Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.

基于K-Means聚类和Boosting算法的配电网线损计算方法

传统线损计算方法所需电气参数较多且计算过程烦琐,导致配电网线损计算结果精度较低,因此提出了一种基于K-Means聚类和Boosting算法的配电网线损计算方法。先采用K-Means聚类算法挖掘配电网的线路负荷有功电量、线路负荷无功电量、线路长度及线路负载率等电气特征指标,再将电气特征指标作为Boosting算法线损预测模型的输入数据,经过模型训练完成配电网线损的预测计算。实验结果表明,该设计方法的线损计算值与真实值之间的误差仅为4.27%,具有较高的配电网线损计算精度。

Energy Loss Analysis of Distributed Rooftop Photovoltaics in Industrial Parks

The analysis of the loss of distributed photovoltaic power generation systems involves the interests of energy users,energy-saving service companies,and power grid companies,so it has always been the focus of the industry and society in some manner or another.However,the related analysis for an actual case that considers different cooperative corporations’benefits is lacking in the presently available literature.This paper takes the distributed rooftop photovoltaic power generation project in an industrial park as the object,studies the analysis and calculation methods of line loss and transformer loss,analyzes the change of transformer loss under different temperatures and different load rates,and compares the data and trend of electricity consumption and power generation in industrial parks before and after the photovoltaic operation.This paper explores and practices the analysis method of the operating loss of distributed photovoltaic power generation and provides an essential reference for the benefit analysis and investment cost estimation of distributed photovoltaic power generation systems in industrial parks.The analyzed results reveal that the change loss is stable after the photovoltaic is connected,and there is no additional transformer loss.And before and after the photovoltaic system installation,there was no significant change in the total monthly data difference between the total meter and the sub-meter.

Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.

Conflict between wild boars(Sus scrofa) and farmers:distribution, impacts,and suggestions for management of wild boars in the Three Gorges Reservoir Area

Economic losses caused by wild boars(Sus scrofa)continue to increase in the Three Gorges Reservoir Area.We selected 206 farmers in Wuqiaohe Basin as survey respondents using random stratified sampling.The distribution and impacts of wild boars were examined using a questionnaire survey and spatial analysis.The results showed that:(1)The density of farmer settlements is low and most peasants’households are located on the forest edge.Due to large-scale labor migration,a high proportion of farmland(25.85%)has been abandoned and an even higher proportion of farmland(40.48%)has been returned to forest.(2)The probability of wild boar damage to peasant households was 67.96%.Yields of corn and sweet potato decreased by 24.87%and 28.24%,respectively,with a total economic loss of approximately 2,590,100 RMB/yr in the Wuqiaohe basin.Sixty-five percent of the affected cultivatedlands were located within approximately 150 m of the forest edges.(3)The wild boar damage coefficient was significantly and negatively correlated with the distance between the peasants’household and the forest edge,the density of the settlements,and the area of cultivated land.The wild boar damage coefficient shows a significant positive correlation with the area of farmland returned to forest.(4)The damage activity caused by wild boars began to occur7-8 years ago,and the destruction has been more frequently and seriously observed in the last 4-5 years.(5)We make suggestions for wild boar control and management measures including developing a compensation policy.The compensation standard is related to the land area returning from farmland to forests and the actual cultivated land area.

Distribution Characteristics of High Temperature Damage and Its Influence on the Rice Yield in the Area along Huaihe River

[Objective] The research aimed to study the distribution characteristics of high temperature damage and its influence on the rice yield in the area along Huaihe River.[Method] The meteorological data of 10 stations in the area along Huaihe River during 1965-2009 and the yield data of Anhui single-season middle rice during 1967-2006 were selected.The occurrence characteristic of summer high temperature weather and the intensity of high temperature damage in the area along Huaihe River were analyzed.Based on the previous high temperature damage index of rice,Changfeng County where was the typical rice planting zone in the area along Huaihe River was as the representation,and the yield damage loss rate risk of high temperature damage in Changfeng was analyzed by combining with the historical yield data.[Result] The high temperature weather in the area along Huaihe River frequently happened.The high temperature damage presented 'N' shape trend from west to east.The occurrence frequency of high temperature weather in Huainan and Bengbu where were in the middle area along Huaihe River was more and was less in Huoqiu and Shouxian where were near the south mountain area of Anhui.The occurrence time mainly focused from the middle and last dekads of July to the first dekad of August after the plum rain.At this time,it was the booting,heading and flowering periods of single-season middle rice,and the influence on the rice yield was obvious.The damage loss rate of single-season middle rice yield in Changfeng County along Huaihe River continued to increase as the increasing of high temperature damage duration.But the occurrence probability decreased.The intensity grade of high temperature damage disaster loss rate which happened frequently concentrated mainly in levels I and II.The longer the high temperature damage duration in the reproductive growth stage of rice was,the bigger the damage loss rate was.But the corresponding occurrence probability was small,and vice versa.[Conclusion] The research provided the reference for assessing the high temperature disaster risk.

