Authors: Hachimenum N. Amadi, Sopakiriba Maxwell West, Richeal Chinaeche Ijeoma
Abstract: The incessant national grid collapse has become a global embarrassment; from 2015 to May 2024 the Transmission Company of Nigeria (TCN) has recorded 105 cases of grid collapse. Phasor Measurement Units (PMUs) are necessary for the extensive use and efficient running of international power networks, the present Supervisory Control and Data Acquisition (SCADA) system used in Nigeria does not provide a robust and dependable solution that improves the power grid’s real time monitoring and control capabilities. PMU will reduce the frequency of power grid breakdowns and also resolve fault location troubleshooting safely and timely. The optimal placement of Phasor Measurement Units (PMUs) is an important requirement in power systems research, particularly for the localization of transmission line faults. This research has proposed a Modified Sparsity Genetic Algorithm Optimizer (MS-GAO) for optimal placement of Phasor Measurement Units (PMUs) in Power Systems over the standard Genetic Algorithm (GA) approach used in various related studies. To further validate the performance, the time complexity studies were performed to determine the better technique considering enhanced PMU placement. The proposed approach has been applied to two IEEE power system networks – the IEEE 6-Bus and 14-bus power networks. The simulations were performed using the MATLAB software tool and results compared with the standard Genetic Algorithm (sGA) on the basis of the percentage Classification Efficiency (CE) and the number of trial iteration runs (iters) used per simulation. The results showed that the proposed MS-GAO gave comparable CEs when compared to sGA with 100% CE for 100iters. However, it was found that reducing the iterations to about 50iters resulted in a degradation of CE. Thus, a compromise should be made between the number of iterations required and the level of CE needed in the problem solution. In addition, computational run-time complexity results considering the 6-bus power network revealed that the MS-GAO will give better run-times when compared to the sGA with an average run-time reduction of about 0.5s. Thus, it is recommended that the MS-GAO be employed for a higher power bus networks as the computational demands will obviously be higher using a sGA.
