“智慧能源系统信息支撑技术”专题特约主编寄语

当前,世界各国电力能源系统发展的两大驱动力主要来自新能源的高效开发利用和用户侧互动参与。信息理论和信息通信技术,作为一种革命性变革驱动技术,在推动电力能源系统演化革新方面发挥了重要作用,以新能源和信息技术深度融合为特征的能源革命正推动传统能源系统向智慧能源系统发展。

基于直流互联的交流电网频率稳定控制研究

直流互联交流电网已经成为解决大电网互联问题的一种有效手段。互联交流电网的直流联络线可以实现区域备用互补,提高电网运行经济效益,并改善电网受到扰动后的频率波动情况。该文基于直流联络线控制提出一种用于互联交流电网频率支援及恢复策略。该策略可以实现事故时的紧急功率控制,减小交流电网事故后的频率急剧下降和波动。同时,在事故后参与到电网恢复过程,起到保障电网在安全频率范围内运行和加速电网频率恢复的作用。最后,通过仿真软件PSCAD/EMTDC进行仿真验证,结果表明所提策略充分利用了直流调频能力,提高了互联电网的经济性和稳定性。

采用稳健统计与B样条函数处理频率扰动记录单元异常数据

北美电网监测系统(FNET)是在配网侧实时采集电网频率的广域测量系统。由于硬件故障或网络中断,频率扰动记录单元(FDRs)采集的数据不可避免地包含尖峰或缺失数据段等异常数据,在剔除尖峰同时常用的一维中值滤波,弱化频率波动的细节信息无法弥补缺失数据段。针对此一问题,提出了融合稳健统计和B样条函数的频率异常数据处理方法,它通过设定阈值辨识尖峰值,采用B样条基函数的线性组合重构原始频率序列,引入曲线粗糙度控制B样条基函数学习过程中存在的过拟合问题。该方法仅在局部范围内处理频率异常数据,能最大限度地保留频率波动信息,且计算简洁,能实现任意阶B样条函数的构造及学习,易于推广到其他时间序列的数据预处理。

Automated Two-stage Conversion of Hourly Optimal DC Flow Solution to AC Power Flow

Conversion of hourly dispatch cases derived using DC optimal power flow(DCOPF)to AC power flow(ACPF)case is often challenging and requires arduous human analysis and intervention.This paper proposes an automated two-stage approach to solve ACPF formulated from DCOPF dispatch cases.The first stage involved the use of the conventional Newton Raphson method to solve the ACPF from flat start,then ACPF cases that are unsolvable in the first stage are subjected to a hotstarting incremental method,based on homotopy continuation,in the second stage.Critical tasks such as the addition of reactive power compensation and tuning of voltage setpoints that typically require human intervention were automated using a criteriabased selection method and optimal power flow respectively.Two datasets with hourly dispatches for the 243-bus reduced WECC system were used to test the proposed method.The algorithm was able to convert 100%of the first set of dispatch cases to solved ACPF cases.In the second dataset with suspect dispatch cases to represent an extreme conversion scenario,the algorithm created solved ACPF cases that satisfied a defined success criterion for 77.8%of the dispatch cases.The average run time for the hotstarting algorithm to create a solved ACPF case for a dispatch was less than 1 minute for the reduced WECC system.

Measurement-based Frequency Dynamic Response Estimation Using Geometric Template Matching and Recurrent Artificial Neural Network

Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.

Data quality issues for synchrophasor applications Part Ⅱ:problem formulation and potential solutions

This work investigates the data quality issue for synchrophasor applications, and pays particular attention to synchronization signal loss and synchrophasor data loss events. First, the historical synchronization signal loss events are analyzed and the potential reasons and solutions are discussed. Then, the scenario of a small amount of synchrophasor data loss is studied and a Lagrange interpolating polynomial method is used to adaptively estimate the incomplete and missing data. The performance of proposed method is demonstrated with simulation results.Specifically, the proposed method considers the trade-off between the estimation accuracy and the hardware cost,and could be efficiently employed in reality.

Data quality issues for synchrophasor applications PartⅠ:a review

Synchrophasor systems, providing low-latency,high-precision, and time-synchronized measurements to enhance power grid performances, are deployed globally.However, the synchrophasor system as a physical network,involves communication constraints and data quality issues, which will impact or even disable certain synchrophasor applications. This work investigates the data quality issue for synchrophasor applications. In Part I, the standards of synchrophasor systems and the classifications and data quality requirements of synchrophasor applications are reviewed. Also, the actual events of synchronization signal accuracy, synchrophasor data loss, and latency are counted and analyzed. The review and statistics are expected to provide an overall picture of data accuracy,loss, and latency issues for synchrophasor applications.

