Power system state estimation using a robust crow search algorithm based on PMUs with limited number of channels

In modern electrical power systems, the state estimation results provide real-time data about the current operating state of the system. The state of the system can be estimated by using the system model and the available measurement data set. Measurement data are collected from measuring devices called Phasor Measurement Units (PMUs). The optimal placement of PMUs is determined by observing the entire system and taking into account the cost of placement. Moreover, the number of channels of PMUs is an important constraint that determines the number of measuring meters. This constraint can change the number of the optimal placement of the PMUs. Firstly, the PMUs are optimally placed by taking into account the limited number of channels. Secondly, the states of the system are estimated using the measurement data collected from the placed PMUs. In this study, a robust Crow Search Algorithm (CSA), which is one of the meta-heuristic methods, is proposed for the first time to solve both the optimal placement of PMUs and the optimal static estimate for the entire system. The proposed CSA has been tested on IEEE 14, 30, 57, and 118 bus test systems. The effectiveness of the CSA-based state estimator over other well-known algorithms, which are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Swarm Optimization (ABSO), is shown by comparing the obtained results. The wellknown Newton-Raphson power flow solutions are accepted as benchmarks, and the proposed CSA-based state estimator has given reliable results with better accuracy in all test systems compared to other methods. Thus, it has been proved that the proposed CSA is an alternative solution method for the state estimation problem. State estimation results are used in the analysis, operation, and planning of the power system. This study contributes to the more accurate and reliable operation of the system by the operators.