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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Yang, Jinye Cao, Yongji Zhang, Hengxu Mi, Yuanze Sun, Leshu Li, Chen Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China Technical University of Denmark Department of Electrical Engineering Lyngby Denmark State Grid Jinan Electric Power Company State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9781665485081

Considering the problems of power flow fluctuation and voltage excursion caused by the large-scale integration of renewable energies, a layered and partitioned planning method is proposed for wind-PV clusters. Firstly, based on transmission line impedance, a partition method is proposed for the power grid. Secondly, a multi-indicator assessment framework including voltage excursion, power flow fluctuation and economic operation is provided. Finally, an optimization model is established to minimize the curtailment of renewable energies. The model is solved using the second-order cone approximation of AC power flow. Finally, the effectiveness of the proposed model was verified on the IEEE-39 bus system. © 2022 IEEE.

关键词： Electric load flow

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Visualization of CNN Transient Voltage Classification Based on Feature Recognition and Enhancement

Visualization of CNN Transient Voltage Classification Based ...

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2021 IEEE Sustainable power and Energy Conference, iSPEC 2021

作者： Li, Zhendong Liu, Yutian An, Jun Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of Education Jilin China

ISBN: (纸本)9781665414395

The deep learning algorithm represented by CNN has achieved remarkable results in complex feature extraction, classification and regression. Since the CNN adopts an end-to-end training process, the carrier of acquisition knowledge is a high-dimensional non-linear function, which is hard to understand by humans. Therefore, it is difficult to be widely used in online decision-making engineering scenarios such as the fast assessment of power system transient voltage stability. In this paper, the key input elements extracted by CNN are identified by Grad-CAM, and the key features captured by CNN in the assessment process are enhanced based on AM, so as to finally obtain the visual expression of key areas and variables of the original input data. Besides, a sample construction method based on single line diagram is proposed to improve the visualization effect of Grad-CAM and AM. Simulation results and analyses of IEEE 39bus test system demonstrates the effectiveness of the proposed method. © 2021 IEEE.

关键词： Visualization

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Inverter-Data-Driven Second-Level power Forecasting for Photovoltaic power Plant

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第8期68卷 7034-7044页

作者： Meng, Xiangjian Gao, Feng Xu, Tao Zhou, Kangjia Li, Wei Wu, Qiang Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

Globally, the installed capacity of photovoltaic (PV) power plants is undergoing rapid growth. However, the random output power fluctuation of PV plants has brought great challenge to power system stable operation. One of the most effective approaches to counteract the impact of power fluctuation is to carry out power forecasting under ultra-short-term scales, among which the second-level power forecasting technique is still absent since the pure historical data analysis of PV plant output power cannot deal with the sudden output power change induced by moving cloud and the traditional cloud image detection methods actually fail to achieve accurate PV power forecasting in high temporal and spatial resolutions. This article proposes an inverter-data-driven method to achieve the second-level PV power forecasting. In specific, multilayer feed-forward artificial neural network based on the error back propagation algorithm is applied to first establish the mapping relations from shading conditions of PV array to its corresponding output power. Combining with output power of neighboring PV arrays as well as their geographical location information, the shading conditions of those shaded PV array can be reversely deduced. And then, the deduced shading conditions of all PV arrays will be converted into one virtual cloud image to identify the instant cloud characteristics. In consequence, the consecutively generated virtual cloud images can depict the moving direction, speed, and shape of shading cloud and then help easily realize the second-level power forecasting of PV plant. Case study and experimental results verified the performance of the proposed method.

关键词： power generation Forecasting Cloud computing Arrays Artificial neural networks Inverters Clouds Artificial neural network (ANN) inverter photovoltaic (PV) power generation power forecasting virtual cloud image

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Distribution Network Topology Identification Based on Time Series Voltage Mutual Information and Probability Density Function

Distribution Network Topology Identification Based on Time S...

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China International Electrical and Energy Conference (CIEEC)

作者： Xiang Lin Wei Cong Chenyan Diao Xiaofei Shao Chunhua Lin Xiaochen Shan Key Laboratory of Power System Intelligent Dispatch and Control (Shandong University) Jinan China State Grid Liaoning Electric Power Company Limited Dalian Power Supply Company line 2-name of organization acronyms acceptable Dalian China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Recognition of distribution network topology is usually based on switching and node related data. It is difficult to frequently interact with multi-dimensional and real-time data, but the reliability of the identification is not high only by uploading the switching quantity. In this paper, a topology identification method based on node voltage magnitude is investigated to accurately recognize the topology of distribution networks. First., take the example of a radial distribution network., mutual information is used to analyze the similarity of two nodes' timing voltages. Then., probability density distribution curves are used to determine whether multiple nodes are in the same branch. Next., the topology identification process is proposed: monitoring whether the topology is changed., determining which branch is disconnected., determining the exact location of the breakpoint., verifying whether the switch is correct., and reconstructing the topology. Finally., the IEEE33 node system is taken as an example and simulated in MATlab for verification. The topology derived from the identification is accurate., proving the effectiveness of the method.

