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A Predictive control Method for LCC HVDC Participated Black Start With Commutation Failure Prevention

IEEE TRANSACTIONS ON power DELIVERY 2023年第4期38卷 2348-2359页

作者： Li, Yong Wang, Hongtao Yang, Huiting Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250000 Peoples R China

Line-commutated converter (LCC) based HVDC systems can significantly speed up the restoration of infeed power systems in blackout states. But they are fragile to commutation failure (CF) under the adverse black-start conditions. To improve the adaptability of LCC HVDC systems in such situations, a predictive control method is proposed in this paper. It includes the linear discrete time predictive model, receding horizon regulator, state estimator, and CF prevention (CFPREV) module. The model is based on the quasi-dynamic and transient-state analysis of load disturbances, where the black-start state is forecasted according to voltage sags caused by inrush currents. The regulator operates in real-time to optimize reference inputs for the HVDC controller. In the estimator, process error and measurement noise are modeled and the optimal system state is estimated with a Kalman filter. Moreover, the dc current setting for CFPREV is calculated using the predicted and estimated state variables. The CFPREV module and regulator work in coordination for secure and fast dc power recovery under abnormal conditions. Small-signal stability analysis and EMT simulations are implemented on the CIGRE benchmark model based black start test grid. The results show that the proposed method i) improves dc power response to black start load startups and ii) presents better recovery performance after the fault.

关键词： Black start commutation failure prevention dc power control LCC HVDC power system restoration

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On the Feasibility of Harmonic Adaptability Remote Testing Method

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第10期69卷 8156-8166页

作者： Jiang, Zimin Liu, Yutian Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China

As a significant part of grid code compliance verification, harmonic adaptability (HA) capability on-site testing is indispensable for offshore wind turbines. Although the HA remote testing method having no restrictions of harsh environment and resulting access limitation is regarded as an alternative to the existing testing method, the application scope and condition are not indicated explicitly, which enormously reduces its practicability in real testing. This article investigates the feasibility of the HA remote testing method. Based on the analysis of its working principle and a comprehensive caparison, two dominating factors affecting the feasibility are identified: the capacity of the applied active power filter and the power transmission capability of submarine cable. An assessment method comprising two indicators, i.e., the maximum resonance amplitude that can be suppressed and the feasible testing distance, is developed for the feasibility study. The application scope of the HA remote testing method is obtained with the proposed corresponding algorithm for the derived indicators. Extensive simulation results show that the HA remote testing method is viable and promising within the specified scope.

关键词： Testing Harmonic analysis power cables power system harmonics Underwater cables Harmonic distortion Cable feasibility grid code compliance harmonic adaptability (HA) offshore wind turbine (WT) remote testing

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State Estimation for Reconfigurable Distribution systems Using Graph Attention Network

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IEEE ACCESS 2023年 11卷 107237-107250页

作者： Ren, Zhengxing Chu, Xiaodong Ye, Hua Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250100 Peoples R China

Obtaining the real-time state of the distribution system is the basis for intelligent operation of the power system. With the surge in new energy generation and the volatility in load demands, traditional state estimation methods face significant challenges. The distribution system requires frequent topology reconfigurations to maintain stable operation. However, current data-driven methods typically cater only to specific topologies. To address this issue, a complete distribution system state estimation (DSSE) framework is proposed to adapt to frequent topology reconfigurations. To ensure data quality, a measurement device configuration algorithm using node importance was designed. Then, a convolutional neural network-based topology identification model is utilized to provide real-time topology data to the DSSE, which uses measured data pre-processed by the Gramian corner field. Finally, we use graph attention network to model the DSSE as a node-level regression prediction problem on a graph abstracted from the distribution system. Simulation results on IEEE 33-bus and IEEE 118-bus distribution systems illustrate the feasibility and efficiency of the proposed framework. Further experiments show that the proposed framework has good robustness.

