Optimizing The Inspection Routine For The Detection Of Electrical Energy Theft In Aes Eletropaulo In São Paulo, Brazil

This work describes the development of a non-invasive and low-cost process that allows for the improvement of the energy theft inspection routine, increasing the field inspection team productivity and reducing the customer's embarrassment in cases where no irregularity is found. This new process is based on the development of an electronic Ah meter device that can be installed on the customer's pole input connections to the power lines. Using the recorded Ah value in the device, it is possible to estimate, within a margin of error, the energy consumption of the customer during a small period, typically one week. This energy value is compared to the customer's regular energy meter reading for the same period. A comprehensive statistical study performed with a database of more than 80000 customers in distribution area of the utility company AES Eletropaulo in São Paulo, Brazil concludes that the comparison between these readings can clearly indicate when tampered or defective meters are found.728089Depuru, S., Wang, L., Devabhaktuni, V., Gudi, N., Measures and setbacks for controlling electricity theft (2010) IEEE North American Power Symposium-NAPS(2009) Bureau for Economic Growth, Agriculture and Trade, , U. S. Agency for International Development Washington, D. C., Transforming Electricity Consumers into Customers: Case Study of a Slum Electrification and Loss Reduction Project in São Paulo, BrazilOnat, N., Transmission and distribution losses of Turkey's power system (2010) Proceedings of the 4th WSEAS Conference on Advances in Energy Planning, Environmental Education and Renewable Energy SourcesSmith, T., Electricity theft comparative analysis (2003) Energy Policy, 32, pp. 2067-2076Fitch, M., Graham, C., (2000) Electricity and Gas Theft, , Centre for Utility Consumer Law, University of Leicester, UKKenny, C., Soreide, T., (2008) Grand Corruption in Utilities, , World Bank Policy Research Working Paper 4805Sarpa, C., (2008) Electricity Theft and Non-payment: Impact on the sa Generation Capacity Crisis, , Yelland, Conference PaperOnat, N., Techno-economic analysis of illegal electricity usage in Turkey and policy proposals (2010) WSEAS Transactions on Power Systems, 3, pp. 213-222Stajic, Z., Janjic, A., Simendic, Z., Power quality and electrical energy losses as a key drivers for smart grid platform development (2011) Proceedings of the 15th WSEAS International Conference on Systems - Recent Researches in System Science, pp. 417-422Kadurek, P., Blom, J., Cobben, J., Kling, W., Theft detection and smart metering practices and expectations in the Netherlands (2010) Innovative Smart Grid Technologies Conference Europe (ISGT Europe) IEEE PESOliveira-De Jesus, P., Alvarez, M., De Ponce Leao, M., Yusta, J., A novel approach to evaluate incremental transmission losses (2009) WSEAS Transactions on Power Systems, 1, pp. 12-21Ghajar, R.F., Khalife, J., Cost/benefit analysis of an AMR system to reduce electricity theft and maximize revenues for Électricité du Liban (2003) Applied Energy, 76 (1-3), pp. 25-37. , DOI 10.1016/S0306-2619(03)00044-8Nagi, J., Mohammad, A., Yap, K., Tiong, S., Ahmed, S., Non-technical loss analysis for detection of electricity theft using support vector machines (2008) Proceedings of the 2nd IEEE International Conference on Power and Energy (PECon 08), pp. 907-912(2008) MSP430FE42x Mixed Signal Microcontroller Data-sheet, , Texas Instruments(2011) CC2550 Low-cost Low-power 2.4 GHz RF Transmitter Data-Sheet, , Texas InstrumentsMorais, F., (2011) Development of an Electronic Wireless RMS Current Meter for Applications in Detection of Electrical Energy Theft, , M. Sc. Thesis in Electrical Engineering, Faculty of Computer and Electrical Engineering, University of Campinas, BrazilPomilio, J., Deckmann, S., Characterization and compensation of harmonics and reactive power of residential and commercial loads (2007) IEEE Transactions on Power Delivery,

Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

Detection of Non-Technical Losses in Smart Distribution Networks: a Review

With the advent of smart grids, distribution utilities have initiated a large deployment of smart meters on the premises of the consumers. The enormous amount of data obtained from the consumers and communicated to the utility give new perspectives and possibilities for various analytics-based applications. In this paper the current smart metering-based energy-theft detection schemes are reviewed and discussed according to two main distinctive categories: A) system statebased, and B) arti cial intelligence-based.Comisión Europea FP7-PEOPLE-2013-IT