Smart distribution power losses estimation: A hybrid state estimation approach

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

doi:10.1109/pesgm.2016.7741760

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…The load for any ℎ feeder section, ( ( )), during the time period can be derived by aggregating the load at each downstream load point ( ( )) at all = 0 … . For numbers of feeder sections and load points, the load profile for the first feeder section can be calculated as the coincident sum of all the load profile at load point 1 to as shown in (5).The second feeder section is derived based on coincident sum from load profile at load point 2 to ; so on and so forth. The 30-days energy loss (in MWh) for each ℎ feeder section (ℑ ) can be estimated based on its peak power loss (℘ ) , loss factor (ℒ ) and the time period (1 month = 720 hours), as shown in (6) and (7).…”

Section: Methodsmentioning

confidence: 99%

“…[%] = 1127 Where: = total energy losses (generation, transmission and distribution) = energy delivered (measured) at the generation system point = energy consumed and billed at end-customer Distribution energy losses can be broken down into technical losses (TL) and non-technical losses (NTL) [3,4]. TL are associated with the inevitable and inherent loss of energy due to energized equipment and current flowing through resistive distribution components [5]. TL can be measured using energy meters or computed based on the network's electrical properties, such as resistance, reactance, voltage, current, and power [6,7].…”

Section: Introductionmentioning

confidence: 99%

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A new methodology for technical losses estimation of radial distribution feeder

Ibrahim

Gan

2019

IJEECS

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<span lang="EN-US">Power distribution feeders is one of the key contributors of technical losses (TL) as it is typically large in numbers and scattered over large geographic areas. Traditional approach using classical formulation or time series load flow simulations to determine TL in each and every feeder and feeder sections in all distribution network require is an expensive exercise as it requires extensive modelling of the feeders and voluminous data. This paper presents a simple analytical approach to estimate monthly TL of a radial distribution feeder using analytical approach. TL for each feeder sections are evaluated on a monthly basis based on estimation of the load profile of the load points, peak power loss characteristics and loss factor. Total feeder TL are then estimated as the sum of all TL contributed by each feeder section. The developed models and procedure have been demonstrated through case studies performed on three (3) typical and representative feeders characterized by the different area served, number of feeder sections, load distribution and feeder length. The results shows close agreement (less than 5% differences) when compared with time series load flow simulations. With this model, the approach could be extended and applied to estimate TL of any radial distribution feeders of different configurations and characteristics</span>

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new methodology for technical losses estimation of radial distribution feeder

Ibrahim

Gan

2019

IJEECS

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“…Monitoring of consumer load profiles for energy theft detection can be found in the literature [17]- [20]. The most common methods for fraud detection are Support Vector Machines ( [21]- [23]), Artificial Neural Networks ( [24], [39]), Bayesian Networks and Decision Trees [25], Extreme Learning Machines [26], Optimum-Path Forest [27], Fuzzy Clustering [28], Anomaly Detection [29] and Deep Learning which has recently achieved unprecedented performance in many areas of computer applications [30]- [32]. From the above technics, Support Vector Machines and Artificial Neural Networks are the leading technics due to good performance and easy adaption to different areas of research [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining Knowledge Discovery Process

Blazakis¹,

Stavrakakis²

2019

IJAIA

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Energy theft constitutes an issue of great importance for electricity operators. The attempt to detect and reduce non-technical losses is a challenging task due to insufficient inspection methods. With the evolution of advanced metering infrastructure (AMI) in smart grids, a more complicated status quo in energy theft has emerged and many new technologies are being adopted to solve the problem. In order to identify illegal residential consumers, a computational method of analyzing and identifying electricity consumption patterns of consumers based on data mining techniques has been presented. Combining principal component analysis (PCA) with mean shift algorithm for different power theft scenarios, we can now cope with the power theft detection problem sufficiently. The overall research has shown encouraging results in residential consumers power theft detection that will help utilities to improve the reliability, security and operation of power network.

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“…Em ambas as hipóteses, para consumidores monofásicos é sorteada uma das outras duas fases remanescentes e para consumidores bifásicos são reclassificados nas outras duas configurações remanescentes, por exemplo: caso a UC seja bifásica "AB"será sorteada uma configuração "BC"ou "AC". Consumidores trifásicos não tem suas fases trocadas, pois no contexto deste trabalho, como não há medição individual das fases, as três possuem demandas sendo tratadas como perdas não técnicas ou comerciais.Além da onerosa consequência comercial para distribuidoras e principalmente consumidores (a parte não faturada da energia vendida é cobrada de todos), as perdas não técnicas tem grande influência na operação das redes, fato que motiva abordagens de estimadores, como a deRossoni et al (2016). Neste trabalho, os autores apresentam uma abordagem para um estimador de estado híbrido para SDs que detecta, identifica e corrige as perdas não técnicas usando uma EG) em medidas sejam feitas pelo teste Chi-Quadrado e pela análise dos resíduos normalizados (ABUR; EXPóSITO, 2004).…”

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Estimação de demanda trifásica em tempo real para sistemas de distribuição radiais

Avelino¹

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Aos meus pais, Jair e Rose, e tios, Cida e Ivanir, que por amor incondicional me ensinaram, não tudo que sei, mas sim tudo que sou. À minha irmã e cunhado, Ariane e Fernando, pelo imenso carinho e toda demonstração de apoio ao acompanharem de perto esta etapa. Ao Evandro, por dividir comigo este capítulo de nossas vidas. Pelo amor, dedicação e companheirismo. Pela força que encontramos um no outro. Por me fazer absolutamente feliz. Ao professor João Bosco por ter confiado na minha capacidade e ter dado a oportunidade desta experiência que considero de muito valor. E não só pela orientação, mas pela paciência, dedicação e apoio. À minha amiga Marina pela reciprocidade de nossa amizade e toda atenção e carinho, digna de uma amiga longa data. Ao Julio, companheiro de laboratório, não só pela amizade, mas também pela absoluta disposição em ajudar sempre e por todo conhecimento que, com muita generosidade, comigo compartilhou. Aos amigos do LACOSEP que tornaram o ambiente de trabalho mais leve e de apoio mútuo. Agradeço a Deus por todas as bençãos em minha vida, principalmente pela vida das pessoas a quem pude aqui agradecê-las.

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Smart distribution power losses estimation: A hybrid state estimation approach

Cited by 9 publications

References 9 publications

A new methodology for technical losses estimation of radial distribution feeder

A new methodology for technical losses estimation of radial distribution feeder

Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining Knowledge Discovery Process

Estimação de demanda trifásica em tempo real para sistemas de distribuição radiais

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