Abstract
For mechanism analysis and high-performance control of synchronous reluctance machine (SynRM), accurate and reliable parameter identification of nonlinear magnetic model is always required. However, the accuracy and robustness of traditional heuristic algorithms are restricted by incomplete individual performance evaluation and single population evolution mechanism. In this paper, we propose a self-adaptive synergistic optimization (SSO) algorithm for extracting the parameters of the model. A novel synergistic-performance evaluation is first established to classify candidates automatically. Then, a self-organized mechanism is proposed to select optimal evolution strategies designed for classified candidate solutions. Around the current best candidate, the exploration is guaranteed in priority. Meanwhile, a self-adaptive mechanism is introduced to select other candidates to construct more promising evolutionary direction. Thus, achieving a good balance between exploration and exploitation. The parameter estimation performance of SSO algorithm is evaluated through standard datasets of SynRM magnetic model obtained by the finite element analysis. Comprehensive experiment results demonstrate the competitiveness and effectiveness of the proposed SSO algorithm compared with other algorithms, especially in terms of the accuracy and robustness. According to these superiorities, it can be concluded that the proposed algorithms are promising parameter identification methods for SynRM nonlinear magnetic model.
Highlights
In recent years, to cope with the energy consumption, cost, and over-dependence on rare earth elements rare-earth of AC motor, many efforts have been focused on the research of Synchronous Reluctance Motor (SynRM) [1]
Other algorithm variants such as self-adaptive differential evolution algorithm (SHDE) [12], dynamic encoding algorithm searches (DEAS) [13], fast parallel co-evolutionary particle swarm optimization algorithms (PSO) [14], genetic algorithm (GA) assisted PSO algorithm [15] and dynamic PSO algorithm with learning strategies [16] are employed to identify the parameters of motor model
We focus on the model in d − q reference frame synchronous to the rotor
Summary
To cope with the energy consumption, cost, and over-dependence on rare earth elements rare-earth of AC motor, many efforts have been focused on the research of Synchronous Reluctance Motor (SynRM) [1]. The direct current, no-load and locked-rotor tests results are used for parameter estimation Other algorithm variants such as self-adaptive differential evolution algorithm (SHDE) [12], dynamic encoding algorithm searches (DEAS) [13], fast parallel co-evolutionary PSO [14], GA assisted PSO algorithm [15] and dynamic PSO algorithm with learning strategies [16] are employed to identify the parameters of motor model. A self-adaptive synergistic optimization (SSO) algorithms is proposed to identify the parameters of SynRM magnetic model accurately and reliably. Based on the probability of the evaluation result, the candidates of the classification are chosen to construct different evolution directions using the proposed self-organizing evolution mechanism In this way, the fitness and diversity contribution of the candidate solution to current population are comprehensively utilized. To evaluate the effectiveness of the proposed SSO algorithm, we compared them with other well-established algorithms on parameters identification problems of the SynRM magnetic model.
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