关键词： Distribution system graph attention network state estimation topology identification

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An Automatic Faulted Line Section Location Method for Electric power Distribution systems Based on Multisource Information

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IEEE TRANSACTIONS ON power DELIVERY 2016年第4期31卷 1542-1551页

作者： Sun, Kongming Chen, Qing Gao, Zhanjun Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

This paper presents an automatic faulted line section location method for distribution systems. It can be divided into two parts: faulted line section location method for medium-voltage distribution feeders and faulted area identification technique for low-voltage distribution systems. In the first part, an adjacency list that shows the adjacency relationship and status of fault indicating devices is developed. Then, an iterative search technique is devised to traverse the adjacency list to determine the faulted line section. In the second part, a relational table that shows the association relationship between smart meters, meter boxes, and distribution transformers is developed. Based on the table and the voltage of smart meters received from the customer electric information acquisition system, the faulted area can be located. Several test cases studied in a real distribution system demonstrate the effectiveness of the proposed method.

关键词： Faulted line section location low-voltage (LV) distribution system medium-voltage (MV) distribution system multisource fault information

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Real-time optimal voltage control using measurement-based aggregate load model

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ELECTRIC power systemS RESEARCH 2014年第Nov.期116卷 293-300页

作者： Zhang Yan Zhang Wen Chu Xiaodong Liu Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

Focused on long-term voltage stability, this paper presents a real-time voltage control scheme using measurement-based aggregate load model to restore system voltages following a contingency. According to the load power restoration feature and wide area measurements, the load model is formulated and corrected in receding horizons, which transforms the hybrid differential-algebraic equations of the power system model into nonlinear algebraic equations with discrete events. A deformed Euler state predictor is proposed to predict system response based on the measurement-based model. The optimal control behaviors are calculated using model predictive control method. By eliminating dynamic equations using wide area measurements, the computation time is significantly reduced. The validation and effectiveness of the proposed method is verified by the simulation analysis on the Nordic 32 20-generator 52-bus test system. (c) 2014 Elsevier B.V. All rights reserved.

关键词： Real-time voltage control Measurement-based load model Model predictive control Long-term voltage stability Load power restoration

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Voltage sag source location technology based on corresponding sequence components

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第9期9卷 820-827页

作者： Zhu, Ke Wang, Yixuan Yin, Peng Ni, Jian Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250100 Peoples R China

Determining the relative region where voltage sags originates is useful to identify the responsible party causing the power quality problem and monitor system or equipment operating condition. As different kinds of voltage sag will cause different sequence components to change according to the neutral point operation mode in the distribution system, a modified incremental impedance-based method for determining voltage sag source (VSS) location is proposed in this study. This study starts with the VSS direction theory based on incremental impedance, and derives location criteria for different types of VSSs according to the neutral point operation mode of the Chinese distribution network. The VSS location strategy is finally accomplished through the analyses of voltage sag's capacity for passing through the service transformer and the impact of system transient on incremental impedance calculation. The method locates various VSSs based on existing PQ measure points and has clear physics concept. Theoretical analysis, simulation and lab experiments have verified its effectiveness.

关键词： power supply quality power transformers voltage sag source location technology sequence components power quality problem power monitor system equipment operating condition neutral point operation mode distribution system modified incremental impedance-based method service transformer system transient

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Time Integration-Based IGD Methods for Eigen-Analysis of Large Delayed Cyber-Physical power system

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IEEE TRANSACTIONS ON power systemS 2020年第2期35卷 1376-1388页