关键词： Voltage measurement Network topology Density measurement Estimation Distribution networks Switches Topology

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STEPS: A Portable Dynamic Simulation Toolkit for Electrical power system Studies

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IEEE TRANSACTIONS ON power systemS 2021年第4期36卷 3216-3226页

作者： Li, Changgang Wu, Yue Zhang, Hengxu Ye, Hua Liu, Yutian Liu, Yilu Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Univ Tennessee Sch Elect Engn & Comp Sci Knoxville TN 37996 USA

Numerical simulation is the key technique for large-scale power system analysis. Redistribution of global renewable power via international interconnections requires new simulation tools to study the interconnected systems with different nominal frequencies as a whole. This paper introduces an open-source simulation toolkit for electrical power systems (STEPS) hosted at Github. Its kernel is coded in C++ with major functions of power flow and electro-mechanical dynamic simulation. Flexible options are provided and configurable to improve power flow solution and dynamic simulation. Common devices and models are supported in STEPS for AC/DC hybrid system studies. The study of interconnected systems with different nominal frequencies is supported in STEPS for research of international interconnection. Application program interfaces are provided and wrapped with Python to enable high-level interfaces for general applications. STEPS is thread-safe, and parallel computation is supported in both kernel-level and task-level parallelization to accelerate simulation. It is portable and works on Windows and GNU/Linux platforms. Cases from small to large-scale systems are thoroughly tested to validate the toolkit with commercial packages as benchmarks.

关键词： power system dynamics Load flow power system stability Load modeling Computational modeling Open source software Object oriented modeling power system open-source simulation package power flow electro-mechanical dynamic

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A Reduced Disjunctive Model for Transmission Expansion Planning Considering Unit Commitment and Optimal Transmission Switching

A Reduced Disjunctive Model for Transmission Expansion Plann...

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2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2021

作者： Hu, Yuxi Wang, Mingqiang Lian, Xiaoyang Liu, Shuai Zhang, Qiang Ministry of Education Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China Shandong Electric Company Jinan China

ISBN: (纸本)9781665441285

The rapid growth of wind power brings uncertainty and fluctuation and it is crucial to improve the flexibility of power system. In this paper, a reduced disjunctive model (RDM) for transmission expansion planning is proposed. The proposed model considers unit commitment and optimal transmission switching simultaneously in order to improve the flexibility and wind power accommodation capacity. Then the RDM reduces the number of variables compared with the traditional disjunctive model (DM). Finally, numerical results of case studies indicate that the proposed model saves the total cost, decreases the wind curtailment and load shedding, and improves the computational efficiency. © 2021 IEEE.

关键词： Computational efficiency

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Voltage control for Black-Start Based on Energy Storage Station and Wind Farm

Voltage Control for Black-Start Based on Energy Storage Stat...

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intelligent power and systems (ICIPS), International Conference on

作者： Yuanzhen Zhu Xinyu Wei Tianshu Li Yiheng Chen Ning Zhou Qiao Fang Kang Zhao Runjia Sun Shandong Smart Grid Technology Innovation Center State Grid Shandong Electric Power Company Electric Power Science Research Institute Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Jinan China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

Energy storage station (ESS) and wind farm (WF) have the potential to serve as a black-start sources to accelerate the power system restoration after blackout. However, the voltage security problem when start the auxiliary machines of power plants limit their reliability as black-start sources. A voltage control method is proposed for ESS and WF combined black-start sources to improve the voltage security during black-start. Firstly, the black-start process based on grid-forming ESS and WF is presented, and the voltage control framework is established. Then, a predictive control method considering the impact of active power on voltage variations is proposed, in which different optimization modes are established to obtain the active and reactive control sequences for WF and ESS. Finally, black-start simulation with Matlab/Simulink demonstrate the proposed method can improve the voltage security level when starting auxiliary machines of power plants.

