Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researche...Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researches have tried their best to extract a wide variety of load features based on transient or steady state of electrical appliances,it is still very difficult for their algorithm to model the load decomposition problem of different electrical appliance types in a targeted manner to jointly mine their proposed features.This paper presents a very effective event-driven NILM solution,which aims to separately model different appliance types to mine the unique characteristics of appliances from multi-dimensional features,so that all electrical appliances can achieve the best classification performance.First,we convert the multi-classification problem into a serial multiple binary classification problem through a pre-sort model to simplify the original problem.Then,ConTrastive Loss K-Nearest Neighbour(CTLKNN)model with trainable weights is proposed to targeted mine appliance load characteristics.The simulation results show the effectiveness and stability of the proposed algorithm.Compared with existing algorithms,the proposed algorithm has improved the identification performance of all electrical appliance types.展开更多
Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually.Prior studies have mos...Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually.Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances.Low-power consumer appliances have comparable power consumption patterns,which can complicate the detection task and can be mistaken as noise.This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures.A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power appliances.Fractal dimension,Hurst exponent,multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various‘turn-on’appliance signatures for classification.Four classifiers,i.e.,deep neural network,support vector machine,decision trees,and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted features.The simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers,achieving an accuracy of up to 96%in classifying low-power appliances.展开更多
Non-Intrusive Load Monitoring(NILM)has gradually become a research focus in recent years to measure the power consumption in households for energy conservation.Most of the existing algorithms on NILM models independen...Non-Intrusive Load Monitoring(NILM)has gradually become a research focus in recent years to measure the power consumption in households for energy conservation.Most of the existing algorithms on NILM models independently measure when the total current load of appliances occurs,and NILM usually undergoes the problem of signatures of the appliance.This paper presents a distingue NILM design to measure and classify the appliances by investigating the inrush current pattern when the alliances begin.The proposed method is implemented while the five appliances operate simultaneously.The high sampling rate of field-programmable gate array(FPGA)is used to sample the inrush current,and then the current is converted to be image patterns using the kurtogram technique.These images are arranged to be four groups of data set depending on the number of appliances operating simultaneously.Furthermore,the five proposed modifications convolutional neural networks(CNN),which is based on very deep convolutional networks(VGGNet),are designed by adjusting the size to decrease the training time and increase faster operation.The proposed CNNs are then implement as a classification model to compare with the previous models.The F1 score and Recall are used to measure the accuracy classification.The results showed that the proposed system could be achieved at 99.06 accuracy classification.展开更多
Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility o...Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility or load aggregators who only have total load data.Most existing studies require subloads for supervised disaggregation or prior knowledge for unsupervised disaggregation,but such information is hard to obtain.It is necessary to develop an effective,completely unsupervised non-intrusive monitoring method to obtain the HVAC load data.In this study,a multiple seasonal-trend decomposition using the LOESS(MSTL)method is proposed to disaggregate the HVAC load from the total metered electricity data of office buildings.The effects of periodic types(daily,weekly,monthly,etc.),periodic sequences,and parallel/serial structures are analyzed.The proposed method is verified based on the historical electricity data of ten buildings.The results show that the proposed MSTL can accurately disaggregate the HVAC load with a coefficient of variation of the root mean square error(CVRMSE)of 10.94%,a normalized root mean squared error(NRMSE)of 2.1%,and a weighted absolute percentage error(WAPE)of 8.52%.Compared to single-cycle STL,the proposed method can significantly improve load disaggregation performance,with a maximum reduction of 16.36%in CVRMSE,5.3%in NRMSE,and 12.91%in WAPE.Backward-chain-based MSTL is recommended with higher accuracy and robustness.The proposed method provides an effective solution for utilities or load aggregators to improve demand response management and grid stability.展开更多
Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial ...Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.展开更多
Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation.Non-intrusive load monitoring(NILM)offers many promising applications in the context o...Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation.Non-intrusive load monitoring(NILM)offers many promising applications in the context of energy efficiency and conservation.Load classification is a key component of NILM that relies on different artificial intelligence techniques,e.g.,machine learning.This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis.Moreover,this study also analyzes the role of input feature space dimensionality in the context of classification performance.For the above purposes,an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households.Based on the presented analysis,it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data.The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier.Furthermore,it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.展开更多
