Optimal load frequency control of hybrid renewable energy system using PSO and LQR

Shankar, Gauri; Lakshmi, Shubh; Nagarjuna, Nandala

doi:10.1109/icpace.2015.7274942

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…Optimal control is also an important domain when using renewable energy sources. The basic control problems of such systems are described in [13]. With hybrid energy harvesting sources, one has to take into account the variations in the daytime and nighttime energy supply for the solar part.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems

et al. 2021

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This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.

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Section: Introductionmentioning

confidence: 99%

Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems

et al. 2021

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“…Linear quadratic regulator (LQR) control design has been successfully utilized in frequency regulation problems, mostly due to large stability margins of its stabilizing solution, with the fundamental work of [34] being a benchmark approach to LQR-based LFC of multi-area power systems. Ever since, considerable research has been carried out on this topic; [35][36][37][38] represent some recent works. Over the past few years, there has been a renewal of interest in control of networks composed of a large number of interacting systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed LQR Design for a Class of Large-Scale Multi-Area Power Systems

2019

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Load frequency control (LFC) is one of the most challenging problems in multi-area power systems. In this paper, we consider power system formed of distinct control areas with identical dynamics which are interconnected via weak tie-lines. We then formulate a disturbance rejection problem of power-load step variations for the interconnected network system. We follow a top-down method to approximate a centralized linear quadratic regulator (LQR) optimal controller by a distributed scheme. Overall network stability is guaranteed via a stability test applied to a convex combination of Hurwitz matrices, the validity of which leads to stable network operation for a class of network topologies. The efficiency of the proposed distributed load frequency controller is illustrated via simulation studies involving a six-area power system and three interconnection schemes. In the study, apart from the nominal parameters, significant parametric variations have been considered in each area. The obtained results suggest that the proposed approach can be extended to the non-identical case.

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“…Different control methodologies are reported in the literature for realizing LFC [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Optimal control approaches as LQR, OPS and H  are applied for LFC problem.…”

Section: Introductionmentioning

confidence: 99%

“…Optimal control approaches as LQR, OPS and H  are applied for LFC problem. These methods are characterized by fast response [6][7][8]. However, a steady-state frequency error is unavoidable in these techniques.…”

Section: Introductionmentioning

confidence: 99%

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Chaos Algorithm versus Traditional and Optimal Approaches for Regulating Line Frequency of Steam Power System

El-Hafez¹,

Yosuf²

2018

IJEEE

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Load Frequency Control (LFC) is a basic control strategy for proper operation of the power system. It ensures the ability of each generator in regulating its output power in such way to maintain system frequency and tie-line power of the interconnected system at prescribed levels. This article introduces comprehensive comparative study between Chaos Optimization Algorithm (COA) and optimal control approaches, such as Linear Quadratic Regulator (LQR), and Optimal Pole Shifting (OPS) regarding the tuning of LFC controller. The comparison is extended to the control approaches that result in zero steady-state frequency error such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Ziegler-Nicholas method is widely adopted for tuning such controllers. The article then compares between PI and PID controllers tuned via Ziegler-Nicholas and COA. The optimal control approaches as LQR and OPS have the characteristic of steady-state error. Moreover, they require the access for full state variables. This limits their applicability. Whereas, Ziegler-Nicholas PI and PID controllers have relatively long settling time and high overshoot. The controllers tuned via COA remedy the defects of optimal and zero steady-state controllers. The performance adequacy of the proposed controllers is assessed for different operating scenarios. Matlab and its dynamic platform, Simulink, are used for stimulating the system under concern and the investigated control techniques. The simulation results revealed that COA results in the smallest settling time and overshoot compared with traditional controllers and zero steady-state error controllers. In the overshoot, COA produces around 80% less than LQR and 98.5% less than OPS, while in the settling time, COA produces around 81% less than LQR and 95% less than OPS. Moreover, COA produces the lowest steady-state frequency error. For Ziegler-Nicholas controllers, COA produces around 53% less in the overshoot and 42% less in the settling time.

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Optimal load frequency control of hybrid renewable energy system using PSO and LQR

Cited by 14 publications

References 14 publications

Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems

Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems

Distributed LQR Design for a Class of Large-Scale Multi-Area Power Systems

Chaos Algorithm versus Traditional and Optimal Approaches for Regulating Line Frequency of Steam Power System

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