Design of Power System Stabilizers based on &amp;#x03BC;-controller for power system stability enhancement

Ellithy, K.; Said, S.; Kahlout, O.

doi:10.1109/epecs.2013.6712982

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…The SMIB power system model is often used as control object for evaluating PSS performance (Chitara et al, 2018). The linearized model of the SMIB power system with PSS used in this paper is cited from Ellithy et al (2013) and given by the following equation…”

Section: Power System Modelmentioning

confidence: 99%

“…In addition, K 6 is the change in terminal voltage with a change in flux in the d -axis linkage for a constant rotor angle. The value of the related parameters can be obtained from Appendix in Ellithy et al (2013). Δ Vref , Δ Tm , and Δ ω represent the excitation reference voltage sudden change, mechanical torque disturbance, and rotor speed deviation, respectively.…”

Section: Power System Modelmentioning

confidence: 99%

“…The simulation model of the SMIB power system with an MFO-FPID controller is shown in Figure 6, and the relevant parameters of this system can be obtained from Ellithy et al (2013). Δ ω and Δ ω · are used as the input of the MFO-FPID, while Δ ω is used as the input of PID and CPSS.…”

Section: Simulation Studymentioning

confidence: 99%

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Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

Gao

Xiong

et al. 2023

Transactions of the Institute of Measurement and Control

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Since low-frequency oscillation seriously threatens the safe operation of the power system, the power system stabilizer (PSS) can effectively suppress the oscillation. In this paper, a hybrid parameter optimization method combining the moth-flame optimization (MFO) algorithm and fuzzy logic controller (FLC) is proposed to address the problem of poor adaptability of the parameter tuning method in the conventional power system stabilizer (CPSS). This method can optimize the parameters of PSS in different processes. Initially, the optimal parameters of PSS under the current perturbation are given by the MFO algorithm. During the online operation of the system, as perturbation changes, the parameters of the PSS will also be adaptively tuned by the FLC in real-time when the system operating conditions change. According to this method, a fuzzy adaptive proportional–integral–differential (FPID) controller is designed based on the moth-flame optimization algorithm (MFO-FPID), and it is used as PSS to improve dynamic stability performance during oscillation. Moreover, its parameters can be adaptively adjusted in different perturbation scenarios. The designed MFO-FPID controller is applied to the single machine infinite bus (SMIB) power system to compare the dynamic performance with other controllers, that is, proportional–integral–differential (PID) and CPSS. The result shows that the MFO-FPID controller can suppress the oscillation very well, and the control effect is the best.

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Section: Power System Modelmentioning

confidence: 99%

Section: Power System Modelmentioning

confidence: 99%

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Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

Gao

Xiong

et al. 2023

Transactions of the Institute of Measurement and Control

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“…These HILF events cause perturbations in the voltage, grid frequency, current flows, and rotating generator torque that can cascade beyond the immediate location of the fault and affect adjacent infrastructure and regions. Mitigation strategies to address these stability issues include distributed control strategies (Lee et al, 2016;Ayar et al, 2017), generator control circuits (Ellithy et al, 2013;Derafshian and Amjady, 2015), fast-acting power electronics devices (Ma et al, 2016;Yu et al, 2018), and distributed energy resources (DERs) and microgrids (Krismanto et al, 2021). Areas of the grid with high amounts of renewable generation and DERs may be at higher risk to outages if the variability in their output power is not complemented with advanced power electronics or sufficient operating reserves from storage, controllable loads, and dispatchable generation (Ogunjuyigbe et al, 2016;Khani et al, 2012;Dulău et al, 2014;Resource and MW Fault Induced Solar Photovoltaic, 2017).…”

Section: Introductionmentioning

confidence: 99%

Unified dispatch of grid-connected and islanded microgrids

Wodicker,

Nelson,

Johnson

2024

Front. Energy Res.

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This work develops microgrid dispatch algorithms with a unified approach to model predictive control (MPC) to (a) operate in grid-connected mode to minimize total operational cost, (b) operate in islanded mode to maximize resilience during a utility outage, and (c) utilize weighting factors in the grid-connected objective function to preserve islanded capability (on-site fuel reserves, battery state of charge) to enhance resilience in the potential event of an unplanned grid outage. Resilience is defined using microgrid survivability (probability to serve 100% of critical load), autonomy (duration of time to serve 100% of critical load), and unserved energy (curtailed critical load) for a target of 7 days during a grid outage. The developed methods are applied to a military microgrid with 2,250 kW of diesel generation, 3,450 kW/13,800 kWh battery storage, and 16,479 kW of solar photovoltaics. Sensitivity analysis is conducted to determine the selection of weighting factors to have the best impact on three developed objectives: grid-connected economics, islanded resilience, and carbon intensity. Optimal weighting factors reduce operating costs by 0.1%, increase survivability by 3.9%, increase autonomy by 16.7%, reduce unserved energy by 94.1%, and increase carbon intensity by 2.5%.

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Artificial Optimizer Algorithm for Power System Stabilizer design problem and multidisciplinary engineering applications

Singh,

Kaur,

Houssein

2024

Heliyon

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Design of Power System Stabilizers based on μ-controller for power system stability enhancement

Cited by 7 publications

References 6 publications

Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

Unified dispatch of grid-connected and islanded microgrids

Artificial Optimizer Algorithm for Power System Stabilizer design problem and multidisciplinary engineering applications

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