Application of metaheuristic control strategies to voltage regulation

Ramjug-Ballgobin, R.; Fowdar, Roopesh

doi:10.1007/s42452-019-1802-8

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…The third objective is achieved by adjusting the voltage regulation to a desired value. The timing solution of the power system's transient stability problem with a minimum overshoot is possibly achieved by optimizing the integral of the time-multiplied absolute value of the speed error (ITAE) as a first objective function J 1 , and equally by optimizing the integral of the time-multiplied absolute terminal voltage error as a second objective function J 2 [45,46]. For this purpose, the Jaya optimization algorithm is used as a means of minimizing the objective function J, incorporating the sum of the two preceding functions, which are effectively weighed by the coefficients α and β.…”

Section: Problem Formulationmentioning

confidence: 99%

A Backstepping Control Strategy for Power System Stability Enhancement

et al. 2023

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Secure power system operation relies extensively on the analysis of transient stability and control. The dynamics involved in power system control are often complex and nonlinear. Most of the currently existing works approach these frequent problems with nonlinear control techniques, leading to a requirement for specific controller parameter adjustments. In these veins, this paper proposes a new method for stabilizing electric power systems, using nonlinear backstepping control by optimizing the controller’s parameters. The Jaya algorithm and Genetic algorithm are utilized as a powerful meta-heuristic optimization technique to search parameters of an optimal controller. Improvement in system damping, transient stability, and voltage regulation has been achieved by minimizing the integral time absolute error (ITAE) as the objective function. Numerical simulations on an SMIB power system under different fault conditions showed that the proposed method outperforms classical power system stabilizer (PSS) methods, reducing overshoots and settling times and eliminating steady-state errors. These findings highlight the effectiveness of the proposed approach and its potential contribution to the development of advanced nonlinear control techniques for electric power systems. The suggested optimization methods demonstrate superior performance, compared to classical methods, and achieve a reduction of 27.5% in overshoot and 87% in transient time in addition to complete elimination of static error.

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Section: Problem Formulationmentioning

confidence: 99%

A Backstepping Control Strategy for Power System Stability Enhancement

et al. 2023

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“…, , and represent gains in the transfer functions of the amplifier, exciter, generator and sensor, while , , and represent time constants, respectively. Typical values of these parameters are given in Table 1 [10]- [13], [15], [16], [18]. The transfer function of the closed loop AVR system with PID controller is given in Equation 1.…”

Section: Avr System Modelmentioning

confidence: 99%

“…In the literature, there are studies using the CSA algorithm in the control of AVR system. Ballgobin et al [15] studied transient response of the system by comparing the performance of CSA and several optimization algorithms. Bhullar et al [16] compared the performance of CSA and enhanced CSA (ECSA) utilized in controller design process for an AVR system.…”

Section: Introductionmentioning

confidence: 99%

Effects of objective function in PID controller design for an AVR system

Burnaz

Ayas

2020

International Journal of Applied Mathematics Electronics and Computers

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Regulation capability of an automatic voltage regulator (AVR) system still needs to be improved to keep the output voltage of the generator within the AVR system at the desired level. Researchers have been developing new control structures and designing controllers to improve the performance of the AVR system. Designing of PID controller, which is commonly preferred controller due to its simple structure and robustness against to system parameter changes, has an important place among these studies. Especially with the development of metaheuristic algorithms, more successful PID controller designs are emerging by using these algorithms than traditional design methods. Undoubtedly, the objective function utilized also has a significant effect on this success. Therefore, effects of the objective function in PID controller design process for an AVR system are examined in this study. Two different PID controllers are designed using two different metaheuristic algorithms, namely, crow search algorithm (CSA) and ant colony optimization (ACO) algorithm. The parameters of the PID controllers are optimally tuned by using five different objective functions in both algorithms. These objective functions are: Integral of absolute error (IAE), integral of squared error (ISE), integral of time-weighted absolute error (ITAE), integral of time-weighted squared error (ITSE), and a commonly used user-defined objective function. The performance of the designed PID controllers are compared in terms of transient response characteristics and performance metrics. In addition, in order to evaluate the stability of the AVR system with the designed controllers, bode analysis, pole-zero map analysis and robustness analysis are performed.

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A hybrid metaheuristic optimisation technique for load frequency control

Ramjug-Ballgobin

Ramlukon

2021

SN Appl. Sci.

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This paper investigates the problem of load frequency control by the application of metaheuristic optimisation techniques. In order to analyse the effect of the system model on the performance of the algorithm, two different two-area systems are considered, modelled using the Matlab/Simulink package. The first one consists of a reheat thermal area and a hydro area while the second one is made up of identical reheat thermal areas. To take into consideration the effects of practical constraints, nonlinearities such as Generation Rate Constraint, Governor Deadband and Boiler Dynamics are introduced. The Shuffled Frog Leaping Algorithm and Teaching Learning Based Optimisation are applied followed by a proposed hybrid of both algorithms to tune Proportional-Integral-Derivative (PID) controllers for the different areas of the systems, taking into account step load changes as inputs. The aim of this proposed algorithm is to merge the qualities of the individual algorithms to provide a more efficient one, converging faster to the optimal gains of the controllers. The results obtained proved the satisfactory performances of all algorithms and superiority of the hybrid Shuffled Frog Leaping Algorithm -Teaching Learning Based Optimisation technique in controlling frequency level in both systems investigated, where the main control measures such as peak values, settling times and steady-state values have been considered. Article Highlights When considering a system of electrical mechanisms installed to produce, distribute and use electric energy, it is important to match supply with demand in order to keep frequency almost constant so as to ensure the safe and reliable use of equipment and maintain stability. The combination of two different algorithms efficiently improved the performance of the control system since the new system benefitted from the qualities of both strategies. Algorithm-based controllers provided robust and reliable frequency control and may be used to increase the quality of electrical energy under increasing and constantly changing demand.

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Application of metaheuristic control strategies to voltage regulation

Cited by 5 publications

References 21 publications

A Backstepping Control Strategy for Power System Stability Enhancement

A Backstepping Control Strategy for Power System Stability Enhancement

Effects of objective function in PID controller design for an AVR system

A hybrid metaheuristic optimisation technique for load frequency control

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