According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural ne...Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.展开更多
For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously u...For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously under limited and non-linear high-power amplifier conditions.In this paper,different from the traditional link supportability designs aiming at minimizing the total transponder output power,a maximal sum Shannon capacity optimization objective is firstly raised subject to link supportability constraints.Furthermore,an efficient multilevel optimization(MO) algorithm is proposed to solve the considered optimization problem in the case of single link for each terminal.Moreover,in the case of multiple links for one terminal,an improved MO algorithm involving Golden section and discrete gradient searching procedures is proposed to optimize power allocation over all links.Finally,several numerical results are provided to demonstrate the effectiveness of our proposals.Comparison results show that,by the MO algorithm,not only all links' supportability can be guaranteed but also a larger sum capacity can be achieved with lower complexity.展开更多
This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power ...This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.Firstly,the key influencing factors of the power loads were screened based on the grey relational degree analysis.Secondly,in view of the characteristics of the power loads affected by various factors and time series changes,the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network.The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features,and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects.In the end,the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.展开更多
The DC microgrid has the advantages of high energy conversion efficiency,high energy transmission density,no reactive power flow,and grid-connected synchronization.It is an essential component of the future intelligen...The DC microgrid has the advantages of high energy conversion efficiency,high energy transmission density,no reactive power flow,and grid-connected synchronization.It is an essential component of the future intelligent power distribution system.Constant power load(CPL)will degrade the stability of the DC microgrid and cause system voltage oscillation due to its negative resistance characteristics.As a result,the stability of DC microgrids with CPL has become a problem.At present,the research on the stability of DC microgrid is mainly focused on unipolar DC microgrid,while the research on bipolar DC microgrid lacks systematic discussion.The stability of DC microgrid using CPL was studied first,and then the current stability criteria of DC microgrid were summarized,and its research trend was analyzed.On this basis,aiming at the stability problem caused by CPL,the existing control methods were summarized from the perspective of source converter output impedance and load converter input impedance,and the current control methods were outlined as active and passive control methods.Lastly,the research path of bipolar DC microgrid stability with CPL was prospected.展开更多
The variability characteristics of Guangdong daily power load from 2002 to 2004 and its connection to meteorological variables are analyzed with wavelet analysis and correlation analysis. Prediction equations are esta...The variability characteristics of Guangdong daily power load from 2002 to 2004 and its connection to meteorological variables are analyzed with wavelet analysis and correlation analysis. Prediction equations are established using optimization subset regression. The results show that a linear increasing trend is very significant and seasonal change is obvious. The power load exhibits significant quasi-weekly (5 – 7 days) oscillation, quasi-by-weekly (10 – 20 days) oscillation and intraseasonal (30 – 60 days) oscillation. These oscillations are caused by atmospheric low frequency oscillation and public holidays. The variation of Guangdong daily power load is obviously in decrease on Sundays, shaping like a funnel during Chinese New Year in particular. The minimum is found at the first and second day and the power load gradually increases to normal level after the third day during the long vacation of Labor Day and National Day. Guangdong power load is the most sensitive to temperature, which is the main affecting factor, as in other areas in China. The power load also has relationship with other meteorological elements to some extent during different seasons. The maximum of power load in summer, minimum during Chinese New Year and variation during Labor Day and National Day are well fitted and predicted using the equation established by optimization subset regression and accounting for the effect of workdays and holidays.展开更多
To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided ...To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.展开更多
Began from early 1992, Chongqing Power Supply Bureau had spent 3 and half years to build up a power load management system consisting of I master station, 6 relay stations, 1280 terminals and the distributed monitorin...Began from early 1992, Chongqing Power Supply Bureau had spent 3 and half years to build up a power load management system consisting of I master station, 6 relay stations, 1280 terminals and the distributed monitoring device. This system distributes in the hilly and mountainous areas where geographically complicated and the load widely scatters, it can supervise about 72% load and curtail more than 15% load展开更多