The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to ma...The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)programs.Appliance flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort level.In this paper,a dynamic appliance flexibility analysis approach using the smart meter data is presented.In the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user model.During operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal information.The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the appliance.The HEMS can choose the appliance with high flexibility to participate in the DR programs.The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.展开更多
This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an...This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an intelligent and feasible non-intrusive load monitoring(NILM)algorithm is urgently needed.However,most recent researches on NILM have not dealt with practical problems when applied to power grid,i.e.,①limited communication for slow-change systems;②requirement of low-cost hardware at the users’side;and③inconvenience to adapt to new households.Therefore,a novel NILM algorithm based on biology-inspired spiking neural network(SNN)has been developed to overcome the existing challenges.To provide intelligence in NILM,the developed SNN features an unsupervised learning rule,i.e.,spike-time dependent plasticity(STDP),which only requires the user to label one instance for each appliance while adapting to a new household.To upgrade the feasibility in NILM,the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor(RC)circuit.In addition,a distributed computing system has been designed that divides the SNN into two parts,i.e.,smart outlets and local servers.Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM,the high-frequency data can be easily compressed as the spike times,and are sent to the local server with limited communication capability,whereas it is unable to handle the traditional NILM.Finally,a series of experiments are conducted using a benchmark public dataset.Meanwhile,the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.展开更多
Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on...Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample training.Based on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this study.The algorithm can effectively depict the load state without the need for prior training.First,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models.The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm.The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal.With low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing cost.Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.展开更多
Non-intrusive load monitoring(NILM) can provide appliance-level power consumption information without deploying submeters for each load, in which load event detection is one of the crucial steps. However, the existing...Non-intrusive load monitoring(NILM) can provide appliance-level power consumption information without deploying submeters for each load, in which load event detection is one of the crucial steps. However, the existing event detection methods do not efficiently detect both the starting time of an event(STE) and the ending time of an event(ETE), and their adaptability to scenarios with different sampling rates is limited. To address these problems, in this paper, an event detection method based on robust random cut forest(RRCF) algorithm, which is an unsupervised learning method for detecting anomalous data points within a dataset, is proposed. First, the meanpooling preprocessing is applied to the aggregated load power series with a high sampling rate to minimize fluctuations. Then, the power differential series is obtained, and the anomaly score of each data point is calculated using the RRCF algorithm for preliminary detection. If an event has been preliminarily detected, misidentification caused by fluctuation will be further eliminated by using an adaptive power difference threshold approach. Finally, linear fitting is used to finely and accurately adjust the STE and ETE. The proposed method does not require any pretraining of the detection model and has been validated with both the BLUED dataset(with high and low sampling rates) and the REDD dataset(with low sampling rate). The experimental results demonstrate that the proposed method not only meets real-time requirements, but also exhibits strong adaptability across multiple scenarios. The precision is greater than 92% in distinct sampling rate scenarios, and the F1 score of phase B on the BLUED dataset reaches 94% in the scenario with a high sampling rate. These results indicate that the proposed method outperforms other state-of-the-art methods.展开更多
With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising te...With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.展开更多
Technological advances,innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems.The lack of measu...Technological advances,innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems.The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models,generating inefficiency in the analysis and processing of informa-tion to validate the flexibility potential that large consumers can contribute to the network operator.In this sense,the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the appli-cation of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.展开更多
This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on e...This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.展开更多
In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a d...In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.展开更多
