A. Bahmanyar, H. Borhani-Bahabadi, S. Jamali,
Volume 16, Issue 3 (9-2020)
Abstract
To realize the self-healing concept of smart grids, an accurate and reliable fault locator is a prerequisite. This paper presents a new fault location method for active power distribution networks which is based on measured voltage sag and use of whale optimization algorithm (WOA). The fault induced voltage sag depends on the fault location and resistance. Therefore, the fault location can be found by investigation of voltage sags recorded throughout the distribution network. However, this approach requires a considerable effort to check all possible fault location and resistance values to find the correct solution. In this paper, an improved version of the WOA is proposed to find the fault location as an optimization problem. This optimization technique employs a number of agents (whales) to search for a bunch of fish in the optimal position, i.e. the fault location and its resistance. The method is applicable to different distribution network configurations. The accuracy of the method is verified by simulation tests on a distribution feeder and comparative analysis with two other deterministic methods reported in the literature. The simulation results indicate that the proposed optimized method gives more accurate and reliable results.
Mojtaba Ajoudani, Seyed Reza Mosayyebi, Ramazan Teimouri Yansari,
Volume 22, Issue 2 (3-2026)
Abstract
The increasing penetration of distributed generation (DG) significantly complicates Distribution System State Estimation (DSSE) by introducing stochasticity and uncertainty. This paper proposes a novel DSSE framework that unlike conventional methods simultaneously estimates the system state, load demands, and DGs output power through a unified constrained optimization model. The model is efficiently solved using the Whale Optimization Algorithm (WOA), whose unique balance of exploration and exploitation enables robust solution search in complex, active distribution networks. Simulation studies on standard IEEE 37-bus and 69-bus test systems reveal that the proposed WOA-based approach achieves outstanding accuracy. For the 37-bus system, WOA attains a Maximum Individual Relative Error (MIRE) of 1.15% and a Maximum Individual Absolute Error (MIAE) of 2.303 on load estimation. On the larger 69-bus system, the method further reduces these errors yielding a MIRE of 0.886% and a MIAE of 1.12 for load, and 0.73% and 1.058 for DG power estimation, respectively. Across all experiments, WOA consistently outperforms leading metaheuristics including ABC, PSO, and GA highlighting its superior accuracy, scalability, and robustness for real-world DSSE challenges.
