Conditional abnormality detection based on AMI data mining

Han, Seon Yeong; No, JaeGoo; Shin, Jin-Ho; Joo, Yongjae

doi:10.1049/iet-gtd.2016.0048

Cited by 27 publications

(13 citation statements)

References 9 publications

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“…5.1.1.6. Nearest neighbour (k-NN) [36,37,54]. Within the supervised classification algorithms for non-technical losses detection, the Nearest neighbours are the simplest methods which is mainly used a baseline for comparisons with other algorithms.…”

Section: Rule Inductionmentioning

confidence: 99%

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Review of Non-Technical Losses Identification Techniques

Pal¹

2021

IJRITCC

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Illegally consumption of electric power, termed as non-technical losses for the distribution companies is one of the dominant factors all over the world for many years. Although there are some conventional methods to identify these irregularities, such as physical inspection of meters at the consumer premises etc, but it requires large number of manpower and time; then also it does not seem to be adequate. Now a days there are various methods and algorithms have been developed that are proposed in different research papers, to detect non-technical losses. In this paper these methods are reviewed, their important features are highlighted and also the limitations are identified. Finally, the qualitative comparison of various non-technical losses identification algorithms is presented based on their performance, costs, data handling, quality control and execution times. It can be concluded that the graph-based classifier, Optimum-Path Forest algorithm that have both supervised and unsupervised variants, yields the most accurate result to detect non-technical losses.

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Section: Rule Inductionmentioning

confidence: 99%

“…5.1.2.2.Clustering algorithms[54][55][56][57][58][59]. Clustering algorithms have been applied in many fraud detection…”

mentioning

confidence: 99%

Review of Non-Technical Losses Identification Techniques

Pal¹

2021

IJRITCC

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“…The "distance" is often used to describe the degree of similarity. The larger the distance, the smaller the similarity between the two data samples [12]. A data sample can be two numbers, two sequences, or more generally, two vectors.…”

Section: Temporal Correlation Judgmentmentioning

confidence: 99%

Localization and recognition algorithm for fuzzy anomaly data in big data networks

Zhang¹,

Zhang²,

Hanafiah³

2018

Open Physics

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In order to accurately detect the fuzzy anomaly data existing in big data networks, it is necessary to study the localization and recognition algorithm. The current algorithms have problems related to poor noise reduction, low recognition efficiency, high energy consumption and low accuracy. A novel localization and recognition algorithm for fuzzy anomaly data in big data networks is proposed. The multi-wavelet denoising method is used to remove the noise signals existing in the network. The k-means algorithm is utilized for network clustering, and the association mode between nodes and the unitary linearity regression model is adopted to eliminate spatially and temporally redundant data that exist in big data networks. The similarity anomaly detection method based on multi-feature aggregation identifies fuzzy anomaly data existing in big data networks, establishes an anomaly data localization model, and completes the localization and recognition of fuzzy anomaly data. Experimental results show that the proposed method has good noise reduction, high recognition efficiency, low energy consumption and high accuracy of localization and recognition.

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“…The UI score further helps in setting an upper limiting value to separate fraudster consumers from the rest of the data. In Reference 11, power theft consumers were identified by formulating a number of rules that were based on conditional probability and K‐means clustering techniques. In another study, 12 the authors developed an NTL detection model based on the amalgamation of hierarchical clustering and decision trees technique to identify the abnormalities in the user's consumption data.…”

Section: Introductionmentioning

confidence: 99%

A novel unsupervised feature‐based approach for electricity theft detection using robust PCA and outlier removal clustering algorithm

Hussain¹,

Mustafa²,

Jumani

et al. 2020

Int Trans Electr Energ Syst

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This paper presents a novel data‐oriented unsupervised machine learning‐based theft detection approach for efficiently identifying the fraudster consumers. It accomplishes the above‐mentioned objective by exploiting the intelligence of the robust principal component analysis (ROBPCA) algorithm in conjunction with the outlier removal clustering (ORC) algorithm. To avoid the irregularities in acquired consumers’ data from a power utility, the statistical features are extracted from each consumer's consumption patterns using an anomalous time series extension. Based on the extracted features, the consumers with most similar features are initially grouped into two categories using the ROBPCA algorithm. In order to evade any overlapping between the two newly formed groups, the ORC algorithm is utilized to categorize the consumers distinctly as “suspicious” and “non‐suspicious”. Finally, a very selective onsite inspection is proposed, thus, saving the considerable time, resources, and overall cost of the utilities. The effectiveness of the proposed theft detection method is validated by comparing its performance with nine most widely used outlier detection methods on the basis of seven of the most prominent performance metrics. The accuracy and detection rate of the proposed technique are found as 94.34% and 92.52%, respectively, which is significantly higher than that of other studied conventional methods.

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Conditional abnormality detection based on AMI data mining

Cited by 27 publications

References 9 publications

Review of Non-Technical Losses Identification Techniques

Review of Non-Technical Losses Identification Techniques

Localization and recognition algorithm for fuzzy anomaly data in big data networks

A novel unsupervised feature‐based approach for electricity theft detection using robust PCA and outlier removal clustering algorithm

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