Neural Network Based Speed Estimation of Induction Motor Using Indirect Field Oriented Control Methods

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Addis Ababa University


Induction motor is one of the most widely used machines in industrial applications due to its high reliability, relatively low cost, and less maintenance requirements. Vector control of ac machine behaves similar to separately excited dc machine in which the torque and flux are controlled independently. Nevertheless, conventional vector controlled induction motor drive has the disadvantage of required speed sensor and parameter sensitive, unknown variation during operation causes incorrect decoupling of flux and torque which leads to deterioration of drive performance. Sensorless speed control of induction machine has improvement of reducing the system size, cost and high system reliability. The aim of this thesis is to design robust speed estimation of induction motors based on NN by reducing the system components and achieve high dynamic performance. A neural network is an information processing system that is highly interconnected processing element working in unison used as an approach, stator voltage and current used as an input of NN and rotor speed as an output. The performance of the proposed system has investigated through simulations by allowing for system response (at no-load and load condition) and variation of motor parameter such as, variation of stator and rotor resistance, and speed tracking. The error of simulation result between actual and estimated speed have been approximately less than 0.25% for transient response and 4% for speed tracking. Lastly, the simulation result is established in MATLAB/SIMULINK and the pictures have been draw in Enterprise Architect and Microsoft Visio.



Computer Engineering