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
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
A novel adaptive power loading algorithm with the constraint of target overall bit error rate （BER） for orthogonal frequency division multiplexing （OFDM） systems is proposed in this article. The proposed algorithm...A novel adaptive power loading algorithm with the constraint of target overall bit error rate （BER） for orthogonal frequency division multiplexing （OFDM） systems is proposed in this article. The proposed algorithm aims to minimize the required transmit power with fixed data rate and uniform （nonadaptive） bit allocation, while guaranteeing the target overall BER. The power loading is based on the unequal-BER （UBER） strategy that allows unequal mean BERs on different subcarriers. The closed-form expressions for optimal BER and power distributions are derived in this article. Simulation results indicate the superiority of the proposed algorithm in terms of BER performance and algorithmic complexity.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
In this study,the present situation and characteristics of power supply in remote areas are summarized.By studying the cases of power supply projects in remote areas,the experience is analyzed and described,and the ap...In this study,the present situation and characteristics of power supply in remote areas are summarized.By studying the cases of power supply projects in remote areas,the experience is analyzed and described,and the applicability of related technologies,such as grid-forming storage and power load management,is studied,including grid-connection technologies,such as grid-forming converters and power load management.On this basis,three power-supply modes were proposed.The application scenarios and advantages of the three modes were compared and analyzed.Based on the local development situation,the temporal sequences of the three schemes are described,and a case study was conducted.The study of the heavy-load power supply mode in remote areas contributes to solving the problem of heavy-load green power consumption in remote areas and promoting the further development of renewable energy.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
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
A large-area high-power radio-frequency（RF） driven ion source was developed for positive and negative neutral beam injectors at the Korea Atomic Energy Research Institute（KAERI）. The RF ion source consists of a dr...A large-area high-power radio-frequency（RF） driven ion source was developed for positive and negative neutral beam injectors at the Korea Atomic Energy Research Institute（KAERI）. The RF ion source consists of a driver region, including a helical antenna and a discharge chamber, and an expansion region. RF power can be transferred at up to 10 kW with a fixed frequency of 2 MHz through an optimized RF matching system. An actively water-cooled Faraday shield is located inside the driver region of the ion source for the stable and steady-state operations of high-power RF discharge. Plasma ignition of the ion source is initiated by the injection of argongas without a starter-filament heating, and the argon-gas is then slowly exchanged by the injection of hydrogen-gas to produce pure hydrogen plasmas. The uniformities of the plasma parameter,such as a plasma density and an electron temperature, are measured at the lowest area of the driver region using two RF-compensated electrostatic probes along the direction of the shortand long-dimensions of the driver region. The plasma parameters will be compared with those obtained at the lowest area of the expansion bucket to analyze the plasma expansion properties from the driver region to the expansion region.展开更多
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
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
Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented ...Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.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
As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy....As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.展开更多
The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the...The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the unbalance occurs,the safe operation of the electrical equipment will be seriously jeopardized.This paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance prediction.Following the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power load.We applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north China.We experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real currents.The results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and stability.The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation.展开更多
The validity of electric power system simulation or prediction models depends on static load model. Measurement- based approach is the unique method to identify them adequately. The measured power depends on both load...The validity of electric power system simulation or prediction models depends on static load model. Measurement- based approach is the unique method to identify them adequately. The measured power depends on both load reaction to supply voltage alteration and random process of load alteration Basically, there is no any universal method that can single out the inherent static load model from experimental data. The paper offers a proprietary technique which is the particular solution of the task. The technique considers the selection of neighboring measurement pairs with the supply voltage altering significantly be-tween them, the exclusion of selected pairs by load power factor and subsequent selection of the inherent static load model presented as the polynomial load model. The usage of the technique to identify static load model at “Fenster” industrial enterprise （in Borisov city） is presented. The ideas considered in the paper can be used for future development of static load model identification methods with the data obtained during both active experiment and in other operating models of electric power systems.展开更多
This article provides a survey of recently emerged methods for wind turbine control. Multivariate control approaches to the optimization of power capture and the reduction of loads in components under time-varying tur...This article provides a survey of recently emerged methods for wind turbine control. Multivariate control approaches to the optimization of power capture and the reduction of loads in components under time-varying turbulent wind fields have been under extensive investigation in recent years. We divide the related research activities into three categories： modeling and dynamics of wind turbines, active control of wind turbines, and passive control of wind turbines. Regarding turbine dynamics, we discuss the physical fundamentals and present the aeroelastic analysis tools. Regarding active control, we review pitch control, torque control, and yaw control strategies encompassing mathematical formulations as well as their applications toward different objectives. Our survey mostly focuses on blade pitch control, which is considered one of the key elements in facilitating load reduction while maintaining power capture performance. Regarding passive control, we review techniques such as tuned mass dampers, smart rotors, and microtabs. Possible future directions are suggested.展开更多
Since the beginning of the year 2000, the power demands in Guangdong, Zhejiang provinces and Beijing Tianjin-Tangshan district have been increasing dramatically, power supply shortages have appeared again. This paper...Since the beginning of the year 2000, the power demands in Guangdong, Zhejiang provinces and Beijing Tianjin-Tangshan district have been increasing dramatically, power supply shortages have appeared again. This paper analyzes the reasons for the current power supply shortages in Shenzhen district and the problems existing presently in Shenzhen power system. It indicates that, to strengthen power demand forecast, to speed up power construction steps and with ’to develop power ahead of the rest’ as a fundamental target, are the precondition to the long term, steady development of power industry.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
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.
