Optimal placement of PMU in wide area monitoring system of transmission network using quasi-oppositional-based artificial rabbit optimization

Due to the massive volume of data generated by the PMU implementation in the current power system during the data collecting process, the data transmission system becomes overburdened. The trade-off between installation cost, communication data congestion, and full system observability makes it difficult to decide where PMUs should be placed in large-scale transmission networks. Additionally, the placement of PMUs in the best possible position has a significant impact on both installation costs and traffic congestion. The wide area monitoring system (WAMS) is a quick fix for this data system congestion. Additionally, the incorporation and integration of the zero injection bus (ZIB) into the current system may allow for a further decrease in the number of PMU necessary to achieve full system observability. To achieve perfect observability of a PMU placement problem, the researchers in this admirable study developed a hybrid quasi oppositional-based artificial rabbit optimisation. In order to survive, rabbits use detour foraging, random hiding and energy shrink. Rabbits imitate other foragers while disregarding their own. This tactic helps with exploration. The rabbits can choose a random borrow from among their own borrows to hide in, lowering the likelihood that the predator would find them and kill them. These tactics assist in exploitation. A balance between exploration and exploitation is finally maintained by the energy shrink. In the current work, the authors used these special techniques to examine total observability, WAMS data traffic, ZIB, and cost installation index in the PMU placement problem. On the IEEE 14-bus, IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus, the proposed techniques have been tested. In order to demonstrate the superiority of the suggested technique in the white scenario, the computed results were compared with other published studies. The outcomes of the suggested methods also show a faster convergence and speedier data scenario. • Addresses the challenge of massive data congestion in PMU-based wide-area monitoring systems (WAMS). • Proposes optimal PMU placement considering installation cost, data traffic, and full system observability. • Incorporates Zero Injection Bus (ZIB) to minimize the number of PMUs required. • Develops a hybrid Quasi-Oppositional Artificial Rabbit Optimization (QOARO) algorithm balancing exploration and exploitation. • Validated on IEEE 14, 30, 57, and 118-bus test systems with superior convergence speed and performance compared to existing methods.