Investigating the Intelligent Methods of Loss Minimization in Induction Motors

Induction motors are widely used in industry. Given the increasing demand for electric machines in different industries, optimization of these machines to achieve a high efficiency with low cost is of utmost importance. Loss-minimization in motor is done in three ways: 1) optimizing motor selection and design; 2) improving motor power supply waveforms; and 3) using appropriate controlling methods in drives. Often, inductive motors provide the maximum efficiency in their nominal load. In most applications it is necessary for a motor to work in light loads for a long time, e.g. in conveyors, elevators, etc. In these conditions, the machine load is not the nominal load, and a higher percentage of the input power is lost. So, in the case of variable load, the first and second methods cannot increase the efficiency; but the third method provides a large flexibility in decreasing motor losses. In this paper, the application of the third method in loss-minimization is reviewed. These motor losses are mostly related to the controlling strategy and basically occur in light-load conditions. There are various strategies to decrease this kind of losses, which are generally divided into two categories: classic methods and intelligent methods. In this paper, first the classic methods, including losses model control (LMC), flux control as a function of torque and search control (SC), are discussed. Then the intelligent methods, such as genetic algorithm, PSO, fuzzy logic and artificial neural network are investigated. This paper is presented while the last methods of efficiency improvement are being investigated and each method is described briefly

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Operation Efficiency Optimization for Permanent Magnet Synchronous Motor Based on Improved Particle Swarm Optimization

In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advantages of the attraction and repulsion PSO and the distributed PSO that can help the search controller to find the optimal d - axis air gap current quickly and accurately under non-stationary operating conditions, thereby minimizing the air gap flux and then improving the motor efficiency. To verify the effectiveness and stability of this proposed algorithm, the operating efficiency of PMSM as using this proposed algorithm is compared with that of traditional search controller under non-stationary operating conditions. The results show that the proposed algorithm can improve the operating efficiency of PMSM by 6.03% on average under non-stationary operation conditions. This indicates that the search controller based on the improved PSO has a better adaptation to the variation of external operating conditions, and can improve the operation efficiency of PMSM under non-stationary condition

Multiple Objective Co-Optimization of Switched Reluctance Machine Design and Control

This dissertation includes a review of various motor types, a motivation for selecting the switched reluctance motor (SRM) as a focus of this work, a review of SRM design and control optimization methods in literature, a proposed co-optimization approach, and empirical evaluations to validate the models and proposed co-optimization methods. The switched reluctance motor (SRM) was chosen as a focus of research based on its low cost, easy manufacturability, moderate performance and efficiency, and its potential for improvement through advanced design and control optimization. After a review of SRM design and control optimization methods in the literature, it was found that co-optimization of both SRM design and controls is not common, and key areas for improvement in methods for optimizing SRM design and control were identified. Among many things, this includes the need for computationally efficient transient models with the accuracy of FEA simulations and the need for co-optimization of both machine geometry and control methods throughout the entire operation range with multiple objectives such as torque ripple, efficiency, etc. A modeling and optimization framework with multiple stages is proposed that includes robust transient simulators that use mappings from FEA in order to optimize SRM geometry, windings, and control conditions throughout the entire operation region with multiple objectives. These unique methods include the use of particle swarm optimization to determine current profiles for low to moderate speeds and other optimization methods to determine optimal control conditions throughout the entire operation range with consideration of various characteristics and boundary conditions such as voltage and current constraints. This multi-stage optimization process includes down-selections in two previous stages based on performance and operational characteristics at zero and maximum speed. Co-optimization of SRM design and control conditions is demonstrated as a final design is selected based on a fitness function evaluating various operational characteristics including torque ripple and efficiency throughout the torque-speed operation range. The final design was scaled, fabricated, and tested to demonstrate the viability of the proposed framework and co-optimization method. Accuracy of the models was confirmed by comparing simulated and empirical results. Test results from operation at various torques and speeds demonstrates the effectiveness of the optimization approach throughout the entire operating range. Furthermore, test results confirm the feasibility of the proposed torque ripple minimization and efficiency maximization control schemes. A key benefit of the overall proposed approach is that a wide range of machine design parameters and control conditions can be swept, and based on the needs of an application, the designer can select the appropriate geometry, winding, and control approach based on various performance functions that consider torque ripple, efficiency, and other metrics

