2022
DOI: 10.1016/j.epsr.2021.107708
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Damped Nyquist Plot for the Phase and Gain Optimization of Power System Stabilizers

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Cited by 9 publications
(4 citation statements)
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“…Ideally, the Nyquist plot has a semicircular section in the high frequency region and a Warburg line (slope at 45°) at low frequencies. 56,65 The low curvature of the first part of the Nyquist diagram indicates a low charge transfer resistance (Rct) and the sharper second part of the diagram with a slope of nearly 90°c onfirms the ideal pseudocapacitive behavior of the system. 66 According to the obtained Nyquist plots of proposed electrodes, the PB/AEGO Nsh graphite sheet electrode has a lower R ct than other electrodes, which indicates the high kinetics of electron Table II.…”
Section: Spmentioning
confidence: 99%
“…Ideally, the Nyquist plot has a semicircular section in the high frequency region and a Warburg line (slope at 45°) at low frequencies. 56,65 The low curvature of the first part of the Nyquist diagram indicates a low charge transfer resistance (Rct) and the sharper second part of the diagram with a slope of nearly 90°c onfirms the ideal pseudocapacitive behavior of the system. 66 According to the obtained Nyquist plots of proposed electrodes, the PB/AEGO Nsh graphite sheet electrode has a lower R ct than other electrodes, which indicates the high kinetics of electron Table II.…”
Section: Spmentioning
confidence: 99%
“…The sliding mode control (SMC) is presented in paper [5], a farmland fertility algorithm (FFA) in [6] but the FFA is highly reliant on accurate and extensive data which can be challenging to acquire, a genetic algorithm in [7] which requires many iterations and evaluations, making it slow for complex problems like multi-machine system, a particle swarm optimization (PSO) in [8] but this algorithm performance critically depends on fine-tuning parameters, making it complex and less robust, a chaotic sunflower optimization algorithm in [9] which requiring careful tuning of chaotic parameters, struggles with local optima, prioritizing exploitation (refining good solutions) over exploration (finding new, potentially better regions) leading to missed global optima, a moth search algorithm in [10] but it lacks the rigorous mathematical backing of some established optimization methods, raising concerns about stability and global convergence guarantees, bio-inspired algorithms in [11] which is highly dependent on fine-tuning specific parameters, impacting effectiveness and requiring more expertise, a sliding mode control in [12]. Moreover, artificial intelligence-based training and tuning techniques have been used to develop a PSS as a Deep reinforcement learning-based method in [13], a neuro-adaptive predictive control in [14], a fuzzy-based controller in [15][16][17], damped Nyquist plot for the phase and gain optimization in [18] but all these algorithms may require intensive computations compared to simpler algorithms, especially for complex problems. Furthermore, robust control theories have been employed in the design of H∞-based robust power system stabilizers [19] in the case of one machine connected to the electrical grid.…”
Section: Introductionmentioning
confidence: 99%
“…The results of the simulations proved the robustness of the designed stabilization method in this paper. In [17], damped Nyquist is presented to select the optimal parameters of the PSSs. In this reference, first the participation coefficients are used for PSSs placement and then the proposed damped Nyquist presents a model of stabilizers.…”
Section: Introductionmentioning
confidence: 99%