Abstract

As one of the key components of the circuit breaker operating mechanism, the relay can experience performance degradation due to harsh environmental factors, such as salt spray, during operation, which can ultimately affect the normal operation of the circuit breaker and threaten the safe and stable operation of the power system. To effectively evaluate the condition of the relay operating in salt spray environments over the long term, this study conducted accelerated aging experiments on a certain model of auxiliary switch under salt spray conditions using an existing test platform. The relay’s pull-in voltage, release voltage, pull-in time, and release time were analyzed as characteristic parameters affected by salt spray. The results showed that the intrusion of salt spray caused a decline in the coil performance of the relay, which required higher voltage to provide the electromagnetic force needed for operation, leading to an increase in operating voltage. On the other hand, coil degradation also caused a decrease in the electromagnetic force generated by the step voltage, resulting in a slower operating time. Subsequently, a Genetic Algorithm_Back Propagation Neural Network GA_BP) algorithm model suitable for identifying the relay status in this study was established. After optimization by a Genetic Algorithm Neural Network (GA), the recognition accuracy increased from 78.6% to 91.8%, which showed significant improvement. By clarifying the changes in the characteristic parameters of the relay in a salt spray environment and relying on experimental data, this study established a relevant state recognition model. The research results have important engineering application value for understanding the relay operating status of the circuit breaker operating mechanism in salt spray environments, conducting circuit breaker operating mechanism relay life prediction, and preventing circuit breaker operating mechanism failures.

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