In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model. Using actual power load data from Wuxi, Jiangsu Province, China, as a dataset, the model makes predictions. The results indicate that the model is effective because the enhanced sparrow algorithm optimizes the bi-directional long- and short-term memory network model for predicting the power load data with a relative error of only 2%, which is higher than the prediction accuracy of the other models proposed in the paper.

An improved fault localization method combining total aggregate empirical modal decomposition (MEEMD) and Teager energy operator (TEO) is proposed to address the fault localization issue of three-terminal multi-segment overhead line–cable hybrid transmission lines. This method solves the fault localization problem caused by wave impedance discontinuity in hybrid lines. First, the MEEMD algorithm, which improves modal aliasing, and the Teager energy operator, which reflects transient energy changes, are combined for the accurate detection of faulty traveling wave heads. The fault line section determination condition within the fault branch is used to determine the overhead line section or cable section where the fault is located after determining the faulty branch line. This condition is based on the time difference between the initial traveling wave of the fault arriving at each end measurement point and the T-node. Ultimately, the fault distance is determined using the double-ended traveling wave technique. By combining PSCAD and MATLAB for simulation verification, the accuracy of the ranging method is compared and analyzed, and the results show that the method is able to ensure the accuracy of ranging at different fault locations and transition resistances. Additionally, the localization error is basically kept within 30 m, which significantly improves the accuracy of fault localization.