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 (60802033, 60873190)
文摘A novel adaptive power loading algorithm with the constraint of target overall bit error rate （BER） for orthogonal frequency division multiplexing （OFDM） systems is proposed in this article. The proposed algorithm aims to minimize the required transmit power with fixed data rate and uniform （nonadaptive） bit allocation, while guaranteeing the target overall BER. The power loading is based on the unequal-BER （UBER） strategy that allows unequal mean BERs on different subcarriers. The closed-form expressions for optimal BER and power distributions are derived in this article. Simulation results indicate the superiority of the proposed algorithm in terms of BER performance and algorithmic complexity.
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.
文摘In this study,the present situation and characteristics of power supply in remote areas are summarized.By studying the cases of power supply projects in remote areas,the experience is analyzed and described,and the applicability of related technologies,such as grid-forming storage and power load management,is studied,including grid-connection technologies,such as grid-forming converters and power load management.On this basis,three power-supply modes were proposed.The application scenarios and advantages of the three modes were compared and analyzed.Based on the local development situation,the temporal sequences of the three schemes are described,and a case study was conducted.The study of the heavy-load power supply mode in remote areas contributes to solving the problem of heavy-load green power consumption in remote areas and promoting the further development of renewable energy.
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Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
基金supported by the Ministry of Science,ICT and Future Planning of the Republic of Korea under the ITER Technology R&D ProgramNational R&D Program Through the National Research Foundation of Korea(NRF)Funded by the Ministry of Science,ICT&Future Planning(NRF-2014M1A7A1A03045372)
文摘A large-area high-power radio-frequency（RF） driven ion source was developed for positive and negative neutral beam injectors at the Korea Atomic Energy Research Institute（KAERI）. The RF ion source consists of a driver region, including a helical antenna and a discharge chamber, and an expansion region. RF power can be transferred at up to 10 kW with a fixed frequency of 2 MHz through an optimized RF matching system. An actively water-cooled Faraday shield is located inside the driver region of the ion source for the stable and steady-state operations of high-power RF discharge. Plasma ignition of the ion source is initiated by the injection of argongas without a starter-filament heating, and the argon-gas is then slowly exchanged by the injection of hydrogen-gas to produce pure hydrogen plasmas. The uniformities of the plasma parameter,such as a plasma density and an electron temperature, are measured at the lowest area of the driver region using two RF-compensated electrostatic probes along the direction of the shortand long-dimensions of the driver region. The plasma parameters will be compared with those obtained at the lowest area of the expansion bucket to analyze the plasma expansion properties from the driver region to the expansion region.
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Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.
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 the National Science and Technology Basic Work Project of China Meteorological Administration(2005DKA31700-06)Innovation Fund of Public Meteorological Service Center of China Meteorological Administration(M2020013)。
文摘As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.
基金This study is supported by the National Natural Science Foundation of China(No.61801019).
文摘The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the unbalance occurs,the safe operation of the electrical equipment will be seriously jeopardized.This paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance prediction.Following the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power load.We applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north China.We experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real currents.The results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and stability.The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation.
文摘The validity of electric power system simulation or prediction models depends on static load model. Measurement- based approach is the unique method to identify them adequately. The measured power depends on both load reaction to supply voltage alteration and random process of load alteration Basically, there is no any universal method that can single out the inherent static load model from experimental data. The paper offers a proprietary technique which is the particular solution of the task. The technique considers the selection of neighboring measurement pairs with the supply voltage altering significantly be-tween them, the exclusion of selected pairs by load power factor and subsequent selection of the inherent static load model presented as the polynomial load model. The usage of the technique to identify static load model at “Fenster” industrial enterprise （in Borisov city） is presented. The ideas considered in the paper can be used for future development of static load model identification methods with the data obtained during both active experiment and in other operating models of electric power systems.
基金This work is supported in part by the US National Science Foundation （CMM11300236）.
文摘This article provides a survey of recently emerged methods for wind turbine control. Multivariate control approaches to the optimization of power capture and the reduction of loads in components under time-varying turbulent wind fields have been under extensive investigation in recent years. We divide the related research activities into three categories： modeling and dynamics of wind turbines, active control of wind turbines, and passive control of wind turbines. Regarding turbine dynamics, we discuss the physical fundamentals and present the aeroelastic analysis tools. Regarding active control, we review pitch control, torque control, and yaw control strategies encompassing mathematical formulations as well as their applications toward different objectives. Our survey mostly focuses on blade pitch control, which is considered one of the key elements in facilitating load reduction while maintaining power capture performance. Regarding passive control, we review techniques such as tuned mass dampers, smart rotors, and microtabs. Possible future directions are suggested.
文摘Since the beginning of the year 2000, the power demands in Guangdong, Zhejiang provinces and Beijing Tianjin-Tangshan district have been increasing dramatically, power supply shortages have appeared again. This paper analyzes the reasons for the current power supply shortages in Shenzhen district and the problems existing presently in Shenzhen power system. It indicates that, to strengthen power demand forecast, to speed up power construction steps and with ’to develop power ahead of the rest’ as a fundamental target, are the precondition to the long term, steady development of power industry.
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.
