scholarly journals An analytical approach for LQR design for improving damping performance of multi-machine power system

2022 ◽  
Vol 25 (1) ◽  
pp. 51
Author(s):  
Sreenivas Uravakonda ◽  
Vijaya Kumar Mallapu ◽  
Venkateswara Reddy Annapu Reddy
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Power System ◽  
Small Perturbation ◽  
Optimal Control Theory ◽  
Analytical Approach ◽  
Low Frequency ◽  
Test System ◽  
Operating Conditions ◽  
Algebraic Equations

In a multi-machine environment, the inter-area low-frequency oscillations induced due to small perturbation(s) has a significant adverse effect on the maximum limit of power transfer capacity of power system. Conventionally, to address this issue, power systems were equipped with lead-lag power system stabilizers (CPSS) for damping oscillations of low-frequency. In recent years the research was directed towards optimal control theory to design an optimal linear-quadratic-regultor (LQR) for stabilizing power system against the small perturbation(s). The optimal control theory provides a systematic way to design an optimal LQR with sufficient stability margins. Hence, LQR provides an improved level of performance than CPSS over broad-range of operating conditions. The process of designing of optimal LQR involves optimization of associated state (Q) and control (R) weights. This paper presents an analytical approach (AA) to design an optimal LQR by deriving algebraic equations for evaluating optimal elements for weight matrix ‘Q’. The performance of the proposed LQR is studied on an IEEE test system comprising 4-generators and 10-busbars.

scholarly journals Theory of Active Suspension Design

1995 ◽  
Vol 7 (4) ◽  
pp. 280-284
Author(s):  
Kunihiko Ichikawa ◽  
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Optimal Control Theory ◽  
Design Theory ◽  
Reference Signal ◽  
Low Frequency ◽  
Active Suspension ◽  
Model Matching ◽  
Frequency Range ◽  
Initial State

Active suspension design has been developed as the application of optimal control theory. However, optimal control theory is only suitable for the design of regulator, where transient responses starting from any initial state are required to converge to zero. The active suspension system is not a simple regulator because road surface unevenness acts only as disturbance in the low frequency range, while it acts not only as disturbance but also as reference signal in the high frequency range. Thus, optimal control theory is not considered suitable for active suspension design. As an alternative to optimal control theory, a new design theory based on exact model matching (EMM) with a disturbance predictor is developed in this paper. One of the peculiarities of this problem is the need to prepare a separate control law for each frequency range. The other is that the outer signal is inaccessible. The former problem is solved by introducing a weighing rational function. The latter problem is fortunately settled by the fact that disturbance and outer signal have a simple relation to each other.

Application of an Optimal Control Theory to a Power System

1970 ◽  
Vol PAS-89 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Yao-nan Yu ◽  
Khien Vongsuriya ◽  
Leonard Wedman
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Power System ◽  
Optimal Control Theory

scholarly journals Multimachine Power System Stabilizer Based on Decentralized Optimal Control Theory

1988 ◽  
Vol 21 (11) ◽  
pp. 137-143
Author(s):  
Huang Mei ◽  
Chen Huaijin ◽  
Han Yinduo ◽  
Wang Zhonghong
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Power System ◽  
Optimal Control Theory ◽  
Power System Stabilizer ◽  
System Stabilizer

scholarly journals Adaptive Hybrid Fuzzy PI-LQR Optimal Control using Artificial Immune System via Clonal Selection for Two-Area Load Frequency Control

2020 ◽  
Vol 12 (3) ◽  
pp. 667-685
Author(s):  
Muhammad Abdillah ◽  
Keyword(s):  
Optimal Control ◽  
Power System ◽  
Clonal Selection ◽  
Frequency Control ◽  
Dynamic Performance ◽  
Test System ◽  
Pi Controller ◽  
Operating Conditions ◽  
Load Frequency Control ◽  
Load Frequency

Load frequency control (LFC) problem has been a foremost issue in electrical power system operation and is becoming more important recently with growing size, changing structure, and complexity in interconnected power systems. In general, LFC system utilizes simple proportional integral (PI) controller. However, due to the PI control parameters are commonly adjusted based on classical or trial-error method (TEM), it is incapable of obtaining good dynamic performance for a wide range of operating conditions and various load change scenarios in a multi-area power system. This paper introduces a novel control scheme for load frequency control (LFC) using hybrid fuzzy proportional integral (fuzzy PI) and linear quadratic regulator (LQR) optimal control, where fuzzy logic control (FLC) is used to adjust the gains KP and KI of PI controller which called fuzzy PI in this paper, while the LQR optimal control method is employed to obtain the feedback gain KOP through Algebraic Riccati Equation (ARE). The merit of both control strategies is to tune their control feedback gains, which are KP, KI and KOP, regarding various system operating conditions. Artificial immune system (AIS) via clonal selection is utilized to optimize the membership function (MF) of fuzzy PI and weighting matrices Q and R of LQR optimal control in order to obtain their optimal feedback gains. To examine the efficacy of the proposed method, LFC of two-area power system model is utilized as a test system. The amalgamation of fuzzy PI-LQR is applied to improve the dynamic performance of two-area LFC. Other control schemes such as PI controller, hybrid PI controllerLQR, and hybrid fuzzy PI-LQR are also investigated to the studied a test system. The obtained simulation results show that the proposed method could compress the settling time and decrease the overshoot of LFC which is better than other approaches that are also employed to the tested system in this study.

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