Performance optimization on axial-flux permanent magnet coreless generator using novel hybrid computational method based on genetic algorithm and pattern search / Lok Choon Long - Lok , Choon Long (2016)
Complex real-world problems can be solved by heuristic optimization efficiently. Improved hybrid optimization method using Pattern Search (PS) and Genetic Algorithm (GA) onto Axial-Flux Permanent Magnet (AFPM) Coreless generator is presented in this thesis, and the optimization is based on the popular multi-objective sizing equation. This hybrid model utilizes concepts from GA and invents new generation chromosomes not only through mutation and crossover operation but also by mechanism of PS. In the design procedure, hybrid optimization model with some predefined constraints for the objective function have been taken into consideration which include the physical limitations and performance characteristics. The dimensions of the machine optimized with multiple adjustments to the number of magnet pole, the number of winding turns and air-gap distance in order to gain the highest power density within desired dimensional constraints. By using the proposed hybrid optimization method, the objective function has obtained a more accurate maximum power density with the least execution time over population compared with GA and PS. In addition, electromagnetic field and electromagnetic characteristics of the chosen generator is subject to Finite-Element Analysis (FEA). A finalized low power Axial-flux permanent magnet (AFPM) generator is fabricated, examined and testified to produce desired output. It has been observed that the experiment result agreed with the simulation result.