An Improvement of Load Flow Solution for Power System Networks using Evolutionary-Swarm Intelligence Optimizers

Load flow report which reveals the existing state of the power system network under steady operating conditions, subject to certain constraints is being bedeviled by issues of accuracy and convergence. In this research, five AI-based load flow solutions classified under evolutionary-swarm intelligence optimizers are deployed for power flow studies in the 330kV, 34-bus, 38-branch section of the Nigerian transmission grid. The evolutionary-swarm optimizers used in this research consist of one evolutionary algorithm and four swarm intelligence algorithms namely; biogeography-based optimization (BBO), particle swarm optimization (PSO), spider monkey optimization (SMO), artificial bee colony optimization (ABCO) and ant colony optimization (ACO). BBO as a sole evolutionary algorithm is being configured alongside four swarm intelligence optimizers for an optimal power flow solution with the aim of performance evaluation through physical and statistical means. Assessment report upon application of these standalone algorithms on the 330kV Nigerian grid under two (accuracy and convergence) metrics produced PSO and ACO as the best-performed algorithms. Three test cases (scenarios) were adopted based on the number of iterations (100, 500, and 1000) for proper assessment of the algorithms and the results produced were validated using mean average percentage error (MAPE) with values of voltage profile created by each solution algorithm in line with the IEEE voltage regulatory standards. All algorithms proved to be good load flow solvers with distinct levels of precision and speed. While PSO and SMO produced the best and worst results for accuracy with MAPE values of 3.11% and 36.62%, ACO and PSO produced the best and worst results for convergence (computational speed) after 65 and 530 average number of iterations. Since accuracy supersedes speed from scientific considerations, PSO is the overall winner and should be cascaded with ACO for an automated hybrid swarm intelligence load flow model in future studies. Future research should consider hybridizing ACO and PSO for a more computationally efficient solution model

Assessment and Evaluation of Soil Effect on Electrical Earth Resistance: A Case Study of Woji Area, Port-Harcourt, Nigeria

The properties of different soil types that affect the resistance of a buried electrical earthing material were studied, with the objectives of achieving a lowest possible earthing resistance by enhancing the soil at the grounding site Soil conduction mechanism, general practical earthing electrodes were analysed using known techniques for electrical earth resistance measurement. In the area under test, there is indication of previous grounding installation and how good is the aim. Based on literature review, the soil samples obtained from the sites under enhanced conditions and unenhanced condition were analysed. It was observed that each soil sample had varying characteristics under different conditions at the installation site. In view of all the factors analysed, temperature had little effect on the electrical earth resistance, whereas soil structure, chemical constituent, and electrode depth are the major contributing factors that affect electrical earth resistance of a grounding system as seen from the general assessment. Specifically, soil sample A (very moist loam soil) showed a very low earth resistance of 75Ω with electrode depth of 0.38m (1.3ft), 62Ω at 0.76m (2.6ft), and at 1.14m (3.9ft) recorded resistance was 53.7Ω. Soil sample F (dry sandy soil) has the highest earth resistance, 2483Ω. In the area of optimization (when other compounds are mixed with natural soils combination) the optimized soil sample BCH (Loamy, clay + hydrogen peroxide mix) has the lowest resistance of 241Ω at depth of 1.14m (3.9ft). Sample BFH (Clay, dry sandy soil + hydrogen Peroxide) had a reading of 318Ω at a depth of 1.14m (3.9ft), whereas the biochar optimized sample BFW (Clay, dry sandy soil + wood char) showed a resistance of 366Ω at the same depth of 1.14m (3.9ft). The optimized samples showed that electrical conduction capacity of the soil was enhanced by hydrogen peroxide compared to that of biochar as seen from the result presented in Table 2, using fall of potential, etc., method conducted in the early morning hours of the day, when temperature is 26 ̊C