Fault diagnosis methods of photovoltaic power array based on deep belief network
In order to timely and accurately diagnose the fault of photovoltaic power array,firstly,the deep confidence network was used as the fault diagnosis algorithm of photovoltaic array,which was based on the simulation model of photovoltaic array output characteristics built in Matlab/Simulink.Then,the number of neurons in each hidden layer of the network was optimized by bat algorithm,and the optimized bat algorithm-deep belief network(BA-DBN)was used as the fault diagnosis model.Four parameters of photovoltaic array output characteristics under different operating conditions were obtained as characteristics samples,and then were input into the BA-DBN fault diagnosis model after normalization to realize the fault diagnosis of photovoltaic array.It is proved that the correct ratio of grid-connected photovoltaic power generation system based on the proposed BA-DBN algorithm is much higher than KNN,BPNN,and the original DBN algorithms.It is more suitable for photovoltaic array fault diagnosis and has better classifieation performance.
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