关键词： Simulation Wind farms Regulation Security Reliability Voltage control power generation Predictive control Grid forming Energy storage

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Inertia Response Coordination Strategy of Wind Generators and Hybrid Energy Storage and Operation Cost-Based Multi-Objective Optimizing of Frequency control Parameters

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IEEE ACCESS 2021年 9卷 74684-74702页

作者： Rahimi, Tohid Ding, Lei Kheshti, Mostafa Faraji, Rasoul Guerrero, Josep M. Tinajero, Gibran David Agundis Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Due to the high penetration of renewable energy resources in microgrids (MGs), the grid inertia becomes low which leads to the grid to be vulnerable to large disturbances. The energy storage devices can play an important role to enhance the inertia of MGs. However, due to the high investment cost of storages or their dp/dt limitation, the installed energy storages cannot cover the challenge of high df/dt. A prominent solution to solve the problem is to use the inertia response of the wind generators. However, relatively high second frequency nadir is the main drawback of using the inertia response of the wind generators which may impose an extensive disturbance to MGs. Accordingly, a coordinated operation strategy for MGs between wind generator and hybrid energy storage (HES) system is proposed in this paper. In addition, to improve the inertia response of the MG;providing high-quality communication infrastructures with low delay and increasing the Ultracapacitor capacity have been paid attention. In this paper, the costs of the installed Ultracapacitor and quality of communication services are defined as the operation cost. Guaranteeing enough frequency damping for the MG with low operation cost are two conflict objectives. Therefore, a multi-objective optimization method is used to set the controllers' values and reduce the operation cost. The results confirmed that the effectiveness of the proposed strategy to control hybrid power storage in coordination with the wind generator and the frequency recovery process is improved. Also, employing the optimum values guaranteed the frequency damping effectively with low operation cost. The Integral Absolute Error (IAE) value and operation cost are reduced by 13.6% and 32%, respectively. Also, the simulation results show that the maximum MG frequency deviation and maximum df/dt is well compatible with different standards in the presence of load perturbations and different wind speeds.

关键词： Frequency control Wind turbines Generators Wind speed Renewable energy sources Energy storage Batteries Microgrid (MG) frequency control wind generator hybrid energy storage (HES) ultra-capacitor inertia response multi-objective optimization

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Generalizability Improvement of Deep Learning-Based Non-Intrusive Load Monitoring system Using Data Augmentation

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IEEE TRANSACTIONS ON SMART GRID 2021年第4期12卷 3265-3277页

作者： Rafiq, Hasan Shi, Xiaohan Zhang, Hengxu Li, Huimin Ochani, Manesh Kumar Shah, Aamer Abbas Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Shandong Univ Technol Sch Elect & Elect Engn Zibo 255049 Peoples R China

Practical application of deep learning based non-intrusive load monitoring (NILM) system requires the deep neural network model to generalize on new unseen data. Existing NILM solutions are not suitable for real-world application due to their poor disaggregation accuracy on new unseen data. In order to address this problem, this paper presents a NILM algorithm that uses data augmentation to generate synthetic data for training deep convolutional neural network models for each target appliance. Proposed data augmentation technique works by combining on and off-durations of a target appliance from various datasets, and forms a unified and comprehensive synthetic aggregate and sub-meter profiles. Apart from proposed algorithm, this paper also proposes an evaluation approach that relies on total predicted energy and ground-truth energy of an appliance to provide detailed insights about total overlapping energy, missing energy and extra energy predicted by the algorithm. Comparison results on our proposed evaluation approach showed that proposed disaggregation algorithm was able to predict energy that was 60% overlapping with ground truth energy and 36% energy was extra. Overall results showed that overlapping energy was 2.5 times more, and extra-predicted energy was 60% less than state-of-the-art algorithms in unseen test cases.

关键词： Home appliances Aggregates Training data Monitoring Deep learning Prediction algorithms Energy consumption Convolutional neural network data augmentation deep learning energy consumption energy disaggregation non-intrusive load monitoring

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A Deep Reinforcement Learning Based control Strategy for Combined Wind Energy Storage system

A Deep Reinforcement Learning Based Control Strategy for Com...

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2021 IEEE Sustainable power and Energy Conference, iSPEC 2021

作者： Xiang, Guangwei Yang, Ming Huang, Shiying Yu, Guangyuan Yin, Aihui Liu, Xiao State Grid Corporation of China Heilongjiang Electric Power Company Harbin China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9781665414395

For renewable energy producers, the uncertainties of power generation and time-varying electricity prices may lead to economic losses. This paper proposes a deep reinforcement learning (DRL) based control model combined energy storage system (ESS) to maximize the income of the wind power producer. It is able to adapt the uncertainties by itself that can avoid the errors caused by modeling the uncertainties. It can also learn and improve the strategy based on feedbacks got by the mechanism of interacting with the environment. Moreover, Rainbow, an improved DRL algorithm, is introduced to formulate the optimal model. Finally, the effectiveness of the proposed model is verified based on the wind farm located in Jiangsu, China. © 2021 IEEE.

关键词： Wind power

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