Mato Mišković

Application of a DSP-based control system in a course in synchronous machines and excitation systems

The paper presents educational tools for students of an undergraduate course in synchronous generators and excitation systems. Laboratory exercises are based on the work with a laboratory model which consists of a digital control system and a synchronous generator connected to the power network. The code development is supported by two software tools where the code can be developed in the C or C++ programming language or in a graphical environment. In the Electrical Machines Laboratory, the presented laboratory model allows students to develop the code for excitation control, to learn how a conventional excitation control structure and various types of power system stabilisers function, to carry out experiments with a step change in the active power reference or voltage reference, and to record generator responses. The proposed approach offers students the opportunity to apply the knowledge they have acquired in lectures through practical work in the laboratory.

Dual Heuristic Neural Programming Controller for Synchronous Generator

This chapter describes the development of voltage control system of a synchronous generator based on neural networks. Recurrent (dynamic) neural networks are used, as a type that has great capabilities in approximation of dynamic systems. Two algorithms are used for training – Dual Heuristic Programming (DHP) and Globalized Dual Heuristic Programming (GDHP). The algorithms have been developed for the optimal control of nonlinear systems using dynamic programming principles.

Synchronous Generator Advanced Control Strategies Simulation

Three different structures for the excitation control of a synchronous generator were simulated in Matlab Simulink: the first structure is a conventional control structure which includes a PI voltage controller, while the second structure includes a fuzzy logic controller, and the third structure includes a neural network- based voltage controller. Performances of the proposed algorithms were tested for step changes in voltage reference in the excitation system of a synchronous generator, which was connected to an AC network through a transformer and a transmission line. For the performance analysis of the proposed control structures two numerical criteria were used: the integral of absolute error and the integral of absolute error derivative. In the comparison with the PI voltage controller neural network-based controller and the fuzzy logic controller show a significant damping of oscillations. It is important to emphasize that the stabilizer was not used in the conventional control structure, which would definitely reduce the difference between the conventional and the proposed control structures. The simulation results show justification for the use of the advanced control structure based on neural networks and fuzzy logic in the excitation control system of a synchronous generator. Also, using the software package Matlab Simulink allows users to easily test the proposed algorithms.