Non-technical loss detection in limited-data low-voltage distribution feeders

Firoozi, Hooman; Mashhadi, Habib Rajabi

doi:10.1016/j.ijepes.2021.107523

Cited by 7 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike the discrete "true" or "false" values, it utilizes a "degree of truth" that can assume any value between zero and one, with the extreme values representing truth and falsehood, respectively. Instances of employing this nuanced logic are illustrated in [25,26].…”

Section: Methods Of Identification and Assessment Of Nontechnical Los...mentioning

confidence: 99%

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Pazderin,

Kamalov,

Gubin

et al. 2023

Energies

View full text Add to dashboard Cite

Nontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms.

show abstract

Section: Methods Of Identification and Assessment Of Nontechnical Los...mentioning

confidence: 99%

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Pazderin,

Kamalov,

Gubin

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…Additionally, unauthorized and illegal energy consumption poses a significant hurdle to the widespread adoption of smart grids [1][2][3][4]. This problem includes issues like electricity theft, technical errors, and the improper use of network equipment and facilities, as highlighted in previous studies [5][6][7][8][9][10][11][12][13][14][15][16]. Addressing these challenges requires estimating line currents and node voltages, as well as developing mathematical models for the distribution network [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Original methods using Artificial Intelligence (AI) applications have been documented in [9][10][11][12][13][14][15][16][17][18]. It is worth noting that these methods seem to be some of the most reliable diagnostic approaches, capable of providing more accurate and dependable results.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that these methods seem to be some of the most reliable diagnostic approaches, capable of providing more accurate and dependable results. A representative sample of existing AI methods applicable for detecting electricity theft in smart grids can be found in [9][10][11][12]. Presently, all the AI-based methods mentioned can be grouped into two main categories.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring Energy Flows for Efficient Electricity Control in Low-Voltage Smart Grids

Alymov,

Averbukh

2024

Energies

View full text Add to dashboard Cite

Modern low-voltage distribution lines, especially those linked with renewable energy sources, face technical hurdles like unaccounted and illegal electricity use, increased power losses, voltage control issues, and overheating. Tackling these challenges effectively requires continuously monitoring power flows and identifying problematic network spots. This study introduces a method involving ongoing energy flow monitoring from distribution transformers and other sources to end-users through auxiliary facilities. The algorithm seamlessly integrates with consumers’ existing smart power meters and supporting infrastructure, eliminating the need for extra equipment or data. Deployed in several distribution networks totaling about 40 GWh/year over two years, this diagnostic system showed promising results. It notably cut total power consumption by around 6% by detecting and mitigating illegal energy waste and addressing technical issues. Additionally, it reduced technical personnel involvement in operational tasks by approximately twentyfold, significantly enhancing network profitability overall.

show abstract

“…NTL affects both developing and developed nations' economies. For example, every year, the power sector of Brazil and India lost 3 billion US dollars and 44.5 billion US dollars, respectively, due to NTL [10]. Energy theft is a big challenge in the way of strengthening the economy of these countries.…”

Section: Introductionmentioning

confidence: 99%

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

et al. 2022

View full text Add to dashboard Cite

In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%.

show abstract

Non-technical loss detection in limited-data low-voltage distribution feeders

Cited by 7 publications

References 29 publications

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Monitoring Energy Flows for Efficient Electricity Control in Low-Voltage Smart Grids

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

Contact Info

Product

Resources

About