虚拟电厂VPP(virtual power plant)旨在将容量小、数量大的分布式电源DGs(distributed generations)聚合利用。构建了聚合储能、电动汽车及风机的VPP分段参与火电机组二次调频的负荷频率控制LFC(load frequency control)模型,提出1种包...虚拟电厂VPP(virtual power plant)旨在将容量小、数量大的分布式电源DGs(distributed generations)聚合利用。构建了聚合储能、电动汽车及风机的VPP分段参与火电机组二次调频的负荷频率控制LFC(load frequency control)模型,提出1种包含VPP的集中式自动发电控制策略。解析了虚拟电厂聚合资源的调频模型,考虑各调频环节的约束限制,以最低调频成本及区域控制偏差最小为目标,利用模型预测控制优化分配虚拟电厂内部DGs的功率输出。利用MATLAB/Simulink对所提2区域电力系统的LFC模型进行仿真,比较在虚拟电厂参与系统调频前后的频率误差及联络线功率偏差,仿真结果表明,虚拟电厂参与下多区域LFC可以减轻功率波动所带来的频率扰动及联络线功率偏差,所提控制策略能够实现有功出力优化分配,相较于传统PID调节具有更好的控制效果。展开更多
In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the di...In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark.In view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault classifiers.The data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation current.In addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor current.The package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification algorithms.The database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are provided.The developed code is available online,and is free to use.展开更多
Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substant...Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training.This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices,an increase in renewable penetration,a change in operational policies,etc.While the dip in model accuracy for unseen data is a cause for concern,what is more,challenging is not knowing when the ML model would respond in such a manner.Such uncertainty makes the power market participants,like bidding agents and retailers,vulnerable to substantial financial loss caused by the prediction errors of EPF models.Therefore,it becomes essential to identify whether or not the model prediction at a given instance is trustworthy.In this light,this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques.The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input.These scores are formulated in two stages:in the first stage,the coarse version of the score is formed using correlations of local and global explanations,and in the second stage,the score is fine-tuned further by the Shapley additive explanations values of different features.Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders.A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm.Results show that the algorithm has more than 85%accuracy in identifying good predictions when the data distribution is similar to the training dataset.In the case of distribution shift,the algorithm shows the same accuracy level in identifying bad predictions.展开更多
文摘虚拟电厂VPP(virtual power plant)旨在将容量小、数量大的分布式电源DGs(distributed generations)聚合利用。构建了聚合储能、电动汽车及风机的VPP分段参与火电机组二次调频的负荷频率控制LFC(load frequency control)模型,提出1种包含VPP的集中式自动发电控制策略。解析了虚拟电厂聚合资源的调频模型,考虑各调频环节的约束限制,以最低调频成本及区域控制偏差最小为目标,利用模型预测控制优化分配虚拟电厂内部DGs的功率输出。利用MATLAB/Simulink对所提2区域电力系统的LFC模型进行仿真,比较在虚拟电厂参与系统调频前后的频率误差及联络线功率偏差,仿真结果表明,虚拟电厂参与下多区域LFC可以减轻功率波动所带来的频率扰动及联络线功率偏差,所提控制策略能够实现有功出力优化分配,相较于传统PID调节具有更好的控制效果。
基金This work was supported by the Natural Science Foundation of Jilin Province,China(20210101390JC).
文摘In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark.In view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault classifiers.The data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation current.In addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor current.The package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification algorithms.The database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are provided.The developed code is available online,and is free to use.
文摘Advanced machine learning(ML)algorithms have outperformed traditional approaches in various forecasting applications,especially electricity price forecasting(EPF).However,the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training.This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices,an increase in renewable penetration,a change in operational policies,etc.While the dip in model accuracy for unseen data is a cause for concern,what is more,challenging is not knowing when the ML model would respond in such a manner.Such uncertainty makes the power market participants,like bidding agents and retailers,vulnerable to substantial financial loss caused by the prediction errors of EPF models.Therefore,it becomes essential to identify whether or not the model prediction at a given instance is trustworthy.In this light,this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques.The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input.These scores are formulated in two stages:in the first stage,the coarse version of the score is formed using correlations of local and global explanations,and in the second stage,the score is fine-tuned further by the Shapley additive explanations values of different features.Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders.A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm.Results show that the algorithm has more than 85%accuracy in identifying good predictions when the data distribution is similar to the training dataset.In the case of distribution shift,the algorithm shows the same accuracy level in identifying bad predictions.
