Application of Swarm Intelligence Approach for Improved Power System Diagnosis
This paper seeks to proffer an optimal power flow solution to the non-linear Euler-based load flow equations. Three Swarm intelligence (SI) optimization algorithms, namely, spider monkey (SM), artificial bee colony (ABC) and ant colony (AC) are used for optimal power flow diagnosis of the 330kV Nigerian transmission grid network. All algorithms are modeled and simulated in MATLAB environment. The results after simulation for 100 iterations revealed the strengths and weaknesses of the three algorithms. The power mismatch (error) value produced by SMO, ABCO, and ACO are 5420.94, 2499.35 and 616.72kVA, respectively, after 25, 93 and 64 iterations. Evidently, the results have shown the superiority of ACO over ABCO and SMO in accurately solving the non-linear load flow equations with a minimal error value. Future researchers should consider leveraging the strengths of ACO and SMO for an automated power flow solution.