The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the ...The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the design of stabilizing controllers. A PWM-based current-sensorless robust sliding mode controller is developed that requires only the measurement of the output voltage. An extended state observer is developed to estimate a lumped uncertainty signal that comprises the uncertain load power and the input voltage, the converter parasitics, the component uncertainties and the estimation of the derivative of the output voltage needed in the implementation of the controller. A linear sliding surface is used to derive the controller, which is simple in its design and yet exhibits excellent features in terms of robustness to external disturbances, parameter uncertainties, and parasitics despite the absence of the inductor’s current feedback. The robustness of the controller is validated by computer simulations.展开更多
The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the ...The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the design of stabilizing controllers. In this work, a robust nonlinear controller based on the uncertainty and disturbance estimator (UDE) scheme is successfully developed to tightly regulate the output voltage of the boost converter. A systematic procedure is developed to select the controller gains to achieve a satisfactory output response. Using simulation, the effectiveness of the proposed controller is validated and compared to a recent robust nonlinear controller.展开更多
This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collob...This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, Feed-Forward Neural Networks (FFNN), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads given temperature predictions, holiday information and historical loads. The models are trained on-hourly data from the New England Power Pool (NEPOOL) region from 2004 to 2007 and tested on out-of-sample data from 2008. Experimental results indicate that the proposed LP-SVR method gives the smallest error when compared against the other approaches. The LP-SVR shows a mean absolute percent error of 1.58% while the FFNN approach has a 1.61%. Similarly, the FFNN method shows a 330 MWh (Megawatts-hour) mean absolute error, whereas the LP-SVR approach gives a 238 MWh mean absolute error. This is a significant difference in terms of the extra power that would need to be produced if FFNN was used. The proposed LP-SVR model can be utilized for predicting power loads to a very low error, and it is comparable to FFNN and over-performs other state of the art methods such as: Bagged Regression Trees, and Large-Scale SVRs.展开更多
Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To bui...Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.展开更多
We consider two non-iterative algorithms of adaptive power loading for multicarrier modulation (MCM) system, The first one minimizes the average power of the system transmitter and ensures the preset average bit-error...We consider two non-iterative algorithms of adaptive power loading for multicarrier modulation (MCM) system, The first one minimizes the average power of the system transmitter and ensures the preset average bit-error rate, while the second reduces the average transmitting power subject to the given values of demanded bit-error rate and of the outage probability. The algorithms may be used for power-efficient management of the up-link in cellular communication, where mobile terminals use rechargeable batteries, or of the downlink in satellite communication with solar power source of a transponder. We present performance analysis of the adaptive MCM systems supported by computer simulation for the case of the m-Nakagami fading and additive white Gaussian noise in the forward and backward channels. Evaluation of the power gain of the proposed strategies and its comparison with uniform power loading shows that the gain depends on the fading depth and average signal to noise ratio in the system sub-channels.展开更多
In DC microgrid systems,interleaved boost converters(IBCs)are widely used to boost the output voltage of renewable energy sources on the source side of a DC bus owing to their high voltage gain and low current ripple....In DC microgrid systems,interleaved boost converters(IBCs)are widely used to boost the output voltage of renewable energy sources on the source side of a DC bus owing to their high voltage gain and low current ripple.However,because power electronic converters on the load side behave as constant power loads(CPLs)with negative impedance characteristics,their high penetration can degrade the system stability.Therefore,this article proposes a fractional-order nonlinear controller integrated with an extended nonlinear disturbance observer(ENDO)for N-phase IBCs.First,the reduced-order model of the IBC is transformed into a canonical form using the differential geometric method.Subsequently,with the ENDO,the dynamic performance can be enhanced by estimating the disturbances,and a fractional-order nonlinear sliding surface is established to avoid the singularity problem and increase control flexibility.In addition,the stability of the proposed controller is analyzed using Lyapunov’s theorem.In a CPL variation test,the proposed controller exhibited a faster dynamic performance and lower tracking error thanconventional controllers,with at least a 27%improvement in the integral squared error(ISE).Both simulation and experimental results demonstrated the effectivenessof the controller,which can ensure large-signal stability and improved dynamic performance in DC microgrid systems.展开更多
Modern electric power systems have increased the usage of switching power converters.These tightly regulated switching power converters behave as constant power loads(CPLs).They exhibit a negative incremental impedanc...Modern electric power systems have increased the usage of switching power converters.These tightly regulated switching power converters behave as constant power loads(CPLs).They exhibit a negative incremental impedance in small signal analysis.This negative impedance degrades the stability margin of the interaction between CPLs and their feeders,which is known as the negative impedance instability problem.The feeder can be an LC input filter or an upstream switching converter.Active damping methods are preferred for the stabilization of the system.This is due to their higher power efficiency over passive damping methods.Based on different sources of damping effect,this paper summarizes and classifies existing active damping methods into three categories.The paper further analyzes and compares the advantages and disadvantages of each active damping method.展开更多