Non-intrusive measurement technology is of great interest for the electrical utilities in order to avoid an interruption in the normal operation of the supply network during diagnostics measurements and inspections. I...Non-intrusive measurement technology is of great interest for the electrical utilities in order to avoid an interruption in the normal operation of the supply network during diagnostics measurements and inspections. Inductively coupled electromagnetic sensing provides a possibility of non-intrusive measurements for online condition monitoring of the electrical components in a Medium Voltage (MV) distribution network. This is accomplished by employing Partial Discharge (PD) activity monitoring, one of the successful methods to assess the working condition of MV components but often requires specialized equipment for carrying out the measurements. In this paper, Rogowski coil sensor is presented as a robust solution for non-intrusive measurements of PD signals. A high frequency prototype of Rogowski coil is designed in the laboratory. Step-by-step approach of constructing the sensor system is presented and performance of its components (coil head, damping component, integrator and data acquisition system) is evaluated using practical and simulated environments. Alternative Transient Program-Electromagnetic Transient Program (ATP-EMTP) is used to analyze the designed model of the Rogowski coil. Real and simulated models of the coil are used to investigate the behavior of Rogowski coil sensor at its different stages of development from a transducer coil to a complete measuring device. Both models are compared to evaluate their accuracy for PD applications. Due to simple design, flexible hardware, and low cost of Rogowski coil, it can be considered as an efficient current measuring device for integrated monitoring applications where a large number of sensors are required to develop an automated online condition monitoring system for a distribution network.展开更多
The purpose of this paper is to investigate the effect of PD （partial discharge） activity within medium voltage XLPE （cross-linked polyethylene） cables. The effect of partial discharge was studied by means of a nu...The purpose of this paper is to investigate the effect of PD （partial discharge） activity within medium voltage XLPE （cross-linked polyethylene） cables. The effect of partial discharge was studied by means of a number of simulations. The simulations were based on the well-known three capacitor model for partial discharge. An equivalent circuit was derived for partial discharge due to a single void in the insulation material of a power cable. The results obtained from the simulations will form the basis of the design proses of a non-intrusive condition monitoring technique. The technique is based on the classification of discharge activity according to five levels of PD. Future work will include the improvement of the simulation model by investigating the high frequency model of a power cable as well as the statistical nature of PD activity. This will improve the accuracy of the simulation results when compared to actual measurements. The work discussed in this paper will be used to construct and calibrate a practical model which will make use of PD measurements for non-intrusive condition monitoring of medium voltage electrical cables.展开更多
<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I t...<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID. </div>展开更多
The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing maj...The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.展开更多
Bridge deformation monitoring usually adopts contact sensors,and the implementation process is often limited by the environment and observation conditions,resulting in unsatisfactory monitoring accuracy and effect.Gro...Bridge deformation monitoring usually adopts contact sensors,and the implementation process is often limited by the environment and observation conditions,resulting in unsatisfactory monitoring accuracy and effect.Ground-Based Synthetic Aperture Radar(GBSAR)combined with corner reflectors was used to perform static load-loaded deformation destruction experiments on solid model bridges in a non-contact manner.The semi parametric spline filtering and its optimization method were used to obtain the monitoring results of the GBSAR radar’s line of sight deformation,and the relative position of the corner reflector and the millimeter level deformation signals under different loading conditions were successfully extracted.The deformation transformation model from the radar line of sight direction to the vertical vibration direction was deduced.The transformation results of deformation monitoring and the measurement data such as the dial indicator were compared and analyzed.The occurrence and development process of bridge deformation and failure were successfully monitored,and the deformation characteristics of the bridge from continuous loading to eccentric loading until bridge failure were obtained.The experimental results show that GBSAR combined with corner reflector can be used for deformation feature acquisition,damage identification and health monitoring of bridges and other structures,and can provide a useful reference for design,construction and safety evaluation.展开更多
Generally,an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control.Different from the traditional intrusive...Generally,an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control.Different from the traditional intrusive modeling,a non-intrusive modeling method based on two-stage generative adversarial network(TS-GAN)is proposed for integrated energy system(IES).By using this method,non-intrusive modeling for the IES including photovoltaic,wind power,energy storage,and energy coupling equipment can be carried out.First,the characteristics of IES are analyzed and extracted based on the meteorological data,energy output,and energy price,and then the characteristic database is established.Meanwhile,the loads are classified as uncontrollable loads and schedulable loads based on frequency domain decomposition to facilitate energy management.Furthermore,TS-GAN algorithm based on the Stackelberg game is designed.In the TS-GAN,the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring,and the second-stage GAN distinguishes the accumulated data generated by first-stage GAN and further modifies the generator models of the first-stage GAN.Finally,the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region.展开更多
基金supported by National Natural Science Foundation of China(No.61531007).
