C. Thanga Raj

Energy Efficient Control of Three-Phase Induction Motor - A Review

Due to robustness, reliability, low price and maintenance free, induction motors (IMs) used in most of the industrial applications. The influence of these motors (in terms of energy consumption) in energy intensive industries is significant in total input cost. This paper presents a review of the developments in the field of efficiency optimization of three-phase induction motor through optimal control and design techniques. Optimal control covers both the broad approaches namely, loss model control (LMC) and search control (SC). Optimal design covers the design modifications of materials and construction in order to optimize efficiency of the motor. The use of Artificial Intelligence (AI) techniques such as artificial neural network (ANN), fuzzy logic, expert systems and nature inspired algorithms (NIA), Genetic algorithm and differential evolution in optimization are also included in this paper. Experimental and simulation examples on efficiency optimization are illustrated.

IN SITU EFFICIENCY DETERMINATION OF INDUCTION MOTOR: A COMPARATIVE STUDY OF EVOLUTIONARY TECHNIQUES

Induction motor is the most frequently used electric machine in industrial and commercial applications because of its well-known advantages, including robustness, low price, maintenance free, and line start. The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and its in situ efficiency determination. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. In this present study, particle swarm optimization, differential evolution, and some of their recent variants are used for in situ efficiency determination of induction motor (5 hp) without performing no-load test. Detailed information about the methods for efficiency measurement is given. Results are compared with genetic algorithm and a physical efficiency measurement method, called torque-gauge method. The performances in terms of objective function (error in the efficiency) and convergence time prove the effectiveness of optimization algorithms used in this article.

Performance Analysis of a Three-Phase Squirrel-Cage Induction Motor under Unbalanced Sinusoidal and Balanced Non-Sinusoidal Supply Voltages

As a result of the increasing use of electronic devices and other non-linear loads, the waveforms of the electricity supply voltage are being distorted and inequalities are appearing between the phases. This deterioration is associated with problems of electromagnetic incompatibility and reductions in the efficiency of loads such as motors. This paper investigates the negative effects of unbalanced sinusoidal voltage (0.96% unbalance and 2.9% THD) which always present in the power supply over balanced (inverter supply) non-sinusoidal voltage (23.7% THD) on the performance of induction motor in terms of line currents, power factor and efficiency. Under both supply conditions, the performance of a 5 HP three-phase squirrel cage induction motor was measured through a real load test. According to the test results and analysis, the negative effects of unbalanced sinusoidal voltage are more than the balanced non-sinusoidal voltage on the motors performance. The financial losses caused by unbalanced voltage of the same motor are determined.

DESIGN OPTIMIZATION OF INDUCTION MOTORS WITH DIFFERENTIAL EVOLUTION ALGORITHMS WITH AN APPLICATION IN TEXTILE SPINNING

This article deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machines in the textile industry, using three newly developed versions of differential evolution (DE) algorithms called modified DE versions (CMDE, GMDE, and LMDE). Efficiency, which decides the operating or running cost of the motor (industry), is considered as the objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance-related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries, and less costly to operate, certain constraints are modified in accordance with the demands of the spinning application. Comparison of the optimum designs with the industrial (existing) motor reveals that the motor designed by the proposed algorithms consumes less power input.

Realization on PSO Based Induction Motor Design Via SPEED/PC-IMD

This paper presents an optimal design and its realization of poly-phase induction motor using Particle Swarm Optimization (PSO). The optimization algorithm considers the efficiency, starting torque and temperature rise as objective function (which are considered separately) and nine performance related items as constraints. The PSO algorithm was implemented on a test motor and the results are compared with the Simulated Annealing (SA) technique and normal design. From the test results PSO gave better results and more suitable to motor’s design optimization. Optimized variables are realized by PC-IMD (Induction Motor Drives) of SPEED (Scottish Power Electronics and Electric Drives) software. C++ code is used for implementing entire algorithms.