With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property...With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property of data and numerical experiments,we find either the linear interpolation(LI)or the singular value decomposition(SVD)based method is superior to other methods depending on the smoothness of the data.We construct an index to measure the smoothness of data,and propose the SVDLI algorithm which adaptively selects different algorithms for data completion according to the index.Numerical simulations show that irrespective of the smoothness of data,the data complementing results of SVDLI are comparable to or better than the best of SVD or LI algorithms.The present study is verified using the measurements in China,and the public data of the Australian electricity distribution company and Lawrence Berkeley National Laboratory.展开更多
Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting s...Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load.Then,set order for the time series model by Akaike information criterion(AIC)rule and acquire model coefficients to establish ARMA(2,1)model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the electric power load data.Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA(2,1)model.Besides,variance is reduced by two orders,and every coefficient of stochastic noise is reduced by one order.The filter method based on time series model does reduce stochastic noise of electric power load,and increase measurement accuracy.展开更多
Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was establi...Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was established.The input parameters of RFR model were determined by means of the grid search algorithm.The prediction results for this model were compared with those for several other common machine learning methods.It was found that the coefficient of determination(R^(2))of test set based on the RFR model was the highest,reaching 0.514 while the corresponding mean absolute error(MAE)and the mean squared error(MSE)were the lowest.Besides,the impacts of the air conditioning system used in summer on the power load were discussed.The calculation results showed that the introduction of indexes in the field of Heating,Ventilation and Air Conditioning(HVAC)could improve the prediction accuracy of test set.展开更多
For OFDM systems with hundreds or thousands subcarriers with the adaptive bit and power loading according to each subcarrier , the signaling overhead will be awfully large. However, the adaptive bit and power loading ...For OFDM systems with hundreds or thousands subcarriers with the adaptive bit and power loading according to each subcarrier , the signaling overhead will be awfully large. However, the adaptive bit and power loading according to “subband” is an effective solution to this problem, with which the signaling overhead is expected to be dramatically decreased at the cost of some performance loss. In this paper, based on Ref . [5] but with some modification to the subband bit and power loading algorithm, we apply the algorithm to the IEEE 802.16e OFDM system. The results show that the modified subband bit and power loading algorithm can achieve better BER performance and the signaling overhead is reduced by 75% at the cost of performance loss less than 1 dB if the number of subcarriers per subband is 4 when the BER is around 10^-3.展开更多
Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution.However,as fishery power load is of greatly uniq...Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution.However,as fishery power load is of greatly unique meteorology sensitivity,it continues to be a difficult problem.Therefore,the research of fishery meteorology is an important part of the rational development of fishery resources,the protection of production safety,and the pursuit of high and stable yield.This paper makes a deep study on the power load of the fishery energy internet under the influence of fishery meteorology and takes onshore fish pond as the research object.First of all,the power load is divided into three parts:oxygen enrichment power load,feeding power load,and water replenishment and drainage power load.The impact mechanism of fishery meteorology(including temperature,surface wind speed,precipitation,relative humidity,etc.)on it is described,and then the overall power load is obtained through modeling and integration.Finally,taking the Yuguang Complementary Project in Zhouquan Town,Tongxiang,Zhejiang Province,China as an example,using the meteorological data of its typical spring day and using the MATLAB tool to solve,the hourly comparison of the three types of power loads,the comprehensive power load demand,the full-day electricity charge forecast and the total annual power consumption are calculated.The annual power consumption per hectare and per kilogram of output calculated by simulation are basically consistent with the order of magnitude of the survey data,which proves the validity of the model proposed.The model established in this paper is an original work,and the exploration of fishery energy internet can draw lessons from it.展开更多
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
基金supported by the Major Project of Basic and Applied Research in Guangdong Universities (2017WZDXM012)。
文摘Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.