文摘Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researches have tried their best to extract a wide variety of load features based on transient or steady state of electrical appliances,it is still very difficult for their algorithm to model the load decomposition problem of different electrical appliance types in a targeted manner to jointly mine their proposed features.This paper presents a very effective event-driven NILM solution,which aims to separately model different appliance types to mine the unique characteristics of appliances from multi-dimensional features,so that all electrical appliances can achieve the best classification performance.First,we convert the multi-classification problem into a serial multiple binary classification problem through a pre-sort model to simplify the original problem.Then,ConTrastive Loss K-Nearest Neighbour(CTLKNN)model with trainable weights is proposed to targeted mine appliance load characteristics.The simulation results show the effectiveness and stability of the proposed algorithm.Compared with existing algorithms,the proposed algorithm has improved the identification performance of all electrical appliance types.
文摘Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually.Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances.Low-power consumer appliances have comparable power consumption patterns,which can complicate the detection task and can be mistaken as noise.This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures.A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power appliances.Fractal dimension,Hurst exponent,multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various‘turn-on’appliance signatures for classification.Four classifiers,i.e.,deep neural network,support vector machine,decision trees,and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted features.The simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers,achieving an accuracy of up to 96%in classifying low-power appliances.
基金This research has received funding support from the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation(Grant number BO4G640045)Also,this research is supported by the National Research Council of Thailand(NRCT).NRISS No.144276,2589514(FFB65E0712)and 2589488(FFB65E0713).
文摘Non-Intrusive Load Monitoring(NILM)has gradually become a research focus in recent years to measure the power consumption in households for energy conservation.Most of the existing algorithms on NILM models independently measure when the total current load of appliances occurs,and NILM usually undergoes the problem of signatures of the appliance.This paper presents a distingue NILM design to measure and classify the appliances by investigating the inrush current pattern when the alliances begin.The proposed method is implemented while the five appliances operate simultaneously.The high sampling rate of field-programmable gate array(FPGA)is used to sample the inrush current,and then the current is converted to be image patterns using the kurtogram technique.These images are arranged to be four groups of data set depending on the number of appliances operating simultaneously.Furthermore,the five proposed modifications convolutional neural networks(CNN),which is based on very deep convolutional networks(VGGNet),are designed by adjusting the size to decrease the training time and increase faster operation.The proposed CNNs are then implement as a classification model to compare with the previous models.The F1 score and Recall are used to measure the accuracy classification.The results showed that the proposed system could be achieved at 99.06 accuracy classification.
文摘Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility or load aggregators who only have total load data.Most existing studies require subloads for supervised disaggregation or prior knowledge for unsupervised disaggregation,but such information is hard to obtain.It is necessary to develop an effective,completely unsupervised non-intrusive monitoring method to obtain the HVAC load data.In this study,a multiple seasonal-trend decomposition using the LOESS(MSTL)method is proposed to disaggregate the HVAC load from the total metered electricity data of office buildings.The effects of periodic types(daily,weekly,monthly,etc.),periodic sequences,and parallel/serial structures are analyzed.The proposed method is verified based on the historical electricity data of ten buildings.The results show that the proposed MSTL can accurately disaggregate the HVAC load with a coefficient of variation of the root mean square error(CVRMSE)of 10.94%,a normalized root mean squared error(NRMSE)of 2.1%,and a weighted absolute percentage error(WAPE)of 8.52%.Compared to single-cycle STL,the proposed method can significantly improve load disaggregation performance,with a maximum reduction of 16.36%in CVRMSE,5.3%in NRMSE,and 12.91%in WAPE.Backward-chain-based MSTL is recommended with higher accuracy and robustness.The proposed method provides an effective solution for utilities or load aggregators to improve demand response management and grid stability.
基金The completion of this research was made possible thanks to The Natural Sciences and Engineering Research Council of Canada(NSERC)and a start-up grant from Concordia University.
文摘Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.
文摘Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation.Non-intrusive load monitoring(NILM)offers many promising applications in the context of energy efficiency and conservation.Load classification is a key component of NILM that relies on different artificial intelligence techniques,e.g.,machine learning.This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis.Moreover,this study also analyzes the role of input feature space dimensionality in the context of classification performance.For the above purposes,an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households.Based on the presented analysis,it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data.The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier.Furthermore,it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.
