Study on power transformer protection based on chaos particle swarm optimization

Power transformers are important parts in power supply systems. The stability, safety and reliability of transformers are especially important for electricity systems. This paper uses dissolved gas value as a feature parameter and creates a power transformer fault detection model by support vector machine classifier. In order to address the difficulty of determining the parameters for support vector machine, an optimization algorithm based on chaos particle swarm is proposed. The approach is not easy to be trapped into local optimum and improves population diversity and particle ergodicity. The experiment results prove that the support vector machine using chaos particle swarm optimization can achieve classification with higher precision and faster speed in power transformer fault diagnosis, compared to the support vector machine using traditional optimization algorithm.