A Hybrid CNN-LSTM Model for Flashover Alert on Polluted Insulators Using UHF and Acoustic PD Signals

Insulators are critical components in the transmission of electrical energy, and their correct operation is essential to guarantee the safety and continuity of the electrical supply. However, their constant exposure to environmental conditions such as pollution can affect their performance and lead to costly power outages. Proper monitoring and maintenance of this equipment are essential to avoid unexpected failures. Although significant progress has been made in the implementation of new monitoring methodologies and instruments, a solution that enables accurate and reliable online monitoring remains a challenge. This work presents a novel insulator flashover alert system that uses an acoustic sensor and an ultrahigh-frequency (UHF) antenna to detect partial discharge (PD) activity and generate alerts in three different states. The system uses hybrid deep learning models that combine a convolutional neural network (CNN) and a long short-term memory (LSTM) network that process the information provided by the sensors and generate local alerts to produce the general decision. This system can indicate whether the monitored insulator is in a normal state, an attention state, or an alarm state, which could help specialists take preventive measures and avoid sudden insulator failures.