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IEEE/IAS Industrial and Commercial power System Asia (I&CPS Asia)

作者： Li Li Qirong Lin Hanbing Qu Bo Liu Yuanzhen Zhu Changgang Li Yutian Lui State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143040

With the development of HVDC transmission and changes of load composition and characteristics, the short-term voltage problem seriously threatens the safety and stable operation of power systems. A short-term voltage stability assessment method based on deep learning neural network for AC/DC receiving-end power grid is proposed in this paper. The stacked denoising autoencoder (SDAE) and adaptive moment estimation (ADAM) algorithms are used to build a rapid evaluation model, and the training sample set is used to train the transient voltage stability rapid evaluation model. The steady-state power flow features are used as inputs and an index of quantifying short-term voltage stability of commutation bus is utilized as the output. Simulation results of real multi-infeed AC/DC power grid demonstrate the effectiveness of the proposed method.

关键词： power system stability power grids Transient analysis Stability criteria Training

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Aggregated power Flexibility of Active Distribution Network Considering Reliability and Uncertainty

Aggregated Power Flexibility of Active Distribution Network ...

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IEEE/IAS Industrial and Commercial power System Asia (I&CPS Asia)

作者： Yichen Song Mingqiang Wang Mengxia Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University) Jinan China State Grid Zhengzhou Power Supply Company Zhengzhou China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143040

With the emergence of more and more distributed energy sources (DERs) in distribution network, the traditional distribution network is changing from passive to active, and it is expected to be capable of providing flexibility for transmission system. To effectively quantify the aggregated power flexibility of active distribution network (ADN) provided to upstream gird, the situation inside ADN must be respected. This paper proposes two aggregated power flexibility quantification models considering the uncertainties of DERs and load, and the reliability in ADN. The uncertainties are modelled by distributions and the system reliability is guaranteed by the expected energy not supplied (EENS) constraint. The optimization is solved by mixed integer linear programming (MILP). The performance of the proposed method is tested on the modified IEEE 33-bus distribution system.

关键词： Uncertainty Reliability Load modeling Reactive power Biological system modeling Optimization Aggregates

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Event-Driven Fast Frequency Response for Large Active power Disturbances

Event-Driven Fast Frequency Response for Large Active Power ...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yongji Cao Hengxu Zhang Changgang Li Leshu Sun Qi Guo Yihua Zhu Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

With the rapid development of renewable energy sources, the equivalent inertia of power systems is decreasing, which leads to more drastic dynamic frequency response. This paper proposes an event-driven fast frequency response (EFFR) approach to counter frequency decline after large power disturbances. Based on the data from the wide area measurement systems, a centralized control framework is established to perform the EFFR. Then, the reference power for EFFR can be determined promptly via monitoring active power deficits. Considering the control costs and power reserves, a utility index is created to quantify the priority of each unit to participate in EFFR, on which the reference power is distributed. Furthermore, a recovery strategy is designed to restore the normal operation states of units when the power system enters the quasi-steady state. According to a droop slope, units exit the EFFR mode in sequence, of which the distributed reference power decreases gradually. A case study is presented to validate the feasibility and effectiveness of the proposed approach.

关键词： Stability criteria power system stability Frequency response Indexes Monitoring Frequency control Centralized control

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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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IEEE Student Conference on Electric Machines and Systems

作者： Menglin Li Ming Yang Yixiao Yu Peng Li Zhiyuan Si Jiajun Yang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China Electric Power Research Institute of State Grid Shaanxi Electric Power Corporation Xi’an China

ISBN: (数字)9781728156224

ISBN: (纸本)9781728156231

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 models Wind power generation Predictive models Probabilistic logic Forecasting Wind forecasting Marine vehicles

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Design of Micro Dynamic Simulation System for power System with Wind power and VSC-HVDC

Design of Micro Dynamic Simulation System for Power System w...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Leshu Sun Hengxu Zhang Yongji Cao Changgang Li Qi Guo Yihua Zhu Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

The excessive use of fossil energy has huge impacts on environment. The energy revolution with optimized energy structure has been advocated. At this background, the main development direction of energy structure is to increase the proportion of renewable energy to the grid. The two main kinds of power system analysis methods is digital simulation relying on electromagnetic or electromechanical transient software and the dynamic simulation composed by physical elements. Traditional dynamic system can only meet the requirement of AC transmission system simulation and is difficult to be expanded with new elements. In this paper, a micro dynamic simulation system is designed according to the similarity theorem. Moreover, the wind power module and VSC-HVDC module are added to meet the requirement of simulating future power systems. In addition, an experimental system based on DSP and FPGA is designed to monitor and control different dynamic elements.

关键词： Analytical models power system dynamics Wind power generation Data models Systems simulation Integrated circuit modeling Transient analysis

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An improved fault recovery strategy for active distribution network considering LVRT capability of DG 8

An improved fault recovery strategy for active distribution ...

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8th Renewable power Generation Conference, RPG 2019

作者： Cao, Yongji Liu, Weisheng Zhang, Yi Zhang, Hengxu Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9781839531255

The growing integration of distributed generation (DG) has changed the operation characteristics of traditional distribution network, and so the active distribution network (ADN) has emerged. Fault recovery is significant to reduce the loss of power failure, but the traditional control strategy without consideration of DG is no longer suitable for ADN. An improved fault recovery strategy is proposed to coordinate the fault reclosing and the low voltage ride through (LVRT) capability of DG. The influence of fault reclosing on the voltage variation of the point of common coupling (PCC) of DG is analyzed, with different relative locations of fault and PCC, and a critical LVRT voltage is extracted from the scenario where fault reclosing fails. In order to avoid DG outage, a blocking criterion of fault reclosing is presented according to the fault location and the first voltage dip of PCC. Then, the ADN can be split into planned islands considering the balance of DG power and loads and the load priorities, once the fault reclosing is blocked. Moreover, the WAMS Light is implemented for high-speed synchronous data collection and online central control. A case study is conducted to verify the effectiveness of proposed approach. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Distributed power generation

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Energy Storage Sizing Based on Asymmetric Copula for Wind Farm to be Black-Start Source

Energy Storage Sizing Based on Asymmetric Copula for Wind Fa...

