Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

MigroGrid(MG)has emerged to resolve the growing demand for energy.But because of its inconsistent output,it can result in various power quality(PQ)issues.PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources.Similarly,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system.Thus,this research focuses on power quality enhancement in the MG using hybrid shunt filters.However,the performance of the filter mainly depends upon the design,and stability of the controller.The efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)controller.The performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this approach.Further,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in MG.From the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

A Review on Intelligent Detection and Classification of Power Quality Disturbances:Trends,Methodologies,and Prospects

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more prominent.It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids.In order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse effects.This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly presented.Finally,comments on the future evolution of PQD detection techniques are given.Unlike the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Design of an Intelligent Self-Healing Smart Grid using a Hybrid Multi-Agent Framework

This paper discusses the applications of a hybrid multi-agent framework for self-healing applications in an intelligent smart grid system following catastrophic disturbances such as loss of generators or during system fault.The proposed hybrid multi-agent framework is a hybrid of both centralized and decentralized scheme to allow distributed intelligent agent in the smart grid system to make fast local decision while allowing the slower central controller to judge the effectiveness of the decision made by the local agents and to suggest more optimal solutions.

基于人工智能的海量电网GIS数据动态调度方法

针对电网GIS数据量大,导致数据调度结果不理想、调度响应时间过长的问题,提出了基于人工智能的海量电网GIS数据动态调度方法。压缩海量电网GIS数据,使用人工智能调度的神经网络方法,构建误差修补函数,修改神经网络权值,生成人工智能调度模型。在R树数据结构下对所有数据进行缓存,改变当前视野范围,并将查询到的压缩数据以格式字符串形式返回给客户端。结合人工智能调度的数据动态渲染方法,实现了基础地理和电网设备符号的实时绘制,缩短了数据动态调度响应时间,由此完成数据动态调度。实验结果表明,所提方法与实际数据存在最大为10 kB的误差,且调度响应时间为100 s,具有较好的调度效果,能够有效缩短调度响应时间。

基于CPS的钢铁企业电网智能管控

针对钢铁企业电网智能化运维水平不高,电网运行控制自动化程度低下,企业电网信息化集成性不足的难题,面向千万吨级钢铁厂开发一套企业级电网智能管控系统。通过将信息化系统和企业电网深度融合,创建了面向钢铁企业电网智能管控的CPS信息物理系统体系架构,实现了电网潮流控制、机网协调、电网管控、电网设备运维管理、电能质量监测等控制功能,全面解决钢铁厂内部电网建设、管理、运维中的各种问题,最大限度利用自发电。

基于PSO-GRNN和D-S证据理论的电网分区故障诊断

针对大电网中保护和断路器误动、拒动、信息丢失等不确定的电网故障信息以及现有电网分区方法的不足,提出了基于粒子群优化广义回归神经网络(PSO-GRNN)和D-S证据理论的电网分区故障诊断方法。首先,通过改进图形分割法将大电网划分为相互重叠的不同区域,降低故障诊断难度。然后在各个区域建立PSOGRNN诊断模块,根据故障警报信息,并行完成各自的故障诊断任务。最后,采用D-S证据理论对相邻区域的重叠区域进行分析,以实现对重叠区域的综合故障诊断。仿真结果表明,该方法能有效识别非重叠区域和重叠区域的故障,容错能力强,诊断准确率高。

基于Dijkstra算法的电力光通信网络智能运维设备脱网故障区段定位方法

智能运维设备发生脱网故障后,往往难以定位,为实现脱网故障区段与故障电流分布间映射关系的确定、寻找脱网故障区段与故障电流分布间映射关系的最短路径,设计一种基于Dijkstra算法的电力光通信网络智能运维设备脱网故障区段定位方法。选择数据驱动方法中的LightGBM算法,通过大量样本确定离网故障区段与故障电流分布之间的映射关系。基于Dijkstra算法寻找映射关系的最短路径,实施脱网故障区段搜索。设计电力光通信网络智能运维设备脱网故障区段定位平台,实现脱网故障点即智能运维设备信号丢失的定位与告警。对比实验显示,在SDH环网电路区域中的光缆中断导致的脱网故障区段定位、多路由段的光缆故障下的脱网故障区段定位,以及在环状电力区域中的多路由段的光缆故障下的脱网故障区段定位场景中,该方法的定位结果与实际位置最贴近,定位最准确。

