Automatic generation control of a multi-area power system with renewable energy source under deregulated environment: adaptive fuzzy logic-based differential evolution (DE) algorithm

Ajithapriyadarsini, S.; Mary, P. Melba; Iruthayarajan, M. Willjuice

doi:10.1007/s00500-019-03765-2

Cited by 28 publications

(13 citation statements)

References 28 publications

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“…where, simulation time (t sim ) in (14) has been set-up to 120 seconds. All the gains (K) and derivative filter coefficient (N) in (15) have been tuned in the range of 0.01 to 6 and 0.1 to 500, respectively. The SSA is a metaheuristic optimization technique that is developed by Mirjalili et al in 2017.…”

Section: Cost Functionmentioning

confidence: 99%

“…Adaptive fuzzy logic-based DE and bacterial foraging (BF) algorithms are utilized to tune gains of the PID and fractional-order PID (FOPID) controllers for LFC of an RPS, respectively in. 15,16 A novel firefly algorithm (FA) tuned hybrid fuzzy-based PIDF is offered for the LFC of a complex power system. 17 Fathy et al simulated a multi-area power system and proposed a mine blast algorithm (MBA) tuned optimal fuzzy-based PID (FPID) regulator for AGC.…”

Section: Introductionmentioning

confidence: 99%

“…The control engineers have solved these concerns to some extent by utilizing metaheuristic methodologies. Adaptive fuzzy logic‐based DE and bacterial foraging (BF) algorithms are utilized to tune gains of the PID and fractional‐order PID (FOPID) controllers for LFC of an RPS, respectively in 15,16 . A novel firefly algorithm (FA) tuned hybrid fuzzy‐based PIDF is offered for the LFC of a complex power system 17 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Frequency stabilization in deregulated energy system using coordinated operation of fuzzy controller and redox flow battery

Sharma¹,

Dhundhara

Arya³

et al. 2020

Int J Energy Res

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The increasing integration of regional power grids, rising penetration of multigeneration sources, and day-ahead power agreements have raised the operational challenges on deregulated modern electric energy systems. Among various challenges, frequency regulation becomes more prominent in the restructured power system (RPS) due to increased uncertainties and intricacies among other operational challenges. A minor deviation in system frequency affects the safety, quality, stability, and operation of the interconnected power system (IPS). An appropriate control mechanism and energy storage device are essential to regulate the system's dynamic responses during continuously varying loading conditions. This study proposed a novel Salp swarm algorithm (SSA) optimized fuzzy-based proportional-integral-derivative filter (FPIDF) controller with redox flow battery (RFB) to regulate the frequency of a realistic multi-area multi-source (thermal-hydro-gas) interconnected power system. All probable contract transaction scenarios are simulated that can be possible in a deregulated power industry. Various nonlinearities such as time delay (TD), governor dead band (GBD), and generation rate constraint (GRC) are incorporated to resemble the realistic operational conditions of the IPS. The effectiveness of the suggested controller has been validated by comparing the system performances with a recently published sine-cosine algorithm (SCA) based proportional-integral (PI) controller and SSA-PID controller. The investigation has been further extended by incorporating RFB in the proposed system. The study reveals that the suggested controller provided superior system control under various system uncertainties in all contact scenarios of the competitive electricity market. Additionally, the system performances have been significantly enhanced due to quick response and precise control offered by RFB.

