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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 lossminimization 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.

In this paper, a novel optimization algorithm based on Particle Swarm Optimization (PSO) is proposed for solving the non-convex non-linear economic dispatch (ED) problem in a system integrated with combined heat and power. In the proposed method, which is known as Advanced Modified PSO (AMPSO), one third of the population is updated based on conventional PSO, and the remaining members are updated randomly based on the boundaries of variables. In order to improve the efficiency of the algorithm, the required coefficients were estimated by the Taguchi method. The optimization problem is the nonlinear non-convex ED in which the effects of steam valves and the power loss have been included. The proposed algorithm is applied on different systems, and the results are compared with those of some well-regarded methods developed by other researchers. The results show the superiority of the proposed AMPSO in comparison to other methods.