作者： Ye, Hua Li, Tairan Liu, Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

Two spectral discretization methods based on linear multistep and implicit Runge-Kutta discretization of infinitesimal generator (IGD-LMS/IRK) are presented in this paper for eigen-analysis of large delayed cyber-physical power system (DCPPS) with large time delays. First, the whole delay interval is partitioned into several sub-intervals by time delays in the system. The piecewise partitioning allows the avoidance of interpolations in evaluating system states at the delay points. Second, two time integration methods, i.e., LMS and IRK, are utilized to discretize infinitesimal generator on each sub-interval, resulting in highly structured and sparse discretization matrices. Third, IGD-LMS/IRK are efficiently implemented to compute the system's electromechanical oscillation modes. The shift-invert preconditioning technique is used and the sparsity in augmented system state matrices is exploited to guarantee the efficiency and scalability of IGD-LMS/IRK in dealing with large DCPPS. Numerical results on the two-area four-machine test system and a real-life large transmission grid validate the improved efficiency and accuracy of the proposed methods in analyzing DCPPS with large time delays.

关键词： Cyber-physical power system eigen-analysis low frequency oscillation small signal stability spectral discretization time delay wide-area damping control wide-area measurement system

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Hybrid Reinforcement Learning for power Transmission Network Self-Healing Considering Wind power

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2023年第9期34卷 6405-6415页

作者： Sun, Runjia Liu, Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

Transmission network self-healing considering uncertain wind power becomes crucial with increasing penetration of wind power. A hybrid reinforcement learning (HRL) method combining offline self-learning with online Monte Carlo tree search (MCTS) is designed to deal with the strong uncertainty induced by wind power restoration. The HRL method trains a policy network with offline self-learning based on historical wind and transmission system data. It then applies the policy network to guide MCTS to realize step-by-step transmission network self-healing based on real-time and forecast data in different wind power scenarios. Besides, a model predictive control method for active power dispatch is proposed to improve wind power generation credibility during self-healing. Simulation results of both test and real-life power systems demonstrate that the proposed method can realize online transmission system self-healing reliably. Comparisons among different reinforcement learning methods indicate that the number of scenarios dominated by HRL is more than twice that dominated by MCTS and a dozen times that dominated by deep Q-network. Meanwhile, the online method is more flexible in uncertain wind power scenarios than optimization methods.

关键词： Wind power generation Generators Wind farms power systems Voltage Training Reliability Monte Carlo tree search (MCTS) power system restoration reinforcement learning (RL) self-healing

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Dead-time elimination method of nine-switch converter

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IET power ELECTRONICS 2014年第7期7卷 1759-1769页

作者： Gao, Feng Tian, Hao Li, Nan Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China

This study presents a novel dead-time elimination method for operating nine-switch converter. The proposed method assumes the unique switching cells of nine-switch converter to operate under several subintervals divided during a fundamental period. Owing to the unidirectional conduction characteristics of switching cells, the nine-switch converter can operate without dead-time protection. Besides, for a special subinterval during which none of switching cells can be adopted, a novel switching signal transformation method is proposed to fully eliminate the dead-time protection during the operation of nine-switch converter. Matlab simulations and experimental results verify the effectiveness of proposed modulation scheme.

关键词： switching convertors dead-time elimination method nine-switch converter unidirectional conduction characteristics switching cells switching signal transformation method Matlab simulations modulation scheme

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Window-Varying Particle Filter for Parameter Identification of Space Thermal Model

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2017年第1期66卷 165-176页

作者： Cheng, Dingyi Zhang, Wen Liu, Jingyu Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

Due to the large proportion of heating, ventilating, and air-conditioning (HVAC) devices in electricity demand, energy conservation can be realized by applying appropriate control strategies on HVAC loads. For this purpose, a proper space thermal model is needed, in which parameter identification is an essential problem. The identification results of parameters are usually sensitive to measurement noise and initial value using conventional particle filter method. To overcome these issues, a modified particle filter algorithm, called window-varying particle filter (WVPF), is proposed to identify time-varying parameters of space thermal model by adaptively changing the prediction time window in this paper. Furthermore, a measurement system is developed to collect data for parameter identification and to verify the accuracy of space thermal model. Experiment and simulation results confirm the desirable performance of the proposed method for parameter identification.

关键词： Energy conservation parameter identification Runge-Kutta space thermal model window-varying particle filter (WVPF)

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