Utilizing Genetic Algorithm and Machine Learning to Optimize a Control System in Generators : Using a PID controller to damp terminal voltage oscillations

Abstract: Hydropower is an important part of renewable power production in Sweden. The voltage stability of the already existing hydropower needs to be improved. One way to do this is by improving the control system that damp terminal voltage oscillations. If the oscillations in the power system are not damped it could lead to lower power outputs or in the worst case a blackout. This thesis focuses on the automatic voltage regulator (AVR) system with a proportional, integral, derivative (PID) controller. The PID controller’s parameters are optimized to dampen the terminal voltage instability in a generator. The aim is to develop a machine learning model that predicts the optimal gain parameters for a PID controller. The model is using the tuned gains from the Ziegler-Nichols (Z-N) method and the amplifier gain as inputs and gives the optimal gains as output. A linearized model of an AVR system, based on transfer functions was developed in a MATLAB script. This model was used to simulate the behaviours of an AVR system when a change in load occurs. The Z-N method and the genetic algorithm (GA) with different settings and fitness functions were used to tune a PID controller. The best performing method is GA with the fitness function developed by Zwe-Lee Gaing (ZL).  The best performing settings are: roulette selection, adapt feasible mutation, and arithmetic crossover. The GA (ZL) was used in the development of a machine learning model. Two different models were developed and tested: the support vector regression (SVR) and the gaussian process regression (GPR). The data that was used to train the models were generated by changing the transfer functions’ time constants 4096 times. At each step, the Z-N, and the GA (ZL) were run. The GPR model is shown to be the superior model with a lower root mean square error (RMSE) and a higher ratio of variation (R^2). The RMSE for GPR is 0.1091, 0.0815, 0.0717 and the R^2 is 87 %, 59 %, and 86%. The result shows that the developed model has capabilities to optimize the PID controller gains of any AVR-system without knowing the characteristics of the components.