基于改进GA-BP混合算法的电力变压器故障诊断

将改进遗传算法(GA)和误差反向传播(BP)算法相结合构成的混合算法用于训练人工神经网络.该混合算法有效地解决了常规BP算法学习网络权值收敛速度慢、易陷入局部极小和GA算法独立训练神经网络速度缓慢等缺点,并对其应用于电力变压器故障诊断进行了仿真,仿真结果表明了该算法具有较快的收敛速度和较高的计算精度,故障诊断结果证实了该算法应用于电力变压器故障诊断的有效性.

POWER TRANSFORMER FAULT DIAGNOSIS BY IMPROVED HYBRID ALGORITHM BASED ON GENETIC ALGORITHM AND BACK PROPAGATION ALGORITHM

The hybrid algorithm which combines improved genetic algorithm (GA) with error back-propagation algorithm (BP) is used to train artificial neural network. The defects of conventional BP algorithm, i.e., easy to fall into local minimum, slow convergence speed of the weight value of learning network, and that of GA, i.e., the training speed is too slow when GA is used to train the neural network effectively improved by itself, are effectively improved by the hybrid algorithm. The application of the hybrid algorithm to power transformer fault diagnosis is simulated, the results show that the hybrid algorithm possesses faster convergence speed and higher calculation accuracy.