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3rd IEEE Student Conference on Electrical Machines and systems (SCEMS)

作者： Hou, Wei Wang, Mingqiang Wang, Xin Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan Peoples R China State Grid Shandong Elect Power Construct Co Jinan Peoples R China

ISBN: (纸本)9781728156224

With the development of China's power market, more and more large industrial customers are participating in electricity market transactions, and they gradually began to pay attention to economic power consumption issues. Based on real data, this paper analyzes how large industrial customers make the best decisions to lower their electricity payment considering various real characteristics such as order requirement of production lines, specific production process, and continuity of power consumption, etc. The proposed model realizes the economic power consumption by adjusting the daily production schedule of large industrial customers. The feasibility and effectiveness of the proposed model are illustrated by case studies.

关键词： economic power consumption electricity market trading interruptible load large customer

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Online dynamic security assessment of wind integrated power system using SDAE with SVM ensemble boosting learner

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2021年 125卷 106429-106429页

作者： Rizwan-ul-Hassan Li, Changgang Liu, Yutian Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China NFC Inst Engn & Fertilizer Res Faisalabad 38000 Pakistan

The increasing trend of decarbonization, particularly high penetration of wind power generation (WPG), has resulted in an increased operational uncertainty of the power system. This emerging trend of wind integration requires novel and effective methods of power system security assessment. This paper proposes a method for dynamic security assessment (DSA) based upon stacked de-noising auto-encoder (SDAE) using support vector machine (SVM) ensemble with boosting learning approach. A two-step feature reduction method is introduced after screening the redundant features among original feature set using mutual information theory. Multi-level features of original input data are extracted using multi-layer SDAE. SVM ensemble classifier is used to perform classification with data from all hidden layers of SDAE. The output of multi-layer SDAE and SVM ensemble are combined together as an input to ensemble boosting learning method which gives the final DSA result. The proposed method is tested on modified New England 39 bus system and extended on simplified AC/DC hybrid power system of Shandong province, China. The effectiveness and accuracy of the proposed method is demonstrated by simulation results and by comparisons with other methods.

关键词： Dynamic security assessment Stacked de-noising auto-encoder Support vector machine Boosted ensemble learning Hybrid power system

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Defect Analysis of power system Secondary Equipment Based on Cluster Analysis and Association Rule

Defect Analysis of Power System Secondary Equipment Based on...

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Electrical Engineering and Automation (IFEEA), International Forum on

作者： Maoran Zheng Jinshuang Mu Zhanjun Gao Zhaohui Chen Jiang Yu Zhenghong Li China Southern Power Grid Co Ltd. Guangzhou China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

In order to effectively improve the operation level of secondary equipment in the power system, based on the historical defect data of the secondary equipment, this paper takes the mining of the correlation between the secondary equipment defect data as the starting point, it proposes a method that combines cluster analysis and association analysis. First, this paper analyzes the basic ideas of cluster analysis and association analysis, then establishes the defect analysis model of the secondary equipment of this method. In the model, it uses cluster analysis to classify defect data firstly, clustering related defect data into a cluster, then uses association rules for further defect analysis. The result shows, this method can quickly and effectively dig out the correlation between defect data, able to further analyze the family defects of secondary equipment, it can provide certain guidance for equipment maintenance and daily operation.

关键词： Electrical engineering Analytical models Correlation Automation Clustering algorithms Maintenance engineering Data processing

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Analytical target cascading based real-time distributed voltage control for MV and LV active distribution networks

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2024年 159卷

作者： Jiao, Wenshu Wu, Qiuwei Chen, Jian Tan, Jin Song, Guocheng Huang, Sheng Wang, Chenshan Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV &LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV &LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV &LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV &LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme.

关键词： Analytical target cascading (ATC) distributed generation (DG) distribution network (DN) Reactive power control Voltage control

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A fault diagnostic method for oil-immersed transformer based on multiple probabilistic output algorithms and improved DS evidence theory

