基于不确定非线性智能电网感知网络控制系统方法研究

针对传统的不确定时滞的鲁棒控制系统不能满足现代电力系统发展的需求,提出一种基于不确定非线性智能电网感知云网络化控制识别方法。使用传统的数据值和基于电力网络到网络系统模型作为电力感知网络到网络模型的电力云网络,控制数据优化使用电源感知网络,而不是利用控制节点的预测值,突破了鲁棒控制的不确定时滞系统的控制效果。运用感知模型中的正向云算法修正加权系数,仿真实验证明,该识别方法能够很好地优化传统的不确定时滞鲁棒控制电力系统,具有良好的适应性和鲁棒控制性,进一步提高了智能电网传感云网络控制的各项性能指标。

基于多源数据融合的配电网调度技术支持系统建设与应用

针对配电网存在基础薄、欠账多、投资少等问题,围绕配电网中“盲调”、配电网抢修“盲指”的难题,开拓配电网数据接入新思路,以调度技术支持系统为平台,突破专业界限,创新接入营销用电信息采集系统、电能量计量系统、移动专网智能柱上开关数据信息,在设备“零改造”下,通过多时间尺度、多系统、跨安全区的配电网数据接入,建设配电网调度技术支持系统,实现了配电网首端开关-中间分支开关-末端配电变压器量测数据监视,全面提升配电网感知能力,配电网运行管控水平大幅提升。

基于数据挖掘的电网状态全感知数据压缩存储系统设计

为了实现电网大规模数据的压缩与存储,设计一种基于数据挖掘的电网状态全感知数据压缩存储系统。设计的系统由四个模块构成,其中数据处理模块能够实现挖掘的电网状态全感知数据的上传、存储、分析处理等功能;数据挖掘模块使用多维时序滑动窗口关联规则算法,对电网状态全感知数据进行挖掘;数据压缩模块通过小波变换进行电网状态全感知数据信号的压缩和重建,可以不失真地恢复数据;数据存储模块使用三种数据库及其对应访问与封装技术对压缩的电网状态全感知数据存储。测试结果表明,系统具有良好的性能,其中信号采样压缩比最高可达9.68。

基于不确定感知结构电网网络控制系统研究

针对传统的确定时滞的最优控制系统不能满足信息电网技术的发展,而本论文提出一种基于不确定感知结构电网感知网络控制方法优化传统的确定时滞最优控制系统;同时本论文运用模型与方法对接进行仿真分析,该感知结构方法能够很好的优化传统的确定时滞最优控制电力系统。

电力物联网在电网的应用方案研究

[目的]电力物联网是电网公司数字化转型、提升电网智能化水平的重要手段。[方法]介绍了电力物联网概况与架构,重点说明了各层级功能要求与发展目标,针对综合电力公司、配电网公司、售电公司的差异,提出了各自发展电力物联网的技术方向。[结果]在输电网、配电网、营销与用电、企业管理领域提出了应用解决方案。[结论]该方案在各类电网公司得到了良好应用。

一种非线性自适应电网云模型网络控制系统方法探索

针对传统的线性时滞的鲁棒控制系统不能满足信息电网技术的发展,而本论文提出一种基于非线性自适应电网云模型感知网络控制方法优化传统的线性时滞鲁棒控制系统;同时本论文运用自适应方法能够很好的优化传统的线性时滞鲁棒控制电力系统,同时,能够提高电网感知器的运行指标。

