Q.H. Wu’s research while affiliated with South China University of Technology and other places

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Publications (593)


Learning-Based Stochastic Multi-Objective Optimizer for Uncertain Power System Scheduling
  • Preprint

January 2025

Bin Deng

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Mengshi Li

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Tianyao Ji

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Q.H. Wu

Transient Frequency-Voltage Support Strategy for VSC-MTDC Integrated Offshore Wind Farms Based on Perturbation Observer and Funnel Control

January 2025

IEEE Transactions on Sustainable Energy

Wen Gao

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Kaishun Xiahou

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[...]

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Yihua Zhu

To address the transient frequency and voltage stability challenges posed by low-inertia offshore wind farms (OWFs) connected through voltage source converter (VSC) based multi-terminal high voltage direct current (VSC-MTDC) system, a nonlinear perturbation observer and funnel controlbased transient frequency and voltage support (PFTFVS) control strategy is proposed for VSC-MTDC integrated OWFs system. This approach utilizes improved estimation ability of the observer and adaptive feature of the funnel controller to enhance transient support capability and disturbance rejection performance of the system. The strategy comprises three parts: 1) an adaptive transient frequency support and rotor speed control method for wind turbines is devised to enhance the frequency support capability of OWFs; 2) considering the energy storage capability of DC capacitor in VSC-MTDC system, a transient frequency support controller is designed for VSC station to swiftly manage transient frequency variations; 3) utilizing the rapid power regulation ability of VSC-MTDC, a transient voltage support controller is developed for VSC station to enhance voltage stability and boost the power transmission capacity. Finally, dynamic simulations of VSC-MTDC integrated OWFs system are built to verify the validity and robustness of the proposed strategy




Advancing GIS Operational Monitoring: A Novel Voiceprint Recognition Method Using Grassmann Manifold and Multi-Kernel Functions

October 2024

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1 Citation

IEEE Transactions on Power Delivery

Gas insulated switchgear (GIS) is essential for the reliability and stability of power systems, requiring precise recognition of its operating conditions, which are typically represented by voiceprint data. Traditional methods, predominantly based in Euclidean space, often struggle to differentiate specific intricate operating conditions in GIS. Addressing this gap, this paper proposes a novel method anchored in Riemannian space. Specifically, this method begins by employing Mel frequency cepstral coefficient (MFCC) and singular value decomposition (SVD) to extract features from voiceprint data. Subsequently, it projects these features within Riemannian space and maps them onto a Grassmann manifold, effectively capturing its intricate and nonlinear characteristics. A key innovation of this method is the use of three kernel functions, namely projection, Binet-Cauchy, and canonical correlation, optimized via empirical kernel alignment techniques to select the most suitable adaptive parameters, thereby enhancing classification accuracy. Further refinement is achieved through kernel linear discriminant analysis (KLDA), which integrates the aligned kernel into a lower-dimensional subspace for more efficient classification. Experiments on real data from ZF23-126 type GIS, encompassing 20 different operational conditions, show our method achieves an accuracy of 95.21%, precision of 95.38%, specificity of 99.75%, and F1-score of 95.22%, significantly outperforming all baseline methods. Moreover, the method demonstrates robust performance across various classifiers, with accuracy consistently exceeding 90%. Additional ablation experiments confirm its effectiveness and generalizability.







Citations (58)


... Despite its drawbacks, the extrinsic approach allows for the use of any Euclidean-based clustering algorithms or distance measures, offering flexibility in method selection. While we have considered commonly adopted embedding techniques from the literature [43,59] in this work, our results are sufficient to draw conclusions. ...

Reference:

Multivariate Time Series Clustering with State Space Dynamical Modeling and Grassmann Manifold Learning: A Systematic Review on Human Motion Data
Advancing GIS Operational Monitoring: A Novel Voiceprint Recognition Method Using Grassmann Manifold and Multi-Kernel Functions
  • Citing Article
  • October 2024

IEEE Transactions on Power Delivery

... 8.8 years and carbon emission by almost 96 ton/year. However, integration of these technological involve challenges such as intermittent nature of solar energy, variations in free cooling sources and electricity price fluctuations [15]. In a study by Comodi et al. [16], it can be deduced that rooftop PV, cold thermal energy storage and DC networks forms an integral part of an optimal solution for "low carbon" local energy community in the context of Singapore. ...

