During the past decade, the permanent magnet synchronous machines have been becoming more and more prevailing in industrial applications. Substantial optimization approaches are employed to improve the performances of the machine drives. It is well known that artificial intelligence techniques bring competitive solutions to machine design and control problems. In this paper, we provide a survey on the stateof-the-art control and optimization methods used in the permanent magnet synchronous machine drives. A large variety of control schemes and up-to-date optimization methods are reviewed here.

A Review of State-of-the-art Control and Optimization Methods in Permanent Magnet Synchronous Machine Drives

Comparison and analysis of hybrid optimization algorithms for water level control of drum boiler system

Aiming to solve the problems of poor dynamic response characteristics and the weak anti-jamming capability of the conventional proportional–integral–derivative (PID) controlled pump-motor servo system (PMSS) under the actual working environment, this study created a brand new hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithm to determine the best parameters of the PID controller for the PMSS speed control to make the PMSS achieve a constant speed control. We developed a GWOPSO-PID controller and compared it with a conventional PID controller, GWO-PID, PSO-PID, and GA-PID. In comparison to the other four control methods, the simulation and experimental results demonstrate that the designed GWOPSO-PID control had better dynamic response characteristics, with its rise times being reduced by 78.6%, 64.7%, 67.1%, and 41.5%, respectively. Additionally, the system under the GWOPSO-PID control exhibits a good stability and robustness even in the face of different load circumstances, with decreases in the re-equilibration times of 59.6%, 23.4%, 53.2%, and 41.9%, respectively, with a significantly improved immunity to disturbances.

/pdf/grey-wolf-particle-swarm-optimized-pump-motor-servo-system-39kdv386.pdf

Grey Wolf Particle Swarm Optimized Pump–Motor Servo System Constant Speed Control Strategy

This article is devoted to the biggest problem of our time: the development of modern and highly efficient equipment for energy systems. We propose improved mathematical models for starting asynchronous motors in isolated power systems. The results of experiments carried out on a model of an isolated network are presented. Both frequency-dependent and frequency-independent models were used. A comparison of various models is given. The advantages of the frequency-dependent model, which provides a more accurate representation of the processes, are shown. The obtained results were discussed, and the possibility of their use for future research was assessed.

Optimization of Equipment Operation in Power Systems Based on the Use in the Design of Frequency-Dependent Models

In this research, we use a variety of optimization techniques to perfect the settings of a PID controller that manages the water level in a boiler's drum. In this study, Particle Swarm Optimization (P_S OPTIMIZATION) is used alongside other optimization techniques and compared to each other. Optimization that combines bacterial foraging with Particle swarms (BFP_S) and Hybridising the Gravitational Search Algorithm along with Particle swarm (GSAP_S) seeks to improve search results. The forementioned methods were tested, and their results were compared, utilising metrics such as IS ERROR, IA ERROR, ITA ERROR, and time domain specifications. 

Drum Boiler System Water Level Control: A Study of Existing Hybrid Optimization Algorithms

This paper represents a new method optimal parameters PID based on nature inspired optimization algorithms application speed control of phases Permanent Magnet Synchronous Motors (PMSM). The methods for speed control stabilization of the PMSM using the Adaptive Particle Swarm Optimization controllers (APSO) and Hybrid Bacterial Foraging Particle Swarm Optimization controllers (BFPSO). The response results of the speed control PMSM Servo Systems used APSO and BGPSO methods are compared and the conclusions are given by simulation.

Application of PSO and Bacterial Foraging Optimization to Speed Control PMSM Servo Systems

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Application of PSO and Bacterial Foraging Optimization to Speed Control PMSM Servo Systems