Semi-Markovian Model of Two-Line Queuing System with Losses

In the present paper, to build model of two-line queuing system with losses GI/G/2/0, the approach introduced by V.S. Korolyuk and A.F. Turbin, is used. It is based on application of the theory of semi-Markov processes with arbitrary phase space of states. This approach allows us to omit some restrictions. The stationary characteristics of the system have been defined, assuming that the incoming flow of requests and their service times have distributions of general form. The particular cases of the system were considered. The used approach can be useful for modeling systems of various purposes.

Method of Fault Area ＆ Section Location for Non-solidly Earthed Distribution System

中国中压配电网以架空线为主，多为小电流系统，单相接地故障占到电网故障总数的80％以上，但中国配网自动化系统基本上没有小电流接地故障定位功能，使配网自动化系统在提高可靠性的作用上大打折扣。给出一种小电流接地故障区段定位新方法，在线路上配置广域相量测量固定测点，获取小电流电网单相接地故障特征信息。基于测点相邻矩阵区段起始测点标识向量和故障路径标识向量概念，提出确定故障区间边界节点算法。物理模拟实验和挂网测试表明：该故障分区分段定位方法能够在线求解小电流接地故障段边界节点，缩小线路维护巡视范围。确定故障区间边界节点算法还可用于确定故障区相关负荷开关，为线路维护和馈线自动化提供依据。

Spatial distribution and sampling of larvae of Phthorimaea operculella(Zeller) and its harmfulness

Studies in tobacco fields were conducted in 1993. The results showed that the distribution pattern of the larva was aggregative,and the aggregation did not change with the densities of population of the larva. The characteristics of the vertical distribution of the larva on tobacco plants was more in the lower leaves than in the upper. The difference of population density among the tobacco fields with an elevation of 490 meters and 900 meters was not significant. The number of sampling was given under different precisions by using two-stage sampling technique. The average of leaf area loss caused by the larva in tobacco fields was 12.654 cm2.

Binary Gravitational Search based Algorithm for Optimum Siting and Sizing of DG and Shunt Capacitors in Radial Distribution Systems

This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.

基于TASSA-Mg LSTM的配电网线损预测方法

为更精确地预测配电网线损,提出了一种基于瞬态自适应麻雀搜索算法TASSA(transient adaptive sparrow search algorithm)优化Mogrifier LSTM(Mg LSTM)的配电网线损预测方法。首先通过斜率灰色相关性分析方法,得到12个电气特征参数与线损之间的关联度,并经过配电网数据的预测校验,确定最佳的电气特征参数体系。然后使用TASSA优化Mg LSTM神经网络中4个重要参数,确定最佳的Mg LSTM网络结构,进而构建基于TAS⁃SA-Mg LSTM的神经网络线损预测模型。最后,通过甘肃省某地区配电网进行实例分析,验证了所提方法具有较高的预测精度。

基于PDCA的配电网降损措施

在日常生活中,电能通过配电线路进行传输时不可避免地会产生能量损耗,这部分损耗称为线损。线损率往往是衡量供电企业经营管理质量的关键经济指标,同时该指标可以反映出供电企业的技术水平。因此,如何有效降低线损,成为供电企业面临的重要难题。将计划-执行-检查-处理(Plan-Do-Check-Act,PDCA)循环理论与线损问题融合起来,提出了一种基于PDCA的线损降损措施,并计划将其应用到C供电局配电网线损管理工作中,旨在为相关企业降损技术水平的提高提供思路。

地铁车站低压配电系统电动机设备采用AC 660V配电的研究

通过分析电动机定子绕组接线形式,输入电压与电磁功率、转矩关系,以及电动机负荷配电系统元件、电缆,从而得出地铁电动机设备均可采用AC 660V供电的结论。通过对比分析说明地铁电动机设备采用AC 660V供电时,具有大幅节约有色金属,降低初期投资及减小线缆损耗的优势。

配电网理论线损概率分布函数的计算与分析

线损是衡量配电系统节能减排和运行效率的重要指标。随着“双碳”目标的提出,大量随机性和间歇性的可再生能源接入配电网,给配电网的线损计算带来新的挑战。该文从概率角度出发,在基于高斯混合模型的节点注入电流基础上,结合广义非中心卡方分布推导了任意电流分布下配电网理论线损概率分布的解析表达式,并首次给出配电网理论线损的均值、方差和矩生成函数的准确表达式。考虑到理论线损概率分布难以直接计算,提出一种基于Raphaeli级数的线损概率密度函数和累积概率分布函数的数值计算方法。基于IEEE 33节点系统的算例分析表明,提出的线损计算公式能够准确、高效地计算线损均值和概率分布函数。

