Control of Hydraulic Power System by Mixed Neural Network PID in Unmanned Walking Platform

To speedily regulate and precisely control a hydraulic power system in a unmanned walking platform(UWP),based on the brief analysis of digital PID and its shortcomings,dual control parameters in a hydraulic power system are given for the precision requirement,and a control strategy for dual relative control parameters in the dual loop PID is put forward,a load and throttle rotation-speed response model for variable pump and gasoline engine is provided according to a physical process,a simplified neural network structure PID is introduced,and formed mixed neural network PID(MNN PID)to control rotation speed of engine and pressure of variable pump,calculation using the back propagation(BP)algorithm and a self-adapted learning step is made,including a mathematic principle and a calculation flow scheme,the BP algorithm of neural network PID is trained and the control effect of system is simulated in Matlab environment,real control effects of engine rotation speed and variable pump pressure are verified in the experimental bench.Results show that algorithm effect of MNN PID is stable and MNN PID can meet the adjusting requirement of control parameters.

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.

Challenges and Improvements of Wind Power Dispatch in Northwest China

Besides common characteristics of wind power,there are some special characteristics in China power system,including large-scale,long distance transmission and lack of flexible regulating power sources.These special characteristics make power dispatch more challenging in China.Many studies have been carried out and some improvements are presented including wind power monitoring and control as well as evaluation of wind power integration capabilities.As a demonstration project,the technologies are integrated into the energy management system and are implemented in the Northwest China power system.They provide effective measures for wind power dispatch in the grid.

Application of the Network Flow Method for Eigenvalue Control in Power System Dispatch

A kind of dispatch method for power system eigenvalue control is proposed-in this paper. With the help of this method, not only the low-frequency oscillation of a power system can be prevented and controlled, but also the probabilistic power oscillatoin on the interconnection lines of an interconnected power system can be reduced. The proposed method has the advantages of high calculation speed and good convergency. Therefore, the method has much prospect of on-line application.

MicroGrid Designer:user-friendly design,operation and control assist tools for resilient microgrid and autonomous community

During this decade,many countries have experienced natural and accidental disasters,such as typhoons,floods,earthquakes,and nuclear plant accidents,causing catastrophic damage to infrastructures.Since the end of 2019,all countries of the world are struggling with the COVID-19 and pursuing countermeasures,including inoculation of vaccine,and changes in our lifestyle and social structures.All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous,so that vital lifelines are secured during calamities.A paradigm shift has been taking place toward reorganizing the energy social service management in many countries,including Japan,by effective use of sustainable energy and new supply schemes.However,such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service.Therefore,new social infrastructures and novel management systems to supply energy and social service will be required.In this paper,user-friendly design,operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance.

Hierarchical Cluster Coordination Control Strategy for Large-scale Wind Power Based on Model Predictive Control and Improved Multi-time-scale Active Power Dispatching

The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.

Research on Wind Farm Participation in Power Grid AGC Control

Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid planning and construction, and will make a heavy impact on the safe and reliable operation of power systems. To deal with the diff iculties of large scale wind power dispatch, this paper presents a new automatic generation control (AGC) scheme that involves the participation of wind farms. The scheme is based on ultra-short-term wind power forecast. The author establishes a generation output distribution optimization mode for the power system with wind farms and verif ies the feasibility of the scheme by an example.

漂浮式海上风力机自适应超螺旋滑模桨距控制

针对额定风速以上漂浮式海上风力机系统的输出功率稳定、浮式平台运动和疲劳载荷抑制任务目标,考虑系统非线性和模型参数摄动及风浪扰动,提出一种基于自适应超螺旋二阶滑模和干扰观测补偿的桨距角鲁棒控制方案。首先,实现漂浮式海上风力机的仿射非线性不确定系统建模;其次,构建考虑风力机额定转速和平台纵摇的滑模函数,设计控制增益自适应调节的超螺旋二阶滑模控制律;再次,采用干扰观测器补偿模型参数摄动和风浪扰动不确定项;最后,基于FAST与Matlab/Simulink在不同风浪环境下进行仿真,验证所提方案有效性和优越性。时域和频域仿真结果表明,与传统比例积分控制方案相比,所提方案对稳定风力机系统输出功率、抑制浮式平台运动及减少塔基载荷具有更好的控制效果。