基于B/S架构的电力客户业扩报装流程智能化管理系统

为解决传统电力业扩报装系统程序复杂、效率低等问题,设计一种基于浏览器/服务器架构的电力客户业扩报装流程智能化管理系统。以业扩报装施工阶段为出发点,计算配电网各段线路建设费用,使用蚂蚁算法对业扩报装线路选取最优路径;利用.NET操作平台将系统划分成表示层、业务逻辑层和数据层,运用java服务器页面技术实现流程智能化管理。仿真结果表明,本文系统有效发挥B/S模式的优越性,缩短了客户等候时间,大幅提升工作效率。

基于SCADA数据的电网故障和异常智能分析的辅助决策系统

为了有效制定辅助决策、明确电网事故原因、帮助调控人员解决故障和异常事故,设计基于SCADA数据的电网故障和异常智能分析的辅助决策系统。利用数据库处理SCADA数据,建立SCADA数据库,业务逻辑层通过ArcGIS Engine组件技术实现辅助决策系统内各模块间的数据交互,应用层中故障和异常诊断模块以电网内断路器与继电保护等状态信息为适配指标,并将其当成目标函数,建立电网故障和异常诊断模型,通过带自适应变异的粒子群优化算法求解该模型,获取电网故障和异常诊断结果,通过辅助决策模块智能分析故障和异常诊断结果,确定事故原因,制定事故处理的辅助决策。实验证明:该系统可精准采集SCADA数据;不同通信轮次时,所提出的系统数据传输的网络节点平均剩余能量较高,数据传输能耗较低;在不同故障和异常情况下,系统均可精准诊断故障和异常情况,有效智能分析事故原因,制定合理的辅助决策。

Key technologies and applications of collaboration between digital power grid and Internet of Things

The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business scope.The key technologies of power IoT and the data flow process are summarised first.The IoT technology and application scenario requirements of power generation,transmission,loading,and storage of new power systems are studied.Thus,the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems.The key problems and so-lutions faced by the power IoT under the digital transformation are described,and the cross-integration of key technologies and promotion of application scenario innovation are prospected.Finally,the key issues of future technological development were dis-cussed,providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.

An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression

The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.

我国电网智能安全技术的关键专利分析

新安全格局下,为促进电网安全管理由传统模式向安全生产全过程数字化、信息化、智能化模式的转变,基于2008—2023年我国电网智能安全专利数据,采用网络中心度分析和主路径分析等方法,描绘专利地图;提取关键专利技术子群,并系统分析电网智能安全的技术发展情况、技术发展重点;基于专利地图和专家意见,运用熵权-优劣解距离法(TOPSIS),就我国既有智能安全技术社群对电网安全智能化的支撑度进行评价。结果表明:我国电网智能安全技术已形成11类关键技术社群,其中,电网智能安全过程技术是最丰富的知识群组。11类电网智能安全技术社群中,对电网智能安全支撑度较高的技术是智能控制技术、智能调度技术和网络通信安全技术。智能安全技术对电网传统安全目标中的安全管控能力和安全稳定能力有较好的支撑度,并对电网的现代安全目标中的安全治理能力和主动安全能力有较好的支撑度,但对安全韧性能力的支撑度不高。

大电网调控人机混合增强智能:概念内涵、应用框架、关键技术以及系统验证

伴随深度学习等人工智能技术的飞速发展,通过数字化赋能电力来实现电网的信息化、数字化和智能化成为未来新型电力系统发展的必然趋势。人工智能技术在电网调控应用研究广泛,但是实际应用过程中,由于电网不确定性、开放性和脆弱性造成的模型可信性和可解释性等问题,使得单纯的人工智能技术应用于电网调控领域难以满足安全可靠应用的需求。为了解决上述问题,需要引入人类的监督与互动,融合调控人员的智能与机器智能,实现电网调控人机智能的混合增强。该文首先介绍混合增强智能技术的概念和内涵以及在不同领域的研究现状。随后,针对电网调控业务特点,从人工智能和调控应用两个维度,提出电网调控人机混合增强智能的应用框架以及电网调控人机交互协同的目标和基本原则。在此基础上,分析和讨论电网调控人机混合增强智能所涉及的关键技术,并研发大电网调控人机混合紧急控制决策支持验证系统,用于省级规模电网的热稳调整、频率控制等多种电网调控场景应用验证,为人机混合增强智能技术在电网调控领域的应用提供参考和借鉴。

