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

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Method for detection and location of non-technical losses in distribution systems exploring smart meters

Orientadores: Walmir de Freitas Filho, Fernanda Caseño Trindade ArioliDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: No Brasil e em muitos outros países, há uma elevada preocupação com perdas não técnicas, as quais são relacionadas com roubo de energia, inadimplência e erros na tarifação. Os números associados a este problema impressionam. Prevê-se que cerca de US$200 bilhões sejam perdidos anualmente devido a roubo de energia ou falha nos equipamentos em todo o mundo. No Brasil, o prejuízo causado por perdas não técnicas é estimado em cerca de 8,1 bilhões de reais, mas os valores de perdas não técnicas registradas em diferentes regiões brasileiras são bastante distintos entre si. Em 2011, por exemplo, a CELPA (Centrais Elétricas do Pará) registrou cerca de 40$ 120 milhões anuais de perdas no âmbito da CPFL. Alguns métodos foram desenvolvidos com o objetivo de identificar tais perdas, contudo ainda não há um procedimento consolidado. Além disso, atualmente novas funções têm sido integradas aos medidores eletrônicos de energia, permitindo o acesso a informações adicionais. Com o aumento destas funcionalidades e a comunicação de dados, estes medidores inteligentes podem ser utilizados para o desenvolvimento de técnicas integradas para o gerenciamento de redes de distribuição. Neste contexto, este trabalho de mestrado apresenta um novo método de análise de perdas não técnicas, mais sensível do que os métodos existentes, permitindo assim melhorar o nível de detecção e localização de perdas não técnicas nos sistemas de distribuição. Tal método baseia-se em medidas feitas pelos medidores inteligentes e em um fluxo de carga modificado que permite utilizar as relações entre as grandezas medidas e calculadas para identificar casos fora dos padrõesAbstract: In Brazil and in several other countries, there is a growing awareness of non-technical losses, which are associated to energy theft, nonpayment and measurements inaccuracy. The numbers associated to this problem are impressive. One can estimate that all around the world about US$200 billion are lost annually because of energy theft and equipment failures. In Brazil, the non-technical losses is estimated to be around R$ 8.1 billion, however its regional values considerably differ from each other. In 2011, for example, the utility called CELPA (abbreviation for Centrais Elétricas do Pará) registered around 40% of nontechnical loss while CPFL Energia (another Brazilian utility) registered only 2%. Even though 2% is a low percentage value, it represents a cost of R$ 120 million for CPFL Energia. Several methods have been proposed to identify such losses; however there is no established technique. Additionally, nowadays new functions have been integrated to the electronic energy meters, allowing access to additional information. With the increase in such functions and data communication, these smart meters can be used for the development of integrated techniques associated to the management of distribution systems. In this context, this work presents a new method to reduce nontechnical losses, which is more sensible than the existing ones, allowing the improvement of detection and location of nontechnical losses in distribution systems. The proposed method is based on measurements from smart meters and on a modified load flow algorithm that allows using the relationships between measured and calculated greatness to identify irregular situationsMestradoEnergia EletricaMestre em Engenharia Elétrica2013/09765-0FAPES

Intelligent system for non-technical losses management in electricity users

This thesis is focused on the problem associated with non-technical losses of electrical energy that are product of fraudulent connections. As a result of the research, a novel methodology is presented for the detection of users who are fraudulently connected to the electricity distribution network. The identification of users with anomalous behaviors is carried out through the use of a system based on computational intelligence techniques. The proposed intelligent system performs the analysis of the energy consumption behavior of the users through three stages. The first is a hybrid cluster between self-organizing maps and genetic algorithms that allows grouping users with similar consumption curves. The second models the users consumption profiles using ARMA/ARIMA models and intelligently corrects them through the use of neural networks, this stage allows predicting the future consumption of customers. The final stage is a classifier based on random forest, which receives the outputs of the previous stages and a set of characterization variables to determine if a user is fraudulent or not. The system was applied on a real case study and the results obtained were compared with proposals of other authors, as well as with the current detection process that is used on the users of the case study. It was found that the proposed system allows to obtain satisfactory results, placing itself in a good position within the reviewed works and significantly exceeding the process currently used on the users of the case study

Artificial Intelligence for the Detection of Electricity Theft and Irregular Power Usage in Emerging Markets

