Detection of commercial losses in electric power distribution systems using data mining techniques

Monteiro, Marina Dietze; Maciel, Renan Silva

doi:10.1109/sbse.2018.8395889

Cited by 4 publications

(8 citation statements)

References 8 publications

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“…Generally, their forecast is uncertain (stochastic nature), since it is not known where, how, and when they occur. They are computed as the difference between the total losses and the technical losses of the distribution system [18,20,21].…”

Section: Country 2008mentioning

confidence: 99%

“…Nearest neighbor (k-NN) [27,28] Decision trees [4][5][6][27][28][29][30][31] Artificial neural network (ANN) [3,[31][32][33][34][35][36] Support vector machine (SVM) [29,32,35] Optimum path forest (OPF) [10,27,37] Bayesian classifiers [5,6,27] Rule induction [4,5,11,12,33,38] Unsupervised learning Self organizing map (SOM) [31,38] Cluster K-means [21,38,39] Cluster K-menoids [21] Regression models [27,35] Fuzzy c-means [38,40,41] Outlier detection [38,42] Network-oriented methods [16,19,[43][44]…”

Section: Supervised Learningmentioning

confidence: 99%

“…The advantage of this technique is efficiency when handling large data sets, and as a disadvantage, it is essential to know the number of formed groups. Another disadvantage is the sensitivity to noise in calculating the groups referring to a center; any atypical data could alter this centroid; therefore, low group formation can impair the response [21].…”

Section: Unsupervised Techniquesmentioning

confidence: 99%

“…The traditional method used by distribution companies to mitigate this problem is periodic random inspections on-site, a method that requires a high amount of financial and technical resources [21][22][23].…”

mentioning

confidence: 99%

“…This method requires a large amount of quality information to apply the model; the electricity distribution company must have data labeled with the variables of fraud and not fraud [20,22]. (b) Unsupervised learning: The function of these algorithms is to determine patterns to acquire training according to the available variables; generally, these algorithms use databases whose variables do not have labels or when the sample does not have a sufficient amount of data [20][21][22].…”

mentioning

confidence: 99%

See 4 more Smart Citations

Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

et al. 2021

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Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project.

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Section: Country 2008mentioning

confidence: 99%

Section: Supervised Learningmentioning

confidence: 99%

Section: Unsupervised Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

et al. 2021

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Impact of Switching to 20 KV to Reduce Losses in the Distribution System-Simulation with Power Factory Software

Bula

Zejnullahu

Bisenieks

et al. 2022

2022 IEEE 63th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)

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Fraud Detection of the Electricity Consumption by combining Deep Learning and Statistical Methods

AZZOUGUER,

SEBAA,

HADJOUT

2024

EEA

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An important issue for the electricity distribution companies is the non-technical loss (NTL), also known as electricity fraud. This issue has a significant impact on the economies of all countries in the world. In this context, we studied the problem of the imbalance between the electrical energy invoiced and the electrical energy supplied within the Algerian economic sector. This article presents an approach to detecting electrical fraud using a combination of Long Short-Term Memory (LSTM) and robust Exponential and Holt-Winters Smoothing (EHWS) methods in order to enhance the accuracy and efficacy of fraud detection mechanisms. The proposed approach investigates the fraudulent behaviour of electricity consumers and unfolds in several key phases. In the first step, monthly consumption forecasts are made and the model with the most accurate results is selected. Then, the phase of detection of anomalies in economic meters and detection of cases of fraud by economic customers begins. This phase relies on the robust exponential and Holt-Winters Smoothing methods for uncovering irregular patterns indicative of potential fraud instances. The proposed model was trained and evaluated, and several experiments were carried out using a large dataset of real users from the economic sector. The dataset comprised approximately 2,000 customers and encompassed 14 years of monthly electricity usage in Bejaia, Algeria. The results of the experiments demonstrate promising performance, underscoring the efficacy of our proposed solution in effectively detecting instances of fraud, leading us to conclude that this proposition is robust and can help improve the accuracy of locating abnormal consumer behaviour and increase the company's profits.

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Detection of commercial losses in electric power distribution systems using data mining techniques

Cited by 4 publications

References 8 publications

Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

Impact of Switching to 20 KV to Reduce Losses in the Distribution System-Simulation with Power Factory Software

Fraud Detection of the Electricity Consumption by combining Deep Learning and Statistical Methods

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