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2022年 137卷 107828-107828页

作者： Hua, Yue Sun, Yuanyuan Xu, Gongde Sun, Shengya Wang, Erdong Pang, Yanqing Shandong Univ Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Shandong Taikai Robot Co Ltd Tai An 271000 Shandong Peoples R China State Grid Wuxi Power Supply Co Wuxi 214061 Jiangsu Peoples R China

power transformer is one of the most vital equipment in the electric power systems, which serves as the connection of power networks at different voltage levels. However, the power transformers in service are subjected to various stresses like thermal stress and electrical stress, which may lead to several faults such as arching, partial discharge and overheating. In order to detect these incipient faults and ensure the normal operation of power transformers, the diagnostic methods based on Dissolved Gas Analysis (DGA) have been developed. However, the DGA methods suffer from low diagnostic accuracy. On the other hand, with the development of Artificial Intelligence (AI), a lot of intelligent algorithms have been applied in this area. However, most traditional AI algorithms are hard classification, which may cause the wrong diagnosis of faults. To overcome those disadvantages, a method based on the improved DS evidence theory and multiple probabilistic output algorithms is proposed in this paper to improve the performance of fault diagnosis of oil-immersed transformers. Firstly, the performance of the soft and hard classification algorithms is compared and analyzed. After that, three diagnostic models based on the Multiclass Relevance Vector Machine (MRVM), Multiclass Support Vector Machine (MSVM) and Back Propagation Neural Network (BPNN) are established. In order to improve the accuracy of these models, Particle Swarm Optimization (PSO) is used to optimize their hyper-parameters. Finally, the combination of these three probabilistic output models is achieved based on the improved Dempster-Shafer (DS) evidence theory, which can help to obtain a more accurate fault diagnosis result by fusing multiple algorithm results. The case study results show that the proposed method achieves a probabilistic output for the fault diagnosis of oil immersed transformers and overcomes the deficiency of traditional DGA methods which are difficult to get accurate diagnosis results and

关键词： Oil-immersed transformer Fault diagnosis Probabilistic output algorithm DS evidence theory

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Emergency control Strategy for Line Overload Considering power Source and Load Fluctuation 3

Emergency Control Strategy for Line Overload Considering Pow...

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3rd International Conference on Environmental Prevention and Pollution control Technologies, EPPCT 2021

作者： Liu, Zhiqing Wang, Fei Qiu, Jifu Chen, Fan Lu, Zhipeng Chen, Ming Niu, Qingda Dong, Xiaoming State Grid Shandong Electric Power Company Shandong Province Jinan250001 China Dezhou Electric Power Supply Company of State Grid Shandong Electric Power Electric Power Company Dezhou Shandong Province253011 China Qingdao Electric Power Supply Company of State Grid Shandong Electric Power Electric Power Company Shandong Province Qingdao266002 China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan Shandong Province250061 China

Uncertain factors such as the integration of renewable energy into the grid and load fluctuations have brought more risks to the safe dispatch of the grid. The random power flow is used to calculate the impact of distributed power sources and load fluctuations on the grid, and to quantify the probability distribution of line power, which can avoid the drawbacks of deterministic power flow that cannot fully reflect the line overload risk. Combining the line overload level with the traditional load shedding method can effectively prevent the system from cascading failures caused by the risk of line overload, improve the optimistic calculation results caused by the load shedding strategy in emergency situations that cannot consider source load fluctuations, and make the grid control more safe and reliable. © Published under licence by IOP Publishing Ltd.

关键词： Probability distributions

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A Three-Phase Adaptive Reclosure Technology for Distribution Feeders Based on Parameter Identification

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IEEE TRANSACTIONS ON power DELIVERY 2019年第6期34卷 2173-2181页

作者： Zhu, Ke Lv, Rundong Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China

A scheme is proposed to improve the decreasing success rate of reclosing in a non-effectively grounded system with a wide application of cables. An inverter source is momentarily connected to the low-voltage side of a service transformer by controlled thyristors to apply a transient high voltage to the de-energized feeder before reclosing. Based on the idea of parameter identification, the internal structure of the de-energized feeder is investigated according to the frequency characteristics of the reference parameters to determine whether the feeder still experiences the fault. The influence of the firing angle on the strength of the frequency components in the transient waveforms is analyzed to determine its range for accurate measurement of feeder harmonic impedance. A sensitivity analysis on the fault property detection criterion is conducted, and the selection of the inverter source power impacted by the firing angle is provided. The results of the theoretical analysis, computer simulation, and lab experiments confirmed the effectiveness of the proposed scheme.

