The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the...The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.展开更多
In this paper,a new simulating method is presented,using only the normal magnetizing curve (B-H) of the transformer core material,its geometric dimensions,the no-load power loss data and the concept of instantaneous p...In this paper,a new simulating method is presented,using only the normal magnetizing curve (B-H) of the transformer core material,its geometric dimensions,the no-load power loss data and the concept of instantaneous power. At the end of this paper the simulating calculation using EMTP has been also performed for the same transformer. The comparison shows that the two sets of results are very close to each other,and proves the correctness of the new method. The new method presented in this paper is helpful to verify the correctness of the power transformer design,analyze the behavior of the transformer protection under switching and study the new transformer protection principles.展开更多
In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(...In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.展开更多
Oil immersed power transformer is the main electrical equipment in power system.Its operation reliability has an important impact on the safe operation of power system.In the process of production,installation and ope...Oil immersed power transformer is the main electrical equipment in power system.Its operation reliability has an important impact on the safe operation of power system.In the process of production,installation and operation,its insulation structure may be damaged,resulting in partial discharge and even breakdown inside the transformer.In this paper,S9-M-100/10 oil immersed distribution transformer is taken as the research object,and the distribution laws of electromagnetic field and temperature field in transformer under normal operation,inter turn short circuit and inter layer short circuit are simulated and analyzed.The simulation results show that under normal conditions,the temperatures at the oil gap between the transformer core and the high and low voltage windings and the middle position of the high-voltage winding are high.When there are inter turn and inter layer short circuit faults,the electromagnetic loss of the fault part of the transformer increases,and the temperature rises suddenly.The influence of the two faults on the internal temperature field of the transformer is different,and the influence of the inter turn short circuit fault on the temperature nearby is obvious.The analysis results can provide reference for the thermal fault interpretation and fault classification of transformer.展开更多
Structured microgrids(SμGs)and Flexible electronic large power transformers(FeLPTs)are emerging as two essential technologies for renewable energy integration,flexible power transmission,and active control.SμGs prov...Structured microgrids(SμGs)and Flexible electronic large power transformers(FeLPTs)are emerging as two essential technologies for renewable energy integration,flexible power transmission,and active control.SμGs provide the integration of renewable energy and storage to balance the energy demand and supply as needed for a given system design.FeLPT’s flexibility for processing,control,and re-configurability offers the capability for flexible transmission for effective flow control and enable SμGs connectivity while still keeping multiscale system level control.Early adaptors for combined heat and power have demonstrated significant economic benefits while reducing environmental foot prints.They bring tremendous benefits to utility companies also.With storage and active control capabilities,a 300-percent increase in bulk transmission and distribution lines are possible without having to increase capacity.SμGs and FeLPTs will also enable the utility industry to be better prepared for the emerging large increase in base load demand from electric transportation and data centers.This is a win-win-win situation for the consumer,the utilities(grid operators),and the environment.SμGs and FeLPTs provide value in power substation,energy surety,reliability,resiliency,and security.It is also shown that the initial cost associated with SμG and FeLPTs deployment can be easily offset with reduced operating cost,which in turn reduces the total life-cycle cost by 33%to 67%.展开更多
Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accura...Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.展开更多
Paper deals with a comparison of selected properties of several vegetable oil representatives along their accelerated thermal ageing at the temperature of 90 ℃. These properties are compared to two widely used and co...Paper deals with a comparison of selected properties of several vegetable oil representatives along their accelerated thermal ageing at the temperature of 90 ℃. These properties are compared to two widely used and commercially available mineral transformer oils. A combined insulating system （an oil-paper system） was created with the usage of mentioned oils for measurement purposes. Dissipation factor, capacity and volume resistance are characteristics measured along a thermal ageing of the oil-paper systems. Infrared spectroscopy was used as an additional method. After 1,000 hours of ageing, the dissipation factor of all systems based on vegetable oils did not exceed the value of 0.015. The volume resistance of systems containing mineral oils was approx, twice as high as the volume resistance of those with vegetable oils. The capacity on the other hand was slightly lower in the case of mineral oils application. An experiment also showed that the paper combined with the vegetable oil dries more quickly than in combination with the mineral oil. Infrared spectroscopy has not shown any expressive changes in the chemical structure of aU tested oils yet （up to 1,000 hours of ageing）.展开更多
