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Simulation and risk assessment of power system with cascading faults caused by strong wind weather

INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2022年 143卷

作者： Chen, Lizheng Li, Changgang Xin, Zheng Nie, Shurui Shandong Jianzhu Univ Sch Informat & Elect Engn Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China State Grid Shandong Power Supply Co Jinan Jinan 250001 Peoples R China

Global warming has led to the frequent occurrence of extreme weather events and threatened the stable operation of the power system. The faults caused by extreme weather events are of long time and massive scale characteristics, which pose new challenges to assessing power system operation risk. The model of the relationship between strong wind information and power grid faults was constructed considering the uncertain of line galloping and complexity of line trajectory. A meteorological-electric hybrid simulation approach was constructed with alternative and adaptive simulation strategies to simulate the cascading faults scenarios under the extreme weather of strong wind. The risk assessment method and index combining risk and fragility were proposed based on the proposed hybrid simulation approach. Case studies were performed to verify the feasibility of the proposed risk assessment method.

关键词： power system Risk assessment Strong wind Hybrid simulation

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Review on deep learning applications in frequency analysis and control of modern power system

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2022年 136卷 107744-107744页

作者： Zhang, Yi Shi, Xiaohan Zhang, Hengxu Cao, Yongji Terzija, Vladimir Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Jingshi Rd Jinan Shandong Peoples R China Skolkovo Inst Sci & Technol Ctr Energy Sci & Technol Moscow Russia

The penetration of renewable energy resources (RES) generation and the interconnection of regional power grids in wide area and large scale have led the modern power system to exhibit more and more complex dynamic features, such as time-varying nonlinearity, uncertainty, data diversity, and local observability. The increasing complexity of power system's dynamic characteristics makes the traditional analysis and control methods inefficient, even invalid. As a new technology path of Machine Learning, Deep learning (DL) has distinct advantages in solving complex problems such as power system frequency analysis and control due to its powerful ability of data analysis, prediction, and classification. This paper reviews the history, state of art and the future of the DL's application in power system frequency analysis and control. Firstly, the basic principle and research progress of DL, the training methods, typical structures, and application peculiarity of DL were introduced. Secondly, the application status of DL in frequency situation awareness, frequency security and stability assessment, and frequency regulation and control were summarized, and the adaptability of DL application to each kind of issue was discussed. Finally, the development trend of DL and its application in power system frequency were prospected.

关键词： power system Deep learning Frequency analysis Artificial intelligence Smart grid Renewable energy

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Equivalent model of multi-type distributed generators under faults with fast-iterative calculation method based on improved PSO algorithm

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Protection and control of Modern power systems 2021年第1期6卷 371-382页

作者： Puyu Wang Jinyuan Song Fangyu Liang Fang Shi Xiangping Kong Guangen Xie Xiao-Ping Zhang Xinxin Gu School of Automation Nanjing University of Science&Technology(NUST)Nanjing210094JiangsuChina Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan250061China Jiangsu Electric Power Research Institute Jiangsu Electric Power CompanyNanjing211103JiangsuChina School of Engineering University of BirminghamEdgbastonB152TTBirminghamUK Nanjing GWDR Relays Technologies Co. Nanjing210032JiangsuChina

There are various types of distributed generators (DGs) with different grid integration strategies. The transient characteristics of the fault currents provided by the DGs are different to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specific operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efficiency while avoiding local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simulation system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efficiency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.

关键词： Multi-type distributed generators(DGs) Fault current characteristics Equivalent model Fast-iterative calculation method Improved particle swarm optimization(PSO)

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5G Communication Based Distributed Fault Recovery Scheme of Active Distribution Network

5G Communication Based Distributed Fault Recovery Scheme of ...

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International Conference on Energy, Environment and Bioengineering (ICEEB)

作者： Li, Xin Gao, Houlei Yuan, Tong Xu, Bin Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jian 250061 Shandong Peoples R China

As more and more distributed power sources are connected to low and medium voltage distribution networks, the traditional single-ended passive distribution networks have evolved into multi-terminal, multi-source active distribution networks. When distributed generations with high permeability are connected to a distribution network, the structure and power flow of this network will change significantly, thus the original fault detection method and reclosing scheme may be challenged, which may cause incorrect action of protection or failure of reclosing. On basis of that, this paper proposes an active distribution network fault recovery scheme based on 5G wireless communication, in which the topology recognition technology and smart terminal units with peer-to-peer communication capability are applied. To prove the method's feasibility, delay of 5G communication is analysed and tested online. In addition, a model of 10 kV active distribution network is built on Real Time Digital Simulation system. Principle investigation and simulation indicate that the proposed scheme can adapt to the change of network structure and implement the fault self-healing quickly.