关键词： Adaptive reclosure de-energized feeder transient fault thyristor

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Operating Region and Boundary control of Modular Multilevel Converter Station Under Unbalanced Grid Conditions

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IEEE TRANSACTIONS ON power DELIVERY 2020年第3期35卷 1146-1157页

作者： Hao, Quanrui Li, Bowei Sun, Yifan Wu, Linlin Wang, Shuying Shandong Univ Dept Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China State Grid Jibei Elect Power Res Inst Beijing 100045 Peoples R China Jinan Power Supply Co State Grid Jinan 250000 Peoples R China

To determine the operating region of modular multilevel converter (MMC) station under unbalance grid conditions, this paper proposes the analytical and point-scanning methods. First, the imbalance of PCC voltages is expressed in terms of two proposed indexes for single phase fault (SPF), which can be directly calculated from the grid parameter and grounding impedance. Next, the algebraic equation to solve the PCC voltage for the given PCC power is presented. The existence of PCC voltage is considered as one constraint. Moreover, the other constraints for transformer rating, converter rating, ac current and output voltage are also analyzed to adapt the unbalance conditions. Based on the proposed method, the effect of the grid imbalance on the operating region of MMC is analyzed and then the common constraints to confine the operating region are determined. Following that, the boundary control is proposed to limit the operation of MMC on the boundary of the operating region under unbalanced conditions. The open and modular control structure allows more constraints to be considered for the boundary control. The simulation results validate the correctness of the proposed methods to define the operating region and the effectiveness of the boundary control.

关键词： Voltage control Circuit faults Multilevel converters Grounding HVDC transmission Inductance Sun Modular multilevel converter operating region boundary control unbalanced grid conditions asymmetrical faults

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Voltage Stability Evaluation Considering Successive Commutation Failure Risk

Voltage Stability Evaluation Considering Successive Commutat...

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control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Rui Ding Hengtong Wang Zhaohao Jia Yuanzhen Zhu Changgang Li Yongji Cao State Grid Huangdao Power Supply Company State Grid Shandong Electric Power Company Qingdao China State Grid Zibo Power Supply Company State Grid Shandong Electric Power Company Zibo China State Grid Jinan Power Supply Company State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Company State Grid Shandong Electric Power Research Institute Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Academy of Intelligent Innovation Shandong University Jinan China

In order to evaluate the risk of successive commutation failure caused by voltage drop, a deep learning-based method is proposed in this paper. First, the mechanism of successive commutation failures is analyzed and the minimum second voltage drop value of the commutation bus after short-circuit fault is utilized as the index to quantify the severity of line outage contingencies. Then, to quickly evaluate the impact of multi-line outage failures on DC systems, a deep learning-based assessment network is built, of which the output is the minimum second voltage drop value of the commutation bus and inputs are steady-state features related to network structure. Moreover, a bus impedance matrix formation approach based on Woodbury formula is proposed, which is used in the calculation of input features and search index. Finally, simulation results based on a real-life AC/DC power system demonstrate that the proposed method significantly improves the risk evaluation efficiency of successive commutation failure.

关键词：

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Diagnosis of the single phase-to-ground fault in distribution network based on feature extraction and transformation from the waveforms

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6079-6086页

作者： Shi, Fang Zhang, Linlin Zhang, Hengxu Xu, Kai Vladimir, Terzija Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Jingshi Rd Jinan Shandong Peoples R China State Grid Jinan Power Supply Co Jinan Shandong Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

The increasing permeation of the distributed generators in the power system brings great challenges for fault diagnosis, especially for the distribution networks with ungrounded neutral or grounded by Peterson coil as the fault current is limited and easily affected by the noises and interferences. A single phase-to-ground fault section identification method is proposed based on feature extraction of the synchronous waveforms and the calculation of the eigenvalues for the time-sequenced features. First, several fault features are defined and extracted from the synchronous current waveforms obtained by the fault recorders. Then, the topology related fault feature matrix is constructed using the time-series features obtained from different measurement sites, and the eigenvalues of the matrix are calculated based on the random matrix theory. Lastly, using the distribution characteristics of the eigenvalues, improved K-means clustering algorithm is utilised in classifying the fault cases and identifying the faulty sections. The effectiveness of the proposed scheme is verified by IEEE 34 nodes test system and a multi-feeder distribution network.

关键词： feature extraction power distribution faults fault diagnosis earthing distributed power generation fault location eigenvalues and eigenfunctions pattern clustering fault currents time series feature extraction distributed generators power system fault diagnosis Peterson coil fault current single phase-to-ground fault section identification method eigenvalues time-sequenced features fault features synchronous current waveforms fault recorders topology related fault feature matrix time-series features random matrix theory distribution characteristics fault cases multifeeder distribution network improved K-means clustering algorithm

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