Optimal operation for district cooling systems coupled with ice storage units based on the per-unit value form
  • Citing Article
  • May 2024

Energy

... Afterwards, CNN's framework was developed to identify FDI assaults. Chen et al. 18 presented a recognition structure of FDIA for PSSE based on GECCN, which utilizes topology data, edge feature, and node feature. Over the structure of a deep graph, the association of data is efficiently extracted to begin the connection between the assessed measurement value and the real state of power models. ...

Detection of false data injection attacks on power systems using graph edge-conditioned convolutional networks
  • Citing Article
  • Full-text available
  • April 2023

Protection and Control of Modern Power Systems

... The quality of the starting point greatly influences the result and convergence efficiency of the optimization algorithm, especially for the non-convex and constrained alternating current OPF problem [78][79][80][81][82]. Forecasting warm start points is similar to directly mapping OPF variables. ...

Efficient contingency analysis of power systems using linear power flow with generalized warm-start compensation
  • Citing Article
  • February 2024

International Journal of Electrical Power & Energy Systems

... The optimization process of reinforcement learning methods does not require an exact model of the optimization objective. Reinforcement learning also does not require too much a priori experience related to the state of the environment and is an online learning method that can be applied to real-time environments [26]. Therefore, reinforcement learning is more suitable for handling more complex ADN equivalence modelling problems. ...

Multi-layer double deep Q network for active distribution network equivalent modeling with internal identification for EV loads
  • Citing Article
  • September 2023

Applied Soft Computing

... W IND turbine generators (WTGs), such as full-rate WTGs and DFIG-WTs, synchronize with the external power gird by the implementation of a PLL [1]- [3]. The PLL measures the phase angle and rotational frequency of the terminal bus voltage vector of a WTG, which are used as the phase and frequency reference of voltage outputs of the WTG [4], [5]. Owning to the fast tracking dynamics of the PLL, voltage outputs of the WTG can accurately follow the grid frequency [6], [7]. ...

Direct Power Regulation of Grid-Connected Voltage-Source Inverters Based on Bang-Bang Funnel Control
  • Citing Article
  • January 2023

IEEE Transactions on Industrial Electronics

... A hybrid of robust optimization (RO), and stochastic programming (SP) model has been proposed by [36] for an energy distribution company with IEDS constraints. In [37], a mixed integer linear programming based on risk assessment is proposed for modelling EDS, DHS, and NGN in the presence of CHP units. A decision-making strategy based on stochastic optimization is investigated in [38] for short-term management of a multi-energy carrier system in the presence of power-to-gas technology. ...

An optimal load curtailment method for operational risk assessment of integrated electricity, natural gas and heating systems
  • Citing Article
  • August 2023

Sustainable Energy Grids and Networks

... The above optimizations are all based on the optimization ideas of centralized modeling and unified solutions without highlighting the multi-level ideas that match the PMES structure [24,25]. Moreover, for more VPP aggregation and massive heterogeneous information scenarios, the use of centralized modeling and unified solution methods increases the difficulty of solving [26,27], and the privacy is inferior [28,29]. ...

Optimal design for a multi-level energy exploitation unit based on hydrogen storage combining methane reactor and carbon capture, utilization and storage
  • Citing Article
  • June 2023

Journal of Energy Storage

... However, these types of DGs are only reliable and productive in grid-connected mode when the national grid power is adequate [2,3]. Islanding detection requires special attention because unintentional islanding is one of the most dangerous situations in such GCDGNs [4][5][6][7]. Unintentional islanding phenomena arise when the national grid supply is cut off from the DGs as a result of any fault or switching events, hence posing severe safety and protection troubles [8,9]. Following IEEE and IEC prerequisites and standards, the islanding detecting relay's essential function detaches the islanded DGs in 1-2 s. ...

A New Method of Unintentional Islanding detection for Multiple Distributed energy resources based Power System
  • Citing Conference Paper
  • March 2023

... The Kernel function (KF) employed in KDE must be symmetrical, with an area under the curve summing to one, and the density value must be non-negative [109]. A typical Gaussian KF for an RV is defined in (10). ...

Random Fuzzy Power Flow Analysis for Power System considering the Uncertainties of Renewable Energy and Load Demands