关键词： Computer system recovery

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A bottom-up approach to evaluate the harmonics and power of home appliances in residential areas

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APPLIED ENERGY 2020年第0期259卷 114207-000页

作者： Sun, Yuanyuan Xie, Xiangmin Wang, Qingyan Zhang, Linghan Li, Yahui Jin, Zongshuai Shandong Univ Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Guangdong Elect Power Ltd Co Dongguan Power Supply Co Dongguan 523008 Peoples R China

With expansion of the urban population, the power consumed by Chinese residential load is increasing rapidly. At the same time, power-electronic devices are widely adopted in home appliances, injecting a great amount of harmonics into the power system, which makes the harmonic problem in residential areas more serious than ever before. In this paper, a bottom-up approach is proposed to evaluate the harmonics of residential energy use. Firstly, a harmonic coupled model is proposed to calculate the harmonics generated by individual nonlinear home appliance. The model directly exhibits the couplings between different order harmonic voltages and currents. Secondly, based on the Markov chain Monte Carlo method, a random usage model is established to determine the usage pattern of home appliances. Various behavioral factors that affect the domestic use of energy in China are considered, and corresponding correction factors are proposed to improve the model accuracy. Thirdly, based on the proposed electrical and random usage models, a bottom-up approach is used to evaluate the harmonics and power of large-scale residential loads. Simulation results are compared with field measurements to verify the accuracy and validity of the proposed method. Furthermore, the proposed methodologies are applied to predict the harmonic generation and power consumption of some rapidly developing harmonic sources such as electric vehicles.

关键词： Home appliances Harmonic evaluation Harmonic coupled model Random usage pattern power prediction Bottom-up method

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Protection in active distribution network against short circuit fault and broken line fault 4

Protection in active distribution network against short circ...

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4th IEEE Conference on Energy Internet and Energy system Integration, EI2 2020

作者： Wang, Zhiyuan Gao, Zhanjun Zheng, Maoran Yu, Jiang Zong, Peng Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China China Southern Power Grid Guangzhou China

ISBN: (纸本)9781728196060

Under extreme weather, the active distribution network will not only have short circuit fault, but also have broken-line fault. At present, the distribution protection method does not consider multiple fault types comprehensively. This paper proposes an active distribution protection method for short circuit fault and broken-line fault. Based on the analysis of short-circuit fault and broken-line fault, it is concluded that the amplitudes of the current on both side of the fault line and their adjacent currents are different. Based on this feature, a protection criterion for comparing current amplitude is proposed, and the validity of the criterion is verified by simulation. © 2020 IEEE.

关键词： Timing circuits

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Probabilistic Prediction of Regional Wind power Based on Spatiotemporal Quantile Regression

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2020年第6期56卷 6117-6127页

作者： Yu, Yixiao Han, Xueshan Yang, Ming Yang, Jiajun Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China State Grid Shaanxi Elect PowerCompany Econ Res In Xian 710004 Peoples R China

Different from power prediction for a single wind farm, the regional wind power prediction is to predict the total power of multiple wind farms located in the specific region. The regional wind power prediction involves more data that implicate abundant information on spatiotemporal correlations and nonlinearity. So that addressing the massive data and extracting the representative features became the crucial issues to construct an effective regional wind power prediction model. This article proposes a spatiotemporal quantile regression (QR) algorithm to perform the short-term nonparametric probabilistic prediction of regional wind power, incorporating the advantages of the hybrid neural network (HNN) and QR. In the approach, the high-dimensional input data are reorganized into a feature graph that is ready for feature extraction by the HNN. Therefore, the advantages of HNN can be utilized to extract the representative features and construct nonlinear regression models. Meanwhile, by following the QR rules, the model obtains quantiles and perform probabilistic prediction. By properly addressing the explanatory variable selection issue, the approach provides a specific solution for regional wind power probabilistic prediction with the massive input data. The test results in a region with ten wind farms demonstrate the effectiveness of the proposed approach.

