基于GA-BPNN的多频超声波变压器油密度检测研究

Transformer oil density based on GA-BPNN method and multi-frequency ultrasound

变压器油是电力变压器中的主要绝缘物质之一,油的密度指标与变压器的安全运行息息相关。文中基于多频超声波、遗传算法-反向传播神经网络(GA-BPNN)的原理,对变压器油密度进行了预测研究。以电网公司110组变压器油为例,其中100组为训练集,10组为预测集。建立了基于BPNN的变压器油密度预测模型,并将242维多频超声数据作为输入,密度作为输出。通过试验法确定了BPNN的隐层神经元个数,由此建立非线性映射关系,并用遗传算法优化BPNN的各层连接权值及阈值。结果表明,与传统的标准BPNN模型相比,GA-PNN模型的变压器油密度值与实际值拟合度更高,误差更小。研究结果为检测变压器油的其他参数提供了可靠的依据。

Transformer oil is one of the main insulating material in power transformers.The density index of oil is closely related to the safe operation of transformers.On the basis of the principle of multi-frequency ultrasound, genetic algorithm GA and back propagation neural network BPNN, this paper proposed a prediction study of density of transformer oil. Taking 110 sets of transformer oil belonged to China southern power grid as an example, a prediction model of density of transformer oil was established based on BPNN, with the 242 dimensional multi-frequency ultrasonic data of oil sample as the input and density as the output. By adjusting the number of hidden layer neurons, the network was trained. Moreover, the genetic algorithm GA was introduced to optimize the network parameters. All results show that compared with the traditional standard BPNN model, the output value of density of transformer oil with the GA-BPNN model is much close to the real value with small errors, which lays a solid foundation to test transformer oil other parameters with tell multi-frequency ultrasonic technology.