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CSEE Journal of Power and Energy Systems 2021年第2期7卷 295-306页

作者： Tianhao Wen Yang Liu q.h.wu Luonan qiu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China

This paper proposes a cascaded sliding-mode observer for systems with high relative degree and studies its applications in output feedback controller *** part I,the working principle and parameter design of the proposed observer are discussed in *** is proved that,within a sufficiently short period of time,the states of the proposed observer will reach the intersection of all the sliding *** sliding surfaces,the observation error of the proposed observer will converge to sufficiently small *** with traditional high-gain observers,the proposed observer has smaller gain *** addition,the peaking-phenomenon occurred in the proposed observer is less ***,the proposed observer has a convergence rate of observation error as fast as that of traditional high-gain *** studies are carried out on a fifth-order system to verify the properties of the proposed observer.

关键词： Sliding-mode observer stability output feedback control

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Fault Identification in Power Network Based on Deep Reinforcement Learning

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 721-731页

作者： Mengshi Li huanming Zhang Tianyao Ji q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou510641China

With the integration of alternative energy and renewables,the issue of stability and resilience of the power network has received considerable *** basic necessity for fault diagnosis and isolation is fault identification and *** conventional intelligent fault identification method needs supervision,manual labelling of characteristics,and requires large amounts of labelled *** enhance the ability of intelligent methods and get rid of the dependence on a large amount of labelled data,a novel fault identification method based on deep reinforcement learning(DRL),which has not received enough attention in the field of fault identification,is investigated in this *** proposed method uses different faults as parameters of the model to expand the scope of fault *** addition,the DRL algorithm can intelligently modify the fault parameters according to the observations obtained from the power network environment,rather than requiring manual and mechanical tuning of *** methodology was tested on the IEEE 14 bus for several scenarios and the performance of the proposed method was compared with that of population-based optimization methods and supervised learning *** obtained results have confirmed the feasibility and effectiveness of the proposed method.

关键词： Artificial intelligence deep q network deep reinforcement learning fault diagnosis fault identification parameter identification power network

Fault Location in Distribution System Using Convolutional Neural Network Based on Domain Transformation

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 472-484页

作者： Yang Yu Mengshi Li Tianyao Ji q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China

Distribution lines are integral parts of the modern power system,which can affect the security and stability of power supply *** effective power system protection scheme should be able to detect all occurring faults as soon as *** are two tasks in fault *** is the fault classification,where high accuracy rates have already ***,this paper focuses on the other task,*** *** by Fourier transform,this paper proposes an online data-driven method,which transforms signals from time domain to image domain through signal-to-image(SIG)algorithm and then process the transformed images with framework based on convolutional neural network(CNN).On the one hand,we can extract more crucial characteristic and information from image *** the other hand,the CNN-based structure is much smaller than *** needs less memory space and would be easier to be transplanted to hardware ***,the proposed algorithm does not require synchronous *** numerical comparison shows that the proposed SIG-CNN fault location model achieves robust and accurate results compared with other data-driven algorithms.

关键词： Convolutional neural network distribution lines domain transformation fault location

Stochastic Economic Dispatch Considering the Dependence of Multiple Wind Farms Using Multivariate Gaussian Kernel Copula

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CSEE Journal of Power and Energy Systems 2022年第5期8卷 1352-1362页

作者： Yantai Lin Tianyao Ji Yuzi Jiang q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China

Wind farms usually cluster in areas with abundant wind ***,spatial dependence of wind speeds among nearby wind farms should be taken into account when modeling a power system with large-scale wind power *** paper proposes a novel non-parametric copula method,multivariate Gaussian kernel copula(MGKC),to describe the dependence structure of wind speeds among multiple wind *** speed scenarios considering the dependence among different wind farms are sampled from the MGKC by the quasi-Monte Carlo(qMC)method,so as to solve the stochastic economic dispatch(SED)problem,for which an improved meanvariance(MV)model is established,which targets at minimizing the expectation and risk of fuel cost *** this model,confidence interval is applied in the wind speed to obtain more practical dispatch solutions by excluding extreme scenarios,for which the quantile-copula is proposed to construct the confidence interval *** studies are carried out on a modified IEEE 30-bus power system with wind farms integrated in two areas,and the results prove the superiority of the MGKC in formulating the dependence among different wind farms and the superiority of the improved MV model based on quantilecopula in determining a better dispatch solution.

关键词： Multivariate Gaussian kernel copula quasi-Monte Carlo quantile-copula stochastic economic dispatch

Data-Driven Fault Detection of Multiple Open-Circuit Faults for MMC Systems Based on Long Short-Term Memory Networks

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1563-1574页

作者： Chenxi Fan Kaishun Xiahou Lei Wang q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China

This paper presents a long short-term memory(LSTM)-based fault detection method to detect the multiple open-circuit switch faults of modular multilevel converter(MMC)systems with full-bridge sub-modules(FB-SMs).Eighteen sensor signals of grid voltages,grid currents and capacitance voltages of MMC for single and multi-switch faults are collected as sampling *** output signal characteristics of four types of single switch faults of FB-SM,as well as double switch faults in the same and different phases of MMC,are analyzed under the conditions of load variations and control command changes.A multi-layer LSTM network is devised to deeply extract the fault characteristics of MMC under different faults and operation conditions,and a Softmax layer detects the fault *** results have confirmed that the proposed LSTM-based method has better detection performance compared with three other methods:K-nearest neighbor(KNN),naive bayes(NB)and recurrent neural network(RNN).In addition,it is highly robust to model uncertainties and Gaussian *** validity of the proposed method is further demonstrated by experiment studies conducted on a hardware-in-the-loop(hIL)testing platform.