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Section: Cost Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Frequency stabilization in deregulated energy system using coordinated operation of fuzzy controller and redox flow battery

Sharma¹,

Dhundhara

Arya³

et al. 2020

Int J Energy Res

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show abstract

“…in system parameters and random load demands. 29,31 Deregulated Adaptive FLC DE Multi-area Multi-source Thermal, gas, and photovoltaic…”

Section: The Main Contribution Of This Proposed Work Is Given Belowmentioning

confidence: 99%

Automatic generation controller for multi area multisource regulated power system using grasshopper optimization algorithm with fuzzy predictive PID controller

Kumar

Sharma

2020

Int J Numerical Modelling

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This paper presents a fuzzy predictive-proportional integral derivative (FP-PID) controller approach for automatic generation controller (AGC). This new AGC approach aims to balance the total generating system without any power losses and load changes in keeping constant system frequency per tie-lie power flow. But, a sudden load variation in multi-area Interconnected power system (MIPS) creates nonlinearities (frequency deviation & tie-line) in all control areas. Because, penetrating of renewable sources to the power system, the sluggish control action may cause inefficiency in migrating frequency and tie-line power flow. Hence, an accurate and fast-acting controller is required to maintain the nominal value also the quality and stability of power system, because traditional AGC is not feasible for further process. Here, we propose an FP-PID controller in MIPS for controlling large parametric uncertainties. Modeling error is taken into account to reduce the maximum deviation and time of oscillation. The main purpose of FP-PID based AGC is to ensure stable and reliable power system operation. A grasshopper optimization algorithm (GOA) is used to tune the parameters of FP-PID controller with Integral of time multiplied squared error (ITSE) as the objective function. The proposed method is compared with conventional two-area and three-area system, the result are built-in Simulink/MATLAB show that the proposed method has a good dynamic response, fast operation reduced magnitude error and minimized frequency transients for three power areas. The robustness of the controller is, it need not be retuned for wide variations in system parameters and random step load power.

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“…The differential evolution (DE) algorithm was proposed by Rainer Storn and Kenneth Price in 1995 [1] to solve Chebyshev polynomials. Because of its simplicity, robustness, and validity, applications of DE include various domains, such as feature selection [2], [3], image segmentation [4], energy storage [5], economic emission dispatch [6], color image encryption [7], fault identification [8], automatic generation control [9], chemical processing, artificial intelligence, and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Potential-Based Differential Evolution Algorithm With Joint Adaptation of Parameters and Strategies

Tian

Yan

2020

IEEE Access

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In the differential evolution (DE) algorithm, numerous studies have independently performed strategy adaptation and parameter adaptation. However, the strategy and parameters are interrelated in their impact on algorithm performance. It is well known that different problems and evolutionary stages require different appropriate parameters and strategies, but the fact that the same is true for different individuals is ignored. Few studies have focused on the difference in fitness values between two successive generations, which contains substantial evolution information. This study proposes a potential-based DE algorithm with joint adaptation of parameters and strategies (JAPSPDE). In JAPSPDE, a new population classification scheme, a new classification evolution mechanism, and a new joint adaptation mechanism are proposed to circumvent the three abovementioned issues. In the population classification scheme, individuals are divided into potential and unpotential individuals according to the improvement in fitness values between two generations. A classification evolution mechanism is applied by evolving potential individuals and unpotential individuals in two ways. In addition, a three-dimensional probability array is constructed to achieve joint adaptation of parameters and strategies. Finally, after properly combining the above algorithmic components, JAPSPDE can find the most appropriate combination of control parameters and mutation strategies for specific problems, stages, and individuals. The performance of JAPSPDE is evaluated in comparison with five well-known DE algorithms on BBOB2012 and CEC2014 and with six up-to-date evolution algorithms on CEC2014. The comparison results demonstrate the competitive performance of JAPSPDE.

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Automatic generation control of a multi-area power system with renewable energy source under deregulated environment: adaptive fuzzy logic-based differential evolution (DE) algorithm

Cited by 28 publications

References 28 publications

Frequency stabilization in deregulated energy system using coordinated operation of fuzzy controller and redox flow battery

Frequency stabilization in deregulated energy system using coordinated operation of fuzzy controller and redox flow battery

Automatic generation controller for multi area multisource regulated power system using grasshopper optimization algorithm with fuzzy predictive PID controller

Potential-Based Differential Evolution Algorithm With Joint Adaptation of Parameters and Strategies

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