采用SGCMG的两轮单辙平台自平衡控制技术

针对以自行车、摩托车为代表的两轮单辙平台的自然不稳定问题,采用单框架控制力矩陀螺(SGCMG)为平衡控制机构,研究两轮单辙平台不同工况下自平衡控制算法。分析两轮单辙平台横向力矩,推导横滚转动动力学方程,并根据不同状态误差设计了增益调度PID控制器;建立轮-地接触点模型,修正曲线运动时目标横滚角,减小反馈静差,获得更好的动态响应;以前叉转向趋势预估未来时刻转向角度与目标平衡横滚角度,提前调整姿态以重力矩抵消离心力矩,模拟人骑行时的预判压弯行为。相关算法经仿真验证与实车测试,可在静止与曲线运动等工况下保持动态平衡,效果良好,表明了算法的有效性。

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.

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6788中国电机工程学报第44卷用研究广泛,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。

电网数字孪生分析模型建模及应用

为支撑新一代调度技术支持系统全局分析计算业务融合,提出一种基于数据孪生的电网分析模型构建方法。基于实际电网设备的物理和逻辑关系,构建出标准化、对象化的完整电网分析模型,并接入实时量测,使其成为真实空间物理电网的数字孪生。在传统通用数据库与专业计算应用间构建出更贴近电网分析计算业务的数据中台,为在线分析计算应用提供统一数据服务。通过在新一代调度支持系统华中试点工程的建设成效,展示了基于数字孪生模型的分析计算应用的性能提高效果。

海上漂浮式风电机组塔筒优化方法研究

为适应复杂多变的海上环境,高效利用风资源、减少用钢成本,开展漂浮式风电机组塔筒的结构优化设计。以船载风电机组为研究对象,引入漂浮式基础的动态响应,作为影响塔筒弯曲变形的重要约束条件。在保证风电机组稳定功率输出的前提下,以质量最轻为优化目标,以塔筒构造要求和规范为基本约束条件,建立漂浮式基础风电机组塔筒的数值优化模型,并利用遗传算法完成了模型优化。结果显示,在保证风电机组稳定性和正常功率输出要求下,塔筒的质量降低了35%,刚度提高了25%。

欧洲大范围跨国电力互联在极端天气下电力安全保供中的作用分析

随着全球气候变化不断加剧,极端天气对电力系统的影响日益增强,电网在电力安全保供中发挥的作用愈发受到关注。欧洲电网作为世界上规模最大的跨国互联电网,其在极端天气下的电力安全保供中发挥的作用值得研究借鉴。首先,对欧洲电网跨国互联进程、跨国电力配置、跨国互联目的进行梳理,并从电力交易与调度的关系、跨国电网耦合机制、跨国输电通道容量分配机制等角度分析欧洲跨国电网的交易与调度特征。其次,以2022年夏季极端高温干旱天气下的欧洲电力供应紧张事件为例,从电量余缺互济、平衡资源共享、统一电力市场支撑、可再生能源消纳等方面分析欧洲跨国互联电网在电力安全保供中发挥的“四个平台”作用。然后,对欧洲跨国互联电网面临的问题进行剖析。最后,提出对中国电网发展的思考及建议。

居民负荷参与电力系统调控关键技术综述与展望

“双碳”目标下,新能源发电占比的不断升高和用电需求的持续增长,严重影响了电力系统供需平衡。居民负荷以体量大、响应快速和控制灵活的特点,逐渐成为发挥电力系统灵活调控能力的重要资源之一,是近几年研究关注的热点。文章结合国内外最新研究成果,从居民负荷的调控机制、可调节特性、调控策略及应用场景4个方面对居民负荷参与电力系统调控的关键技术进行综述,重点比较了用于生成最优调控策略的多种算法以及居民负荷参与调控的多样化场景。最后,针对现阶段实施调控过程中面临的实际挑战,提出了信息-物理-社会系统视角下的居民负荷调控市场机制建设、调节能力精准快速感知,以及弱通信环境下调控策略优化与执行等重点问题,并对未来研究方向进行了展望。