Partial discharge detection in power transformers is discussed using a new approach that exploit the broad band of the Rogowski coils and the potential of two signal processing tools： discrete wavelet transform and e...Partial discharge detection in power transformers is discussed using a new approach that exploit the broad band of the Rogowski coils and the potential of two signal processing tools： discrete wavelet transform and empirical mode decomposition. Detecting and analyzing incipient activities of partial discharge can provide useful information to diagnostics and prognostics about transformer insulation. So, partial discharge signals embedded in the electric current at ground conductor are measured using the Rogowski coil. These signals are submitted to noise suppression and the partial discharges waveforms are extracted through different ways： using discrete wavelet transform and using empirical mode decomposition. The comparison of these two methods show that the extraction with discrete wavelet transform results in a faster and simpler algorithm than the empirical mode decomposition. But this one produces more precise waveforms due its adaptive characteristic.展开更多
This paper discusses the current state of the art of diagnostics at power transformers. A special focus is set on the UHF-PD-measurement （ultra-high-frequency partial discharge measurement） because at power transfor...This paper discusses the current state of the art of diagnostics at power transformers. A special focus is set on the UHF-PD-measurement （ultra-high-frequency partial discharge measurement） because at power transformers, this diagnostic method has become more important in recent years. The current state, basics and principles of operations, proceedings as well as advantages of PD-measurement methods are covered. Furthermore problems and proposed solutions are discussed. Bushings and tap changers are not discussed in detail. In many cases, one single diagnostic method does not have the ability to sufficiently evaluate a power transformer. Therefore, a variety of diagnostic methods came up over times, which are commonly used by now. To expand the evaluation opportunities of power transformers, science strives to develop new diagnostic methods as well as to improve the existing ones. Furthermore, environmentally friendly and hardly inflammable ester liquids are examined for the use at power transformers and PD-measurement at HVDC （high voltage direct current） converter transformers as well. Potential diagnostic options and respectively current developments and findings in the field of oil-paper-insulation systems are outlined conclusively.展开更多
This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distr...This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.展开更多
The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep learning.To handle this problem,a novel transfor...The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep learning.To handle this problem,a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network(ACGAN)under imbalanced data is proposed in this paper,which meets both the requirements of balancing DGA data and supplying accurate diagnosis results.The generator combines one-dimensional convolutional neural networks(1D-CNN)and long short-term memories(LSTM),which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.The discriminator adopts multilayer perceptron networks(MLP),which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large.The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models,enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%.Furthermore,the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods.Therefore,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets.In addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.展开更多
A control scheme of electronic power transformer (EPT) in a three-phase four-wire distribution system, which included an input section, an isolating section and an output section, was researched under unbalanced loads...A control scheme of electronic power transformer (EPT) in a three-phase four-wire distribution system, which included an input section, an isolating section and an output section, was researched under unbalanced loads. The simple and appropriate control scheme was developed through analyzing the system requirements of the primary side and the load requirements of the secondary side. In the input section, a dual-loop control in synchronous rotating d-q coordinates was introduced, and in the output section, a dual-loop control based on instantaneous output voltage was used. Load characteristics of EPT were investigated by using Matlab/Simulink software. Simulation results showed that, with the proposed control scheme, the EPT has good performances and the sinusoidal input current and constant output voltage can be realized under both balanced and unbalanced loads.展开更多
Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Powe...Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.展开更多
Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a...Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.展开更多
