RESEARCH ON FAULT LINE SELECTION METHOD FOR OFFSHORE WIND FARM COLLECTING LINES BASED ON SPARSE MEASUREMENT
To solve the problem that the fault location of multi-branch collecting lines of offshore wind farm mostly depends on multiple measurement points,a method of fault line selection of collecting lines based on convolutional neural network(CNN)is proposed,which uses local connection based on sparse measurement to realize fault line selection of collector lines.In this method,a small number of node current signals are taken as the characteristic quantity,and a measurement position optimization model is established with the goal of minimizing the initial CNN network loss of sparse samples.The binary particle swarm optimization(BPSO)algorithm is used to solve the model and obtain the optimal measurement position.The example analysis shows that the proposed method can achieve fault line selection with high accuracy under sparse measurements,with low sampling frequency requirements,and is not affected by factors such as fault starting angle,fault resistance,and fault location.It also has good robustness to measurement noise.
offshore wind farmscollector linesconvolutional neural networkbinary particle swarm optimization algorithmfault line selectionmeasurement position
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