Hybrid GA and Improved CNN Algorithm for Power Plant Transformer Condition Monitoring Model

Under the general trend of smart grid development in China, it has especially importance to maintain the stability of power generation, the safety of power operation and the reliability of power supply. However, most power plants need to participate in the frequency regulation market and the power spot market, resulting in frequent load fluctuations and often unstable operating conditions of power generation equipment. In this study, a real-time monitoring method based on a hybrid Genetic Algorithm (GA) and Convolutional Neural Networks (CNN) algorithm is utilized to monitor the operation status of power transformers in power plants in real time. The GA-CNN algorithm model is proposed by analyzing the advantages and disadvantages of CNN and GA. It is proved that the accuracy of the GA-CNN is greatly improved compared with the CNN. In the recognition results, the error rate of the GA-CNN is only 1.86%, while that of the CNN is 4%; the random matrix accuracy of the predicted and actual output values of the GA-CNN model is 98.11%, and the three factors affecting the operating status of the equipment, namely temperature and humidity of the external environment and the daily power generation of the power plant, are also acceptable. The model selected for this study is able to detect abnormalities in the operating state of power transformers and provide timely feedback on changes in the external environment of the equipment.