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Development of new methods for nonintrusive induction motor energy efficiency estimation

Induction motors (IMs) are the most widely used motors in industries. They constitute about 70% of the total motors used in industries and are the largest energy consumers in industrial applications. As a result of the increasing need for energy savings and demand-side management, the development of methods for accurate energy efficiency estimation has become a crucial area of research. While several methods have been proposed for induction motor efficiency determination, majority of the methods cannot be easily applied in the field owing to the intrusive nature of the test procedures involved. This PhD work presents some novel methods for nonintrusive efficiency estimation of induction motors operating on-site using limited motor terminal measurements and nameplate data. The first method is developed for induction motors operating on sinusoidal supply source (line-fed). The method uses a modified inverse Г-model equivalent circuit with series core loss arrangement to mitigate the inherent problems of higher computational burden and parameter redundancy associated with the conventional equivalent circuit method. Furthermore, a new method is presented for estimating the friction and windage loss using the airgap torque and motor nameplate data. The proposed Nonintrusive Field Efficiency Estimation (NFEE) technique was validated experimentally on four different induction motors for both balanced and unbalanced voltage supply conditions. The results demonstrate the accuracy of the proposed NFEE method and confirm its advantage over the conventional equivalent circuit method. In addition to the problem of unbalanced voltage supply, the presence of harmonics significantly affects the operation of induction motors. The second novel approach for estimating efficiency proposed in this PhD work extends the NFEE method to cover for non-sinusoidal supply condition. The method considers the variation of core loss, rotor bar resistance and leakage inductance due to time harmonics and skin effects. Finally, the efficiency estimations are compared to the IEC/TS 60034-2-3 in the case of a balanced non-sinusoidal supply condition. This allows not only the efficiency comparison but also the loss segregation analysis on the various components of the motor losses. In the case of an unbalanced supply, the efficiency results are compared to measured values obtained based on the direct input-output method. In both the first and second methods, a robust Chicken Swarm Optimization (CSO) algorithm has been used for the first time in conjunction with a simplified inverse Г-model EC to correctly determine the induction motor parameters and hence its losses and efficiency while inservice. As Variable Frequency Drives (VFDs) continue to dominate industrial process control, there is a need for stakeholders to quantify the converter-fed motor losses over a wide range of operating frequency and loading conditions. Although there is an increase in legislative activities, particularly in Europe, towards the classification and improvement of energy efficiency in electric drive systems, the handful of available standards for quantifying the harmonic losses are still undergoing validation. One of such standards is the IEC/TS 60034-2-3, which has been lauded as a step in the right direction. However, its limitation to rated motor frequency has been identified as one of its main weaknesses. Therefore, the third method proposed in this research demonstrates how the IEC/TS 60034-2-3 loss segregation methodology at nominal frequency can be extended over the constant-torque region of an induction motor (IM). The methodology has been validated by testing two motors using a 2-level voltage source inverter (VSI) in an open-loop V/F control mode. The results provide good feedback to the relevant IEC standards committee as well as guidance to stakeholders

Permanent Magnet Synchronous Motor Design using Grey Wolf Optimizer Algorithm

Common high-torque low-speed motor drive schemes combine an induction motor coupled to the load by a mechanical subsystem which consists of gears, belt/pulleys or camshafts. Consequently, these setups present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. Thus, the replacement of such a conventional drive with a properly designed low speed permanent magnet synchronous motor (PMSM) directly coupled to the load, provides an attractive alternative. In this context, the paper deals with the design evaluation of a 5kW/50rpm radial flux PMSM with surface-mounted permanent magnets and inner rotor topology. Since the main goal is the minimization of the machine's total losses and therefore the maximization of its efficiency, the design is conducted by solving an optimization problem. For this purpose, the application of a new meta-heuristic optimization method called “Grey Wolf Optimizer” is studied. The effectiveness of the method in finding appropriate PMSM designs is then evaluated. The obtained results of the applied method reveal satisfactorily enhanced design solutions and performance when compared with those of other optimization techniques