Oil-filled transformers are critical assets in electrical power systems,both economically and operationally.Their condition is assessed through insulation system,which is greatly affected by various degradation mechan...Oil-filled transformers are critical assets in electrical power systems,both economically and operationally.Their condition is assessed through insulation system,which is greatly affected by various degradation mechanisms.Hence,effective fault diagnosis is essential to prolong their lifespan.Early detection and correction of incipient faults through Dissolved Gas Analysis(DGA)are crucial to prevent irreversible damage.Current measurement systems have significant limitations that impede their use in routine monitoring and underscore the need for new,accessible technologies that are both technically and economically viable to efficiently detect incipient faults.This study evaluates the performance of various Machine Learning(ML)techniques to predict the concentrations of hydrogen(H₂),methane(CH₄),acetylene(C₂H₂),ethylene(C₂H₄),and ethane(C₂H₆)in oil samples subjected to different types of electrical faults,using data from a novel electronic nose(E-Nose)equipped with eleven MOS-type gas sensors.The evaluated ML techniques include Linear Regression(LR),Multivariate Linear Regression(MLR),Principal Component Regression(PCR),Multilayer Perceptron(MLP),Partial Least Squares Regression(PLS),Support Vector Regression(SVR),and Random Forest Regression(RFR).Experimental results from 218 measurement processes revealed that RFR and MLP models exhibited superior performance,with RFR achieving the highest accuracy for predicting H₂,C₂H₂,and C₂H₆,while MLP excelled for CH₄and C₂H₄.A comparison with a commercial DGA system using the Duval Pentagon Method confirmed the effectiveness of these models in diagnosing transformer faults.These findings underscore the potential of combining E-Noses with ML techniques as an innovative and efficient solution for early fault diagnosis.展开更多
Status of the transformer is highly associated with safe and stable operation of the whole power system.Since the vibration signal is generated along with operation of the transformer and can indicate change of equipm...Status of the transformer is highly associated with safe and stable operation of the whole power system.Since the vibration signal is generated along with operation of the transformer and can indicate change of equipment operation status in real-time,fault diagnosis based on the vibration signal is a feasible method to monitor status of the transformer.In this paper,an end-to-end multi-branch-attention-multiscale CNN(MAMCNN)framework is proposed based on a one-dimensional convolutional neural network,in which multi-branch inputs,multiscale residual learning,and attention mechanism-guided multi-branch fusion techniques are integrated to identify states of the 220 kV transformer.To address the problem of small samples for transformer fault diagnosis,the proposed method first tests on a public dataset of rolling element bearing vibration.Results show that MAMCNN still has good differentiation for fault features under strong noise and fluctuating operating conditions without denoising,and has high accuracy and stability for state identification.MAMCNN is then applied to 220 kV transformer fault diagnosis based on vibration signals,and results exhibit high accuracy,rapid and stable convergence in identifying four transformer states.展开更多
This article deals with the methods of finding partial discharge(PD)location in power transformers using ultra high frequency(UHF)measurements.The UHF technique utilises two methods to find the PD location,that is,the...This article deals with the methods of finding partial discharge(PD)location in power transformers using ultra high frequency(UHF)measurements.The UHF technique utilises two methods to find the PD location,that is,the shortest path method and hyperbolic method.The shortest path method works based on the comparison of the measured data and the ones in the database.In the hyperbolic method,a hyperbolic equation is obtained between each two element subset of sensors.The coordinate that best fits all equations is known as the PD location,and can be obtained in three different ways,that is,iterative algorithms,the Fang method and Chan method.The convergence of iterative algorithms is limited by poor initial estimate,overshoot,mitigation of non-convergence etc.The Fang and Chan methods are two closed-form solutions that are used in the communication system to find the radiation source location.This article explains how to use these two methods to obtain the PD coordinate inside the power transformer.These two methods can find exactly the coordinate that best fits all hyperbolic equations.At the end of this article,several tests are carried out through CST software and the PD locations is estimated by all presented methods.The simulation results show how the Fang and Chan methods can overcome the limitations of the iterative method.展开更多
Currently,the international economic situation is becoming increasingly complex,and there is significant downward pressure on the global economy.In recent years,China’s infrastructure sector has experienced rapid gro...Currently,the international economic situation is becoming increasingly complex,and there is significant downward pressure on the global economy.In recent years,China’s infrastructure sector has experienced rapid growth,with the structure of its power engineering business gradually shifting from traditional infrastructure construction to more diversified areas such as production and operation,as well as emergency repairs.As a result,the transformation of mechanized construction in power transmission and transformation projects has become increasingly urgent.This article proposes a post-evaluation model based on game theory to improve comprehensive weighting and fuzzy grey relational projection sorting,which can be used to evaluate the optimal mechanized construction scheme for power transmission and transformation projects.The model begins by considering the entire lifecycle of power transmission and transformation projects.It constructs