In order to overcome the serious errors of wind farm load abnormal fluctuation forecasting results caused by traditional forecasting methods, a wind farm load abnormal fluctuation forecasting method based on probabilistic neural network is proposed in this paper. The probabilistic density is screened out by probabilistic neural network, and the maximum posterior probability density neuron is used as the output to realize wind farm load forecasting. According to the prediction results, a comprehensive severity subordinate function is constructed based on fuzzy reasoning to classify the severity of wind farm anomalies. According to the fuzzy operation rules, the abnormal fluctuation of wind farm load can be warned. The experimental results show that the operation error of the proposed method is only 0.49, the accuracy of early warning is high, and the effective fitting index is up to 0.95, which shows that the proposed method has high practical application value.

中文翻译：

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基于概率神经网络的风电场负荷异常波动的预警

为了克服传统预测方法对风电场负荷异常波动预测结果造成的严重误差，提出了一种基于概率神经网络的风电场负荷异常波动预测方法。通过概率神经网络筛选出概率密度，以最大后验概率密度神经元作为输出，实现风电场负荷预测。根据预测结果，建立基于模糊推理的综合严重度隶属函数，对风电场异常严重性进行分类。根据模糊运算规则，可以警告风电场负荷的异常波动。实验结果表明，该方法的运算误差仅为0.49，预警准确率高，