PID parameters tuning using modified particle swarm optimization and its application in load frequency control

Singh, Kirtiman; Shankar, Gauri

doi:10.1109/icpes.2016.7584160

Cited by 6 publications

(6 citation statements)

References 8 publications

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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%

“…PI and PID controllers successfully eliminate steady-state frequency error forcing the frequency deviation to zero following severe disturbance [9][10][11]. Different approaches are advised for tuning the parameters of PI and PID controllers.…”

Section: Introductionmentioning

confidence: 99%

“…However, Ziegler-Nicholas empirical approaches, the process reaction and the continuous cycling methods, proved to be efficient, simple and robust techniques for tuning PI and PID controllers. In general, the empirical approaches suffer from inadequate design that could result in sluggish or prolonged response [9][10][11][12].…”

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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Section: Introductionmentioning

confidence: 99%

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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“…However, many technical papers have described how different search or optimization techniques like Genetic Algorithm [5], [6], Neural network [7], Particle Swarm [8], etc. are used to tune PID.…”

mentioning

confidence: 99%

“…In [7], it is mentioned that the training periods are too long to identify the weights in the network because it uses supervised learning algorithm. As explained in [8], a modified Particle Swarm Optimization (PSO) is used to eliminate the problem of getting stuck at the local maxima/minima. Optimization methods such as PSO require more computational time and space.…”

mentioning

confidence: 99%

A Predictive Hill Climbing Algorithm for Real Valued multi-Variable Optimization Problem like PID Tuning

Nagarajan¹,

Cep²

2018

IJMLC

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Abstract-This paper proposes a novel method of applying Hill Climbing algorithm for optimizing a problem which has more than one dependent variable and a very large search space. Tuning of PID controller for complete Black-Box plant model is an example of one such problem, where the search algorithm is applied to find PID gains which satisfy the desired optimization criteria. The traditional Hill Climbing algorithm cannot be directly applied to tune PID since the PID controller has three parameters to be tuned and search space is a large collection of real numbers. Searching the entire 3D infinite space without knowing the step size is not easy for this search method. Hence, the traditional Hill Climbing algorithm is modified to adjust the step size and the direction of search dynamically to make the search faster. The algorithm maintains the direction of search as long as it is approaching the optimal point with increase in step size. When it fails in its prediction, the search is randomized in all directions with decrease in step size. This predictive search method reduces the search time and is effective for a very large search space. This paper explains how the algorithm is successful in tuning PID controller and the performance of the algorithm is analyzed.

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Fractional Order Controller Design for interconnected Power System using BAT optimization Algorithm

Singh

Padhy

2022

2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)

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PID parameters tuning using modified particle swarm optimization and its application in load frequency control

Cited by 6 publications

References 8 publications

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

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

A Predictive Hill Climbing Algorithm for Real Valued multi-Variable Optimization Problem like PID Tuning

Fractional Order Controller Design for interconnected Power System using BAT optimization Algorithm

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