ANN Control for Damping Low-frequency Oscillation using Deep learning

Jhang, Seong-Su; Lee, Heung-Jae; Kim, Cha-Nyeon; Song, Chanho; Yu, Won-Kun

doi:10.1109/aupec.2018.8757923

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Therefore measurement-based estimation techniques combined with ANN-based methods extract useful information from the power system. ANNbased power oscillation damping control is suggested in a few pieces of literature [26,27]. Because of massive data from synchrophasors, initial data is appropriately subjected to preprocessing stage and dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

Identification of Oscillatory Modes in Power System Using Deep Learning Approach

et al. 2022

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The increase in electric power demand pushes the modern power system for more interconnected networks. It leads to a lack of inertia and creates more critical disturbances in the power system. The resulting oscillation, if may not be damped out, results in cascade tripping. Immediate detection of low-frequency oscillatory modes and their parameters will help the power system operator to act on a particular event without consuming much time. This research paper proposes novel strategies for identifying low-frequency modes using deep learning techniques, and the model can predict the LFO modes in different topologies. This work presents the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) approach to predict the instantaneous mode oscillatory parameters in the power system. Once the LSTM-RNN model is trained for different power disturbance situations, it can be used for any events associated with the system. Simulation results are verified using two area Kundur systems at various disturbance conditions. The simulation study results are performed using MATLAB and python tensor flow library. The results are validated using statistical methods, and it confirms the superior viability and adaptability of the proposed approach in predicting the instantaneous mode parameters.

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Section: Introductionmentioning

confidence: 99%

Identification of Oscillatory Modes in Power System Using Deep Learning Approach

et al. 2022

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“…Ref. [28] uses a deep learning WADC, but such a method relies extensively on past data and is unable to adapt to the changes in transmission networks. One observation shows that the exploration of the system does help in capturing such transformations.…”

Section: Introductionmentioning

confidence: 99%

Twin-Delayed Deep Deterministic Policy Gradient for Low-Frequency Oscillation Damping Control

2021

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Due to the large scale of power systems, latency uncertainty in communications can cause severe problems in wide-area measurement systems. To resolve this issue, a significant amount of past work focuses on using emerging technology, including machine learning methods such as Q-learning, for addressing latency issues in modern controls. Although the method can deal with the stochastic characteristics of communication latency, the Q-values can be overestimated in Q-learning methods, leading to high bias. To address the overestimation bias issue, we redesign the learning structure of the deep deterministic policy gradient (DDPG). Then we develop a damping control twin-delayed deep deterministic policy gradient method to handle the damping control issue under unknown latency in the power network. The purpose is to address the damping control issue under unknown latency in the power network. This paper will create a novel reward algorithm, taking into account the machine speed deviation, the episode termination prevention, and the feedback from action space. In this way, the system optimally damps down frequency oscillations while maintaining the system’s stability and reliable operation within defined limits. The simulation results verify the proposed algorithm in various perspectives, including the latency sensitivity analysis under high renewable energy penetration and the comparison with conventional and machine learning control algorithms. The proposed method shows a fast learning curve and good control performance under varying communication latency.

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Adaptability Study of Damping Control Strategies for Low-Frequency Oscillation in Power Grid with High Penetration of Photovoltaic

Di,

Zhen,

et al. 2023

2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2)

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ANN Control for Damping Low-frequency Oscillation using Deep learning

Cited by 5 publications

References 7 publications

Identification of Oscillatory Modes in Power System Using Deep Learning Approach

Identification of Oscillatory Modes in Power System Using Deep Learning Approach

Twin-Delayed Deep Deterministic Policy Gradient for Low-Frequency Oscillation Damping Control

Adaptability Study of Damping Control Strategies for Low-Frequency Oscillation in Power Grid with High Penetration of Photovoltaic

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