A new method for identifying the topological relationship between substation transformers and users was proposed to address the issues of complex and diverse data information,lagging data processing capabilities,and low user utilization in the power substation area.By constructing convolutional neural network(CNN)and long short-term memory(LSTM)neural network models,the active power,reactive power,voltage value,current value,and various electricity consumption data information of the distribution transformer in the substation area were transformed into a CNN-LSTM deep learning neural network model;and the LSTM module was integrated into the CNN model to convert the topological macro data relationship between substation transformers and users into micro data information for recognition,greatly improving the recognition and application capabilities of substation transformers and users topological relationship.By setting different levels of CNN-LSTM deep learning neural network,the topological relationship of substation area and user variables was calculated.Through example analysis,the user recognition ability has been greatly improved.The technical ideas for topology relationship recognition of substation transformers and users is provided.
关键词
卷积神经网络/长短期记忆/户变拓扑关系/识别分析系统/卷积神经网络模型/用户识别
Key words
convolutional neural network(CNN)/long short-term memory(LSTM)/topological relationship between substation transformers and users/recognition analysis system/CNN model/user identification
维普
万方数据