文摘The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)programs.Appliance flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort level.In this paper,a dynamic appliance flexibility analysis approach using the smart meter data is presented.In the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user model.During operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal information.The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the appliance.The HEMS can choose the appliance with high flexibility to participate in the DR programs.The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.
基金supported by the SGCC Science and Technology Program under project“Distributed High-Speed Frequency Control Under UHVDC Bipolar Blocking Fault Scenario”(No.SGGR0000DLJS1800934)。
文摘This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an intelligent and feasible non-intrusive load monitoring(NILM)algorithm is urgently needed.However,most recent researches on NILM have not dealt with practical problems when applied to power grid,i.e.,①limited communication for slow-change systems;②requirement of low-cost hardware at the users’side;and③inconvenience to adapt to new households.Therefore,a novel NILM algorithm based on biology-inspired spiking neural network(SNN)has been developed to overcome the existing challenges.To provide intelligence in NILM,the developed SNN features an unsupervised learning rule,i.e.,spike-time dependent plasticity(STDP),which only requires the user to label one instance for each appliance while adapting to a new household.To upgrade the feasibility in NILM,the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor(RC)circuit.In addition,a distributed computing system has been designed that divides the SNN into two parts,i.e.,smart outlets and local servers.Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM,the high-frequency data can be easily compressed as the spike times,and are sent to the local server with limited communication capability,whereas it is unable to handle the traditional NILM.Finally,a series of experiments are conducted using a benchmark public dataset.Meanwhile,the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.
基金supported by National Natural Science Foundation of China(No.52107117)。
文摘Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample training.Based on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this study.The algorithm can effectively depict the load state without the need for prior training.First,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models.The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm.The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal.With low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing cost.Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.
基金This work was supported by the Zhejiang Provincial Natural Science Foundation of China(No.LGG22F030008)Key R&D Program of Zhejiang Province(No.2021C01113).
文摘Non-intrusive load monitoring(NILM) can provide appliance-level power consumption information without deploying submeters for each load, in which load event detection is one of the crucial steps. However, the existing event detection methods do not efficiently detect both the starting time of an event(STE) and the ending time of an event(ETE), and their adaptability to scenarios with different sampling rates is limited. To address these problems, in this paper, an event detection method based on robust random cut forest(RRCF) algorithm, which is an unsupervised learning method for detecting anomalous data points within a dataset, is proposed. First, the meanpooling preprocessing is applied to the aggregated load power series with a high sampling rate to minimize fluctuations. Then, the power differential series is obtained, and the anomaly score of each data point is calculated using the RRCF algorithm for preliminary detection. If an event has been preliminarily detected, misidentification caused by fluctuation will be further eliminated by using an adaptive power difference threshold approach. Finally, linear fitting is used to finely and accurately adjust the STE and ETE. The proposed method does not require any pretraining of the detection model and has been validated with both the BLUED dataset(with high and low sampling rates) and the REDD dataset(with low sampling rate). The experimental results demonstrate that the proposed method not only meets real-time requirements, but also exhibits strong adaptability across multiple scenarios. The precision is greater than 92% in distinct sampling rate scenarios, and the F1 score of phase B on the BLUED dataset reaches 94% in the scenario with a high sampling rate. These results indicate that the proposed method outperforms other state-of-the-art methods.
文摘With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.
文摘Technological advances,innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems.The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models,generating inefficiency in the analysis and processing of informa-tion to validate the flexibility potential that large consumers can contribute to the network operator.In this sense,the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the appli-cation of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.
文摘This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.
文摘In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.
