A dynamic fuzzy q-learning controller to improve power system transient stability

Zhou, Lv; Swain, Akshya; Naik, Prem Kumar; Nair, Nirmal-Kumar C.

doi:10.1109/powercon.2016.7753910

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…But the generative-adversarial process requires a significant amount of time and is difficult to adapt to the real-time requirements of the power grid. The reference [15] introduces a histogram algorithm to discretize the original data, enhancing the model's robustness to noise and reducing the risk of overfitting in noisy environments. However, it fails to consider the impact of sample imbalance on model evaluation.…”

Section: Introductionmentioning

confidence: 99%

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Qu,

Wang,

Cheng

et al. 2024

ENERGY

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The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of the XGBoost model, enhancing the deep exploration of minority class samples to improve the accuracy of the model evaluation. Furthermore, to improve the generalization ability of the evaluation model, a transfer learning method based on model parameters and sample augmentation is proposed. The simulation analysis on the IEEE 39-bus system demonstrates that the proposed method, compared to the traditional machine learning-based transient stability assessment approach, achieves an average improvement of 2.16% in evaluation accuracy. Specifically, under scenarios involving changes in topology structure and operating conditions, the accuracy is enhanced by 3.65% and 3.11%, respectively. Moreover, the model updating efficiency is enhanced by 14-15 times, indicating the model's transferable and adaptive capabilities across multiple scenarios.

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Section: Introductionmentioning

confidence: 99%

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Qu,

Wang,

Cheng

et al. 2024

ENERGY

View full text Add to dashboard Cite

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“…QL can learn without user feedback [3]. QL can be used for many applications including signal localisation [4], power system controllers [5], network path calculation [6], etc.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Q-Learning within SDN Controlled Static and Dynamic Mesh Networks

Harewood-Gill

Martin

Nejabati

2020

2020 6th IEEE Conference on Network Softwarization (NetSoft)

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Current infrastructures are reaching the point where existing networking methods are unable to cope with the exponential growth of traffic and Quality of Service (QoS) requirements. New techniques are necessary to keep pace. One such technique, Software-Defined Networking (SDN) uses a central controller to program many individual network devices. However, SDN uses heuristic algorithms that do not always select the optimal path. This paper looked at creating three Q-Routing algorithms leveraging SDN and Mesh network topologies. Two algorithms used one network metric each (Latency and Bandwidth) and the third used multiple metrics. Results showed that the single metric Q-Routing algorithms on average performed as well as the K-Shortest Path versions while Q-Routing with multiple network metrics failed to match K-Shortest Path (different combination of metrics means these algorithms are not comparable). Results also showed that Q-Routing was able to calculate paths faster than K-Shortest Path in both static and dynamic networks.

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Optimum design of series compensation to improve power system transient stability using DFQL and PSO based controllers

Zhou

Swain

Naik

et al. 2017

2017 13th IEEE International Conference on Control &Amp; Automation (ICCA)

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A dynamic fuzzy q-learning controller to improve power system transient stability

Cited by 8 publications

References 16 publications

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

The Performance of Q-Learning within SDN Controlled Static and Dynamic Mesh Networks

Optimum design of series compensation to improve power system transient stability using DFQL and PSO based controllers

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