All Outputs (13)

Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise (2024)
Journal Article
Gao, J.-H., Guo, M.-F., Lin, S., & Hong, Q. (online). Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise. International Journal of Circuit Theory and Applications, https://doi.org/10.1002/cta.4260

In addressing the quantization noise challenge in high impedance fault (HIF) localization within resonant distribution networks, we propose a cutting-edge, explainable deep learning approach that significantly advances existing methods. This approach... Read More about Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise.

Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning (2024)
Journal Article
Xie, J., Dong, H., Zhao, X., & Lin, S. (in press). Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning. IEEE transactions on Automation Science and Engineering, https://doi.org/10.1109/TASE.2024.3372713

A reinforcement learning (RL) based fault-tolerant control strategy is developed in this paper for wind turbine torque & pitch control under actuator & sensor faults subject to unknown system models. An incremental model-based heuristic dynamic progr... Read More about Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning.

Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks (2024)
Journal Article
Gao, J.-H., Guo, M.-F., Lin, S., & Chen, D.-Y. (online). Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks. Measurement, Article 114431. https://doi.org/10.1016/j.measurement.2024.114431

Fault localization is crucial for ensuring stability, particularly in high impedance faults (HIF) characterized by low current levels and prolonged transient processes (TP). Existing methods predominantly analyze differences in the fixed-length trans... Read More about Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks.

An incremental high impedance fault detection method under non-stationary environments in distribution networks (2023)
Journal Article
Guo, M. F., Yao, M., Gao, J. H., Liu, W. L., & Lin, S. (2024). An incremental high impedance fault detection method under non-stationary environments in distribution networks. International Journal of Electrical Power & Energy Systems, 156, Article 109705. https://doi.org/10.1016/j.ijepes.2023.109705

In the non-stationary environments of distribution networks, where operating conditions continually evolve, maintaining reliable high impedance faults (HIF) detection is a significant challenge due to the frequent changes in data distribution caused... Read More about An incremental high impedance fault detection method under non-stationary environments in distribution networks.

Semantic segmentation-based intelligent threshold-free feeder detection method for single-phase ground fault in distribution networks (2023)
Journal Article
Hong, C., Qiu, H.-Y., Gao, J.-H., Lin, S., & Guo, M.-F. (in press). Semantic segmentation-based intelligent threshold-free feeder detection method for single-phase ground fault in distribution networks. IEEE Transactions on Instrumentation and Measurement, https://doi.org/10.1109/TIM.2023.3335520

Feeder detection for single-phase ground fault (SPGF) is challenging in a resonant grounded system due to the difference in feeder capacitance to ground and the influence of the arc suppression coil. This paper utilizes semantic segmentation algorith... Read More about Semantic segmentation-based intelligent threshold-free feeder detection method for single-phase ground fault in distribution networks.

A novel flexible fault eliminator with active disturbance rejection and soft grid-connection in distribution networks (2023)
Journal Article
Zheng, Z.-Y., Qiu, H.-Y., Zhang, B.-L., Guo, M.-F., Lin, S., & Cai, W.-Q. (2023). A novel flexible fault eliminator with active disturbance rejection and soft grid-connection in distribution networks. International Journal of Electrical Power & Energy Systems, 154, Article 109425. https://doi.org/10.1016/j.ijepes.2023.109425

Among the possible fault types in the distribution networks, single-line-to-ground (SLG) fault has the highest probability. The SLG fault current and arc can easily cause personal injury and death. This study proposed a flexible fault eliminator (FFE... Read More about A novel flexible fault eliminator with active disturbance rejection and soft grid-connection in distribution networks.

Application of Semantic Segmentation in High-Impedance Fault Diagnosis Combined Signal Envelope and Hilbert Marginal Spectrum for Resonant Distribution Networks (2023)
Journal Article
Gao, J.-H., Guo, M.-F., Lin, S., & Chen, D.-Y. (2023). Application of Semantic Segmentation in High-Impedance Fault Diagnosis Combined Signal Envelope and Hilbert Marginal Spectrum for Resonant Distribution Networks. Expert Systems with Applications, 231, Article 120631. https://doi.org/10.1016/j.eswa.2023.120631

The diagnosis of high-impedance fault (HIF) is a critical challenge due to the presence of faint signals that exhibit distortion and randomness. In this study, we propose a novel diagnostic approach for HIF based on semantic segmentation of the signa... Read More about Application of Semantic Segmentation in High-Impedance Fault Diagnosis Combined Signal Envelope and Hilbert Marginal Spectrum for Resonant Distribution Networks.

Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm (2023)
Journal Article
Huang, Y., Lin, S., & Zhao, X. (2023). Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm. IEEE Transactions on Sustainable Energy, https://doi.org/10.1109/tste.2023.3270761

This paper leverages multi-agent reinforcement learning (MARL) to develop an efficient control system for a wind farm comprising a new type of wind turbines with hydrostatic transmission. The primary motivation for hydrostatic wind turbines (HWT) is... Read More about Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm.

Flexible neutral point displacement overvoltage suppression method based on backstepping control in unbalanced distribution networks (2023)
Journal Article
Zheng, Z.-Y., Xu, J.-F., Zhang, B.-L., Wang, H., Guo, M.-F., & Lin, S. (2023). Flexible neutral point displacement overvoltage suppression method based on backstepping control in unbalanced distribution networks. International Journal of Electrical Power & Energy Systems, 148, Article 108950. https://doi.org/10.1016/j.ijepes.2023.108950

Three-phase AC distribution networks are required to operate as symmetrically as possible for optimal performance, but the three-phase-to-ground parameters are asymmetric in the field due to network construction deviation, resulting in the three-phas... Read More about Flexible neutral point displacement overvoltage suppression method based on backstepping control in unbalanced distribution networks.

Multi-Frequency bands based Pole-to-Ground fault detection method for MMC-Based radial DC distribution systems (2022)
Journal Article
Yang, C., Lin, S., & Guo, M. (2022). Multi-Frequency bands based Pole-to-Ground fault detection method for MMC-Based radial DC distribution systems. International Journal of Electrical Power & Energy Systems, 141, Article 108250. https://doi.org/10.1016/j.ijepes.2022.108250

In a small-current grounding system, the pole-to-ground fault may cause the voltage drop in the fault pole and the voltage rise in the other poles. In flexible DC distribution systems, Severe voltage variation may shorten the insulation lifetime of t... Read More about Multi-Frequency bands based Pole-to-Ground fault detection method for MMC-Based radial DC distribution systems.

Gaussian Distribution-Based Inertial Control of Wind Turbine Generators for Fast Frequency Response in Low Inertia Systems (2022)
Journal Article
Kheshti, M., Lin, S., Zhao, X., Ding, L., Yin, M., & Terzija, V. (2022). Gaussian Distribution-Based Inertial Control of Wind Turbine Generators for Fast Frequency Response in Low Inertia Systems. IEEE Transactions on Sustainable Energy, https://doi.org/10.1109/tste.2022.3168778

Decline of rotating inertia due to the high share of renewable sources cause challenges in controlling grid frequency. With recent grid codes, large-scale wind turbines (WTs) are required to provide frequency support. Existing stepwise inertial contr... Read More about Gaussian Distribution-Based Inertial Control of Wind Turbine Generators for Fast Frequency Response in Low Inertia Systems.

A Decentralized Fault Section Location Method Using Autoencoder and Feature Fusion in Resonant Grounding Distribution Systems (2022)
Journal Article
Li, Z. J., Lin, S., Guo, M. F., & Tang, J. (2022). A Decentralized Fault Section Location Method Using Autoencoder and Feature Fusion in Resonant Grounding Distribution Systems. IEEE systems journal, https://doi.org/10.1109/JSYST.2022.3151630

In industrial applications, the existing fault location methods of resonant grounding distribution systems suffer from low accuracy due to excessive dependence on communication, lack of field data, difficulty in artificial feature extraction and thre... Read More about A Decentralized Fault Section Location Method Using Autoencoder and Feature Fusion in Resonant Grounding Distribution Systems.

Nonlinear modelling and adaptive control of smart rotor wind turbines (2022)
Journal Article
Li, J., Wang, Y., Lin, S., & Zhao, X. (2022). Nonlinear modelling and adaptive control of smart rotor wind turbines. Renewable energy, 186, 677-690. https://doi.org/10.1016/j.renene.2022.01.020

This paper develops a nonlinear mid-fidelity aeroservoelastic model for smart rotor wind turbines and studies the turbulent load alleviation of the wind turbines with trailing edge flaps (TEFs) actuated by a novel proportional-derivative model-free a... Read More about Nonlinear modelling and adaptive control of smart rotor wind turbines.