AUTOMATIC GENERATION CONTROL IN REGULATED AND RESTRUCTURED POWER SYSTEM

Abstract

In an interconnected power system when the load varies, the system frequency and tie-line power interchange also vary. At the operating point of the system, frequency and tie line power flows should be kept at nominal or scheduled values by controlling the real power. A supplementary control action is used to keep the deviations in frequency and tie line flow to zero. The supplementary control adjusts load reference set points of the generators for control action. The automatic control to minimize these transient deviations and to provide zero steady state errors is called Automatic Generation Control (AGC). The overall objective ofAGC is to: • To keep frequency in the interconnected system close to nominal value and • To restore scheduled power interchanges between different areas over tie line. This thesis addresses AGC, a major ancillary service, and presents some novel control designs and strategies in both regulated and restructured power systems. In a restructured power system, AGC objective remains the same as in traditional power system but its application in the system differs. The controller generally used as supplementary controller is of integral or proportional integral type but as these controllers are considerably slow and take more computing time in estimation of suitable parameters hence other types of controllers using artificial intelligence techniques are being used to improve AGC performance. This thesis mainly contributes in the area of Automatic Generation Control (AGC) of single area and multi area power systems in regulated as well as deregulated (restructured) environments. 11 Adetailed literature survey has been carried out during the research work under the following headings: • Power System models with AGC • Control strategies and techniques • AGC schemes with DC links • AGC schemes using energy storage devices • AGC in deregulated power system environment • FACTS devices in AGC Onthe basis of the survey research work it has been found that limited research has been attempted in the area of AGC in both regulated and restructured environments using combined artificial intelligent (AI) techniques supported controller design. AGC performance improvement can also be achieved using AI supported controller in tandem with FACTS devices and HVDC links and with application of combined AI techniques for optimal controller design. The research in this thesis is aimed to meet these gaps and extend the work which have been done till date in the area ofAGC in traditional as well as restructured environments. The primary work done in this thesis can be summarized under the following themes: • Development of MATLAB/SIMULINK models for automatic generation control of two or multi area interconnected power system in regulated and restructured power system environment. • Application ofvarious artificial intelligent techniques such as fuzzy logic (FL), genetic algorithm (GA) and particle swarm optimization (PSO) alone and in combination with conventional methods to improve the overall performance of AGC in the power system. in • Use of modern control system to apply optimal controllers in two and three area systems in regulated and restructured environments to get improved transient response of frequency, tie line power and power generated by GENCOs. • Investigation of AGC performance improvement using an HVDC line in parallel with AC tie line in interconnected power system. • Applications of FACTS devices for different types of system models both in regulated and restructured environments. Attempt has also been made to improve the performances using fuzzy controllers. • Tuning of fuzzy controller using GA and PSO to ensure best dynamic performance under all operating conditions. • Performance comparison of integral, optimal and AI supported controllers in traditional and restructured environments. To improve the performance of the AGC and to overcome the limitations of conventional integral controller the use of intelligent systems are required. Artificial Intelligence techniques like Fuzzy Logic, Particle Swarm Optimization and Genetic algorithms have been applied for this purpose. The Fuzzy Logic Controller (FLC) designed does not provide better dynamic performance in all the cases, hence scaling factors of FLC are tuned with the help of combined intelligence techniques, resulting in PSO tuned Fuzzy Logic Controller and Genetic Algorithm tuned Fuzzy Logic Controller. In the restructured power system, different controllers have been applied for frequency and tie-line power regulation in the new environment. The controller has been modified over conventional integral controller to Fuzzy Logic (FL) controller, GA tuned FL controller and PSO tuned FL controller. An attempt has been made to compare the IV performances of these controllers in terms of peak deviations and time taken to damp out oscillations. Steady state values of frequency, tie line power interchange and power generated by different GENCOs have also been examined for the systems under study. The use of HVDC link in parallel with ac tie line as interconnection between two areas has been applied in restructured power system with controller as optimal controller which gives improved performance as compared to the case without HVDC link in the same system. Different FACTS devices have been applied in power system models to damp out transient response oscillations quickly. These gave encouraging results and further improvement was achieved when fuzzy controllers were replaced by the integral controllers.