A Novel Design Optimization of a Fault-Tolerant AC Permanent Magnet Machine-Drive System

In this dissertation, fault-tolerant capabilities of permanent magnet (PM) machines were investigated. The 12-slot 10-pole PM machines with V-type and spoke-type PM layouts were selected as candidate topologies for fault-tolerant PM machine design optimization problems. The combination of 12-slot and 10-pole configuration for PM machines requires a fractional-slot concentrated winding (FSCW) layout, which can lead to especially significant PM losses in such machines. Thus, a hybrid method to compute the PM losses was investigated, which combines computationally efficient finite-element analysis (CE-FEA) with a new analytical formulation for PM eddy-current loss computation in sine-wave current regulated synchronous PM machines. These algorithms were applied to two FSCW PM machines with different circumferential and axial PM block segmentation arrangements. The accuracy of this method was validated by results from 2D and 3D time-stepping FEA. The CE-FEA approach has the capabilities of calculating torque profiles, induced voltage waveforms, d and q-axes inductances, torque angle for maximum torque per ampere load condition, and stator core losses. The implementation techniques for such a method are presented. A combined design optimization method employing design of experiments (DOE) and differential evolution (DE) algorithms was developed. The DOE approaches were used to perform a sensitivity study from which significant independent design variables were selected for the DE design optimization procedure. Two optimization objectives are concurrently considered for minimizing material cost and power losses. The optimization results enabled the systematic comparison of four PM motor topologies: two different V-shape, flat bar-type and spoke-type, respectively. A study of the relative merits of each topology was determined. An automated design optimization method using the CE-FEA and DE algorithms was utilized in the case study of a 12-slot 10-pole PM machine with V-type PM layout. An engineering decision process based on the Pareto-optimal front for two objectives, material cost and losses, is presented together with discussions on the tradeoffs between cost and performance. One optimal design was finally selected and prototyped. A set of experimental tests, including open circuit tests at various speeds and on-load tests under various load and speed conditions, were performed successfully, which validated the findings of this work

Design optimisation of brushless permanent magnet synchronous motor for electric vehicles

A novel new application of optimisation algorithm “Bess Algorithm” in the design of electric machine is presented in this thesis. The optimisation has the ability to perform global and local search and can be applied on constrained, unconstrained optimisation problem with multi-objective function, which all counted when consider optimisation algorithm for the design of electric machine. The searching procedure of the optimisation algorithm has been described in detailed. Furthermore, novel instructions and recommendation were implemented to tune the optimisation parameters, particularly for the purpose electric machine design, which in turn reduced the search space, increase efficiency and ability to find optimal solution with lower computation time. The optimisation was applied to search for optimal parameters of a benchmark electric machine with multi-objective to reduce the cost and increase the power density, power-volume ratio and efficiency. Throughout the thesis, a full detailed analytical model for the design of brushless permanent magnet synchronous motor that account for electromagnetic and thermal aspects was described. The optimisation was employed to search for optimal parameters of the analytical model that satisfy the design requirements. Then, the generated optimal parameters were evaluated and verified by Finite Element Analysis, FEA. The results from the FEA show good agreement with their corresponding values in the analytical model within acceptable range. At the same operational conditions and output specifications, the results show that the power density, volume to power ratio and cost of the new optimised motor IV were all increased by 19%, 39%, 24% respectively and the efficiency reduced only by -1%. The optimisation was also compared with one of the most usable optimisation algorithm used in the design of electric machine i.e. Genetic Algorithm. The results show that bees algorithm has more ability to cover the search space with less number of recruited bees and less number of iterations and higher computation efficiency

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems