Abstract: In order to overcome the shortcomings of existing adaptive noise filtering algorithms for substation noise control, such as poor adaptive ability and slow convergence speed etc., a new intelligent filter algorithm is proposed based on the error back propagation neural network (BPNN) of particle swarm optimization (PSO). In view of the existing problems of PSO, including difficult reconciliation of local and global search, and the population diversity loss, an improved strategy is used, which uses the particle’s“close” degree to adaptively adjust particle inertia factor and mutat ion rate. The improved particle swarm optimization algorithm is used to replace the gradient descent algorithm for real-time optimization of the weights and thresholds of BPNN, and rapid noise reduction. The gradient descent algorithm is then used for further optimization of the weights and thresholds of BPNN, and as a result, the noise is further subdued. By using the noise signal of a substation transformer for simulation of the sound source, the proposed algorithm, PSO-BPNN algorithm and BPNN algorithm are respectively applied to suppress the sound source. The results show that the performance of the proposed algorithm is superior to the performance of the other two algorithms, and the transformer noise reduction system is improved notably.

Key words: power system, substation, noise control, BPNN, IPSO algorithm, particle intimacy, inertial factor, adaptive mutation