a post-evaluation index system that covers the planning and design stage,on-site construction stage,operation and maintenance stage,and the decommissioning and disposal stage,with corresponding calculation methods for each index.The fuzzy grey correlation projection sorting method is then employed to evaluate and rank the construction schemes.To validate the model’s effectiveness,a case study of a power transmission and transformation project in a specific region of China is used.The comprehensive benefits of three proposed mechanized construction schemes are evaluated and compared.According to the evaluation results,Scheme 1 is ranked the highest,with a membership degree of 0.870945,excelling in sustainability.These results suggest that the proposed model can effectively evaluate and make decisions regarding the optimal mechanized construction plan for power transmission and transformation projects.展开更多
Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity met...Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics.Their accuracy is limited,since they are hard to learn from other algorithms to improve their own performance.To improve the accuracy of transformer fault diagnosis,a novel method for transformer fault diagnosis based on graph convolutional network(GCN)is proposed.The proposed method has the advantages of two kinds of existing methods.Specifically,the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples.Furthermore,the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type.The back propagation algorithm is used to complete the training process of GCN.The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network,multi-layer perceptron,support vector machine,extreme gradient boosting tree,k-nearest neighbors and Siamese network in different input features and data volumes,which can effectively meet the needs of diagnostic accuracy.展开更多
The electric power enterprise devotes considerable attention to the reliability of power transformers particularly when it decides to either maintain these transformers or decommission them altogether from operation.A...The electric power enterprise devotes considerable attention to the reliability of power transformers particularly when it decides to either maintain these transformers or decommission them altogether from operation.Although this process has reduced the risk of transformer faults,the attendant dilemma is of excessive maintenance of transformers,or retiring them prematurely,leading to high economic waste.This paper is inspired by real-time engineering applications,and proposes an improved model to assess the economic life of power transformers.The new model offers a more efficient approach than previous methods of assessment,with a specific focus of using the annual net income as separate criteria for determining the economic indices of continuous operation,overhaul,and retirement strategies of transformers.The economic life of power transformers is divided into three sections according to different strategies to better resolve the quantification problem in this field.A case study is provided to prove the feasibility and validity of the proposed economic life model.The case study achieves the fine management goal when the electric power enterprise is required to make the maintenance and retirement strategy decision.展开更多
基金supported by China Southern Power Grid Science and Technology Innovation Research Project(000000KK52220052).
文摘The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.
文摘In this paper,a new simulating method is presented,using only the normal magnetizing curve (B-H) of the transformer core material,its geometric dimensions,the no-load power loss data and the concept of instantaneous power. At the end of this paper the simulating calculation using EMTP has been also performed for the same transformer. The comparison shows that the two sets of results are very close to each other,and proves the correctness of the new method. The new method presented in this paper is helpful to verify the correctness of the power transformer design,analyze the behavior of the transformer protection under switching and study the new transformer protection principles.
基金Project(50977003) supported by the National Natural Science Foundation of China
文摘In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.
基金Science and Technology Project of State Grid Gansu Electric Power Company(No.52272219000Q)。
文摘Oil immersed power transformer is the main electrical equipment in power system.Its operation reliability has an important impact on the safe operation of power system.In the process of production,installation and operation,its insulation structure may be damaged,resulting in partial discharge and even breakdown inside the transformer.In this paper,S9-M-100/10 oil immersed distribution transformer is taken as the research object,and the distribution laws of electromagnetic field and temperature field in transformer under normal operation,inter turn short circuit and inter layer short circuit are simulated and analyzed.The simulation results show that under normal conditions,the temperatures at the oil gap between the transformer core and the high and low voltage windings and the middle position of the high-voltage winding are high.When there are inter turn and inter layer short circuit faults,the electromagnetic loss of the fault part of the transformer increases,and the temperature rises suddenly.The influence of the two faults on the internal temperature field of the transformer is different,and the influence of the inter turn short circuit fault on the temperature nearby is obvious.The analysis results can provide reference for the thermal fault interpretation and fault classification of transformer.
