Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

The invention of Phasor Measurement Units (PMUs) produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible. PMUs are used in transmitting data to Phasor Data Concentrators (PDC) placed in control centers for monitoring purpose. A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid. This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data. The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power grid. As a result, detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical. A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this paper. The proposed method consists of three modules namely, offline Gaussian Mixture Model (GMM), online GMM for identifying anomalies and clustering ensemble model for classifying the anomalies. The significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset, adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the anomalies. The proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.     

基于泛在电力物联网的普适性智能电表状态实时评估方法

针对当前智能电表状态评估存在精确度低、泛化性差和实时困难等问题,本文采用泛在电力物联网构建状态实时评估方法解决该问题。首先,采用决策树算法实现智能电表的分类,整体增强方法的匹配度和适应性;随后针对不同类别的智能电表,采用Apriori算法对样本集数据的特征集进行识别和提取,从而降低特征维度并增强关联性;接着,基于决策引擎实现对智能电表状态实时评估,并以度量学习实现新增物联网采集数据的有效性评估,反馈优化传感设备部署,从而根据评估结果实现对新增部署传感器及其位置的调整,进而根据应用场景不断优化智能电表状态实时评估应用模式。实验结果表明,本方法可实现智能电表运行状况的实时、普适、精准运维评估,进一步解决泛在电力物联网设备现场部署经验不足、校验无目标等问题。     

新能源发电设备转子安全运行监测系统设计

针对新能源（如风能、抽水蓄能等）发电设备具有运行环境恶劣、工况复杂及负载扰动大等特点,开发了新能源发电设备转子安全运行监测系统,该系统由发电设备智能监测终端和监控中心服务器两部分组成.智能终端完成发电设备的智能在线监测与实时分析;监控中心服务器实现发电设备的远程监测与诊断分析.系统的设计开发为新能源发电设备安全运行提供重要保障,同时也为新能源机组振动特性研究提供了有效工具.     

基于电力线载波的智能家居控制系统研究

摘要：结合电力线载波通信技术的发展状况,提出了基于电力线载波通信技术的智能家居系统设计方案;详细地阐述了该系统的总体框架设计、远程终端系统软件设计以及嵌入式μC／OS系统控制器的硬件设计和软件设计。系统的实验检测结果表明,该方案能够灵活、快捷地实现对家居设备的控制,而且成本较低。     

基于NGOSS构架的流域电力生产通信网络一体化运维支持系统

雅砻江流域电力生产通信网络是以雅砻江公司集控中心作为通信汇聚节点,雅砻江流域电厂为通信子节点而建立的冗余型电力通信骨干网。通过该网络,可实现集控中心与流域电厂的电力生产业务系统数据对接和交互。笔者提出结合通信信息行业的最新动向和技术发展趋势,借鉴电信运营商和电力运营商的先进经验,构建一套基于NGOSS构架的一体化运维平台,以提升对流域电力生产通信网的运维管理水平。本文对该系统的框架、部署、核心功能设计进行了阐述。     

基于状态机的AI/AO模块HART通信软件实现方法

现代电站控制系统正逐渐向数字化、智能化方向发展。HART协议的通信方式具有可靠性高、传输数据量大等优点。基于HART协议的智能仪表正越来越广泛地应用于自动化控制领域,如核电站控制、火电站控制、燃气轮机控制等。针对现有以硬件方式实现HART通信功能存在系统复杂性高、经济性与通用性差等问题,提出了一种基于有限状态机的软件设计方法实现AI/AO模块的HART通信功能。采用软状态机的轮转逻辑设计实现HART协议的主从站切换和主站配置,通过接收状态函数和HART报文切换的逻辑设计完成多路HART从站应答。HART通信软件实现方法具有无需更改AI/AO模块硬件设计的特点,其应用前景十分广泛。测试结果验证了方案的有效性。     

巨型风电并网系统的协同自律控制

巨型风电场并网发电的调度和控制是我国电力系统发展亟需解决的关键难题之一．本文提出了风-水-气协同自律控制的理念和理论,给出了消纳巨型风电的两种基本调度和控制策略,即基于智能调度自动化系统（smart energy manage system,SEMS）的集中式控制和基于协同自律控制的调度策略．本文对比分析了上述两种控制策略的适用情景,指出后者更加合理和高效．进一步,本文探讨了发展基于协同自律控制的风-水-气联合调度系统所需关键技术,试图为解决我国巨型风电并网发电调度难题给出一种方略．     

热电联产机组汽机控制系统分析及仿真建模

该文利用STAR -90模块化仿真支撑系统对热电联产机组进行仿真建模。并对热电联产机组汽机控制系统进行了分析。机组仿真机可以对电厂运行人员及管理人员进行培训和考核 ,使受培人员能熟练掌握机组在各种条件下的启停过程和正常运行中的监视操作技术 ;使受培人员的应急能力及对机组运行的综合分析能力得以提高。可利用仿真机进行机组运行方式的优化和研究 ;对控制系统的设计、性能分析以及现场调试都具有重要意义。     

