【作者】 王静；

【导师】 梁振锋；

【作者基本信息】 西安理工大学 ， 电气工程， 2022， 硕士

【摘要】 随着分布式电源、电动汽车及储能大量接入配电网,配电系统的监测、运行与控制变得愈来愈复杂,高精度的状态估计成为配电网安全评估、网络重构、故障处理、无功优化的重要手段。微型同步相量测量装置(Micro Phasor Measurement Unit,μPMU)作为一种高精度、实时监测的量测装置,因其成本较高,不宜在配电网中大量安装。因此,开展了配电网『PMU配置优化的研究,以提高配电网状态估计的精度。主要研究内容包括:(1)针对μPMU不宜大量配置的问题,提出了一种μPMU配置优化方法。基于μPMU量测数据,将不同类型量测数据融合,并建立了混合量测状态估计的数学模型,通过加权最小二乘法实现配电网状态估计。最后，建立了以状态估计精度为目标函数的μPMU配置优化模型,并通过改进的粒子群算法实现了模型求解。(2)考虑配电网复杂多变的运行方式,提出了一种适用于拓扑变化的μPMU配置优化方法。首先,对配电网每一种运行方式进行μPMU的配置优化;其次,依据各种运行方式优化结果,根据最短距离进行K-means聚类与节点评估,并采用AHP-CRITIC主客观组合赋权的方法确定四种评估指标的权重,选择综合评估指标最高的节点作为配置节点;最后,引入了各种运行方式出现的概率并确定μPMU优化配置方案。(3)考虑到分布式电源大量接入配电网,提出了一种计及分布式电源不确定性的配电网μPMU配置优化方法。采用混合高斯模型处理风电、光伏出力的不确定性,并运用贝叶斯信息准则确定混合高斯模型的分量数,得到风电、光伏的等效高斯函数,以等效高斯参数作为状态估计的伪量测值引入状态估计模型。最后,建立优化模型并采用改进的粒子群算法求解。以IEEE-33与IEEE-118配电网为算例,对本文μPMU优化配置方法进行了仿真验证,结果表明所提方法能有效提高配电网状态估计精度。更多还原

【Abstract】 With the access of distributed generation,electric vehicles and energy storage to distribution network,the monitoring,operation and control of distribution system become more and more complex.High precision state estimation has become an important means of distribution network security assessment,network reconfiguration,fault handling and reactive power optimization.Micro Phasor Measurement Unit(μPMU)is a kind of measuring device with high precision and real-time monitoring.Because of its high cost,it is not suitable for large-scale installation in distribution network.Therefore,the research of distribution network μPMU configuration optimization is carried out to improve the accuracy of distribution network state estimation.The main research contents include:(1)Aiming at the problem that μPMU is not suitable for large-scale configuration,a configuration optimization method for μPMU is proposed.Based on μPMU measurement data,different types of measurement data are fused,and the mathematical model of mixed measurement state estimation is established.The state estimation of distribution network is realized by weighted least square method.Finally,a μPMU configuration optimization model with state estimation accuracy as the objective function is established,and the model is solved by an improved particle swarm optimization algorithm.(2)Considering the complex and changeable operation mode of distribution network,aμPMU configuration optimization method suitable for topology change is proposed.Firstly,the configuration of μPMU is optimized for each operation mode of the distribution network;secondly,according to the optimization results of various operation modes,K-means clustering and node evaluation are carried out according to the shortest distance,and the weights of the four evaluation indexes are determined by AHP-CRITIC subjective and objective combination weighting method,and the node with the highest comprehensive evaluation index is selected as the configuration node.Finally,the probability of various operation modes is introduced and the optimal configuration scheme of μPMU is determined.(3)Considering the large number of distributed generation access to the distribution network,a distribution network μPMU configuration optimization method considering the uncertainty of distributed generation is proposed.The Gaussian mixture model is used to deal with the uncertainty of wind power and photovoltaic output.The Bayesian information criterion is used to determine the component number of the Gaussian mixture model,and the equivalent Gaussian function of wind power and photovoltaic is obtained.The equivalent Gaussian parameter is used as the pseudo measurement value of state estimation to introduce the state estimation model.Finally,the optimization model is established and solved by improved particle swarm optimization algorithm.Taking IEEE-33 and IEEE-118 distribution networks as examples,the optimal allocation method of μPMU in this paper is simulated and verified,and the results show that the proposed method can effectively improve the state estimation accuracy of distribution networks.更多还原