Zhuo Liu

Beijing University of Posts and Telecommunications

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Min Zhang

Beijing University of Posts and Telecommunications

Yueying Zhan

Chinese Academy of Sciences

Xue Chen

Sichuan University

Tianhao Tang

Guangdong Provincial Hospital of Traditional Chinese Medicine

Tianyou Chai

Northeastern University

Wen Yu

Center for Research and Advanced Studies of the National Polytechnic Institute

Jian Tang

Union Hospital

Junfeng Qi

China Academy of Space Technology

Fei Tang

Wuhan University

Azeddine Houari

IMT Atlantique

Cooperative Institutions

Beijing University of Posts and Telecommunications

Chinese Academy of Sciences

China Mobile (China)

Harbin Institute of Technology

Peking University

Wuhan University

Huazhong University of Science and Technology

Tsinghua University

Shanghai Maritime University

Ministry of Industry and Information Technology

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Failure prediction using machine learning and time series in optical network

Optics Express (2017)

Zhilong Wang Min Zhang Danshi Wang Chuang Song Min Liu

In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

Exponential Smoothing

Smoothing

10.1364/oe.25.018553

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Citations (168)

Research on the time to adjust IPC's parameter in interconnected power system

Dianji yu kongzhi xuebao (2005)

Zhuo Liu

Two power systems connected with one Tieline and IPC on one side were simulated. Generic circuit of IPC was established and vector relation was analyzed based on the simple circuit of two complex systems. The current impact in capacitor and the voltage impact on reactor in the control course can be restrained by selecting the right time to adjust IPC' s parameter.

Source

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Electrical power system state forecasting and estimation

Zhou Suquan Zhongwen Gao Liu Youbin Zhuo Liu

This paper discusses the concept of state forecasting and estimation in power systems. A method of forecasting bus injective power and line flow power which makes use of the current information and the historical data from run patterns is presented and the results of state forecasting and estimation are compared with those of static state estimation.< >

Line (geometry)

10.1109/tencon.1993.320673

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Efficient way to estimate the optimum number of factors for trilinear decomposition

Analytica Chimica Acta (2001)

Zeng‐Ping Chen Zhuo Liu Yuzhen Cao Ru‐Qin Yu

Collinearity

Basis (linear algebra)

Factor (programming language)

Data set

Factor Analysis

10.1016/s0003-2670(01)01179-5

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Citations (77)

Small-Signal Stability Modeling and Analysis of Power System with Integrated Photovoltaic Energy Storage

2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2) (2022)

Y. Guo Fei Tang Xiaoqing Wei Jiarui Xie Junfeng Qi

As the current penetration of renewable energy generation in the power system is gradually increasing, small disturbances in the power system are inevitable. Combining the grid-connected photovoltaic (PV) system with energy storage is an effective way to improve the immunity of the new-type power system. This paper firstly uses an eigenvalue-based small-signal stability analysis method to model the small-signal of the grid-connected PV-storage system. Then calculate the eigenvalues of the state matrix to summarize the oscillation modes and main participating variables in different modes. The system stability is judged by observing the trace of the eigenvalues with varied grid strength and controller parameters. The results show that there are nine oscillation modes of the system. The grid strength is the main factor affecting the system's stability. When grid strength is weakened, the system stability decreases. Due to the occurrence of medium frequency oscillations, the system is destabilized under the very weak grid. The controller parameter is the second factor affecting the system stability, and appropriate adjustment of the phase-locked loop (PLL) proportional factors can improve the system stability of small disturbances. The state variables related to energy storage will become the dominant part affecting the stability of the PV-storage system.

