Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : neural network controller

Neural Network-Based Robust Automatic Voltage Regulator (AVR) of Synchronous Generator

Abdullah Sahib Abdulsada; Abdulrahim Thiab Humod

Engineering and Technology Journal, 2011, Volume 29, Issue 7, Pages 1372-1385

The voltage stability and power quality of the electrical system depend on proper operation of AVR. Nowadays, Design technology of AVR is being broadly improved.
Nonlinearities and parametric uncertainties are unavoidable problem faced in
controlling the output voltage of Synchronous Generator (SG) when working alone or with others. This paper proposes a Nonlinear Auto Regressive-Moving Average control (NARMA-L2) as a voltage controller which is one type of Neural Network (NN) plant structure. Nonlinearities due to the effect of saturation in machine between generated voltage and field current, uncertainties arise because variation of the load connected with time and the change of rotors resistance with temperature. Due to this fact, Proportional- Integral- Derivative (PID) controller cannot be used effectively since it is developed based on linear system theory. NN controller shows less over shoot and settling time than PID controller with different conditions of load. Also NN controller shows high robust characteristic than PID controller.

Artificial Neural Network Control of the Synchronous Generator AVR with Unbalanced Load Operating Conditions

Helen J. Jawad; Fadhil A. Hassan

Engineering and Technology Journal, 2010, Volume 28, Issue 17, Pages 5514-5523

This paper proposes the using of artificial neural networks (ANNs') to
control the synchronous generator automatic voltage regulator (AVR), with unbalance load operating conditions. The neural network for control a nonlinear system is described and used to demonstrate the effectiveness of the neural network for control the drives with nonlinearities. In this study, performances of a simulated neural network AVR evaluated for a wide range of unbalanced loads
operating conditions. The variance factors are calculated, as an indicator of optimum operation, and their values are compared for different feedback signals and various unbalanced operating conditions. The optimum control is introduced, which gives an average variance factor in ANN controller is about 1.105%, whereas the average variance factor in traditional PI controller is about 2.035%.