PUGWO-TEABC-MNet: A Hybrid Metaheuristic Framework for Optimal Ice-Melting Device Placement in Power Distribution Networks

Abstract

Ice accumulation on power distribution lines leads to significant reliability issues, operational instability, and economic losses during extreme weather conditions. Accurate prediction and optimal placement of ice-melting devices are essential to mitigate these impacts and ensure continuous power delivery. Conventional optimization approaches often exhibit slower convergence, limited adaptability to nonlinear climatic variations, and inadequate prediction accuracy under uncertain environmental parameters. The objective to develop an intelligent and robust hybrid framework for optimal ice-melting equipment placement, enhancing prediction precision and system resilience. A new hybrid metaheuristic framework, PUGWO-TEABC-MNet, is formulated by integrating a Position-Updated Grey Wolf Optimizer (PUGWO), Tent-Elite Artificial Bee Colony (TEABC), and LSTM-based Memory Network (MNet) to ensure adaptive exploration and exploitation with dynamic memory learning. The framework optimally determines device placement by minimizing prediction error while modeling nonlinear climatic dependencies. Climatic datasets related to icing events, including wind speed, temperature, humidity, and ice thickness, were collected from Kaggle repositories. Data were normalized and partitioned into an 80:20 training–testing ratio. Principal Component Analysis (PCA) was used to retain the eight most significant components representing climatic influence. PUGWO initializes optimal search regions, TEABC refines global optima, and MNet captures sequential temporal patterns to improve predictive stability. The framework was implemented in Python using TensorFlow and Scikit-learn libraries. Performance metrics achieved include R² = 0.992, MAPE = 0.0042, and RMSE = 0.12, outperforming baseline BP and PSO-BP approaches. The proposed framework demonstrates superior forecasting accuracy and resilience, providing a cost-effective strategy for optimal ice-melting device deployment in power distribution networks.