考虑光伏波动性的配电网线损率阈值评估

针对分布式光伏接入后,异常线损率的判定阈值不合理导致漏检率较高的问题,提出考虑光伏波动性的10 kV配电网线损率波动阈值评估方法。首先,分析分布式光伏电量的并网形式以及可能存在的电量异常情况;其次,采用长短时记忆网络(LSTM)拟合输入特征与线损率之间的非线性关系预测线损率的方法,取代基于量测数据的潮流计算线损率的方法;然后,基于天气类型SCF指数,采用Monte Carol模拟体现光伏发电量的波动性,求得考虑光伏发电随机性的配电网线损率概率密度函数,并以此划定线损率的异常判定阈值。最后以IEEE 37节点配电系统为例,实际评估光伏接入后在考虑气象因素条件下配电线路日线损率的概率密度分布,并与电网的传统判定阈值进行对比分析。

基于自编码器与时域卷积神经网络算法的配电网线损分析

复杂的配电网环境中存在线损计算精确性、实时性不足的问题,因此提出基于循环神经网络自编码器改进的TCN-BiGRU配电网线损预测方法。选用擅长处理时间序列的TCN神经网络模型作为主干特征提取网络,在TCN中融入BiGRU单元以有效解决梯度消失问题。在此基础上,结合循环神经网络自编码器对线损异常值进行无监督分类并标记,通过softmax损失函数预测线损率异常原因,并制定相应降损措施,同时利用改进后的TCN-BiGRU算法对线损进行预测及成因分析。实验结果表明,与传统的配电网线损预测方法相比,该线损预测方法的均方根误差相较于传统的EMD-LSTM与PSO-CNN算法分别降低了0.03699和0.00402,在线损成因分析方面的准确率相较于ResNet50与DBN-DNN算法分别提高了1.500%和5.841%,为分布式电源接入后配电网节能降损、实现电网双碳目标提供了科学的参考依据。

基于用电信息采集数据的低压台区异常线损诊断新方法

低压配电网台区的线损分析对发现和解决异常线损问题,减小用电损失以及用户的精细化管理具有重要意义。文章基于全事件用电信息采集系统采集的真实台区数据,提出了一种新的低压台区线损诊断方法。该方法利用电网诊断规则对所采集的原始数据进行质量分析,并通过对台区线损特征地提取和分类,建立了基于电压信息的二分K-means聚类诊断算法和基于电量信息的全局搜索诊断算法,实现了对台户异常电能表的快速定位及台区线损异常的治理。通过剔除异常电能表和实际检验表明该方法具有较高的准确性和一定的实用性。

基于二阶聚类和鲁棒性随机分割森林算法的低压台区线损异常辨识

为精准识别台区的线损异常,保证配电网经济、稳定运行,针对台区线损的异常情况,提出一种基于二阶聚类和鲁棒性随机分割森林(robust random cut forest,RRCF)算法的台区线损异常检测方法。首先,运用二阶聚类将台区不同的运行工况进行聚类,将相同工况的线损节点归并,然后将各类工况的节点线损数据导入RRCF算法中分析,通过删除和插入样本节点,并对插入节点后评判模型的复杂度进行计算,得到线损异常节点的评分值,进一步找出线损异常的节点。最终,通过有关实例验证所提方法的准确性与有效性。

基于多源数据融合的配电网线损研究

多源数据是一种综合多个信息源的复杂数据类型,其主要特点是不同的信息源从不同的角度刻画了样本以及样本之间的关系(具体到配电网领域,不同量测系统针对同一节点所得到的数据是不同的,甚至存在较大差异)。提出了一种适用于配电网线损计算的多源数据综合利用方法,为便于数据融合,对多源量测数据进行转换;为保证量测时间断面一致性,选择同步相量测量单元(phasor measurement unit,PMU)某一量测时刻作为基准,对数据采集与监视控制(supervisory control and data acquistion,SCADA)系统数据进行时间配准与数据填充操作,对智能电表数据采用“量测值+预测值”方式进行时标对齐;对时序数据进行滤波,获得较为准确的配电网数据;基于登普斯特-沙夫特(dempster-shafter,D-S)证据理论法实现多源数据融合。以某10 kV配电网为算例分析计算了配电网线损,结果表明,所提方法可较好地完成多源数据的综合利用,提高配电网线损计算的准确性。

IGA优化的神经网络计算配电网理论线损

针对BP神经网络学习速度慢、容易陷入局部极小的缺点,提出了一种基于免疫遗传算法(IGA)的人工神经网络(artificial neural network,ANN)计算配电网的理论线损。该算法在遗传算法(genetic algorithm,GA)的基础上引入生物免疫系统中的多样性保持机制和抗体浓度调节机制,有效地克服了GA算法的搜索效率低、个体多样性差及早熟现象,扩大了神经网络的权值搜索空间,提高了网络系统的学习效率和精度。实例计算结果表明,同混合遗传算法相比,该算法具有较快的收敛速度和较强的全局收敛性能,比现有其他计算配网线损的方法更为准确。