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Sustainable power and Energy Conference (iSPEC)

作者： Weipeng Liu Yutian Liu Chunyi Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

In local power grid with high wind power penetration, using wind farm (WF) with energy storage system (ESS) as black-start (BS) source will significantly accelerate the restoration after blackout. For enabling WF to be a reliable BS source, an ESS sizing method based on asymmetric copula function is proposed, aiming mainly at smoothing WF output power during the process of starting up generator unit. To model the asymmetric correlation between ESS power sizing and capacity sizing, the joint probability distribution is built by asymmetric copula function. The most suitable copula function is determined by maximum likelihood estimation and a goodness of fit test. Considering sample variation, the parametric bootstrap method is applied for the confidence intervals of dependence parameter estimation. A case study with real WF data demonstrates that the proposed asymmetric copula function is more accurate and ESS sizing cost is lower consequently.

关键词： Maximum likelihood estimation power smoothing Wind farms Wind power generation Probability distribution Generators Energy storage

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Method for Single-phase Electric Meter Phase Discrimination Based on Multiple Linear Regression 8

Method for Single-phase Electric Meter Phase Discrimination ...

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8th IEEE International Conference on Advanced power System Automation and Protection, APAP 2019

作者： Zhang, Liqiang Cong, Wei Dong, Gang Sun, Yun Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9781728117225

The phase information of single-phase electric meter is crucial to improve the level of load management and the quality of modeling. A method for single-phase electric meter phase discrimination based on multiple linear regression (MLR) is proposed. The MLR equation takes the single-phase electric meter voltage as the dependent variable. Meanwhile, it takes the gateway meter voltage, the gateway meter current and the single-phase electric meter current as the independent variables. Each single-phase meter forms a regression equation with the gateway meter A, B and C respectively. The determination coefficients of the three regression equations are calculated. According to the value of the coefficients, the phase of single-phase electric meter is discriminated. An example is given to analyze the proposed method with actual energy meter reading data. The analysis results show that the proposed method has higher accuracy and reliability. © 2019 IEEE.

关键词： Multiple linear regression

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On the Use of Real-Time Transformer Temperature Estimation for Improving Transformer Operational Tripping Schemes

SSRN

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SSRN 2022年

作者： Cong, Yuhang Wall, Peter Osborne, Mark Li, Qi Li, Yigui Terzija, Vladimir School of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom Electricity National Control Centre National Grid Berkshire RG41 5BN United Kingdom Chongqing University 174 Shazheng Road Shapingba District Chongqing400044 China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education School of Electrical Engineering Shandong University Jinan250061 China

During each summer, the super grid transformers (SGTs) inside GB transmission network are maintained according to the schedule which is determined by the asset management strategies. The outage of selected transformer often results in an increased loading on the rest of transformers in the adjacent circuits. Any type of faults occurring on circuits or other elements during this period of time is likely to cause overloads on the remaining transformers which could lead to a breach of the Security and Quality of Supply Standards (SQSS). The transformer operational tripping scheme (OTS) is a technique for protecting transformers from overloading by tripping pre-selected groups of generations so that the loading on the monitored transformers can be relieved during the contingencies. An event-based transformer OTS has been constantly armed in the GB network. This paper presents a novel technique to integrate the real time transformer temperature estimation into an existing transformer OTS. This technique improves the OTS by reducing the number of unnecessary generation tripping, which is initiated from the overly conservative transformer ratings. This novel scheme will generate enormous economic benefits as well as provide significant flexibility to the GB system operator – National grid Electricity System Operator (NGESO). The new scheme is an example of a Wide Area Monitoring, Protection and control (WAMPAC) application. © 2022, The Authors. All rights reserved.

关键词： Temperature distribution

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Ultra-short-term Bus Load Forecasting Method Based on Multi-source Data and Hybrid Neural Network

Ultra-short-term Bus Load Forecasting Method Based on Multi-...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Shixiong Fan Xingwei Liu Weiwei Ma Wei Zhang Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China State Key Laboratory of Advanced Power Transmission Technology Global Energy Interconnection Research Institute co.Ltd. Beijing China National Power Dispatching and Control Center State Grid Corporation of China Beijing China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

Bus load forecasting plays an important role in terms of the safety of power grid dispatching and operation and the accuracy of online analysis and decision-making. In order to improve accuracy of bus load forecasting, this paper proposes an ultra-short-term load forecasting method based on multi-type data and hybrid model. Under the background of power big data, the multi-type data such as historical bus load data, date information and weather information are firstly taken as the input features of the prediction model, which is established based on the fusion of back propagation (BP-ANN) neural network and convolutional neural network(CNN). BP-ANN is used to extract the feature vectors of numeric type and category type data, which is concatenated with feature vectors extracted by CNN from image type data. The combined features are fed to a fully connected multilayer Neural Network to predict the bus load. In this paper, the historical data of active power load at the high voltage side of a 220kV substation in a certain area of China and the corresponding weather information are used for example analysis. The analysis of experimental results shows that the proposed method can make full use of the characteristics of multi-source data to conduct ultra-short-term bus load prediction, and it has higher load prediction accuracy compared to the single model prediction of BP-ANN and CNN.

关键词： Load forecasting Neural networks Predictive models Feature extraction Data models Data mining Load modeling

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