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Sustainable power and Energy Conference (iSPEC)

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

ISBN: (纸本)9781665414401

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.

关键词： Training Visualization Simulation Data visualization Voltage power system stability Feature extraction

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Distributional two-level synergetic unit commitment considering three scheduling states

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ELECTRIC power systemS RESEARCH 2022年第0期213卷

作者： Zhang, Yumin Zhang, Xuan Ji, Xingquan Yang, Ming Han, Xueshan Yin, Ziyang Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

The high penetration of distributed energy resources (DERs) (i.e. solar energy, wind power, photovoltaic and energy storage system) are integrated into the transmission grid (TG) and the active distribution networks (ADNs) in a centralized or decentralized manner or distributed manner, which significantly impose crucial challenges on the frequency regulation of power system. To make full use of the frequency regulation effect in dispatch and control, and considering the topology interlinked and the information sharing between TG+ADNs, in this paper, a distributional TG+ADNs two-level synergetic unit commitment (TLS-UC) considering three scheduling states is proposed. To explore the synergetic capacity of the TG+ADNs accommodating uncertainties of load and DERs, three scheduling states including the automatic synergetic state (i.e. primary frequency regulation (PFR)), the coordinated synergetic state (i.e. secondary frequency regulation (SFR)) and the controlled synergetic state (i.e. economic dispatch (ED)) of TG are defined and implemented to deal with uncertainties through the interconnection of the tie-line between TG+ADNs. A decentralized and parallel analytical target cascading (ATC) algorithm is employed to solve the proposed TLS-UC model. A modified 6-bus TG system with 2 ADNs (T6D2) and IEEE 118-bus TG system with 10 ADNs (T118D10) are used to test the proposed TLS-UC model. Test results illustrate that the proposed method is effective and efficient, and it can enhance the synergic capacity of power system to keep source power balance under uncertainties.

关键词： Analytical target cascading Distributional transmission and distribution synergetic operation Primary and secondary frequency regulation Three scheduling states Unit commitment

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Improved convolutional neural network-based quantile regression for regional photovoltaic generation probabilistic forecast

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IET RENEWABLE power GENERATION 2020年第14期14卷 2712-2719页

作者： Yu, Yixiao Wang, Mengxia Yan, Fangqing Yang, Ming Yang, Jiajun Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China State Grid Shandong Elect Power Co Laiwu Elect Power Supply Co Laiwu 271100 Peoples R China State Grid Shaanxi Elect Power Res Inst Xian 710054 Peoples R China

Nowadays, an increasing number of photovoltaic (PV) plants are becoming integrated into one regional power grid. Under this circumstance, the probabilistic forecast of regional PV power generation is of significance for the regional power system operation and control. This study presents a novel probabilistic forecast method for regional PV generation that integrates the convolutional neural network (CNN) with non-linear quantile regression (QR). In this method, the CNN structure is enhanced to extract the non-linear features of the input data and generate the non-linear QR function. As a result, the improved CNN can effectively process high-dimensional and complex input data and the non-linear QR model can provide quantile forecast information of regional PV power. The validity of the proposed method is verified by using it to forecast the regional PV generation from the clustered PV plants in the Weifang region of China.

关键词： neural nets regression analysis photovoltaic power systems power grids load forecasting convolutional neural network-based quantile regression regional photovoltaic generation probabilistic forecast photovoltaic plants regional power grid regional PV power generation regional power system operation probabilistic forecast method regional PV generation nonlinear quantile regression nonlinear features nonlinear QR function improved CNN high-dimensional input data complex input data nonlinear QR model quantile forecast information clustered PV plants

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A dual-driven linear modeling approach for multiple energy flow calculation in electricity-heat system

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APPLIED ENERGY 2022年 314卷

作者： Tian, Hang Zhao, Haoran Liu, Chunyang Chen, Jian Wu, Qiuwei Terzija, Vladimir Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Tech Univ Denmark Ctr Elect Power & Energy Dept Elect Engn DK-2800 Lyngby Denmark Skolkovo Inst Sci & Technol Ctr Energy Sci & Technol Moscow Russia