电力系统中人工智能的应用与研究

人工智能技术被广泛地应用于求解非线性问题，较之于传统方法有着不可替代的优势。人工智能技术在电力系统的管理、控制及规划等各个领域中发挥着重要的作用。本文描述了电力系统中一些已经应用了该技术来解决的问题与此项技术的今后应用，并针对继电保护给出了具体说明。     

基于模糊云理论模型的智能配电云网络化控制识别方法

由于基于传统的不确定时滞鲁棒控制系统不能满足新型电力云网络化控制用户的需求,提出了一种基于模糊云理论系统模型的感知智能配电云网络化控制识别方法,该方法采用优化传统数据值和电力云网络化控制数据的一种云理论系统模型,并用云理论结果代替被控节点预测值,攻破了不确定滞鲁棒控制系统控制效果不能及时反馈的问题,同时将供电系统性能指标引入到电力云网络化控制计算器中,运用性能指标的云模型中正向运算法修正加权系数,实现了感知智能配电云网络化的最优控制。基于实验仿真证明,该识别方法可以优化传统的不确定滞鲁棒控制系统,并具有良好的适应性、鲁棒性、控制性,同时可以进一步改善智能配电云网络化控制的各种性能指标。     

基于VxWorks网络通信代理实现的分布式变电站自动化监控系统

研究了分层、分布式变电站自动化系统中有关网络通信功能在嵌入式操作系统VxWorks下的实现。综合变电系统中多方面的需求,采用当前最先进的软、硬件技术及体系构架, 在Vxworks嵌入式实时操作系统及PowerPC860处理器的硬件平台上实现变电站内部网的实时数据及控制信息的传输。该系统已成功地实现了工业运行实验。     

基于长输管道SCADA运行数据的诟控操作分析与研究

长输管道SCADA系统建设日趋成熟,在各级油气管道调控中心也搭建了数据小心,实现了对多条管道SCADA运行数据的汇聚,积累了大量的管道生产运行数据,这些数据详实地记录了管道生产运行和调控操作的全过程。而调控中心面临着如何对管道调控操作规律、篱道复杂度和调度漪操作水平进行科学评估与分析的难题。本文介绍了采用数据挖掘中描述性分析技术,基于海量的长输管道SCADA运行数据,对调控操作基础数据进行工况判断规律的提取、分析和完善,最终识别判断出调度人员在各种工况下操作全貌,井基于工况自动判断结果进行各种纬度的统计分析,为调控运行管理提供科学的决策支持数据和有效分析方法。     

Cyber Attack Protection and Control of Microgrids

Recently, the smart grid has been considered as a next-generation power system to modernize the traditional grid to improve its security, connectivity, efficiency and sustainability. Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economical, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional (RSC) code and Kalman filter (KF) based method in the context of smart grids. Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information, which is affected by random noises and cyber attacks. Once the estimated states are obtained by KF algorithm, a semidefinite programming based optimal feedback controller is proposed to regulate the system states, so that the power system can operate properly. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.      

面向智能矿山与新工科的数字孪生技术研究

将数字孪生与人工智能(AI)技术相结合,提出了基于数字孪生+AI的智能矿山建设新思路。探索了智能矿山技术发展路径,研究了数字孪生技术的特征、应用领域及发展趋势,指出数字孪生是数字化矿山发展的必然趋势。提出了基于数字孪生+AI的智能矿山理论架构,构建了矿山数字孪生模型,模型自下而上分别为矿山全要素物理实体、矿山信息物理融合层、矿山数字孪生模型、矿山孪生数据交互层、矿山应用智能服务层,据此实现智能矿山的泛在感知、协同控制和智能决策与优化。从应用实际需求出发,探讨了智能矿山模型构建技术、智能开采数字孪生体技术、矿山智能控制技术、矿山设备故障预测、基于数字孪生的人机交互等关键技术。通过研究数字孪生在智能矿山中的应用,为AI技术在智能矿山应用落地提供思路,为未来智能矿山新工科建设提供理论借鉴。     

A deep learning-enhanced Digital Twin framework for improving safety and reliability in human–robot collaborative manufacturing

In Industry 5.0, Digital Twins bring in flexibility and efficiency for smart manufacturing. Recently, the success of artificial intelligence techniques such as deep learning has led to their adoption in manufacturing and especially in human–robot collaboration. Collaborative manufacturing tasks involving human operators and robots pose significant safety and reliability concerns. In response to these concerns, a deep learning-enhanced Digital Twin framework is introduced through which human operators and robots can be detected and their actions can be classified during the manufacturing process, enabling autonomous decision making by the robot control system. Developed using Unreal Engine 4, our Digital Twin framework complies with the Robotics Operating System specification, and supports synchronous control and communication between the Digital Twin and the physical system. In our framework, a fully-supervised detector based on a faster region-based convolutional neural network is firstly trained on synthetic data generated by the Digital Twin, and then tested on the physical system to demonstrate the effectiveness of the proposed Digital Twin-based framework. To ensure safety and reliability, a semi-supervised detector is further designed to bridge the gap between the twin system and the physical system, and improved performance is achieved by the semi-supervised detector compared to the fully-supervised detector that is simply trained on either synthetic data or real data. The evaluation of the framework in multiple scenarios in which human operators collaborate with a Universal Robot 10 shows that it can accurately detect the human and robot, and classify their actions under a variety of conditions. The data from this evaluation have been made publicly available, and can be widely used for research and operational purposes. Additionally, a semi-automated annotation tool from the Digital Twin framework is published to benefit the collaborative robotics community.     