关键词： Fault detection long short-term memory(LSTM) modular multilevel converter(MMC) open circuit fault

A hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power quality Disturbance Detection and Classification

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 584-592页

作者： Zhi Ding Tianyao Ji Mengshi Li q.h.wu School of Electric Power Engineering South China University of Technology(SCUT)Guangzhou 510641China

In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power quality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.

关键词： hadamard matrix mathematical morphology morphological clustering power quality disturbance Walsh theory

Location of Asymmetric Ground Fault Using Virtual Injected Current Ratio and Two-stage Recovery Strategy in Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 151-161页

作者： haiting Shan Luliang Zhang q.h.wu Mengshi Li School of Electric Power Engineering South China University of Technology(SCUT)Guangzhou 510641China

Sparse measurements challenge fault location in distribution *** paper proposes a method for asymmetric ground fault location in distribution networks with limited measurements.A virtual injected current vector is formulated to estimate the fault line,which can be reconstructed from voltage sags measured at a few buses using compressive sensing(CS).The relationship between the virtual injected current ratio(VICR)and fault position is deduced from circuit analysis to pinpoint the ***,a two-stage recovery strategy is proposed for improving reconstruction accuracy of the current vector,where two different sensing matrixes are utilized to improve the *** proposed method is validated in IEEE 34 node test *** results show asymmetric ground fault type,resistance,fault position and access of distributed generators(DGs)do not significantly influence performance of our *** addition,it works effectively under various scenarios of noisy measurement and line parameter *** on 134 node test feeders prove the proposed method is also suitable for systems with more complex structure.

关键词： Distribution networks fault location reconstruction accuracy two-stage recovery virtual injected current ratio

Convexification of hybrid AC-DC Optimal Power Flow with Line-Commutated Converters

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 617-628页

作者： hongyuan Liang Zhigang Li J.h.Zheng q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China

Line-commutated converter (LCC)-based high-voltage DC (hVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvexity to optimal power flow (OPF) of hybrid AC-DC power grids. A convexification method for the LCC station model could address such nonconvexity but has rarely been discussed. We devise an equivalent reformulation for classical LCC station models that facilitates second-order cone convex relaxation for the OPF of LCC-based AC-DC power grids. We also propose sufficient conditions for exactness of convex relaxation with its proof. Equivalence of the proposed LCC station models and properties, exactness, and effectiveness of convex relaxation are verified using four numerical simulations. Simulation results demonstrate a globally optimal solution of the original OPF can be efficiently obtained from relaxed model.

关键词： AC-DC power system convex relaxation linecommutated converter optimal power flow second-order cone programming

Dispatchable Region for Active Distribution Networks Using Approximate Second-order Cone Relaxation

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 1999-2007页

作者： Zhigang Li Wenjing huang J.h.Zheng q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China

Uncertainty in distributed renewable generation threatens the security of power distribution *** concept of dispatchable region is developed to assess the ability of power systems to accommodate renewable generation at a given operating *** DC and linearized AC power flow equations are typically used to model dispatchable regions for transmission systems,these equations are rarely suitable for distribution *** achieve a suitable trade-off between accuracy and efficiency,this paper proposes a dispatchable region formulation for distribution networks using tight convex *** cone relaxation is adopted to reformulate AC power flow equations,which are then approximated by a polyhedron to improve ***,an efficient adaptive constraint generation algorithm is employed to construct the proposed dispatchable *** studies on distribution systems of various scales validate the computational efficiency and accuracy of the proposed method.

关键词： Dispatchable region distribution system renewable power generation second-order cone relaxation uncertainty

Ultra-short Term Wind Speed Prediction Using Mathematical Morphology Decomposition and Long Short-term Memory

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CSEE Journal of Power and Energy Systems 2020年第4期6卷 890-900页

作者： Mengshi Li Zhiyuan Zhang Tianyao Ji q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China

This paper proposes a new model,which consists of a mathematical morphology(MM)decomposer and two long short term memory(LSTM)networks,to perform ultra-short term wind speed *** MM decomposer is developed in order to improve the forecast accuracy,which separates the wind speed into two parts:a stationary long-term baseline and a nonstationary short-term ***,two LSTM networks are implemented to forecast the baseline and residue,***,this paper makes an integrated forecast that takes into account multiple climate factors,such as temperature and air *** baseline,temperature and air pressure are used as the inputs of baseline network for training and prediction,and the baseline,residue,temperature and air pressure are used as the inputs of residue network for training and *** performance of the proposed model has been validated using data collected from the Australian Meteorological Station,which is compared with least squares-support vector machine(LS-SVM),back-propagation artificial neural network(BPNN),LSTM,MM-LS-SVM,and *** results demonstrate that the proposed model is more suitable to solve non-stationary time-series forecast,and achieves higher accuracy than the other models under various conditions.

关键词： Deep learning long short-term memory network mathematical morphology wind speed forecast