Power grids are critical infrastructure assets that face non-technical losses (NTL), which include, but are not limited to, electricity theft, broken or malfunctioning meters and arranged false meter readings. In emerging markets, NTL are a prime concern and often range up to 40% of the total electricity distributed. The annual world-wide costs for utilities due to NTL are estimated to be around USD 100 billion. Reducing NTL in order to increase revenue, profit and reliability of the grid is therefore of vital interest to utilities and authorities. In the beginning of this thesis, we provide an in-depth discussion of the causes of NTL and the economic effects thereof. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electric utilities are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data. This is due to the latter's propensity to suggest a large number of unnecessary inspections. In this thesis, we compare expert knowledge-based decision making systems to automated statistical decision making. We then branch out our research into different directions: First, in order to allow human experts to feed their knowledge in the decision process, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. Second, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data as well as a selection of master data. The methodology used is specifically tailored to the level of noise in the data. Last, we discuss the issue of biases in data sets. A bias occurs whenever training sets are not representative of the test data, which results in unreliable models. We show how quantifying and reducing these biases leads to an increased accuracy of the trained NTL detectors. This thesis has resulted in appreciable results on real-world big data sets of millions customers. Our systems are being deployed in a commercial NTL detection software. We also provide suggestions on how to further reduce NTL by not only carrying out inspections, but by implementing market reforms, increasing efficiency in the organization of utilities and improving communication between utilities, authorities and customers

Applications of distribution systems state estimation with smart meters

Orientadores: Walmir de Freitas Filho, Fernanda Caseño Trindade ArioliTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Nos últimos anos, em decorrência dos recentes avanços tecnológicos dedicados aos sistemas elétricos de potência, verifica-se um crescente monitoramento das redes de distribuição, acompanhado da implantação de uma infraestrutura avançada de medição. Funções adicionais incorporadas aos medidores de energia tradicionais, associadas à capacidade de comunicação de dados em duas vias, caracterizam os chamados medidores inteligentes (smart meters). Diante da quantidade de dados potencialmente disponibilizada por esses medidores, há grande expectativa em relação ao uso da estimação de estados em sistemas de distribuição e às aplicações que podem ser dadas a essa ferramenta, visando à melhora da qualidade da energia suprida e à redução dos custos operacionais das distribuidoras de energia elétrica. Nesse contexto, esta tese de doutorado apresenta uma abordagem crítica e sistemática do estimador de estados de sistemas de distribuição (EESD), e propõe métodos eficientes dedicados a funções de gerenciamento desses sistemas, explorando-se a potencialidade dos medidores inteligentes e sua instalação em larga escala. Inicialmente, é quantificada a perda de informação do EESD devida à agregação das medidas no tempo, tendo em vista a frequência de envio dos dados dos medidores à central de gerenciamento. Em seguida, as seguintes aplicações do EESD voltadas à supervisão e gerenciamento dos sistemas de distribuição são propostas: uma metodologia de detecção e localização de perdas não técnicas por meio da análise de erros grosseiros; e uma metodologia de detecção de afundamentos e desequilíbrio de tensão. Os resultados obtidos mostram que as soluções apresentadas neste trabalho agregam valor às informações fornecidas pelos medidores inteligentes, possibilitando maior retorno dos investimentos envolvidos na implantação de uma infraestrutura avançada de mediçãoAbstract: Nowadays, technological advances dedicated to electric power systems have motivated the monitoring of distribution networks by the development of an advanced metering infrastructure. New features have been incorporated into the traditional energy meters that, when associated with the possibility of two-way data communication, characterize the so-called smart meters. Given the large amount of available data resulted from this scenario, there is an expectation about the use of the state estimation in distribution networks, mainly related to the applications that can benefit from this tool, which can improve the quality of the energy supplied and reduce the operational costs of distribution utilities. In this context, this work presents a critical and systematic investigation of the use of distribution system state estimator (DSSE) in some functions of the distribution management system and the large-scale deployment of smart meters is the basis of the proposed methodologies. Firstly, the loss of information of the DSSE caused by the frequency of data communication and the measurement aggregation in time is quantified. Then, DSSE applications focusing on the supervision and management of the distribution systems are proposed: a methodology to detect and locate non-technical losses using bad data analysis; and a methodology to detect voltage sags and voltage unbalance. The solutions presented in this work add value to the information provided by the smart meters and allow a higher return of the investments in an advanced metering infrastructureDoutoradoEnergia EletricaDoutora em Engenharia Elétric