Recent Developments of FNET/GridEye—A Situational Awareness Tool for Smart Grid

Wide-area monitoring systems(WAMS)are becoming increasingly vital for enhancing power grid operators’situational awareness capabilities.As a pilot WAMS that was initially deployed in 2003,the frequency monitoring network FNET/GridEye uses GPS-time-synchronized monitors called frequency disturbance recorders(FDRs)to capture dynamic grid behaviors.Over the past ten years,a large number of publications related to FNET/GridEye have been reported.In this paper,the most recent developments of FNET/GridEye sensors,data centers,and data analytics applications are reviewed.These works demonstrate that FNET/GridEye will become a costeffective situational awareness tool for the future smart grid.

A Hybrid Dynamic Demand Control Strategy for Power System Frequency Regulation

The rapid increase in renewable energy integration brings with it a series of uncertainty to the transmission and distribution systems.In general,large-scale wind and solar power integration always cause short-term mismatch between generation and load demand because of their intermittent nature.The traditional way of dealing with this problem is to increase the spinning reserve,which is quite costly.In recent years,it has been proposed that part of the load can be controlled dynamically for frequency regulation with little impact on customers’living comfort.This paper proposes a hybrid dynamic demand control(DDC)strategy for the primary and secondary frequency regulation.In particular,the loads can not only arrest the sudden frequency drop,but also bring the frequency closer to the nominal value.With the proposed control strategy,the demand side can provide a fast and smooth frequency regulation service,thereby replacing some generation reserve to achieve a lower expense.

Machine learning-based fast frequency response control for a VSC-HVDC system

HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation.However,most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control.Since the traditional system frequency control is dominated by the thermal generators,the advantage of the fast response of the HVDC system is not made fully used.The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system.Different from other frequency deviation tracking strategies,a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost.In this paper,a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression(MRFR)algorithm is proposed.The simulation is carried out with an integrated power system model based on the North American interconnections.The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event,while stabilizing the frequency in advance.

SF_(6)decomposition and insulation condition monitoring of GIE:A review

The statistics of the Conference International des Grands Reseaux Electriques(CIGRE)indicate that the operational reliability of SF6 gas‐insulated equipment(GIE)is very high;however,the failure rate of the GIE in operation is much higher than that of the IEC standard,and the fault occurs frequently in the GIE at a high voltage level.The reason is due to the complex and strong on‐site electromagnetic interference environment and fully enclosed structure of GIE.The key method and technology for effective on‐line monitoring and fault diagnosis of GIE are still lacking.Given the partial strong electromagnetic energy and high temperature induced by early latent insulation faults in the equipment,SF6 gas insulation presents different degrees of decomposition.The decomposition products mainly include SO_(2)F_(2),SOF_(2),SO_(2),HF,and H2S.The decomposition characteristics of SF6 are closely related to the property of insulation faults.At present,this area is attracting attention from the power industry and research institutes.This study summarises the current research on SF_(6)decomposition component analysis(DCA).The content mainly includes the latest progress of SF_(6)decomposition characteristics and mechanism under fault conditions,and fault diagnosis methods based on decomposition components.

Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

Controlled islanding plays an essential role in preventing the blackout of power systems.Although there are several studies on this topic in the past,no enough attention is paid to the uncertainty brought by renewable energy sources(RESs)that may cause unpredictable unbalanced power and the observabilit>T of power systems after islanding that is essential for back-up black-start measures.Therefore,a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming(MISOCCP)is proposed to address these issues.First,the uncertainty of RESs is characterized by their possibility distribution models with chance constraints,and the requirements,e.g.,system observability,for rapid back-up black-start measures are also considered.Then,a law of large numbers(LLN)based method is em-ployed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one.Finally,case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model.The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

Impact of Simultaneous Activities on Frequency Fluctuations:Comprehensive Analyses Based on Real Measurement Data from the FNET/GridEye

Simultaneous human activities,such as the Super Bowl game,would cause certain impacts on frequency fluctuations in power systems.With the help of FNET/GridEye measurements,this paper aims to give comprehensive analyses on the frequency fluctuations during Super Bowl LIV held on Feb.2,2020,so as to better understand several phenomena caused by simultaneous activities which will help system operations and controls.First,recent developments of the FNET/GridEye are briefly introduced.Second,the frequency fluctuations of the Eastern Interconnection(El),western electricity coordinating council(WECC),and electric reliability council of Texas(ERCOT)power systems during Super Bowl LIV are analyzed.Third,frequency fluctuations of Super Bowl Sunday and ordinary Sundays in 2020 are compared.Finally,the differences of frequency fluctuations among different years during the Super Bowl and their change trends are also given.Furthermore,several possible explanations,including the simultaneity of electricity consumption at the beginning of commercial breaks and the halftime show,the increasing usage of the Internet,and the increasing size of TV screens,are illustrated in detail in this paper.