文摘Structured microgrids(SμGs)and Flexible electronic large power transformers(FeLPTs)are emerging as two essential technologies for renewable energy integration,flexible power transmission,and active control.SμGs provide the integration of renewable energy and storage to balance the energy demand and supply as needed for a given system design.FeLPT’s flexibility for processing,control,and re-configurability offers the capability for flexible transmission for effective flow control and enable SμGs connectivity while still keeping multiscale system level control.Early adaptors for combined heat and power have demonstrated significant economic benefits while reducing environmental foot prints.They bring tremendous benefits to utility companies also.With storage and active control capabilities,a 300-percent increase in bulk transmission and distribution lines are possible without having to increase capacity.SμGs and FeLPTs will also enable the utility industry to be better prepared for the emerging large increase in base load demand from electric transportation and data centers.This is a win-win-win situation for the consumer,the utilities(grid operators),and the environment.SμGs and FeLPTs provide value in power substation,energy surety,reliability,resiliency,and security.It is also shown that the initial cost associated with SμG and FeLPTs deployment can be easily offset with reduced operating cost,which in turn reduces the total life-cycle cost by 33%to 67%.
基金support of national natural science foundation of China(No.52067021)natural science foundation of Xinjiang(2022D01C35)+1 种基金excellent youth scientific and technological talents plan of Xinjiang(No.2019Q012)major science and technology special project of Xinjiang Uygur Autonomous Region(2022A01002-2).
文摘Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.
文摘Paper deals with a comparison of selected properties of several vegetable oil representatives along their accelerated thermal ageing at the temperature of 90 ℃. These properties are compared to two widely used and commercially available mineral transformer oils. A combined insulating system （an oil-paper system） was created with the usage of mentioned oils for measurement purposes. Dissipation factor, capacity and volume resistance are characteristics measured along a thermal ageing of the oil-paper systems. Infrared spectroscopy was used as an additional method. After 1,000 hours of ageing, the dissipation factor of all systems based on vegetable oils did not exceed the value of 0.015. The volume resistance of systems containing mineral oils was approx, twice as high as the volume resistance of those with vegetable oils. The capacity on the other hand was slightly lower in the case of mineral oils application. An experiment also showed that the paper combined with the vegetable oil dries more quickly than in combination with the mineral oil. Infrared spectroscopy has not shown any expressive changes in the chemical structure of aU tested oils yet （up to 1,000 hours of ageing）.
文摘Partial discharge detection in power transformers is discussed using a new approach that exploit the broad band of the Rogowski coils and the potential of two signal processing tools： discrete wavelet transform and empirical mode decomposition. Detecting and analyzing incipient activities of partial discharge can provide useful information to diagnostics and prognostics about transformer insulation. So, partial discharge signals embedded in the electric current at ground conductor are measured using the Rogowski coil. These signals are submitted to noise suppression and the partial discharges waveforms are extracted through different ways： using discrete wavelet transform and using empirical mode decomposition. The comparison of these two methods show that the extraction with discrete wavelet transform results in a faster and simpler algorithm than the empirical mode decomposition. But this one produces more precise waveforms due its adaptive characteristic.
文摘This paper discusses the current state of the art of diagnostics at power transformers. A special focus is set on the UHF-PD-measurement （ultra-high-frequency partial discharge measurement） because at power transformers, this diagnostic method has become more important in recent years. The current state, basics and principles of operations, proceedings as well as advantages of PD-measurement methods are covered. Furthermore problems and proposed solutions are discussed. Bushings and tap changers are not discussed in detail. In many cases, one single diagnostic method does not have the ability to sufficiently evaluate a power transformer. Therefore, a variety of diagnostic methods came up over times, which are commonly used by now. To expand the evaluation opportunities of power transformers, science strives to develop new diagnostic methods as well as to improve the existing ones. Furthermore, environmentally friendly and hardly inflammable ester liquids are examined for the use at power transformers and PD-measurement at HVDC （high voltage direct current） converter transformers as well. Potential diagnostic options and respectively current developments and findings in the field of oil-paper-insulation systems are outlined conclusively.
文摘This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.
基金The authors gratefully acknowledge financial support of national natural science foundation of China(No.52067021)natural science foundation of Xinjiang Uygur Autonomous Region(2022D01C35)+1 种基金excellent youth scientific and technological talents plan of Xinjiang(No.2019Q012)major science&technology special project of Xinjiang Uygur Autonomous Region(2022A01002-2).