关键词： Wind power generation Wind farms Probabilistic logic Feature extraction Correlation power systems Predictive models Convolutional neural network (CNN) hybrid neural network (HNN) long short-term memory (LSTM) network probabilistic prediction regional wind power prediction spatiotemporal quantile regression (SQR)

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An energy scheduling method for clustering islands with shared power exchanging vessels

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2023年第1期152卷

作者： Xia, Shiwei Tian, Ye Wang, Zizheng Fu, Xueqian Li, Gengyin Zhang, Feng Shahidehpour, Mohammad North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250002 Peoples R China IIT Chicago IL 60616 USA

Pelagic clustering islands are rich sources of renewable energy. However, they are isolated by natural sea areas, making it difficult to realize the interconnectivity of energy generation and utilization among different types of clustering islands. This paper proposes the strategy of coordinated and optimal energy scheduling for pelagic clustering islands based on shared power exchanging vessels (SPEV). First, the energy interconnection model among different types of islands is analyzed based on the concept of SPEV sharing. Second, the space-temporal coupling of SPEV navigation is established by considering SPEV sharing transportation capacity and the charging-discharging state of mobile battery packs. On this basis, a clustering islands energy scheduling model is established to minimize the comprehensive operation costs of clustering islands, considering the battery pack charge-discharge constraints, SPEV navigation route constraints, and passenger traveling constraints. The established energy scheduling model fully explores the space-temporal energy transfer flexibility of SPEV considering meeting the travel demands of passengers. Finally, clustering islands in the South China Sea are considered in the case study, and the simulation results indicate that the proposed SPEV-based clustering islands energy scheduling strategy can effectively improve the renewable energy consumption rate and the operating economy of clustering islands.

关键词： Pelagic clustering islands Shared power exchanging vessel Mobile battery packs Space-temporal coupling Passenger carrying demand Energy scheduling

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Enhancing Integrated Gas and Electricity Networks Operation With Coupling Attention-Graph Convolutional Network Under Renewable Energy Variability

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 277-289页

作者： Bai, Runze Sun, Xianzhuo Zhang, Wen Qiu, Jing Tao, Yuechuan Lai, Shuying Zhao, Junhua Univ Sydney Sch Elect & Comp Engn Sydney NSW 2006 Australia Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy Hong Kong Peoples R China Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China CAFEA Smart City Ltd Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518100 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518100 Peoples R China

The growing integration of renewable energy sources into the power grid necessitates innovative approaches to energy system management. Integrated gas and electricity networks offer a promising solution to this challenge, enabling the efficient, reliable, and sustainable operation of energy systems. This paper presents a novel approach to the optimal scheduling of integrated gas and electricity networks, addressing the challenges posed by high penetration of renewable energy sources. First, a learning-assisted methodology is proposed to leverage Graph Convolutional Networks (GCNs) and Bayesian-based uncertainty models to enhance the accuracy and efficiency of scheduling integrated energy systems. The proposed GCN model effectively captures the complex interactions within the integrated network, facilitating accurate power and gas flow predictions. Meanwhile, the Bayesian-based model adeptly manages the inherent uncertainties associated with renewable energy generation, employing a chance-constrained approach to ensure system reliability. The effectiveness of the proposed methodology is demonstrated through extensive simulations on an IEEE 39-bus electricity network coupled with a 22-node hydrogen network. Results indicate significant improvements in computational efficiency and predictive accuracy compared to traditional model-based methods and existing data-driven techniques.

关键词： Electricity Renewable energy sources Costs Uncertainty Reliability Generators Couplings Optimal scheduling Load modeling Computational modeling power system planning probability capped load curtailment probability risk management

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Optimal Transmission Switching Based on Analytical Target Cascading Algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Li, Benxin Zhang, Xuan Zhang, Yumin Yu, Yixiao Zang, Ying Zhang, Xueqing Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automation Qingdao Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan Peoples R China State Grid Shandong Elect Power Co Yantai Power Supply Co Yantai Peoples R China

The penetration of a high proportion of renewable energy sources (RES) into the power grid intensifies the source-load imbalance, which greatly weakens the network transmission performance and power supply quality, and the effect of relying only on individual regulation within the region is negligible. To enhance the capacity of interconnection and coordination among different areas of power systems and improve the accommodation level of RES and low-carbon efficiency, an optimal transmission switching model based on the bus tearing method is proposed in this article. Firstly, the complex power system is decomposed based on the bus tearing method, and thus, the interconnected power grid structure of the multiarea system is constructed. Secondly, the optimal model of interconnected power grid decomposition and coordination structure considering renewable energy generation is constructed, based on exquisite modeling, to reduce the difficulty of unified analysis and decision-making of the multiarea interconnected power system, and the expression of the model is simplified in the form of the matrix. Then, the analytical target cascading (ATC) method is used to decouple the complex model from the main problem and subproblem and solve the distributed parallel problem, to understand the optimization of the decomposition and coordination structure of the interconnected power grid with source-load coordination. Finally, based on the case studies of the IEEE 14-bus system and IEEE 118-bus system, the effectiveness of the proposed model and method is verified, the coordinated operation of the interconnected power grid and the optimal allocation of network resources are achieved, and the economy of power system operation is improved.

关键词： optimal transmission switching bus tearing method decomposition and coordination analytical target cascading renewable energy source

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