Recognition of Topological Relationship Between Substation Transformers and Users Based on Improved CNN-LSTM Deep Learning Neural Network
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.
convolutional neural network(CNN)long short-term memory(LSTM)topological relationship between substation transformers and usersrecognition analysis systemCNN modeluser identification