文摘The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep learning.To handle this problem,a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network(ACGAN)under imbalanced data is proposed in this paper,which meets both the requirements of balancing DGA data and supplying accurate diagnosis results.The generator combines one-dimensional convolutional neural networks(1D-CNN)and long short-term memories(LSTM),which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.The discriminator adopts multilayer perceptron networks(MLP),which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large.The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models,enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%.Furthermore,the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods.Therefore,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets.In addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.
基金This project is financed by the New Century Outstanding Talents Supporting Program of Ministry of Education and Superior Young Teachers Supporting Program of Ministry of Education.
文摘A control scheme of electronic power transformer (EPT) in a three-phase four-wire distribution system, which included an input section, an isolating section and an output section, was researched under unbalanced loads. The simple and appropriate control scheme was developed through analyzing the system requirements of the primary side and the load requirements of the secondary side. In the input section, a dual-loop control in synchronous rotating d-q coordinates was introduced, and in the output section, a dual-loop control based on instantaneous output voltage was used. Load characteristics of EPT were investigated by using Matlab/Simulink software. Simulation results showed that, with the proposed control scheme, the EPT has good performances and the sinusoidal input current and constant output voltage can be realized under both balanced and unbalanced loads.
文摘Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.
基金supported by Shandong Provincial Natural Science Foundation(ZR2024ME229,ZR2024ZD29).
文摘Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.
基金supported by Agencia Nacional de Investigación y Desarrollo (ANID), through Fondecyt Regular 1230135 and Fondef TA24I10002the Programa de Iniciación a la Investigación Científica (PIIC) from the Dirección de Postgrado y Programas, Universidad Técnica Federico Santa María, Chile+1 种基金the FIC-R IA 40036152-0 Project of the Regional Government of Biobíoand the invaluable contributions of Elohim G. and the Genesis (1/1) Project.
文摘Oil-filled transformers are critical assets in electrical power systems,both economically and operationally.Their condition is assessed through insulation system,which is greatly affected by various degradation mechanisms.Hence,effective fault diagnosis is essential to prolong their lifespan.Early detection and correction of incipient faults through Dissolved Gas Analysis(DGA)are crucial to prevent irreversible damage.Current measurement systems have significant limitations that impede their use in routine monitoring and underscore the need for new,accessible technologies that are both technically and economically viable to efficiently detect incipient faults.This study evaluates the performance of various Machine Learning(ML)techniques to predict the concentrations of hydrogen(H₂),methane(CH₄),acetylene(C₂H₂),ethylene(C₂H₄),and ethane(C₂H₆)in oil samples subjected to different types of electrical faults,using data from a novel electronic nose(E-Nose)equipped with eleven MOS-type gas sensors.The evaluated ML techniques include Linear Regression(LR),Multivariate Linear Regression(MLR),Principal Component Regression(PCR),Multilayer Perceptron(MLP),Partial Least Squares Regression(PLS),Support Vector Regression(SVR),and Random Forest Regression(RFR).Experimental results from 218 measurement processes revealed that RFR and MLP models exhibited superior performance,with RFR achieving the highest accuracy for predicting H₂,C₂H₂,and C₂H₆,while MLP excelled for CH₄and C₂H₄.A comparison with a commercial DGA system using the Duval Pentagon Method confirmed the effectiveness of these models in diagnosing transformer faults.These findings underscore the potential of combining E-Noses with ML techniques as an innovative and efficient solution for early fault diagnosis.
基金supported by the Science and Technology Project of State Grid Corporation of China(5700-202121258A-0-0-00).
文摘Status of the transformer is highly associated with safe and stable operation of the whole power system.Since the vibration signal is generated along with operation of the transformer and can indicate change of equipment operation status in real-time,fault diagnosis based on the vibration signal is a feasible method to monitor status of the transformer.In this paper,an end-to-end multi-branch-attention-multiscale CNN(MAMCNN)framework is proposed based on a one-dimensional convolutional neural network,in which multi-branch inputs,multiscale residual learning,and attention mechanism-guided multi-branch fusion techniques are integrated to identify states of the 220 kV transformer.To address the problem of small samples for transformer fault diagnosis,the proposed method first tests on a public dataset of rolling element bearing vibration.Results show that MAMCNN still has good differentiation for fault features under strong noise and fluctuating operating conditions without denoising,and has high accuracy and stability for state identification.MAMCNN is then applied to 220 kV transformer fault diagnosis based on vibration signals,and results exhibit high accuracy,rapid and stable convergence in identifying four transformer states.
