In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. This paper utilized the Meta heuristic-based method for the optimal size and placement of distributed generation (DG) and capacitor (QG) sources for loss reduction by incorporating network current carrying capacity constraint in the optimization problem. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called Multi-objective Whale Optimization Algorithm (MWOA). Wind and solar photovoltaic sources are utilized as the distributed generation with their probabilistic outputs. The developed method is tested using two feeders of practical Bahir Dar Distribution Network, Ethiopia. The results of loss minimization and voltage profile management with MWOA are compared with multi-objective particle swam optimization (MPSO) with an equal number of iteration to show the superiority of the developed method.

This paper proposes a security algorithm based on thewavelet-alienation-neural technique for detecting, classifying, and locating faults on Thyristor-Controlled Series compensator (TCSC) compensated lines. A fault index has been calculated using wavelet transform and alienation coefficients with post-fault current signals measured/ sampled for quarter cycle time at both near and far end buses for fault detection and classification. The location of the fault is predicted using an Artificial Neural Network (ANN) after the fault has been diagnosed. Approximate coefficients (quarter cycle time) of both voltage and current signals, from both buses, were provided as input to ANN. Various case studies, such as variations in TCSC position, fault location, sampling frequency, power flow path, incipient angle of fault, TCSC control strategy, fault resistance, and load switching conditions, have verified the robustness of the proposed safety system.