Transformers are crucial devices in the power system,primarily used for measurement and protection.However,during long-term operation,transformers are susceptible to various factors such as the environment,temperature,and humidity,leading to performance degradation or even failures.To ensure the normal operation of transformers,real-time monitoring and fault diagnosis are necessary.Traditional transformer monitoring methods mainly rely on manual experience,which is inefficient and prone to misdiagnosis.Therefore,a transformer multi-parameter online detection system based on Convolutional Neural Networks(CNN)is proposed.By collecting key parameters of transformers in real-time and combining the powerful feature extraction and classification capabilities of CNN,the system can achieve real-time monitoring and fault diagnosis of transformer status.
维普
万方数据