Firefly algorithm-based load frequency controller design of a two area system composing of PV grid and thermal generator

Elazim, S.M. Abd; Ali, E. S.

doi:10.1007/s00202-017-0576-5

Cited by 32 publications

(11 citation statements)

References 38 publications

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“…The equivalent circuit of PV cell is depicted in Figure 3, which includes a PV current source parallel with a diode ( D ) and a series resistance ( R ) (Abd-Elazim and Ali, 2018; Davtalab et al, 2020). The generated output power, P G , of the PV system is given by (Lee and Wang, 2008):…”

Section: System Configuration and Mathematical Modellingmentioning

confidence: 99%

“…In the study by Mohamed et al (2012), model predictive control for LFC of multi-area interconnected power system has been reported. In the study by Abd-Elazim and Ali (2018), LFC of photovoltaic (PV) system integrated hybrid power system is handled by using proportional integral controllers tuned by means of firefly algorithm. A hybrid optimization algorithm based on particle swarm optimization and bacteria foraging optimization has been utilized to tune PID controllers for LFC of hybrid power system in the study by Panwar et al (2019).…”

Section: Introductionmentioning

confidence: 99%

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Constrained neural adaptive predictive control of SMES for dynamic performance improvement of power systems

Syed

Mufti

2021

Wind Engineering

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For dynamic performance improvement of modern power systems, the use of fast acting energy systems like superconducting magnetic energy storage (SMES) is imperative. In this paper, incorporation of a small rating SMES in a solar and wind power penetrated multi-area power system is proposed. A non-linear neural adaptive predictive controller is used to generate an optimal power command taking into account the converter rating and energy level constraints of SMES unit. The SMES is represented by a control relevant model comprising of a first order lag compensator cascaded by an integrator to translate the hardware constraints pertaining to its coil current into modified power constraints. Moreover for avoiding the sudden SMES outage, the power thresholds are forcibly varied as the SMES current reaches near its maximum and minimum values. The uprightness of the designed scheme is illustrated by simulation studies performed on a three area power system.

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Section: System Configuration and Mathematical Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constrained neural adaptive predictive control of SMES for dynamic performance improvement of power systems

Syed

Mufti

2021

Wind Engineering

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“…Ali and Abd‐Elazim analyzed the two nonreheat thermal power system LFC using BFOA and validated with the comparative analysis with conventional PI controller, genetic algorithm–tuned PI controller . Ali and Abd‐Elazim analyzed LFC for PV‐thermal system using firefly algorithm and compared with genetic algorithm–based LFC . Jagatheesan et al analyzed LFC for hydro‐hydro power system using FLC.…”

Section: Background Of Lfc In Multiarea Power Systemmentioning

confidence: 99%

Analysis of LFC in PV‐thermal‐thermal interconnected power system using fuzzy gain scheduling

Revathi

Kumar

2020

Int Trans Electr Energ Syst

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Summary In this paper, load frequency control of photovoltaic (PV)‐thermal‐thermal interconnected three area power system is proposed with fuzzy gain scheduling controller. Grid‐connected PV power system has quick rising annual rate and is rapidly becoming a significant part of the energy balance in the power systems. Reduction in the PV modules cost increases the application of PV. In recent days, increase in load demand necessitates the interconnected power systems. Power quality concerning frequency plays a major role in life and efficiency of the load. The fuzzy logic controller is an efficient controller to deal with the unknown data; in the LFC, the load demand is unpredictable; hence, fuzzy is applied for LFC in this analysis. Proportional integral (PI) controller plays a significant role in real‐time LFC. Therefore, in this article, fuzzy gain scheduling controller is proposed to tune gains of PI controller to improve performance of interconnected power systems. To validate the performance of proposed system, it is compared with the conventional PI controller‐based LFC. The entire system is analyzed using Matlab with various loads.

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“…Multiple hierarchical techniques to power system control for single or multi-area microgrids have been recently developed. Typically, the proportional integral (PI)/proportional integral derivative (PID) controller for interconnected power grids is developed with high penetration RES in consideration [15,16].…”

Section: Introductionmentioning

confidence: 99%

Modified Cascaded Controller Design Constructed on Fractional Operator ‘β’ to Mitigate Frequency Fluctuations for Sustainable Operation of Power Systems

et al. 2022

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The demand for energy is increasing at an abrupt pace, which has highly strained the power system, especially with high share of power generation from renewable energy sources (RES). This increasing strain needs to be effectively managed for a continuous and smooth operation of the power system network. Generation and demand exhibit a strong correlation that directly creates an impact on the power system frequency. Fluctuations and disruptions in load frequency can manifest themselves as over-voltages and physical damages in the power grid and, in the worst case, can lead to blackouts. Thus, this paper proposed an effective solution to mitigate the load frequency problem(s), which is initiated by the changing load demand under high penetration of RES. This paper presented an improved cascaded structure, the proportional integral with a fractional operator coupled with proportional derivative . The proposed modifies the (1+PD) controller by introducing fractional properties that improve its tracking efficiency and mitigate frequency fluctuations taking minimal time. The introduction of diversifies its tracking and overall controlling ability, which translates it as a significant controller. The controller optimal parameters are extracted by deploying a dragonfly search algorithm (DSA). The study of the results illustrates that the proposed design displays efficient performance under any disturbance or uncertainty in the power system.

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Firefly algorithm-based load frequency controller design of a two area system composing of PV grid and thermal generator

Cited by 32 publications

References 38 publications

Constrained neural adaptive predictive control of SMES for dynamic performance improvement of power systems

Constrained neural adaptive predictive control of SMES for dynamic performance improvement of power systems

Analysis of LFC in PV‐thermal‐thermal interconnected power system using fuzzy gain scheduling

Modified Cascaded Controller Design Constructed on Fractional Operator ‘β’ to Mitigate Frequency Fluctuations for Sustainable Operation of Power Systems

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