智能电表在电网改造中的应用及配电网潮流数据的可靠性

Sindi Ndaba, I. Davidson
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引用次数: 0

摘要

高电力损耗是南非关注的问题,也是世界上许多电力公司遇到的问题。技术损耗和非技术损耗是造成电力损耗的主要因素。技术损耗是指由网络阻抗、电流流动和辅助电源引起的电力系统损耗。电力盗窃、测量系统错误和不支付账单是造成非技术损失的因素,导致Eskom的收入损失每年都在增加。采用智能电表的智能电网有望成为解决非技术损失问题的有效集成技术。为了分析智能预付费分式电表在Eskom配电网络中实施的有效性,采用自填问卷对334名受访者进行了定量随机抽样评估。采用SPSS 26.0版社会科学统计软件包和Microsoft Excel 2016版对收集的数据进行分析,实现智能预付费分式计量效能的多目标决策,同时兼顾公用事业和客户满意度。不同的推理统计技术使用包括回归,相关性,多因素分析(MFA)和卡方拟合优度检验来解释p值,识别变化点,检测趋势,并为决策制定相关的最佳拟合时间序列。调查结果表明,智能电表作为新兴技术的使用为公用事业提供了多种好处,并为公用事业面临的许多电力相关问题提供了可能的长期解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Implementation of Smart Meters for Electric Grid Improvements and Reliable Power Flow Data on Electrical Power Distribution Network
High electrical power loss is of concern in South Africa and it is an issue that many power utilities around the world encounter. Technical and non-technical losses are the contributing factors of electrical power losses. Technical losses are regarded as the electrical system losses which are caused by network impedance, current flows, and auxiliary supplies. Electricity thefts, measurement system errors and non-payments of bills are the contributing factors to non-technical losses which resulted in revenue loss increasing every year in Eskom. The smart grid with smart meters implemented promises to be an effectively integrated technology to address the issue of non-technical losses. To analyze the effectiveness of smart prepaid split meters implementation on Eskom distribution networks, a quantitative random sample assessment of 334 respondents was evaluated using a self-administered questionnaire. The collected data was analyzed with Statistical Package for the Social Sciences software (SPSS) version 26.0 and Microsoft Excel 2016 version to achieve multi-objective decision-making for smart prepaid split metering effectiveness for both utility and customer satisfaction. The different inferential statistics techniques used include regression, correlations, multifactor analysis (MFA), and chi-square goodness of fit test to interpret the p-values, identify the change-point, detect the trend and to correlated best fit time series for decision making. The findings showed that smart metering usage as emerging technology has offer multiple benefits to utilities and provide a possible long-term solution to many of the power-related problems utilities face.
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