Line loss anomaly identification in power grids using grey wolf algorithm-optimised SVR

The operating conditions of the power grid have strong nonlinear time-varying characteristics, and the line loss data presents non-stationary features, resulting in a decrease in the accuracy of line loss anomaly identification and a high false alarm rate.Therefore, a grey wolf algorithm is proposed to optimise the identification method of power grid line loss anomalies under SVR.Firstly, LSTM network is used to complete the missing power line loss data.Secondly, by simulating the encirclement mechanism and position update strategy of grey wolf hunting behaviour, the SVR parameters are adaptively adjusted.Finally, with the maximum number of iterations as the termination condition, output the optimal parameter combination to identify the abnormal state of line loss.The experimental results show that the accuracy of the proposed method for identifying line loss anomalies always remains in the high range of 96-98%, and the false alarm rate always remains below 2%.