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Performance analysis of SSA optimized fuzzy 1PD-PI controller on AGC of renewable energy assisted thermal and hydro-thermal power systems

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Abstract

Nonlinear structure, vague loading characteristics and parameter uncertainties of the interconnected power system (IPS) have recently given birth to various controllers that better deal with automatic generation control (AGC). AGC plays a key role in ensuring the balance of generation and load demand in IPSs. If this balance is lost, then the system faces large frequency deviations. Thus, this work proposes a new fuzzy 1 + proportional + derivative-proportional + integral (F1PD-PI) controller to escalate AGC performance of different IPSs integrated with renewable energy sources (RES) including wind, solar and fuel cells. Inspiration for the proposed controller is unique and comes from combining the merits of fuzzy, 1PD and PI controllers. Salp swarm algorithm (SSA) is utilized to optimize the proposed controller’s gains as well as fuzzy membership functions. The effectiveness and contribution of the advocated approach are demonstrated on a two-area reheat thermal system and a two-area multi-source hydro-thermal system by realizing an extensive comparison study with the state-of-the-art variants. The results substantiate that SSA optimized F1PD-PI controller has better performance than its competing peers in terms of minimum settling time, peak undershoot, peak overshoot and error-integrating performance criterion of the system responses. Nonlinearities from governor dead band and generation rate constraint are also studied, which verifies the performance of the control strategy in tackling nonlinearities. Additionally, the robustness of the controller is affirmed against parameter uncertainties and load disturbances. Finally, the stability of our proposal is checked using eigenanalysis.

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Data availability

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Abbreviations

\(B\) :

Frequency bias, puMW/Hz

\(R\) :

Speed governor regulation parameter, Hz/puMW

\({K}_{ps}\) :

Power system gain

\({K}_{ae}\) :

Aqua electrolyser gain

\({K}_{fc}\) :

Fuel cell gain

\({K}_{r}\) :

Reheat gain constant

\({K}_{s}, {K}_{t}\) :

Solar thermal generator gains

\({K}_{wtg}\) :

Wind turbine generator gain

\({K}_{n}\) :

Participation factor for solar thermal and wind power systems

\({T}_{12}\) :

Tie line power coefficient, puMW/rad

\({T}_{ps}\) :

Power system time constant, s

\({T}_{ae}\) :

Aqua electrolyser time constant, s

\({T}_{fc}\) :

Fuel cell time constant, s

\({T}_{s}, {T}_{T}\) :

Solar thermal generator time constants, s

\({T}_{wtg}\) :

Wind turbine time constant, s

\({T}_{g}\) :

Thermal governor time constant, s

\({T}_{t}\) :

Thermal turbine time constant, s

\({T}_{r}\) :

Reheater time constant, s

\({T}_{rh}\) :

Time constant of hydro governor transient droop, s

\({T}_{R}\) :

Hydro governor reset time, s

\({T}_{gh}\) :

Hydro governor main servo time constant, s

\({T}_{w}\) :

Water starting time, s

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  1. Department of Electrical and Electronics Engineering, Engineering Faculty, Düzce University, Düzce, Turkey

    Emre Çelik

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Appendix: System data (Arya et al. 2021b)

Appendix: System data (Arya et al. 2021b)

Nominal parameters of two-area RTS:

\({P}_{r}=2000\) MW, \({T}_{ps}=20\) s, \({K}_{ps}=120\) Hz/puMW, \({T}_{g}=0.08\) s, \({T}_{t}=0.3\) s, \({K}_{r}=0.5\), \({T}_{r}=10\) s, \(2\pi {T}_{12}=0.545\) puMW/Hz, \(B=0.425\) puMW/Hz, \(R=2.4\) Hz/puMW, \({F}^{0}=60\) Hz.

Nominal parameters of two-area MSHTS:

\({P}_{r}=2000\) MW, \({T}_{ps}=20\) s, \({K}_{ps}=100\) Hz/puMW, \({T}_{g}=0.08\) s, \({T}_{t}=0.3\) s,\({T}_{rh}=48.7\) s, \({T}_{R}=5\) s, \({T}_{gh}=0.513\) s, \({T}_{w}=1\) s, \({T}_{12}=0.0707\) puMW/Hz, \(B=0.425\) puMW/Hz, \({R}_{1}=2\) Hz/puMW, \({R}_{2}=2.4\) Hz/puMW, \({F}^{0}=60\) Hz.

Nominal parameters of RES:

\({T}_{s}=1.8\) s, \({K}_{s}=1.8\), \({T}_{T}=0.3\) s, \({K}_{t}=1\), \({T}_{wtg}=1.5\) s, \({K}_{wtg}=1\), \({K}_{n}=0.6\), \({T}_{ae}=0.5\) s, \({K}_{ae}=0.002\) \({T}_{fc}=4\) s, \({K}_{fc}=0.01\)

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Çelik, E. Performance analysis of SSA optimized fuzzy 1PD-PI controller on AGC of renewable energy assisted thermal and hydro-thermal power systems. J Ambient Intell Human Comput 13, 4103–4122 (2022). https://doi.org/10.1007/s12652-022-03751-x

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