The multiple energy flow (MEF) model is inherently nonlinear, which challenges the fast steady-state analysis and the efficient optimization of the integrated energy system (IES). This paper presents a hybrid mechanism and data-driven (dual-driven) modeling approach to generate accurate linear models for MEF calculation. A linear regression model for the electricity-heat system is proposed to represent the underlying linear relationship among electrical, thermal, and hydraulic variables. Partial least squares (PLS) regression is used to deal with data collinearity which may lead to over-fitting. Furthermore, a dual-driven modeling approach is proposed to realize the complementarity between mechanism-and data-driven approaches. In this approach, mechanism analysis screens critical features before data processing and provides a fundamental linear model based on physical mechanism. Data-driven regression generates linear models to replace the complicated nonlinear elements and trains an approximation error model to further improve the overall accuracy. Numerical findings on a basic and a complex electricity-heat system confirmed the dual-driven linear model's accuracy advantage over existing linear models. Overall, this study proposes a dual-driven modeling approach that provides high-precision linear models while avoiding the intractable convergence problem in the nonlinear MEF calculation. It also reveals that linear models driven by data and mechanisms can closely fit the energy flows in the nonlinear electricity-heat system.

关键词： Multiple energy flow Integrated energy system Linear model Data-driven Electricity-heat system Partial least squares regression

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Ultra-Short-Term Probabilistic Wind Turbine power Forecast Based on Empirical Dynamic Modeling

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第2期11卷 906-915页

作者： Ma, Jiayi Yang, Ming Lin, You Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China State Grid Shandong Elect Power Co Qingdao Elect Power Co Qingdao 266002 Peoples R China Southern Methodist Univ Dept Elect Engn Dallas TX 75205 USA

This paper proposes a non-parametric probabilistic wind turbine power forecast (WTPF) approach based on the empirical dynamic modeling. According to Takens' theorem, the approach applies historical wind turbine power (WTP) time series to form the reconstructed state space. The moving trajectories of state vectors in the space reflect the evolution laws of WTP, and the divergence of adjacent trajectories indicates the uncertainty existing in the forecast. An enhanced simplex projection algorithm is developed to forecast the probability distribution as well as the prediction interval of WTP with respect to the trajectories. Without any assumptions on the distribution types or physical equations, the proposed approach can overcome the drawbacks of distribution type misspecification or physical model incorrectness in the conventional forecast approaches. The real-time updated state vector set in the reconstructed space further merits the reliability and flexibility of the proposed approach. Case studies employ the proposed approach as well as several benchmarks to achieve 5 min, 15 min, and 30 min ahead probabilistic WTPFs of Penglai wind farm located in Eastern China, and the superior performance demonstrates the effectiveness of the proposed approach.

关键词： Wind forecasting Wind turbines Probabilistic logic Wind power generation Manifolds Predictive models Mathematical model Attractor reconstruction empirical dynamic modeling probabilistic forecast Takens' theorem wind turbine power forecast

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Wind power Probabilistic Forecasting Based on Wind Correction Using Weather Research and Forecasting Model

Wind Power Probabilistic Forecasting Based on Wind Correctio...

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

作者： Li, Menglin Yang, Ming Yu, Yixiao Li, Peng Si, Zhiyuan Yang, Jiajun Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China State Grid Shaanxi Elect Power Corp Elect Power Res Inst Xian Peoples R China

ISBN: (纸本)9781728156224

This paper proposes a wind power probabilistic forecasting (WPPF) method by correcting the wind forecasting (WF) obtained from the weather research and forecasting (WRF) simulation. Short term wind power forecasting highly depends on the numerical weather prediction (NWP), wind forecasting especially, which contributes the most error of the predicted wind power. This method is based on the WRF, it first analyzes the performance on wind forecasting and the relationship between the predicted wind and the observed one. Second, the Hidden Markov Model (HMM) ameliorated by the Gaussian mixture model (GMM) is introduced to extract the association of the predicted wind and forecasting error and the inner temporal ship of error itself, and makes correction on the raw wind forecasting. Finally, the spare Bayesian learning (SBL) is used to generate the wind power probabilistic prediction. Case study demonstrates the proposed method significantly enhances wind forecasting accuracy and lead to a better WPPF.