“数字控制”下劳动秩序的价格逻辑及其竞争法规制——以送餐软件为例

在数字化时代,“数字控制”下的劳动秩序应运而生,经营者依托平台、算法和数据对从业人员的劳动时间、劳动方式、劳动绩效进行精细化管理,进而塑造了一种新型的劳动管理模式。“数字控制”下劳动秩序一方面展现了数字时代经营管理的创新成果,另一方面也暴露了劳动者权益保障的不足和算法权力的过度扩张等隐患,其通过对劳动价格和订单价格的微粒式和个体化调整引发了新的市场竞争担忧。从竞争法的视角出发,运用法律手段规制市场秩序,调整和优化“数字控制”下的劳动秩序显得尤为重要。应通过反垄断和反不正当竞争法规来规范平台运营,引导“数字控制”走向理性化发展;抵制价格垄断行为,确保市场价格的合理性和公正性;维持市场的有效竞争,以保持数字经济行业的活力和创新能力;推进法律制度的完善,构建起一套有效的价格反垄断机制,为数字经济的健康发展提供坚实保障。

人机混合增强决策智能在新型电力系统调控中的应用与展望

新型电力系统的调控运行面临规模及随机性升高、海量多元资源协同困难等难题,现有传统优化和人工经验的调控手段难以应对。以强化学习为代表的决策智能技术在表征能力和决策速度方面具备显著优势,但在应用中存在诸多关键瓶颈。而人机混合增强智能(hybrid human-machine intelligence,HHMI)技术具有突破这些瓶颈、支撑高效智能调控的巨大潜力。目前HHMI理论研究尚处于初期,应用不足。为挖掘HHMI的潜力并探索其应用方案,基于新型电力系统调控与应急调整的实际需求,分析决策智能的优势与局限,在此基础上,论述HHMI的总体框架、关键技术、应用方案以及在实际应用中面临的关键问题,并对其未来的实践进行展望,可为HHMI在新型电力系统调控中的研究与应用提供思路。

论守门人平台市场力量的形成逻辑与认定规则

遵循何种逻辑、设置何种考察因素与标准认定守门人平台的市场力量是数字经济规制的一个基础性问题。现有的考察因素是结合了传统平台经济特性的修正,存在考察因素过于复杂、包含关系混乱导致的适用困境。造成这一困境的根源在于既有的市场力量认定逻辑与考察因素的设置是围绕“产能控制”的底层逻辑,这一逻辑在守门人平台语境下存在适用偏差。守门人平台市场力量的生成与维持逻辑是围绕“需求控制”展开,积累用户规模、利用网络效应、采取策略性商业手段主要以实现需求方规模经济最大化以及阻碍需求转向为基本商业逻辑。因此,在守门人平台市场力量认定的考察因素设置上,应当以是否实现了“需求控制”为基准,以需求方规模经济的实现以及阻碍需求转向为基本分析维度,并需重点考察用户数量大小、网络效应强弱、用户需求依赖性、数字生态系统构建和数据的获取、收集与利用情况。

基于调控云平台的变电站二次系统故障主动预警技术

为避免因变压器实际功率过大而造成电力系统瘫痪问题,针对基于调控云平台的变电站二次系统故障主动预警技术展开研究。按照调控云平台部署情况,搭建IaaS诊断网络,根据云变电系数的实际取值,推导出完整的故障数据集合,实现对变电故障特征的提取。利用风险向量指标,完善故障数据关联规则,再借助已获取的变电故障特征参量,求解预警建模条件,完成基于调控云平台的变电站二次系统故障主动预警方法的设计。实验结果表明,当变压器实际功率达到其额定功率的95%时,应用调控云平台的变电主机就会发出预警,可以有效解决因功率过载而导致的电力系统瘫痪问题。

基于模型预测控制的源荷储低碳经济调度

为提高电力系统风电消纳的经济性以及实现低碳排放的目标,基于模型预测控制方法,构建了多时间尺度下含碳捕集电厂的源荷储低碳经济调度模型。源侧分析碳捕集电厂的运行机理及能量时移特性;荷侧根据需求响应时间尺度的差异性,调用不同响应速度的价格型、动态激励型需求响应资源分别参与日前-日内调度;储能快速调整充放电功率提高运行经济性,日前调度以运行成本和碳排放量最小为目标,通过协调调度源荷储资源优化系统各单元出力计划。日内调度在滚动优化平滑功率波动的基础上,以跟踪和修正日前调度计划为目标,实时应对风光和负荷的功率波动,通过算例验证所提方法可降低运行成本和碳排放量,实现对经济与低碳协同优化。