Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/4528
Title: Load Frequency Control of Multi-Area Power System with Renewable Sources by Intelligent and Optimal Control Techniques
Authors: Kaur, Ramanpreet
Prakash, Surya (Guide)
Keywords: Load frequency control (LFC);Area control error (ACE);Adaptive neuro-fuzzy inference system (ANFIS);Fuzzy logic controller (FLC);Linear quadratic regulator (LQR)
Issue Date: 31-Jul-2017
Abstract: In interconnected power system, some small unexpected load change in one of the areas causes the variation of the frequencies of every area and also there is variation of power in tie line. The most important objects of Load Frequency control (LFC) are, to retain the actual frequency and the preferred power output in the power system and to control the variation in tie line power among the interconnected areas. Therefore, a LFC scheme mainly includes a suitable control system for the power system, which is capable to carry the frequency of each area and power in the tie line back to desired set point values after the load change. In this dissertation work, LFC of the interconnected power system with distributed generation has been developed. As renewable energy resources are in great demand to fulfill the requirement of power. So, the incorporation of renewable energy sources are implemented as these are effective in supplying the power but the frequency variation is more in the hybrid system. The two identical areas are taken in which each area consists of one thermal power generation system with wind turbine, Photovoltaic generation system (PV), fuel cell, battery storage system and aqua electrolyzer. LFC requires a fast and accurate controller to maintain the frequency at preferred value. This proposed research work deals with the Artificial Intelligence technique (Fuzzy PI and Neuro- Fuzzy approach) for LFC. The benefit of Neuro-fuzzy approach is to deal with non-linearities in the intervening time and it is faster than other conventional controllers. The intelligent controller output is better and faster than other conventional controllers. Then LQR is also implemented in the hybrid power system which is proven the best controller in this thesis. The performance of the three controllers is compared with the specific parameters that are settling time, maximum overshoot, and maximum undershoot.
URI: http://hdl.handle.net/10266/4528
Appears in Collections:Masters Theses@EIED

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