Online Fault Diagnosis Method for Photovoltaic Arrays Using Bayesian Optimized Multilayer Perceptron
Photovoltaic arrays running under abnormal conditions will significantly reduce the power generation efficiency of photovoltaic power stations and shorten the service life of photovoltaic components.Timely and accurate identification of fault types becomes particularly important.A real-time remote monitoring and online fault diagnosis method for photovoltaic arrays was proposed,combining Bayesian optimization algorithm(BOA)with multilayer perceptron(MLP).Firstly,a simulation model of the photovoltaic array was established using Matlab/Simulink,simulating four typical fault scenarios and setting seven fault states.I-V and P-V curves were analyzed under different fault conditions.Considering the real-time measurable physical quantities during the operation of the photovoltaic array,fault features were extracted,and a new feature"current ratio"was constructed.Subsequently,the feature data was standardized.Then,BOA was used to optimize the hyper parameters of MLP,resulting in a highly accurate fault diagnosis model for the PV array with a test accuracy of 99.67％,demonstrating excellent diagnostic performance.Finally,a comparative study with algorithms such as random forest and support vector machine was conducted,and simulation experiments verify the accuracy and stability of the proposed method.
万方数据
维普