文摘Non-intrusive measurement technology is of great interest for the electrical utilities in order to avoid an interruption in the normal operation of the supply network during diagnostics measurements and inspections. Inductively coupled electromagnetic sensing provides a possibility of non-intrusive measurements for online condition monitoring of the electrical components in a Medium Voltage (MV) distribution network. This is accomplished by employing Partial Discharge (PD) activity monitoring, one of the successful methods to assess the working condition of MV components but often requires specialized equipment for carrying out the measurements. In this paper, Rogowski coil sensor is presented as a robust solution for non-intrusive measurements of PD signals. A high frequency prototype of Rogowski coil is designed in the laboratory. Step-by-step approach of constructing the sensor system is presented and performance of its components (coil head, damping component, integrator and data acquisition system) is evaluated using practical and simulated environments. Alternative Transient Program-Electromagnetic Transient Program (ATP-EMTP) is used to analyze the designed model of the Rogowski coil. Real and simulated models of the coil are used to investigate the behavior of Rogowski coil sensor at its different stages of development from a transducer coil to a complete measuring device. Both models are compared to evaluate their accuracy for PD applications. Due to simple design, flexible hardware, and low cost of Rogowski coil, it can be considered as an efficient current measuring device for integrated monitoring applications where a large number of sensors are required to develop an automated online condition monitoring system for a distribution network.
文摘The purpose of this paper is to investigate the effect of PD （partial discharge） activity within medium voltage XLPE （cross-linked polyethylene） cables. The effect of partial discharge was studied by means of a number of simulations. The simulations were based on the well-known three capacitor model for partial discharge. An equivalent circuit was derived for partial discharge due to a single void in the insulation material of a power cable. The results obtained from the simulations will form the basis of the design proses of a non-intrusive condition monitoring technique. The technique is based on the classification of discharge activity according to five levels of PD. Future work will include the improvement of the simulation model by investigating the high frequency model of a power cable as well as the statistical nature of PD activity. This will improve the accuracy of the simulation results when compared to actual measurements. The work discussed in this paper will be used to construct and calibrate a practical model which will make use of PD measurements for non-intrusive condition monitoring of medium voltage electrical cables.
文摘<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID. </div>
文摘The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.
基金Science and Technology Innovation Program of Hunan Province(No.2021RC4037)National Natural Science Foundation of China:Deformation Monitoring Key Technology and Damage Mechanism Research on Data Fusion among GB-SAR and Multi-sensors(No.41877283)Scientific Research Project of Hunan Provincial Department of Natural Resources(No.2021-18)
文摘Bridge deformation monitoring usually adopts contact sensors,and the implementation process is often limited by the environment and observation conditions,resulting in unsatisfactory monitoring accuracy and effect.Ground-Based Synthetic Aperture Radar(GBSAR)combined with corner reflectors was used to perform static load-loaded deformation destruction experiments on solid model bridges in a non-contact manner.The semi parametric spline filtering and its optimization method were used to obtain the monitoring results of the GBSAR radar’s line of sight deformation,and the relative position of the corner reflector and the millimeter level deformation signals under different loading conditions were successfully extracted.The deformation transformation model from the radar line of sight direction to the vertical vibration direction was deduced.The transformation results of deformation monitoring and the measurement data such as the dial indicator were compared and analyzed.The occurrence and development process of bridge deformation and failure were successfully monitored,and the deformation characteristics of the bridge from continuous loading to eccentric loading until bridge failure were obtained.The experimental results show that GBSAR combined with corner reflector can be used for deformation feature acquisition,damage identification and health monitoring of bridges and other structures,and can provide a useful reference for design,construction and safety evaluation.
基金supported by the National Key Research and Development Program of China(2018YFA0702200)National Natural Science Foundation of China(No.62073065).
文摘Generally,an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control.Different from the traditional intrusive modeling,a non-intrusive modeling method based on two-stage generative adversarial network(TS-GAN)is proposed for integrated energy system(IES).By using this method,non-intrusive modeling for the IES including photovoltaic,wind power,energy storage,and energy coupling equipment can be carried out.First,the characteristics of IES are analyzed and extracted based on the meteorological data,energy output,and energy price,and then the characteristic database is established.Meanwhile,the loads are classified as uncontrollable loads and schedulable loads based on frequency domain decomposition to facilitate energy management.Furthermore,TS-GAN algorithm based on the Stackelberg game is designed.In the TS-GAN,the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring,and the second-stage GAN distinguishes the accumulated data generated by first-stage GAN and further modifies the generator models of the first-stage GAN.Finally,the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region.
