NTL detection of electricity theft and abnormalities for large power consumers In TNB Malaysia

Abstract

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents an approach towards detection of Non-technical Losses (NTLs) of Large Power Consumers (LPC) in Tenaga Nasional Berhad (TNB) Malaysia. The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in Malaysia to reduce its NTLs in the LPC distribution sector. Remote meters installed at premises of LPC customers transmit power consumption data including remote meter events wirelessly to TNB Metering Services Sdn. Bhd. The remote meter reading (RMR) consumption data for TNB LPC customers is recorded based on half-hourly intervals.The technique proposed in this paper correlates the half-hourly RMR consumption data with abnormal meter events. The correlated data provides information regarding consumption characteristics i.e. load profiles of LPC customers, which helps to expose abnormal consumption behavior that is known to be highly correlated with NTL activities and electricity theft. Pilot testing results obtained from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection of LPC customers in peninsular Malaysia indicate the proposed NTL detection technique is effective with a 55% detection hitrate. With the implementation of this intelligent system, NTL activities of LPC customers in TNB Malaysia will reduce significantly.

NFD: Non-technical loss fraud detection in Smart Grid

“…User behavior analysis is typically employed to detect NTL frauds [38]- [44]. The paper [39] mainly introduces the methods of feature extraction.…”

“…Some other factors are also taken into account, such as weather, location, etc. In the paper [38], a detection tool, named RMADS, is proposed to detect NTL frauds. Different from most existing schemes focusing on residential customers, the targets of RMADS are those large power consumers, such as factories.…”

“…The authors in [14] first propose using SVM to detect NTL frauds. The main steps include data preprocessing, feature extraction, and classification, and this is very similar to user behavior analysis [38]. SVM is also based on user behavior analysis to detect NTL frauds, but the feature extraction and classification processes are automatic and more accurate.…”

Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

“…As mentioned by [6,7,8] the amount of energy loss in the distribution grids varies between 7 -50 % of the total delivered energy (depending of the country and the characteristics of the distribution network), which undoubtedly justifies the strong efforts that utilities are investing towards detecting and inspecting atypical consumption traces to ultimately avoid significant economical losses. As stated in [9], only in US between 1 and 10 billion worth of electricity was stolen in the late 90s, showing an increment between 5-10% in the last two decades with a remark-able 40% and beyond in Southeast Asia [10]. In addition, the identification of atypicalities provides further profitable advantages beyond fraud assessment: by properly characterizing the statistics of the consumption traces registered over the power grid, the power distribution can be optimized by matching generation to consumption, thereby avoiding network under-dimensioning and electrical surge.…”

Distribution line monitoring system for the detection of power theft using power line communication

“…Theft of electricity in the world is one among the major loss of revenue irrespective of being a developed or a developing nation. In United States of America, the NTL are estimated to about 0.5% to 3.5% of the gross annual revenue [3]. In developing countries like India, the loss of electricity due to theft is projected to about 20% to 30% of the overall loss in Power utility.…”