Distribution networks nontechnical power loss estimation: A hybrid data-driven physics model-based framework

Bretãs, Arturo S.; Rossoni, Aquiles; Trevizan, Rodrigo D.; Bretas, N.G.

doi:10.1016/j.epsr.2020.106397

Cited by 20 publications

(10 citation statements)

References 32 publications

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“…The main idea of creating synthetic measurements is to approximate the residual of the measurement model to a normal distribution. In doing so, not only parameter FDI correction is enhanced, as will be presented, but also the global redundancy level of the system increases, which enhances gross error detection and identification [32].…”

Section: Background Information 21 State Estimation Augmented With Synthetic Measurementsmentioning

confidence: 99%

“…Instead, measurements can be created artificially at locations where no real-life measurement or historical data exist. These measurements, named synthetic measurements, were modeled in [32]. One should not confuse synthetic measurements with pseudo measurements, which are created considering available historical data [33].…”

Section: Background Information 21 State Estimation Augmented With Synthetic Measurementsmentioning

confidence: 99%

“…In order to do this, an additional measurement set is generated, called Synthetic Measurements (SM) in this paper, to increase the redundancy level and enable a new SE towards parameter correction. The linear regression prediction used in [29] was used to generate Synthetic Measurements (SM) in a similar way as in [32]. Therefore, the contributions of this work are threefold:…”

Section: Introductionmentioning

confidence: 99%

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A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

et al. 2021

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Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

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Section: Background Information 21 State Estimation Augmented With Synthetic Measurementsmentioning

confidence: 99%

Section: Background Information 21 State Estimation Augmented With Synthetic Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

et al. 2021

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“…Finally, when a new batch of SCADA and AMI measurements is available [34], SM are created and added to the measurement set for the NLSE, which has been shown to be effective in [35]. SM are calculated based on the previously set of state estimates and the current system parameters.…”

Section: Synthetic Measurementsmentioning

confidence: 99%

μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

et al. 2021

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Simultaneous real-time monitoring of measurement and parameter gross errors poses a great challenge to distribution system state estimation due to usually low measurement redundancy. This paper presents a gross error analysis framework, employing μPMUs to decouple the error analysis of measurements and parameters. When a recent measurement scan from SCADA RTUs and smart meters is available, gross error analysis of measurements is performed as a post-processing step of non-linear DSSE (NLSE). In between scans of SCADA and AMI measurements, a linear state estimator (LSE) using μPMU measurements and linearized SCADA and AMI measurements is used to detect parameter data changes caused by the operation of Volt/Var controls. For every execution of the LSE, the variance of the unsynchronized measurements is updated according to the uncertainty introduced by load dynamics, which are modeled as an Ornstein–Uhlenbeck random process. The update of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data. When new SCADA and AMI measurements arrive, the LSE provides added redundancy to the NLSE through synthetic measurements. The presented framework was tested on a 13-bus test system. Test results highlight that the LSE and NLSE processes successfully work together to analyze bad data for both measurements and parameters.

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“…Eles são muito robustos, mas o desafio está na instalação de medidores para garantir a observabilidade e a redundância das medidas. São necessários, em alguns casos, medidores ao longo das linhas (Bretas, et al, 2020) ou até mesmo PMUs (Souza, et al, 2020).…”

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Princípios da Detecção de Perdas Não Técnicas em Rede de Distribuição Usando a Análise de Componentes Principais

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2020

Anais Do Congresso Brasileiro De Automática 2020

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A detecção de perdas não técnicas nas redes de distribuição é um tema muito importante para as distribuidoras. Com a evolução dos sistemas de monitoramento das redes e a inclusão de infraestrutura avançada de medição, abrem-se novas oportunidades para o desenvolvimento de métodos para mitigar esse problema. Neste trabalho é proposto o princípio de um método de detecção e identificação de clientes suspeitos de fraudar os sistemas de medição usando a análise de componentes principais. Por meio de simulações, verificou-se que é possível aproveitar as medições no tempo, ao longo do dia, para detectar perdas não técnicas intermitentes.

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Distribution networks nontechnical power loss estimation: A hybrid data-driven physics model-based framework

Cited by 20 publications

References 32 publications

A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

Princípios da Detecção de Perdas Não Técnicas em Rede de Distribuição Usando a Análise de Componentes Principais

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