Application of Soft Computing Techniques For Automatic Generation Control In Traditional and Deregulated Power Systems

Abstract

The present thesis contributes in the area of Automatic Generation Control (AGC) of newlinepower systems in both traditional and deregulated environment to ensure stable and secure newlineoperation. In this regard, soft computing techniques such as Fuzzy, Teaching Learning Based newlineOptimization (TLBO), Differential Evolution (DE), Firefly Algorithm (FA) and hybrid newlineParticle Swarm Optimization and Pattern Search (hPSO-PS) algorithms are proposed for AGC newlineof interconnected power system. Also various control structures such as conventional newlineProportional-Integral-Derivative (PID) and its variants as well as new controller structures such newlineas Proportional-Integral-Double Derivative (PIDD), Proportional Integral Derivative with newlinederivative Filter (PIDF) and Fuzzy PIDF have been proposed for AGC. newlineTLBO is employed for the design of PIDD controller for AGC of interconnected system. newlineThe superiority of the proposed TLBO based PIDD controller has been demonstrated by newlinecomparing the results with recently published optimization technique for the same interconnected newlinepower system. Also, the proposed approach has been extended to two-area power system with newlinedifferent sources of generation like thermal, hydro, wind and diesel units. The system model newlineincludes inherent nonlinearities such as boiler dynamics, Generation Rate Constraints (GRC) and newlineGovernor Dead Band (GDB) nonlinearities. newline