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

Abstract

Due to the massive volume of data generated by the PMU implementation in the current power system during the data collection process, the data transmission system becomes overburdened. The trade-off between installation cost, communication 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 optimal placement has a significant impact on both installation costs and traffic congestion. The wide area monitoring system (WAMS) is a practical solution 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 PMUs necessary to achieve full system observability. To achieve perfect observability in the PMU placement problem, the researchers in this study developed a hybrid quasi oppositional-based artificial rabbit optimization. In order to survive, rabbits use detour foraging, random hiding, and energy shrinkage. Rabbits imitate other foragers while disregarding their own strategies. This tactic helps with exploration. The rabbits can choose a random burrow from among their own borrows to hide in, lowering the likelihood that the predator would locate and capture 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.