Application of Neuro-Swarm Intelligence Technique ToLoad Flow Analysis
Journal Title: American journal of Engineering Research - Year 2018, Vol 7, Issue 8
Abstract
Load flow analysis (LFA) is essential when planning design or stretching of a new or existing power station. Neuro-Swarm Intelligence is an optimally automated Artificial Intelligence (AI) solution technique which combines Artificial Neural Network (ANN) and Artificial Bee Colony (ABC) algorithms for power flow analysis. The ABC algorithm is used to evolve the solution space and train the ANN for optimal power flow solution. The ANN learning from the ABC, memorizes and registers the best initial data (parameter) setting which yields the best solution and sets the maximum cycle (maxCycle) for optimal load flow analysis (OLFA). Results from test conducted on the Diobu PHEDC 4-bus system using Neuro-Swarm algorithm showed good performance with less computational time and divergence mostly in heavy loading conditions. The results were validated by comparing them with that of Particle Swarm Optimization (PSO). The results showed better performance of Neuro-Swarm Intelligence in terms of flexibility, number of control parameters, computational time and number of iteration.
Authors and Affiliations
Christopher O. Ahiakwo1 ,, Sunny Orike2 ,, Otonye E. Ojuka
Keywords
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