Electricity theft detection system with RF communication between distribution and customer usage

Mutupe, R M; Osuri, S. O.; Lencwe, Mpho J.; Chowdhury, S.

doi:10.1109/powerafrica.2017.7991288

Cited by 8 publications

(4 citation statements)

References 1 publication

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“…However, this solution can filter a locality where NTL is occurring but it fails to identify the specific consumers responsible for NTL. In [31], the authors have proposed to remotely detect NTL by monitoring the difference between the electricity distributed and the electricity consumed. The electricity consumed at the consumer end is monitored by radio transceivers and communicated back to the distribution pole using Wi-Fi space.…”

Section: B Network-based Techniquesmentioning

confidence: 99%

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

et al. 2020

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With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

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Section: B Network-based Techniquesmentioning

confidence: 99%

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

et al. 2020

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“…This approach can be applied in AMI systems as well as in traditional grid systems. A similar approach is used in Mutupe et al (2017). The authors have used meters at the transformer side and the consumer side.…”

Section: Network Based Techniquesmentioning

confidence: 99%

Performance analysis of machine learning classifiers for non-technical loss detection

Ghori

Imran

Nawaz

et al. 2020

J Ambient Intell Human Comput

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Power companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest, K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection.

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“…Beberapa faktor resiko yang terjadi terhadap pencurian arus seperti tegangan menjadi turun, peralatan listrik yang akan mudah rusak dan dapat mengancam kehandalan jaringan dari grid PLN [4]. Apabila kerugian dalam pencruian arus dapat diminimalisir akan berdampak pada langsung pada perekonomian negara yang lebih baik [5].…”

Section: Intisariunclassified

Testing the Reliability of the Current Transformer System in Tackling the Illegal Use of Electrical Energy

et al. 2020

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Meningkatnya kebutuhan dan tarif energi listrik membuat oknum masyarakat memilih untuk mengambil jalan pintas dengan malakukan pecurian secara ilegal listrik milik Negara. Hal tersebut sangat bertentangan dengan hukum dan sangat beresiko terjadinya konsleting listrik yang dapat mengakibatkan kebakaran. Oleh sebab itu, dibutuhkan suatu alat untuk mencegah terjadinya pemakaian arus secara ilegal yang efektif tanpa harus melakukan pengukuran secara langsung ke konsumen. Untuk mendeteksi terjadinya pencurian listrik ilegal dilakukan dengan 2 cara yaitu physical detection methode dan customer consumption methode. Penelitian diperoleh bahwa pemasangan sederhana dengan pemanfaatan transformator arus (current transformer) sangat membantu PLN dalam mencegah penggunaan arus listrik ilegal tanpa harus melakukan pengecekan langsung terhadap konsumen.

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Electricity theft detection system with RF communication between distribution and customer usage

Cited by 8 publications

References 1 publication

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Performance analysis of machine learning classifiers for non-technical loss detection

Testing the Reliability of the Current Transformer System in Tackling the Illegal Use of Electrical Energy

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