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Combined analysis on AGC and ELD of a hybrid power system with D-WCA designed Gaussian type-2 fuzzy controller

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Abstract

This manuscript deals with a maiden research work on automatic generation control (AGC) in constraint to economic load dispatch (ELD) of a hybrid power network with proposing novel approaches. The action of AGC is to obtain stable frequency and rated tie-line power flow under various disturbances. This study employs a Gaussian-interval type-2 fuzzy PID (GIT2-FPID) controller for AGC of hybrid power system. The optimal parameters of this proposed controller are obtained with implementing an innovative discrete-water cycle algorithm (D-WCA) in realization with various dynamic conditions. In regard to the optimum cost for power generation, this research work has been extended for combined analysis on AGC and ELD in a common power system model. The combined AGC-ELD methodology progressively advances the system behaviours both in stability and economic concern. In controller analysis, it is noticed that controller GIT2-FPID improves the settling time of ΔF1 by 235.60% and 292.66% over FL-PID and PID controllers respectively. Under common controller scenario, proposed D-WCA algorithm improves ITAE values by 60.14% and 114.60% as compared to WCA and PSO respectively. In methodology concern, suggested D-WCA optimized GIT2-FPID approach gives improved performance over few standard approaches in response to AGC-ELD of the proposed system.

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Authors and Affiliations

  1. Department of Electrical Engineering, VSSUT, Burla, Sambalpur, India

    Krushna Keshab Baral & Banaja Mohanty

  2. Department of Electrical Engineering, Aryan Institute of Engineering and Technology, Bhubaneswar, Odisha, India

    Prakash Chandra Sahu

  3. Department of Electrical Engineering, CET, Bhubaneswar, Odisha, India

    Ajit Kumar Barisal

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  1. Krushna Keshab Baral
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  2. Prakash Chandra Sahu
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  3. Ajit Kumar Barisal
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  4. Banaja Mohanty
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Correspondence to Krushna Keshab Baral.

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Appendices

Appendix

1.1 Control area_1

Thermal plant Tg = 0.08 s = Time constant (Tcon) of governor, Tr = 10 s = Tcon of reheat turbine, Kr = 0.2 = Gain of reheat, Tt = 0.3 s = Tcon of turbine; Hydro System:Tgh = 0.2 s = Tcon of hydro governor, Trh = 28.749 s = Tcon of water droop, TR = 5 s; TW = 1 s = Water through penstock for time; Gas Station: Bg = loop1 coefficient = 0.5, Cg = loop1 gas constant = 1, XC = loop2 gain = 0.6 s, YC = loop2 constant = 1 s, Tcr = loop3 gain = 0.03 s;;Tfl = loop3 time constant = 0.23 s, Tcd = loop4 time constant = 0.2 s.

1.2 Control area_2

Thermal Plant Tg = 0.08 s = Time constant (Tcon) of governor, Tr = 10 s = Tcon of reheat turbine, Kr = 0.2 = Gain of reheat, Tt = 0.3 s = Tcon of turbine; Hydro System: Tgh = 0.2 s = Tcon of hydro governor, Trh = 28.749 s = Tcon of water droop, TR = 5 s; TW = 1 s = Water through penstock for time; Gas Station: Bg = loop1 coefficient = 0.5, Cg = loop1 gas constant = 1, XC = loop2 gain = 0.6 s, YC = loop2 constant = 1 s, Tcr = loop3 gain = 0.03 s;;Tfl = loop3 time constant = 0.23 s, Tcd = loop4 time constant = 0.2 s.

Nuclear Plant TgN = governor time constant = 0.02 s, Kh = Gain of hp turbine = 1, Th = time constant of LP turbine. 0.003 s, Kr = gain of LP turbine = 0.5, Kth = Gain of LP turbine1 = 0.01; Trh = Time constant of LP turbine1 = 0.002.

Control area_3

Wind TWTG = 1.5 s = Tcon of wind plant = ; Solar: TPV = 1.8 s = Tcon of PV plant; Diesel: TC = TDEG = 2 s = Tcon of diese plant; MT: TMT = 2 s = Tcon of Micro-turbine; FuelCell: TFC = 1.8 s = Tcon of fuel cell, Cn = 0.3 = distribution factor of fuel cell; GT: TG = 0.2 s = Tcon of geo-thermal; TT = 0.5 s; Battery: TBES = 0.1 s = Tcon of battery; Flywheel: TFES = 0.1 s = Tcon of Flywheel; M = Moment of inertia = 0.2; D = Damping coefficient = 0.012.

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Baral, K.K., Sahu, P.C., Barisal, A.K. et al. Combined analysis on AGC and ELD of a hybrid power system with D-WCA designed Gaussian type-2 fuzzy controller. Evolving Systems 14, 263–280 (2023). https://doi.org/10.1007/s12530-022-09454-0

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