Optimal Controller Design for Speed Governors of Hydroelectric Power Plant

Speed governors have critical importance on hydroelectric power plants, which are adjusted to the rotating speed of hydroelectric generation based on load demand of the grid. The rotating speed is the main factor to balance power generation and load demand. The well-designed controller is needed to control speed governors with high accuracy. A well-defined model is needed to obtain desired control structure. Therefore, in this study, initially, the mathematical model of a hydroelectric power plant is obtained by using physical characteristics of a real-world. Then by using this model and corresponding real-world data, a set of controller parameters is designed by using tuning methodologies based on heuristic optimization algorithms, and their performances are compared with each other and with a classical tuning methodology. Evolutionary-based and nature-inspired-based heuristic optimization algorithms are selected as the tuning algorithms not only to compare the performance of these algorithms with a classical method but also with different origins. The performance of the optimized controller improves the performance of the overall system and helps to get desired performance. The results also indicate that as long as the desired performance criteria are defined as accurate as possible, the performance of the optimization algorithms is acceptable

Orthogonal array based performance improvement in the gravitational search algorithm

The gravitational search algorithm (GSA) is a novel heuristic method inspired by Newton's gravity and velocity equations. In addition, it is a population-based algorithm, in which each member (called an agent) in the population has a mass, velocity, and acceleration. Beginning with the first population state, agents influence each other via mass and velocity relations. This mutual effect causes agents to reach the optimum. Hence, the performance of the GSA to attain the optimum is related to the initial population formation, like other population-based algorithms. In this study, the orthogonal array (OA) concept is applied and injected to the GSA algorithm in the initialization phase. Hence, the GSA benefits from the homogenized agent distribution tendency of the OA. The implementation results are utilized to compare the conventional and proposed methods (i.e. conventional GSA and the so-called "OA-GSA"), and the efficiency of the proposed method is demonstrated