A measurement-based approach for power system instability early warning

Early warning of impending instability in a power system under disturbance conditions is important for preventing of system collapse.A measurement-based approach is proposed to assess the potential power system transient instability problem under cascading outages.Where a measurement-based index is obtained as the estimation accuracy of a linear autoregressive exogenous(ARX)model to estimate the dynamic response of the power system and indicate the system stability to some extent after a disturbance.The proposed approach was verified using a set of marginally stable cases in a 179-bus WECC equivalent power system.Then the instability early warning threshold for this system is obtained as 0.44.

Cross-seam Hybrid MTDC System for Integration and Delivery of Large-scale Renewable Energy

To better utilize the diversity of renewable energies in the U. S., this paper proposes a cross-seam hybrid multi-terminal high-voltage direct current(MTDC) system for the integration of different types of renewable energies in the U. S.Based on a developed station-hybrid converter design, the proposed hybrid MTDC system further investigates the connection methods of renewable energies and develops novel flexible power flow control strategies for realizing uninterrupted integration of renewable energies. In addition, the frequency response control of the hybrid MTDC system is proposed by utilizing the coordination between the converters in the hybrid MTDC system.The feasibility of the hybrid MTDC system and the performance of its corresponding control strategies are conducted in the PSCAD/EMTDC simulation. The simulation results indicate that the proposed hybrid MTDC system could realize the uninterrupted integration of renewable energies and flexible power transmission to both coasts of U.S.

Optimized Allocation Method of the VSC-MTDC System for Frequency Regulation Reserves Considering Ancillary Service Cost

In recent years,the interconnection of asynchronous power grids through the VSC-MTDC system has been proposed and extensively studied in light of the potential benefits of economical bulk power exchanges and frequency regulation reserves sharing.This paper proposes an optimized allocation method for sharing frequency regulation reserves among the interconnected power systems and the corresponding frequency regulation control of the VSC-MTDC system under emergency frequency deviation events.First,the frequency regulation reserve classification is proposed.In the classification,the available frequency response capacity reserves of each interconnection are divided into commercial reserves and regular reserves.While the commercial reserves are procured through long-term contracts,the regular reserves are purchased based on market prices of frequency regulation services.Secondly,based on the proposed frequency regulation reserve classification,a novel frequency regulation control is then introduced for the VSC-MTDC system.This control method could minimize the costs of the disturbed power grid for the needed frequency response supports from the other power grids.Simulation verifications are performed on a modified IEEE 39 bus system and a highly reduced power system model representing the North American grids.The simulation verification indicates that the developed frequency regulation control significantly reduced ancillary service costs of the disturbed power grid.

Attack Detection for Spoofed Synchrophasor Measurements Using Segmentation Network

Synchrophasor measurements are essential to realtime situational awareness of the smart grid but vulnerable to cyber-attacks during the process of transmission and invocation.To ensure data security and mitigate the impact of spoofed synchrophasor measurements,this work proposes a novel object detection method using a Weight-based One-dimensional Convolutional Segmentation Network(WOCSN)with the ability of attack behavior identification and time localization.In WOCSN,automatic data feature extraction can be achieved by onedimensional convolution from the input signal,thereby reducing the impact of handcrafted features.A weight loss function is designed to distribute the contribution for normal and attack signals.Then,attack time is located via the proposed binary method based on pixel segmentation.Furthermore,the actual synchrophasor data collected from four locations are used for the performance evaluation of the WOCSN.Finally,combined with designed evaluation metrics,the time localization ability of WOCSN is validated in the scenarios of composite attacks with different spoofed intensities and time-sensitivities.

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Large‐scale power systems exhibit more complex dynamics due to the increasing inte-gration of inverter‐based resources(IBRs).Therefore,there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs.As a pioneering Wide‐Area Measurement System,FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large‐scale power grids.This study provides an overview of the latest progress of FNET/GridEye.The sensors,communication,and data servers are upgraded to handle ultra‐high density synchrophasor and point‐on‐wave data to monitor system dynamics with more details.More importantly,several artificial intelligence(AI)‐based advanced appli-cations are introduced,including AI‐based inertia estimation,AI‐based disturbance size and location estimation,AI‐based system stability assessment,and AI‐based data authentication.