文摘This article deals with the methods of finding partial discharge(PD)location in power transformers using ultra high frequency(UHF)measurements.The UHF technique utilises two methods to find the PD location,that is,the shortest path method and hyperbolic method.The shortest path method works based on the comparison of the measured data and the ones in the database.In the hyperbolic method,a hyperbolic equation is obtained between each two element subset of sensors.The coordinate that best fits all equations is known as the PD location,and can be obtained in three different ways,that is,iterative algorithms,the Fang method and Chan method.The convergence of iterative algorithms is limited by poor initial estimate,overshoot,mitigation of non-convergence etc.The Fang and Chan methods are two closed-form solutions that are used in the communication system to find the radiation source location.This article explains how to use these two methods to obtain the PD coordinate inside the power transformer.These two methods can find exactly the coordinate that best fits all hyperbolic equations.At the end of this article,several tests are carried out through CST software and the PD locations is estimated by all presented methods.The simulation results show how the Fang and Chan methods can overcome the limitations of the iterative method.
文摘Currently,the international economic situation is becoming increasingly complex,and there is significant downward pressure on the global economy.In recent years,China’s infrastructure sector has experienced rapid growth,with the structure of its power engineering business gradually shifting from traditional infrastructure construction to more diversified areas such as production and operation,as well as emergency repairs.As a result,the transformation of mechanized construction in power transmission and transformation projects has become increasingly urgent.This article proposes a post-evaluation model based on game theory to improve comprehensive weighting and fuzzy grey relational projection sorting,which can be used to evaluate the optimal mechanized construction scheme for power transmission and transformation projects.The model begins by considering the entire lifecycle of power transmission and transformation projects.It constructs a post-evaluation index system that covers the planning and design stage,on-site construction stage,operation and maintenance stage,and the decommissioning and disposal stage,with corresponding calculation methods for each index.The fuzzy grey correlation projection sorting method is then employed to evaluate and rank the construction schemes.To validate the model’s effectiveness,a case study of a power transmission and transformation project in a specific region of China is used.The comprehensive benefits of three proposed mechanized construction schemes are evaluated and compared.According to the evaluation results,Scheme 1 is ranked the highest,with a membership degree of 0.870945,excelling in sustainability.These results suggest that the proposed model can effectively evaluate and make decisions regarding the optimal mechanized construction plan for power transmission and transformation projects.
基金This manuscript is supported by the China Scholarship Council.
文摘Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics.Their accuracy is limited,since they are hard to learn from other algorithms to improve their own performance.To improve the accuracy of transformer fault diagnosis,a novel method for transformer fault diagnosis based on graph convolutional network(GCN)is proposed.The proposed method has the advantages of two kinds of existing methods.Specifically,the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples.Furthermore,the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type.The back propagation algorithm is used to complete the training process of GCN.The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network,multi-layer perceptron,support vector machine,extreme gradient boosting tree,k-nearest neighbors and Siamese network in different input features and data volumes,which can effectively meet the needs of diagnostic accuracy.
基金supported by the Funds for Innovative Research Groups of China(51021005).
文摘The electric power enterprise devotes considerable attention to the reliability of power transformers particularly when it decides to either maintain these transformers or decommission them altogether from operation.Although this process has reduced the risk of transformer faults,the attendant dilemma is of excessive maintenance of transformers,or retiring them prematurely,leading to high economic waste.This paper is inspired by real-time engineering applications,and proposes an improved model to assess the economic life of power transformers.The new model offers a more efficient approach than previous methods of assessment,with a specific focus of using the annual net income as separate criteria for determining the economic indices of continuous operation,overhaul,and retirement strategies of transformers.The economic life of power transformers is divided into three sections according to different strategies to better resolve the quantification problem in this field.A case study is provided to prove the feasibility and validity of the proposed economic life model.The case study achieves the fine management goal when the electric power enterprise is required to make the maintenance and retirement strategy decision.
