Optimized Load Frequency Control for Single Area Power System Using Linear Quadratic Gaussian Technique and Coefficient Diagram Method

Energy is one of the vital figures that impact the development of civilization in the 21st century. It has been projected that by the year 2050, global energy needs will be satisfied by renewable sources. Among these renewable energy resources hydropower is available worldwide with relatively cheaper accessibility for most of the communities. Nevertheless, hydropower's control architecture raises concern for the system operators in terms of preserving the Load Frequency Control (LFC) services due to the elongated response time of hydro turbines in catering for the varying load demands. The varying load demands are inevitable in the power system due to different clients’ energy consumption patterns at different times. This, therefore, places changing control framework requests as per the requirement of diverse clients. Hence, the research proposes and demonstrates the connection of the hydro-hydro framework through the AC tie- line for LFC. The Linear Quadratic Regulator (LQR) is a plan for hydro overseeing framework in discrete mode. The application derived is displayed through closed- loop feedback gains and closed-loop eigenvalues. In the expansion model, the positive effect of a Unified Power Flow Controller (UPFC) and Redox Flow Battery (RFB) in LFC studies is investigated. This proposition moreover shows the joint endeavors of Fuzzy Logic (FL) as well as Proportional Integral Derivative (PID), with control gains well-calculated, through Particle Swarm Optimization (PSO) result into a robust FL-PSO-PID for LFC of the connected hydro framework. The different errors are defined to assess the yield as well as the execution of the FL-PSO-PID. The yield appears through a decline in blunder values as well as minimization in framework responses from accurate estimation for the LFC under various working conditions such as non- linearity, random load alteration, and parametric move as a result of a precise estimate. In the expansion, the effect of energy storage devices is also investigated to understand the enhancement provided frequency control of the hydro system, and the result obtained shows their effectiveness. Finally, the outcomes and future extent of this investigation work have been presented.

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Optimal Decentralized LQR Control to Enhance Multi-Area LFC System Stability

Mathematical Problems in Engineering ◽

10.1155/2018/7232915 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Ashraf M. Abdelhamid ◽

Ahmed A. M. El-Gaafary

Keyword(s):

Power System ◽

Frequency Control ◽

Linear Quadratic Regulator ◽

Load Frequency Control ◽

System Stability ◽

Linear Quadratic ◽

Single Input Single Output ◽

Load Frequency ◽

Single Output ◽

Optimal Linear

Many studies have been made in the field of load frequency control (LFC) through the last few decades because of its importance to healthy power system. It is important to maintain frequency deviation at zero level after a load perturbation. In decentralized control, the multi-area power system is decomposed into many single input single output (SISO) subsystems and a local controller is designed for each subsystem. The controlled subsystems may have slow poles; these undesired poles may drive the aggregated overall system into the instability region. Thus, it is required to relocate these poles to much more stable places to avoid their effect upon the overall system stability. This study aims to design a new load frequency controller based on the powerful optimal linear quadratic regulator (LQR) technique. This technique can be applied over subsystem level to shift each subsystem undesired poles one by one into a prespecified stable location which in turn shift the overall system slow poles and reduce the effect of the interaction between the interconnected subsystems among each other. This procedure must be applied many times as the number of undesired poles (pairs) until all the desired poles are achieved. The main objective is considered to get a robust design when the system is affected by a physical disturbance and ±40% model uncertainties. LQR can be applied again over the aggregated system to enhance the stability degree. Simulation results are used to evaluate the effectiveness of the proposed method and compared to other research results.

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