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

Planned production of thermal units for reducing the emissions and costs using the improved NSGA II method

This paper presents a new algorithm for production planning, thermal units called so-called economic dispatch. The selected problem for algorithm implementation is a bounded and non-linear optimization problem. In the present paper, it has been tried to reduce the production costs, as well as the polluting gases emitted while generating energy. The proposed algorithm is based on multi-objective optimization algorithm of Genetic Algorithm, which is a meta-heuristic algorithm. In this paper, an idea is suggested to improve the efficiency of the multi-objective optimization algorithm. After implementation and comparing the results obtained for the multi-objective standard algorithm with the previous works, it was observed that the proposed algorithm can obtain better results. To investigate the results, a 3-generator system with and without considering the losses was used. However, in each state, 3 different demands for energy was considered and the algorithm was implemented on this system. The proposed algorithm, find answers with less than 0.15% improvement in production costs, as well as on average 0.30% improvement on the find answers with less contaminant. Keywords: Economic load dispatch, Emission dispatch, Multi-objective optimization, Multi-objective optimization algorithm based on GA (NSGA II