基于网络交互与智能规则的调度操作指挥模块

针对传统的手打拟票、手工记录以及电话下令的业务开展方式已成为操作效率提升的瓶颈,提出在调度指挥控制系统(dispatch command control system,DCCS)上设计调度操作指挥模块。将电话下令转变为网络交互;构建多种基于智能规则的自动成票手段,取代传统的手打出票方式;操作完成后系统可自动记录设备状态信息并通知相关单位,实现调度操作全流程的网络化、信息化与智能化。模块上线运行结果表明,数据显示对调度操作效率的提升作用显著。

电网混沌系统的T-S模糊非线性PI控制

为抑制电网的混沌现象,提出了一种电网混沌系统的T-S模糊模型的非线性状态比例-积分(PI)控制方法。通过建立电网混沌系统的T-S模糊模型,将具有强非线性的电力系统分解为局部线性化的子系统,针对每一个子系统设计非线性状态比例-积分控制器,理论论证了控制系统的稳定性。最后由仿真实验证实了该方法能够有效抑制电网的混沌现象。

基于现场实验数据的PSS参数智能优化方法

针对目前电力系统稳定器(PSS)参数优化实验工作中人工参与度高的现状,提出了一种基于现场试验数据的PSS参数智能优化方法。首先通过现场小扰动试验数据,将发电机以外的系统等值为无穷大母线电压Vs和系统电抗Xs,然后利用静态等值系统的线性化Heffron-Philips模型计算励磁控制系统的无补偿相位特性,得到PSS参数优化的目标曲线,并根据这一目标曲线,利用改进粒子群算法(SAPSO)优化PSS的时间参数值。通过在PSASP中对华北华中电网算例进行仿真,仿真结果表明采用本算法优化后的PSS能有效、合理地抑制低频振荡,并且能够适应电网不同的运行方式,具有一定的鲁棒性。

电网调度智能操作票系统功能设计分析

电网是保障民生的基础设施。对社会发展而言,推进电网智能化改革是长远发展的必然选择。为解决电力企业供需关系问题,保证系统使用效果,提升电网开票质量,聚焦电网调度智能操作票系统功能设计开展分析,基于实际需求,完善已有系统开发理念,以融合智能操作形式,强化调度操作票体系成效,为提升电网调度操作票工作质量提供参考。

信息驱动的大电网运行态势知识图谱框架及构建模式研究

基于大电网海量、高维的各类实时测量及仿真模拟信息,依托大数据、人工智能、知识图谱等先进信息技术,深度挖掘和表征电网运行态势知识并直观形象展示,是建设大电网在线智能主动安全防御体系的核心。首先,在智能全景系统思想体系下,采用“物理系统+信息系统+知识图谱”融合机制,综合考虑电网不同运行场景的稳定状态评估指标和优化控制策略信息,给出电网运行态势知识图谱的基本概念及内涵,并详细介绍其核心要素及构建目标。然后,依托智能电网调度时空大数据支撑平台,设计电网运行态势知识图谱软件功能架构,包含多源信息融合、时空大数据平台、态势图谱引擎、知识关联推理、图谱业务应用和友好人机交互等6个层次模块。在此基础上,给出自顶向下的电网运行态势知识图谱构建模式,并系统性介绍逻辑架构、技术架构、知识建模、知识抽取、知识融合、知识加工、知识更新、图结构存储、可视化引擎等构建技术。最后,研发大电网运行态势知识图谱智能构建及可视化系统,并在某省级电网开展测试应用,为实现信息驱动的大电网智能全景主动安全防御提供技术支撑。