A PCA-LSTM-Based Method for Fault Diagnosis and Data Recovery of Dry-Type Transformer Temperature Monitoring Sensor

Abstract

The failure that occurs during the dry-type transformer temperature monitoring sensor working will result in wrong data output, which may cause the monitor and monitoring background to respond incorrectly. To solve this problem, a fault diagnosis and data recovery algorithm based on principal component analysis (PCA), long short-term memory neural network (LSTM), and decision tree is proposed. It can realize the fault sensor location, fault diagnosis, and data recovery under dynamic processes. First, a set of temperature monitors was designed to collect the temperature inside the dry-type transformer in real-time by using the collected temperature data to build a PCA-based fault diagnosis model and a LSTM-based data recovery model. A fault location model based on a decision tree was constructed for five typical sensor fault types. Finally, the three models were constructed to obtain the sensor fault diagnosis and recovery algorithm. We then transplanted the algorithm to the temperature monitor. The experimental results showed that the recognition rate of the algorithm for different fault diagnoses of single- or multiple-sensors was above 96%. The diagnosis time was less than 1 ms. The recovery error was within 0.1 °C. The field experiments verified that the algorithm could significantly improve the stability of the monitor. Even if the sensor fails, it can also ensure that the dry-type transformer works within the normal range.