Smart control techniques for design of TCSC and PSS for stability enhancement of dynamical power system

This paper deals with the simultaneous application of thyristor controlled series capacitor based damping controller and power system stabilizer for stability improvement of dynamic power system. The adaptive neuro-fuzzy inference system and Levenberg–Marquardt artificial neural network algorithm are used to develop the control strategy for thyristor controlled series capacitor based damping controller and power system stabilizer. The power system stabilizer generates appropriate supplementary control signal to an excitation system of synchronous generator to damp the frequency oscillations and improves the performance of the power system dynamic. The performance of power system affected due to the system configuration and load variation. In order to achieve the appreciable damping, the series capacitor is suggested in addition to the power system stabilizer. Nonlinear simulations of single machine infinite bus system are carried out using the individual application of power system stabilizer and simultaneous application of power system stabilizer and thyristor controlled series capacitor. The comparison analysis between conventional and smart control strategies based controllers is demonstrated. Single machine infinite bus system is tested under various operating conditions and disturbances to show the effectiveness of proposed control schemes.     

Amalgamation of adaptive control and AI techniques: applications to generator excitation control

Adaptive control can be described as the changing of controller parameters on-line based on the changes in system operating conditions. Whenever an adaptive controller detects changes in system operating conditions, it responds by determining a new set of control parameters.An adaptive controller based on analytical techniques can provide excellent performance and improve the dynamic performance of the plant by allowing the parameters of the controller to adjust as the operating conditions change. However, proper care needs to be taken in the design of the analytical algorithms to make them robust, especially under large disturbances.The controller robustness can be improved by employing artificial intelligence (AI) techniques, such as fuzzy logic and neural networks. It is possible that either the entire algorithm may be implemented using AI techniques or the analytical and AI techniques be integrated such that some functions are performed using analytical approach while the rest are performed using AI techniques. Successful implementation of all three approaches, i.e. purely analytical, purely AI and integrated, is illustrated by application to an adaptive power system stabilizer (PSS) to improve damping and stability of an electric generating unit.     

两总线智能化气体检测系统

根据提供的两总线智能化的气体检测系统的工作原理及设计方法,所制成的探测器采用催化燃烧式传感器的特点:是以棒调节零点和增益;通讯采用直流电源载波方法;信号线与电源线公用.由此,不仅降低了布线成本,而且提高了系统可靠性.     

A Stealth Integrity Targeted Cyber‐Attack in Distributed Electric Power Networks with Local Model Information

Electric power networks are critical infrastructures, and their correct operation is of vital importance. Nowadays, these systems are prone to cyber‐attacks because of new vulnerabilities in the system and access to shared networks. In this paper, a novel Stealth Integrity Targeted Attack (SITA) is proposed in the context of distributed power systems. A distributed power system comprises several sub‐networks, or zones with dedicated control and monitoring centers. The overall system is represented by linear time invariant state space models with coupled dynamical and algebraic equations. In the proposed strategy, the attacker has access to only one of the sub networks; therefore, the attacker only requires local information about one of the power system zones. Primarily, the proposed attack policy is defined based on zero‐dynamics of the sub network. The intruder injects predesigned signals to both the local generation unit controller as well as local unsecured and controllable loads in the attacked zone. Moreover, the local measurement system, or the sensors of the targeted zone are tampered. Furthermore, it will be proved that although the neighbor zones have physical connections with the attacked zone, the injected adversary signals are designed as they do not impact other zones directly in order to conceal the local attack from neighbor control centers as much as possible. We provide some advice to system administrators to make the intrusion unfeasible or to reveal the attack. The simulations on IEEE‐118 bus test system illustrate the validity of the assertions.     

A load control method for small data centers participating in demand response programs

This paper presents a load control method for small data centers, which are rarely studied although they account for more than 50% of all data centers. The method utilizes the data network and the electrical network to control power usage for participation in demand response (DR) programs, which are regarded as the killer applications of the emerging smart grid (SG). Traditional data center power management often directly manipulates energy usage, which may be ineffective or impractical for small data centers due to their limited resources. Both the SG and the data centers are considered to be the cyber-physical systems (CPSs). This article proposes an approach that performs the data center DR load management through the cyberspaces of the SG and the targeted data center. The proposed method instructs the workload dispatcher to select the best-suited algorithm when a DR event is issued. Additionally, this method also adjusts the temperature set-points of the air conditioners. The simulation result shows that this approach can achieve a 30% power reduction for DR.