关键词： Hidden Markov Model WRF model wind forecasting correction wind power probabilistic forecasting

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Optimal operation of integrated energy system considering dynamic heat-gas characteristics and uncertain wind power

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ENERGY 2020年 198卷 117270-000页

作者： Chen, Xi Wang, Chengfu Wu, Qiuwei Dong, Xiaoming Yang, Ming He, Suoying Liang, Jun Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Shandong Peoples R China Tech Univ Denmark Dept Elect Engn Ctr Elect Power & Energy DK-2800 Lyngby Denmark Shandong Univ Sch Energy & Power Engn Jinan 250061 Peoples R China

Natural gas and heating water transmission systems operate at slower rates than electric power, making resource cooperation in integrated energy systems (IESs) difficult. Moreover, large-scale fluctuating wind power integrated into IESs further complicates the coordination of each system. Therefore, to coordinate electric power, gas, and heating systems featuring different time scales, an optimal scheduling model for IESs considering the dynamic characteristics and uncertainty of wind power is proposed to improve the economy and security of IESs. First, conditional value-at-risk is introduced to quantitatively evaluate the wind power uncertainty. Then, heat-gas dynamic characteristics with different inertia time constants are coordinated to establish the optimal operation model of IESs with uncertain wind power. Finally, case studies based on a modified simulation system are conducted to demonstrate the efficiency of the proposed method for improving the flexibility and economy of IES operation. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Heat-gas network Integrated energy system Dynamic characteristics Wind power Optimal operation Conditional value-at-risk

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Coordination control for multi-voltage-level dc grid based on the dc-dc converters

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ELECTRIC power systemS RESEARCH 2020年 178卷 106050-000页

作者： Sun, Kaiqi Li, Ke-Jun Wang, Meiyan Tian, Guanyu Wang, Zhuo-di Liu, Zhijie Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Shandong Peoples R China Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

Dc-dc converters are emerging as a key device for future dc transmission networks. This paper develops a switch mode control and a dc-dc coordination control for dc-dc converters' coordinated operation in the multi-voltage. level dc grid. The switch mode control is a local controller on the dc-dc converter to enable the automatic switch of the dc-dc operation mode. The dc-dc coordination control is an advanced control based on the switch mode control. With the dc-dc coordination control, unbalanced power from one dc grid could be optimally distributed to other grids, in order to avoid serious power fluctuation events. Simulation results are provided to demonstrate the performance of the switch mode control and dc-dc coordination control.

关键词： Dc-dc converter Multi-voltage-level dc grid Switch mode control Dc-dc coordination control

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A Method for Multi-HVDC Grouping Based on Spectral Clustering

A Method for Multi-HVDC Grouping Based on Spectral Clusterin...

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2020 IEEE/IAS Industrial and Commercial power system Asia, I and CPS Asia 2020

作者： Wang, Xiaohui Bai, Yu Gao, Yadong Liu, Dong Zhang, Yan Wang, Zhidong Gao, Feng Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China Zhejiang Huayun Electric Power Engineering Design Consulting Co. Ltd Hangzhou China Beijing China

ISBN: (纸本)9781728143033

To satisfy the increasing power demands in load center, multiple HVDCs have been integrated into the receiving grid in China. The reality raises the coordination issues of HVDCs. A HVDC grouping method for optimizing the management of multiple HVDCs is proposed in this paper. Several kinds of relationship in coordination and complementarity between HVDCs are analyzed and quantified. Based on graph theory, HVDC grouping is transformed into a graph-cut problem and solved by spectral clustering algorithm. The proposed method is implemented on a practical multi-HVDC grid, and the results show its feasibility and effectiveness. © 2020 IEEE.

关键词： Clustering algorithms

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A Method of Fault Diagnosis for Secondary Loop in intelligent Substation Based on Bayesian Algorithm

A Method of Fault Diagnosis for Secondary Loop in Intelligen...

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

作者： Zong, Peng Gao, Zhanjun Yang, Tao Zhou, Fengyuan Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan Peoples R China State Grid Shandong Elect Power Co Shandong Elect Power Dispatching Control Ctr Jinan Peoples R China

ISBN: (纸本)9781728156224

In order to improve the accuracy and maintenance efficiency of fault diagnosis for secondary loop in intelligent substation, this paper proposes a fault diagnosis method based on improved Bayesian algorithm. Firstly, the physical loop and virtual loop models of the secondary loop are established according to the SCD file, and their mapping relationship is established. Secondly, the set of suspicious fault components in the secondary loop is obtained according to the abnormal degree table of physical components. Then, the improved Bayesian algorithm is used to evaluate the components in the set of suspicious fault components. Finally, the output of the Bayesian algorithm is the Bayesian anomaly degree of the suspicious fault components. The component with the highest Bayesian anomaly degree is considered as the fault component. In order to detect the effectiveness and the feasibility of the proposed method, this paper conducts a fault diagnosis case and the diagnosis results demonstrate that the proposed diagnosis method can obtain good discriminant result.

关键词： fault diagnosis abnormal degree secondary loop Bayesian algorithm

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