Research On Fault Location Of Distribution Network Based On Matrix Method

Power load forecasting not only plays an extremely important role in power system planning,but also the basis for maintaining stable operation of the power system.Power system dispatching,planning,and unit overhaul are all inseparable from power load forecasting.Improving the prediction accuracy of short-term load forecasting is not only conducive to the safer and economical operation of the power system,but also conducive to the control of the quality of electrical energy,thereby realizing the further improvement of the economic and social benefits of grid operation.Therefore,it is of great significance to study to improve the accuracy of short-term load forecasting of power systems.This article first introduces the basic principles and classification of short-term load of power system,deeply analyzes the characteristics of short-term load of power system and the main impact factors,through the analysis of short-term load characteristics of power system,it is concluded that short-term load of power system has uncertainty,Conditional and time characteristics;through analysis of different impact factors of short-term power load forecasting,it is concluded that climate factor and time factor are the main factors affecting short-term load forecasting of power system.Secondly,the basic principles and characteristics of convolutional neural networks(CNN)and long-short-term memory networks(LSTM),the classic algorithm models in deep learning,are introduced.Due to the large number of load influencing factors,the existing models cannot well dig the load rules,Leading to low accuracy of load forecasting.In order to overcome the current limitation of low power load forecasting accuracy,this paper introduces a self-attention mechanism that performs well in machine translation tasks to deal with the problem of short-term power forecasting.The self-attention mechanism can effectively highlight the factors that affect the load,thereby improving the accuracy of the prediction model.At the same time,it deeply analyzes the internal structure and principle of the self-attention mechanism to prepare for the combination model proposed in this paper.Then,according to the characteristics of historical load data combined with the advantages of the deep learning model introduced in this article,the prediction model in this paper is proposed,based on the self-attention mechanism CNN-LSTM short-term load prediction model.Because the convolutional neural network has powerful data feature extraction capabilities,this paper first uses the convolutional neural network toextract the feature features of the load and its related data.Then input the extracted feature vector data to LSTM.LSTM uses its own internal memory unit to learn the dependency of the load sequence.In order to better learn the deep dependency of the load sequence and thus further improve the prediction accuracy of the model,this article uses The self-attention mechanism,which is better at capturing the internal correlation of data or features,further studies the data output by LSTM to obtain the law of more load data.Finally,by comparing with the experimental results of CNN-LSTM and LSTM,it is proved that the extraction of load features by CNN and the introduction of self-attention mechanism to the model are beneficial to improve the accuracy of the short-term load forecasting model.