Fruit fly algorithm-based automatic generation control of multiarea interconnected power system with FACTS and AC/DC links in deregulated power environment

Shankar, Ravi; Kumar, Ashiwani; Raj, Utkarsh; Chatterjee, Kalyan

doi:10.1002/etep.2690

Cited by 57 publications

(23 citation statements)

References 40 publications

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“…Many advanced forms of AGC schemes have been witnessed by using intelligent concepts, such as neural networks, 17‐19 fuzzy logic, 20‐22 particle swarm optimization, 23‐25 genetic algorithm (GA), 9,26‐33 honey bee algorithms, 34 gravitational search algorithm, 35,36 BAT algorithms, 37,38 Jaya algorithm, 39,40 whale algorithm 41 , fruit fly algorithm, 42 and so on, which are very effective and efficient in AGC controller design rather than using conventional AGC design techniques. Recently, few works have addressed some deficiencies in GA performance due to premature convergence of this algorithm, which downgrades its search capability 43…”

Section: Introductionmentioning

confidence: 99%

“…In the majority of research works, AGC study is carried out by considering single energy source based power plants in each corresponding control areas. Instead of having single source of power generation in a control area a more practical type of control area structure with multisources of power generation have been taken into consideration in References 5–7, 30–33, 35, 39–42, 48.…”

Section: Introductionmentioning

confidence: 99%

“…Almost all the AGC studies have been identified so far by considering initial system loading is equal to initial power generation at the value of 50% of the area generation capacity by assuming decoupling between AGC and EDC on time scale 1‐42,44‐48 . But the parameters of power system are uncertain due to stochastic nature of power demand of the end users.…”

Section: Introductionmentioning

confidence: 99%

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Generation‐based automatic generation control with multisources power system using bacterial foraging algorithm

Hakimuddin

Nasiruddin

Bhatti

2020

Engineering Reports

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This article presents an application of bacterial foraging algorithm (BFA) for design and implementation of generation‐based PID structured automatic generation control (AGC) in a 2‐area multisources power system with hydro, thermal, and gas power plants incorporated in each area. Most of AGC studies carried out so far have considered the initial system loading to be equal to 50% of the area generation capacity. But, AGC controller parameters are uncertain due to stochastic nature of power demand of the end users. Hence, in this article, the design of AGC controller is proposed on the basis of generation schedule by incorporating changes in power system gain constant, power system time constant, frequency bias constant, and so on. The dynamic responses of power system with BFA tuned AGC controller are compared with the genetic algorithm tuned AGC controller. The parameters of the controllers are evaluated by using these techniques and investigations are carried out to find the best performance of the system. Therefore, it is desirable to find the parameters of the generation‐based controller depending upon the contribution of its constituent hydro, thermal, and gas energy sources in the total power generation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generation‐based automatic generation control with multisources power system using bacterial foraging algorithm

Hakimuddin

Nasiruddin

Bhatti

2020

Engineering Reports

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“…In recent years, in order to cover the shortage of traditional AGC PID controllers, many scholars have tried to apply intelligent control strategies to AGC systems, which have played a guiding role in AGC research [6][7][8]. Scholars have applied advanced control methods including genetic algorithms in [9], fuzzy predictive control an AGC system is designed by combining a black-box model, trained by actual historical data from a power plant, with intelligent control and classical predictive control ideas. It avoids the error between the mathematical model of the AGC system and real data, enables a more realistic simulation, and enhances the anti-disturbance ability of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards

Peng

Zhao

2019

Energies

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In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid.Energies 2019, 12, 4073 2 of 23 in [10], and the combination of various control methods [11] in the AGC system to improve development of the AGC system to some degree. Nanda et al. [12] proposed using the genetic algorithm (GA) to optimize control of the AGC system. However, this method has some shortcomings, as GA only carries out crossover and mutation operations, and it is likely to fall into local optimum or premature convergence. This has resulted in a dramatic reduction in the efficiency and searchability of the algorithm. Gozde and Taplamacioglu [13] developed a gain-scheduling proportional-integral (PI) controller for a dead zone, nonlinear AGC system. The authors used the craziness-based particle swarm optimization algorithm to minimize the standard error and solution time of different objective functions. Rabindra Kumar Sahu et al. [14] changed the controller design problem into a controller optimization problem for automatic generation control (AGC) of multi-area power systems with diverse energy sources. The proposed teaching learning based optimization (TLBO) algorithm was employed to optimize the parameters of the PID controller, and the superiority of the proposed TLBO controller has been demonstrated by simulation comparison experiments.Appropriate models serve for excellent control algorithms. As one of the intelligent algorithms, model predictive control (MPC) is advantageous because of its unique model prediction, rolling optimization, and feedback correction structure [15,16]. An MPC algorithm was presented to control coal convey systems in a coal-fired power plant [17]. Ne...

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“…Intelligent techniques based on modern optimisation algorithms are generally used for the tuning of controller parameters according to the system requirements. Some of the intelligent algorithms used in the literature for the tuning of controller parameters are genetic algorithm (GA) [14], particle swarm optimisation (PSO) [15], quasi-oppositional harmonic search (QOHS) algorithm [16], fruitfly optimisation algorithm (FOA) [17], bacteria foraging algorithm (BFA) [18], whale optimisation algorithm (WOA) [19] and interactive search algorithm (ISA) [20].…”

Section: Introductionmentioning

confidence: 99%

Firefly Optimization Based Control Strategies for Combined Load Frequency Control and Automatic Voltage Regulation for Two-Area Interconnected Power System

Sahani¹,

Raj²,

Shankar³

et al. 2019

ijeei

Self Cite

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In this research work the combined Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR) of a two-area interconnected power system has been considered. Each control area consists of linearized model of thermal and hydropower system. PID controller has been used as the secondary controller for the combined LFC-AVR of the proposed system. A powerful meta-heuristic algorithm Firefly optimisation technique is used for the optimisation of the proposed controller. Comparison of the system dynamics with and without proposed controller is investigated to show effectiveness of the proposed control scheme. Also, a comparison of the proposed control scheme with other popular meta-heuristic techniques shows the superiority of the proposed control scheme. Moreover, small signal stability analysis of the proposed system has been done using the state space representation of the proposed system.

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Fruit fly algorithm-based automatic generation control of multiarea interconnected power system with FACTS and AC/DC links in deregulated power environment

Cited by 57 publications

References 40 publications

Generation‐based automatic generation control with multisources power system using bacterial foraging algorithm

Generation‐based automatic generation control with multisources power system using bacterial foraging algorithm

Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards

Firefly Optimization Based Control Strategies for Combined Load Frequency Control and Automatic Voltage Regulation for Two-Area Interconnected Power System

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