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 a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in peninsular Malaysia to reduce its NTLs in the distribution sector due to abnormalities and fraud activities, i.e., electricity theft. The fraud detection model (FDM) developed in this research study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This approach provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields customer classes which are used to shortlist potential suspects for onsite inspection based on significant behavior that emerges due to fraud activities. Model testing is performed using historical kWh consumption data for three towns within peninsular Malaysia. Feedback from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection indicates that the proposed method is more effective compared to the current actions taken by them. With the implementation of this new fraud detection system TNBD's detection hitrate will increase from 3% to 60%.

Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines

This paper presents a new approach to nontechnical loss (NTL) analysis for utilities using the modern computational technique extreme learning machine (ELM). Nontechnical losses represent a significant proportion of electricity losses in both developing and developed countries. The ELM-based approach presented here uses customer load-profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. This approach provides a method of data mining for this purpose, and it involves extracting patterns of customer behavior from historical kWh consumption data. The results yield classification classes that are used to reveal whether any significant behavior that emerges is due to irregularities in consumption. In this paper, ELM and online sequential-ELM (OS-ELM) algorithms are both used to achieve an improved classification performance and to increase accuracy of results. A comparison of this approach with other classification techniques, such as the support vector machine (SVM) algorithm, is also undertaken and the ELM performance and accuracy in NTL analysis is shown to be superior.

Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method

Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards non-technical loss (NTL) analysis for electric utilities using genetic algorithm (GA) and support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector. This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. GA provides an increased convergence and globally optimized SVM hyper-parameters using a combination of random and prepopulated genomes. The result of the fraud detection model yields classified classes that are used to shortlist potential fraud suspects for onsite inspection. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.

Detection of abnormalities and electricity theft using genetic Support Vector Machines

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 a new approach towards Non-Technical Loss (NTL) analysis for electric utilities using a novel intelligence-based technique, Support Vector Machine (SVM) The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector due to electricity theft The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behavior This approach provides a method of data mining and involves feature extraction from historical customer consumption data The SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behavior that emerges due to irregularities in consumption Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities

Non-Technical Loss analysis for detection of electricity theft using support vector machines

This review paper focuses on the various modeling practices for the identification and apprehension of non-technical losses. The modeling practices are extremely vital to develop, upsurge energy performance, examine and foresee the performance of power transmission & distribution of the electrical system. The data mining based models are innovative and have the subsistence to examine an enormous potential of energy consumption records and performing area profile for preparing housing zone directing the electricity effective living. In this concern, support vector machine model, which classifies illegal customers is a form of advanced mix evolutionary neural network model. Optimum-path forest clustering process is activated to recognize legitimate and irregular profiles of an industry as well as commercial customers to find out theft of electricity. Real time state estimation technique determines a state approximation method in the actual stage for every conversion (transformation) point. Aforementioned allows us to regulate the parts to the maximum extent of non-technical losses through the radial distribution method. The support vector machine with genetic algorithm advances a hybrid method for the non-technical loss investigation and provide an automated assistance to dominate the electricity theft. This model is simplified version of support vector machine. Decision tree and Bayesian regularization networks are appropriated to identify the several kinds of patterns of losses in the electrical system. These practices have been accompanied concerning testing and validation for power system losses in the experimental laboratory. It operates in an influence tool intended to expedite the investigators and scientists. It assists short of spending a massive amount of money, time and energy in experimental events. Prior fabrication modeling methods are remarkably significant in replication of diverse kinds of solar electrical systems. Accordingly, this study concentrates on the base of modeling methods not only saves time but, also preserves the monetary investment in the electrical system. The benefit and imminent opportunity of modeling practices are also conferred in the review paper.

Review of various modeling techniques for the detection of electricity theft in smart grid environment

A focus area of energy management research is the reduction of electrical energy losses occurring in an electricity distribution network. These losses are the technical and nontechnical losses. Reducing these losses ensure that the cost of electricity to customers will be reduced and in turn the efficiency of the distribution network will be improved. The model developed minimizes the nontechnical electrical energy losses of an electrical distribution network. This model simulates the electrical distribution network and includes different parameters that calculate the estimated technical losses of the electricity distribution network. The model is used as a base for developing a strategy that minimizes the electrical energy losses of an electrical distribution network.

J.W. Fourie

Papers

A statistical method to minimize electrical energy losses in a local electricity distribution network