基金supportedin part by Natural Science Foundation under grant No.91338108,91438206Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously under limited and non-linear high-power amplifier conditions.In this paper,different from the traditional link supportability designs aiming at minimizing the total transponder output power,a maximal sum Shannon capacity optimization objective is firstly raised subject to link supportability constraints.Furthermore,an efficient multilevel optimization(MO) algorithm is proposed to solve the considered optimization problem in the case of single link for each terminal.Moreover,in the case of multiple links for one terminal,an improved MO algorithm involving Golden section and discrete gradient searching procedures is proposed to optimize power allocation over all links.Finally,several numerical results are provided to demonstrate the effectiveness of our proposals.Comparison results show that,by the MO algorithm,not only all links' supportability can be guaranteed but also a larger sum capacity can be achieved with lower complexity.
基金Supported by the Shaanxi Provincial Education Department 2022 Key Research Program Project(22JS022)the National Natural Science Foundation of China(51808428)
文摘This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.Firstly,the key influencing factors of the power loads were screened based on the grey relational degree analysis.Secondly,in view of the characteristics of the power loads affected by various factors and time series changes,the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network.The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features,and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects.In the end,the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.
基金supported by National Natural Science Foundation of China(No.51767015)Key Project of Natural Science Foundation of Gansu Province(No.22JR5RA317)Tianyou Innovation Team Support Program of Lanzhou Jiaotong University(No.TY202009)。
文摘The DC microgrid has the advantages of high energy conversion efficiency,high energy transmission density,no reactive power flow,and grid-connected synchronization.It is an essential component of the future intelligent power distribution system.Constant power load(CPL)will degrade the stability of the DC microgrid and cause system voltage oscillation due to its negative resistance characteristics.As a result,the stability of DC microgrids with CPL has become a problem.At present,the research on the stability of DC microgrid is mainly focused on unipolar DC microgrid,while the research on bipolar DC microgrid lacks systematic discussion.The stability of DC microgrid using CPL was studied first,and then the current stability criteria of DC microgrid were summarized,and its research trend was analyzed.On this basis,aiming at the stability problem caused by CPL,the existing control methods were summarized from the perspective of source converter output impedance and load converter input impedance,and the current control methods were outlined as active and passive control methods.Lastly,the research path of bipolar DC microgrid stability with CPL was prospected.
基金Platform for Meteorological Prediction of Power Load in Guangdong Province
文摘The variability characteristics of Guangdong daily power load from 2002 to 2004 and its connection to meteorological variables are analyzed with wavelet analysis and correlation analysis. Prediction equations are established using optimization subset regression. The results show that a linear increasing trend is very significant and seasonal change is obvious. The power load exhibits significant quasi-weekly (5 – 7 days) oscillation, quasi-by-weekly (10 – 20 days) oscillation and intraseasonal (30 – 60 days) oscillation. These oscillations are caused by atmospheric low frequency oscillation and public holidays. The variation of Guangdong daily power load is obviously in decrease on Sundays, shaping like a funnel during Chinese New Year in particular. The minimum is found at the first and second day and the power load gradually increases to normal level after the third day during the long vacation of Labor Day and National Day. Guangdong power load is the most sensitive to temperature, which is the main affecting factor, as in other areas in China. The power load also has relationship with other meteorological elements to some extent during different seasons. The maximum of power load in summer, minimum during Chinese New Year and variation during Labor Day and National Day are well fitted and predicted using the equation established by optimization subset regression and accounting for the effect of workdays and holidays.
文摘To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.
