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Non-Technical Loss Identification by Using Data Analytics and Customer Smart Meters

机译:使用数据分析和客户智能电表的非技术损失识别

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摘要

The smart meters installed at the customer premises are one of the main apparatuses promoting the modernization of the distribution systems. These devices collect a huge amount of data, demanding the development of analytic techniques to transform these data into useful information. In addition, by proposing new applications to data supplied by smart meters, more value is added to this equipment, allowing higher return on the associated investments. Utilities can have a business case by increasing their operational efficiency if smart meters are used, for instance, to identify non-technical losses, which represent an important cause of revenue losses. This paper presents a new data analytic technique for detection and location of non-technical losses caused by illegal connections of loads to distribution systems in the presence of smart meters. The data analytic technique relies on bad data analysis, similar to the ones used in state estimation methods, developed specifically for this application. A real 34-bus low voltage system is used to illustrate the main concepts of the proposed algorithm. Systematic tests are also conducted on a real 1682-bus distribution system to evaluate the method performance considering electricity theft caused by medium and low voltage customers.
机译:安装在客户场所的智能仪表是促进分配系统现代化的主要设备之一。这些设备收集大量数据,要求开发分析技术以将这些数据转换为有用的信息。此外,通过向智能电表提供的数据提出新的应用程序,此设备将更多的值添加了更多的值,允许对相关投资进行更高的回报。如果使用智能电表,例如识别非技术损失,则公用设施可以提高运营效率,以提高其运营效率,以识别非技术损失,这代表了收入损失的重要原因。本文介绍了一种新的数据分析技术,用于检测和在智能电表的存在下负载到分配系统的非技术损失引起的非技术损失的位置。数据分析技术依赖于不良数据分析,类似于在专门为此应用开发的状态估计方法中使用的数据分析。真正的34总线低压系统用于说明所提出的算法的主要概念。还在真实的1682公交车分配系统上进行系统测试,以评估考虑由中低压客户引起的电力盗窃的方法性能。

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