A Power Failure Prediction Algorithm for Station DC Power Supply System Based on Value Function and Strategy Function
In the process of predicting power supply faults in station DC power supply systems,fault prediction mainly relies on a single value function,which cannot cope with dimensional explosions caused by weak changes in strategies,resul-ting in low G-mean values in fault prediction results.Therefore,a power failure prediction algorithm for station DC power supply systems is proposed that combines the value function and strategy function.Normalize the historical operating param-eters of the power supply,select the parameters that meet the requirements through grey analysis,and form a fault predic-tion analysis dataset.Construct a combination value function to describe the variance changes in a data sequence.The rein-forcement learning agent is composed of improved value function and strategy function,and a power failure prediction model including multi-layer perceptron network is designed.After data training and adjusting the model to be in the optimal state,high-quality DC power system power fault prediction results are obtained.The experimental results show that the G-mean value of the proposed method for power supply fault prediction is 0.98 ensuring the accuracy of the fault prediction results.
value functionstrategy functionmultilayer perceptron MLPstation DC power supply systempower fail-urefault prediction
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