文摘Began from early 1992, Chongqing Power Supply Bureau had spent 3 and half years to build up a power load management system consisting of I master station, 6 relay stations, 1280 terminals and the distributed monitoring device. This system distributes in the hilly and mountainous areas where geographically complicated and the load widely scatters, it can supervise about 72% load and curtail more than 15% load
文摘The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the design of stabilizing controllers. A PWM-based current-sensorless robust sliding mode controller is developed that requires only the measurement of the output voltage. An extended state observer is developed to estimate a lumped uncertainty signal that comprises the uncertain load power and the input voltage, the converter parasitics, the component uncertainties and the estimation of the derivative of the output voltage needed in the implementation of the controller. A linear sliding surface is used to derive the controller, which is simple in its design and yet exhibits excellent features in terms of robustness to external disturbances, parameter uncertainties, and parasitics despite the absence of the inductor’s current feedback. The robustness of the controller is validated by computer simulations.
文摘The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the design of stabilizing controllers. In this work, a robust nonlinear controller based on the uncertainty and disturbance estimator (UDE) scheme is successfully developed to tightly regulate the output voltage of the boost converter. A systematic procedure is developed to select the controller gains to achieve a satisfactory output response. Using simulation, the effectiveness of the proposed controller is validated and compared to a recent robust nonlinear controller.
文摘This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, Feed-Forward Neural Networks (FFNN), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads given temperature predictions, holiday information and historical loads. The models are trained on-hourly data from the New England Power Pool (NEPOOL) region from 2004 to 2007 and tested on out-of-sample data from 2008. Experimental results indicate that the proposed LP-SVR method gives the smallest error when compared against the other approaches. The LP-SVR shows a mean absolute percent error of 1.58% while the FFNN approach has a 1.61%. Similarly, the FFNN method shows a 330 MWh (Megawatts-hour) mean absolute error, whereas the LP-SVR approach gives a 238 MWh mean absolute error. This is a significant difference in terms of the extra power that would need to be produced if FFNN was used. The proposed LP-SVR model can be utilized for predicting power loads to a very low error, and it is comparable to FFNN and over-performs other state of the art methods such as: Bagged Regression Trees, and Large-Scale SVRs.
文摘Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.
文摘We consider two non-iterative algorithms of adaptive power loading for multicarrier modulation (MCM) system, The first one minimizes the average power of the system transmitter and ensures the preset average bit-error rate, while the second reduces the average transmitting power subject to the given values of demanded bit-error rate and of the outage probability. The algorithms may be used for power-efficient management of the up-link in cellular communication, where mobile terminals use rechargeable batteries, or of the downlink in satellite communication with solar power source of a transponder. We present performance analysis of the adaptive MCM systems supported by computer simulation for the case of the m-Nakagami fading and additive white Gaussian noise in the forward and backward channels. Evaluation of the power gain of the proposed strategies and its comparison with uniform power loading shows that the gain depends on the fading depth and average signal to noise ratio in the system sub-channels.
基金Supported by the National Natural Science Foundation of China(52077085)Natural Science Foundation of Guangdong Province(2023A1515012273).
文摘In DC microgrid systems,interleaved boost converters(IBCs)are widely used to boost the output voltage of renewable energy sources on the source side of a DC bus owing to their high voltage gain and low current ripple.However,because power electronic converters on the load side behave as constant power loads(CPLs)with negative impedance characteristics,their high penetration can degrade the system stability.Therefore,this article proposes a fractional-order nonlinear controller integrated with an extended nonlinear disturbance observer(ENDO)for N-phase IBCs.First,the reduced-order model of the IBC is transformed into a canonical form using the differential geometric method.Subsequently,with the ENDO,the dynamic performance can be enhanced by estimating the disturbances,and a fractional-order nonlinear sliding surface is established to avoid the singularity problem and increase control flexibility.In addition,the stability of the proposed controller is analyzed using Lyapunov’s theorem.In a CPL variation test,the proposed controller exhibited a faster dynamic performance and lower tracking error thanconventional controllers,with at least a 27%improvement in the integral squared error(ISE).Both simulation and experimental results demonstrated the effectivenessof the controller,which can ensure large-signal stability and improved dynamic performance in DC microgrid systems.