10.1109/ei256261.2022.10116596

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Citations (3)

A second order filter-based fault detection method for five-phase permanent magnet synchronous generators

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society (2020)

Zhuo Liu Azeddine Houari Mohamed Machmoum Mohamed Fouad Benkhoris Tianhao Tang

Comparing with classical three phase generators, multiphase ones attract more and more attention for industrial applications due to their capability of fault tolerance, high quality of output power and so on. In the event of a fault occurrence, before reconfiguring the control algorithms, it is necessary to detect the fault. A second order filter-based fault detection method is thus proposed in this paper and applied to a five-phase permanent magnet synchronous generator. The method is based on the analysis of the phase current in the natural basis when it varies suddenly. Thus, a second order filter is studied with a view to detecting faults and defining a fast indicator extracted from the derivative of the phase current. This approach is then applied to tidal energy conversion chain using a five-phase PMSG with non-sinusoidal EMF. Two faults are investigated successfully: open phase fault of the generator and open switch fault in its serial converter legs.

Three-phase

10.1109/iecon43393.2020.9254869

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Citations (7)

Estimating open boundary conditions from coastal tidal observations by adjoint approach

Science in China Series D Earth Sciences (1998)

Jiang Zhu Zeng Qincun Guo Dongjian Zhuo Liu

Tidal Model

Adjoint equation

Square (algebra)

Tidal current

Open water

Open sea

10.1360/yd1998-41-3-330

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Citations (1)

Numerical Modeling and Experimental Testing of Reflective Semiconductor Optical Amplifier (RSOA) with Modulation Bandwidth Optimization

Mingtao Liu Min Zhang Lei Liu Yueying Zhan Zhuo Liu

The characteristics of commercial RSOA, i.e. fiber-to-fiber gain and small-signal electro-optical conversion (E/O) response are measured. A numerical model for RSOA including spatial variations of carrier density and ASE noise is established and validated via experimental tests. The auger recombination coefficient and active region bulk of commercial RSOA are determined by fitting the stimulated characteristics to the experimental results numerically. Performance of RSOA with different cross-sectional areas of active region is investigated based on the numerical model. The stimulated results indicate that reducing the cross-sectional area can increase its modulation bandwidth. RSOA 3-dB bandwidth is able to increase to 10.0GHz by optimizing its active region bulk.

Amplified spontaneous emission

Modulation (music)

10.1364/acp.2010.798808

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Machine Learning Based Inter-Turn Short Circuit Detection for Three-Phase Power Transformers with Primary Side Currents

2022 IEEE Applied Power Electronics Conference and Exposition (APEC) (2024)

Yujia Cui Kadir Liano Zhijun Liu Zhuo Liu Hao Yang

A novel method is proposed in this paper for inter-turn short circuit detection in three-phase power transformers. This detection method only requires primary side currents and can determine the fault severity and locate the faulty phase(s). Phase shift information is extracted out of primary side currents and can act as an effective fault indicator. Machine learning techniques are adopted for feature generation and decision region separation. Continuous degradation monitoring is feasible with the proposed approach. To improve the robustness and accuracy of the detection algorithm, Gaussian mixture model is introduced from the perspective of probabilistic machine learning. Both simulation and experiment results are provided to validate the algorithm.

Robustness

Three-phase

10.1109/apec48139.2024.10509073

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Nonconvex Multiview Subspace Clustering Framework with Efficient Method Designs and Theoretical Analysis

Zhi Wang Zhuo Liu Dong Hu Tao Jia

Multi-view subspace clustering (MvSC) is one of the most effective methods for understanding and processing high-dimensional data. However, existing MvSC methods still have two shortcomings: (1) they adopt the nuclear norm as the low-rank constraint, which makes it impossible to fully exploit the mutually complementary subspace information, and (2) they do not handle disjoint and confounding points carefully, which may degrade the purity and distinctiveness of cross-view fusion. To address these issues, in this paper we propose a novel MvSC model with nonconvex ℓq regularization. Specially, our proposed model can not only effectively capture the intrinsic global low-rank structure, but also accurately cluster disjoint and confounding data samples into corresponding subspaces. Then, an efficient algorithm is developed with convergence guarantee. Furthermore, we prove that the sequence generated by our proposed algorithm converges to the desirable Karush-Kuhn-Tucker (KKT) critical point. Extensive experiments on various datasets verify the superiority of our proposed model. MATLAB code is available at https://github.com/wangzhi-swu/NLRSC-MvSC.

10.24963/ijcai.2024/571

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