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- W1928996848 abstract "Renewable energy is becoming more and more important with the current environmental issues. Many research projects are being conducted to implement the renewable technologies mainly in developing countries and small island developing states. Another way of reducing the environmental impact is to increase efficiency at the energy utilization end. More than half of the world’s electrical energy generated is consumed by electric machines. Thus, improving the efficiency in electrical drives brings along two benefits as economic saving and reduction of environmental pollution. Efficiency optimisation of three-phase induction motor can be achieved through optimal control and design techniques. Optimal control is more practical because the motor efficiency cannot be improved for every operating point by optimising machine design. A three-phase induction motor can be controlled via indirect field orientation which offers an improvement in the dynamic response of the system. However, it is observed that in many applications, the field orientation operate under constant flux for different amount of load torque and this reduces the efficiency of this control scheme. In this study, a loss model equation is developed for the three-phase induction motor to establish the relationship between the rotor flux and total electrical losses. Imperialist Competitive Algorithm (ICA) is compared to Particle Swarm Optimisation (PSO) to evaluate the optimal rotor flux that minimizes the motor losses at specific load torques. Simulation results show the effectiveness of using field orientation control with swarm intelligence and the superiority of ICA over PSO." @default.
- W1928996848 created "2016-06-24" @default.
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- W1928996848 date "2013-01-01" @default.
- W1928996848 modified "2023-09-27" @default.
- W1928996848 title "Efficiency Optimisation of Three-Phase Induction Motor Using Swarm Intelligence" @default.
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- W1928996848 doi "https://doi.org/10.1007/978-3-642-37753-2_38" @default.
- W1928996848 hasPublicationYear "2013" @default.
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