文摘Modern electric power systems have increased the usage of switching power converters.These tightly regulated switching power converters behave as constant power loads(CPLs).They exhibit a negative incremental impedance in small signal analysis.This negative impedance degrades the stability margin of the interaction between CPLs and their feeders,which is known as the negative impedance instability problem.The feeder can be an LC input filter or an upstream switching converter.Active damping methods are preferred for the stabilization of the system.This is due to their higher power efficiency over passive damping methods.Based on different sources of damping effect,this paper summarizes and classifies existing active damping methods into three categories.The paper further analyzes and compares the advantages and disadvantages of each active damping method.
文摘With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property of data and numerical experiments,we find either the linear interpolation(LI)or the singular value decomposition(SVD)based method is superior to other methods depending on the smoothness of the data.We construct an index to measure the smoothness of data,and propose the SVDLI algorithm which adaptively selects different algorithms for data completion according to the index.Numerical simulations show that irrespective of the smoothness of data,the data complementing results of SVDLI are comparable to or better than the best of SVD or LI algorithms.The present study is verified using the measurements in China,and the public data of the Australian electricity distribution company and Lawrence Berkeley National Laboratory.
基金Science and Technology Project of SGCC(SGTJDK00DWJS1600014).
文摘Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load.Then,set order for the time series model by Akaike information criterion(AIC)rule and acquire model coefficients to establish ARMA(2,1)model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the electric power load data.Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA(2,1)model.Besides,variance is reduced by two orders,and every coefficient of stochastic noise is reduced by one order.The filter method based on time series model does reduce stochastic noise of electric power load,and increase measurement accuracy.
基金supported by National Natural Science Foundation of China(Grant 61273151).
文摘Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was established.The input parameters of RFR model were determined by means of the grid search algorithm.The prediction results for this model were compared with those for several other common machine learning methods.It was found that the coefficient of determination(R^(2))of test set based on the RFR model was the highest,reaching 0.514 while the corresponding mean absolute error(MAE)and the mean squared error(MSE)were the lowest.Besides,the impacts of the air conditioning system used in summer on the power load were discussed.The calculation results showed that the introduction of indexes in the field of Heating,Ventilation and Air Conditioning(HVAC)could improve the prediction accuracy of test set.
文摘For OFDM systems with hundreds or thousands subcarriers with the adaptive bit and power loading according to each subcarrier , the signaling overhead will be awfully large. However, the adaptive bit and power loading according to “subband” is an effective solution to this problem, with which the signaling overhead is expected to be dramatically decreased at the cost of some performance loss. In this paper, based on Ref . [5] but with some modification to the subband bit and power loading algorithm, we apply the algorithm to the IEEE 802.16e OFDM system. The results show that the modified subband bit and power loading algorithm can achieve better BER performance and the signaling overhead is reduced by 75% at the cost of performance loss less than 1 dB if the number of subcarriers per subband is 4 when the BER is around 10^-3.
基金supported by the National Natural Science Foundation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution.However,as fishery power load is of greatly unique meteorology sensitivity,it continues to be a difficult problem.Therefore,the research of fishery meteorology is an important part of the rational development of fishery resources,the protection of production safety,and the pursuit of high and stable yield.This paper makes a deep study on the power load of the fishery energy internet under the influence of fishery meteorology and takes onshore fish pond as the research object.First of all,the power load is divided into three parts:oxygen enrichment power load,feeding power load,and water replenishment and drainage power load.The impact mechanism of fishery meteorology(including temperature,surface wind speed,precipitation,relative humidity,etc.)on it is described,and then the overall power load is obtained through modeling and integration.Finally,taking the Yuguang Complementary Project in Zhouquan Town,Tongxiang,Zhejiang Province,China as an example,using the meteorological data of its typical spring day and using the MATLAB tool to solve,the hourly comparison of the three types of power loads,the comprehensive power load demand,the full-day electricity charge forecast and the total annual power consumption are calculated.The annual power consumption per hectare and per kilogram of output calculated by simulation are basically consistent with the order of magnitude of the survey data,which proves the validity of the model proposed.The model established in this paper is an original work,and the exploration of fishery energy internet can draw lessons from it.