基于“电力-气象一张图”的低碳调度系统的建设与应用

为实现“双碳”目标,构建以新能源为主体的新型电力系统,同时兼顾电力供应的安全性、可靠性、经济性、低碳性,在“电力-气象一张图”的基础上,建设基于“一图两应用”的低碳调度系统。创新融合“电网设备地理信息图”与“动态电力气象图”,形成“电力-气象一张图”,以图形和数据为基础,构建基于数据支撑的电网低碳调度全景展示分析图。建设新能源承载力评估和高效接纳应用,形成新能源接入“前瞻型”规划和“多元型”技术支撑,提高新能源规划、监测以及出力预测水平,优化电网运行方式,充分发挥新能源接纳潜力。基于包含时空环境数据的电网拓扑信息,建立一种考虑高比例新能源接入的电网全息感知与安全管控应用,实现新能源出力及电网负荷精细预测、电网检修方式精益管理、电网运行风险精准预警,提升电网应对恶劣气象条件的能力。

基于多时间尺度状态估计的配电网实时态势预测

为有效提升泛在电力物联网建设过程中的配电网运行安全感知能力,提出了一种基于多时间尺度状态估计的配电网实时态势预测方法,该方法可实现对配电网安全态势的快速、准确预测。首先,基于多元混合量测提出了多时间尺度递归动态状态估计方法,通过状态预测与伪量测递归变换改进了已有的量测线性等效变换方法,缩短了状态更新周期,在递归变换算法中添加了校正算法以消除伪量测波动误差,提高了状态估计算法的计算速度。然后基于状态估计得到历史估计状态,采用分区多元时间序列分析方法建立了实时状态预测模型,实现了配电网的实时安全性态势预测。最后在Matlab仿真平台中基于实际算例对所提方法进行了分析,结果验证了所提配电网实时态势预测方法的准确性和有效性。

基于多智能体深度强化学习的配电网无功优化策略

配电网中光伏、风机设备出力随机波动以及负荷波动带来的电压波动、网损增加等问题,给配电网在线无功优化带来了挑战。本文采用一种无模型的深度确定性策略梯度(MADDPG)算法多智能体强化学习框架,采用集中训练、分散执行的方式解决无功优化问题。MADDPG算法将每一个智能体当作一个行动者(Actor),在离线训练过程中每个Actor可以借助一个评论家(Critic)进行训练。所提策略用深度神经网络拟合可投切电容器、有载调压变压器分接头以及分布式电源逆变器的动作函数,在和配电网环境交互过程中完成深度神经网络的训练。利用该强化学习算法在线实时决策无功调节设备的调度方案,此方法不需要通过精确的潮流建模,也不依赖于日前的数据预测,适用于通信能力较弱的部分观测配电网。最后,通过算例来验证MADDPG算法的有效性。

配电网持续无功优化的深度强化学习方法

高渗透率分布式光伏的出力波动可能导致配电网电压波动大、网损提高和电容器投切需求频繁。但配电网节点监控覆盖率低、潮流建模难度大,需要在上述不利条件下实现对台区内持续电压无功优化。采用深度强化学习的方法提出了适用于低感知度配电网的连续无功优化方法。该方法将原问题转化为一个多步马尔科夫决策过程,以最小化网损和动作成本之和为优化目标,以离散无功调节设备的投切指令为控制变量,并采用基于行动者–评论家(actor-critic)的深度强化学习算法进行求解。针对配电网缺乏完整潮流模型和观测数据的特点,分别设计了用来拟合投切策略的Actor网络和用来拟合动作价值函数的Ctritic网络。所提方法用深度神经网络直接拟合系统状态到离散无功调节设备的投切动作的函数关系,在与实际配电网的交互过程中完成网络训练。相比传统方法,该无需潮流建模和分段决策,且不依赖于日前的负荷与分布式电源出力预测,可以实现在线的多时间断面下的连续无功优化,提高了系统运行经济性。

配电网调度中的自动化技术应用

阐述配电网态势感知与配电网运行趋势分析,自动化技术在配网调度业务中的应用,包括遥信辨识与状态估计、配电网智能操作、大面积停电辅助决策、故障监测与报警。

供电企业数字化保电体系分析

阐述数字化保电体系的创建方案,实现电网状态的全面感知、保电作业的全景在线、保电资源的全域调配、应急处置的全局管控、管理决策的全程支撑,从而实现数字化保电模型。

Reputation-Based Hierarchically Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.

