Application of ANN and ANFIS techniques for PSS tuning in a multimachine power system

Kahouli, Omar; Alshammari, Badr M.; Dhouib, Bilel

doi:10.1109/ssd.2019.8893264

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…However, PSSs developed using Artificial Intelligence (AI) techniques have been used for design and stability studies of non-renewable-sourced power systems. Some of the frequently used techniques for a PSS design are ANN [23,24] and Fuzzy Logic [10,25,26], and support vector regression was used to design an adaptive PSS in [27]. The use of AI to solve stability issues has been categorized into three separate methodologies based on the techniques used: supervised learning, unsupervised learning, and reinforcement learning (RL) [28].…”

Section: Literature Reviewmentioning

confidence: 99%

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Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

2022

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Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controller to damp the oscillations without requiring an accurate system model. In this work, a Q-learning (QL)-based model-free wind speed DFIG was designed on the rotor-side converter (RSC), and a QL-based model-free DC-link voltage regulator was designed on the grid-side converter (GSC) to enhance the stability of the system. In the next step, the TD3 agent was trained to learn the system dynamics by replacing the inner current controllers of the RSC, which replaced the QL-based model. In the first stage, the conventional PSS and Proportional–Integral (PI) controllers were introduced to both the RSC and GSC. Then, the system was trained to become model-free by replacing the PSS and the PI controller with a QL algorithm under very small wind speed variations. In the second stage, the QL algorithm was replaced with the TD3 agent by introducing large variations in wind speed. The results reveal that the TD3 agent can sustain the stability of the DFIG system under large variations in wind speed without assuming a detailed control structure beforehand, while QL-based controllers can stabilize the doubly fed induction generator (DFIG)-equipped wind energy conversion system (WECS) under small variations in wind speed.

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Section: Literature Reviewmentioning

confidence: 99%

“…For grid-side active power in these equations, by applying the synchronously rotating reference frame and aligning the d-axis on the grid voltage vector, the obtained results are v ds = v s and v qs = 0. Applying this to Equations (24) and (25) yields:…”

Section: Grid-side Controllersmentioning

confidence: 99%

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

2022

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“…This technique produces a better performance according to the results that are presented in this paper. Most works use many points to train the neural network, as in 13,20,22,23 . This work presents a new methodology for training the NN that requires far fewer load points to efficiently represent the power system.…”

Section: Introductionmentioning

confidence: 99%

“…This work presents a new methodology for training the NN that requires far fewer load points to efficiently represent the power system. The training method differs from the training methods in 22,23 in only requiring the optimization of the PSS4b gains and not its output, which makes it easier to reproduce in other systems. The methodology proposed to train the NN can be used in other control systems, and six different training functions were also compared.…”

Section: Introductionmentioning

confidence: 99%