A novel weighed cooperative bandwidth spectrum sensing for spectrum occupancy of cognitive radio network

In cognitive radio network(CRN), a secondary user(SU) may utilize the spectrum resource of the primary user(PU) and avoid causing harmful interference to the primary network(PN) via spectrum sensing. In the traditional time spectrum sensing, the SU cannot detect the PU's presence during its transmission, thus increasing interference to the PN. In this work, a novel weighed cooperative bandwidth spectrum sensing method is proposed, which allows multiple SUs to use part of the bandwidth to perform cooperative spectrum sensing throughout the whole frame in order to detect the PU's reappearance in time. The SU's spectrum efficiency is maximized by jointly optimizing sensing bandwidth proportion, number of cooperative SUs and detection probability, subject to the constraints on the SU's interference and the false alarm probability. Simulation results show significant decrease on the interference and improvement on the spectrum efficiency using the proposed weighed cooperative bandwidth spectrum sensing method.

Detection Performance Analysis of Spectrum Sensing in Cognitive Radio Networks with Mobile Secondary Users

The majority of existing papers about spectrum sensing have the assumption that secondary users(SUs) are stationary. However,mobility is an essential feature of mobile communications networks. In this paper,the detection performance of spectrum sensing by mobile SUs was analyzed. Three performance metrics,i.e.,detection probability,miss detection probability and false alarm probability,were thoroughly investigated. In our analysis,a critical variable was the real-time received primary user signal power by a mobile SU. Its probability distribution and mathematical expectation were analytically derived. Moreover,the three performance metrics in single-node spectrum sensing and multi-node collaborative spectrum sensing systems were also derived. Extensive simulations were performed. The results are consistent with the theoretical analysis. And it is concluded that SU mobility has a significant impact on the detection probability and the miss detection probability,but not on the false alarm probability.

Determination of Threshold for Energy Detection in Cognitive Radio Sensor Networks

The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.

构建油田智能电网保障油田生产

构建油田智能电网是为了保障油田生产,是将油田电网生产、管理、运营等能力进行有效集成并实现数字化、智慧化;提高输电资源管理、输电集中监控、输电运维管理、移动应用功能要求巡视管理。通过安装智能感知设备及网络,建立油田电网管理平台、油田电网调度运行平台、油田电网运维服务平台、油田电网运营管控平台,完善统一的数据管控。

Optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing in cognitive radio network

In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.

System Recovery-Aware Virus Propagation Model and Its Steady-State Analysis

Network structures and human behaviors are considered as two important factors in virus defense currently. However, due to ignorance of network security, normal users usually take simple activities, such as reinstalling computer system, or using the computer recovery system to clear virus. How system recovery influences virus spreading is not taken into consideration currently. In this paper, a new virus propagation model considering the system recovery is proposed first, and then in its steady-state analysis, the virus propagation steady time and steady states are deduced. Experiment results show that models considering system recovery can effectively restrain virus propagation. Furthermore, algorithm with system recovery in BA scale-free network is proposed. Simulation result turns out that target immunization strategy with system recovery works better than traditional ones in BA network.

Spectrum sensing sequence prediction in cognitive radio networks

Spectrum sensing is one of the key issues in cognitive radio networks. Most of previous work concenates on sensing the spectrum in a single spectrum band. In this paper, we propose a spectrum sensing sequence prediction scheme for cognitive radio networks with multiple spectrum bands to decrease the spectrum sensing time and increase the throughput of secondary users. The scheme is based on recent advances in computational learning theory, which has shown that prediction is synonymous with data compression. A Ziv-Lempel data compression algorithm is used to design our spectrum sensing sequence prediction scheme. The spectrum band usage history is used for the prediction in our proposed scheme. Simulation results show that the proposed scheme can reduce the average sensing time and improve the system throughput significantly.