NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting

Avila, Nelson Fabian; Figueroa, Gerardo; Chu, Chia-Chi

doi:10.1109/tpwrs.2018.2853162

Cited by 106 publications

(86 citation statements)

References 31 publications

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“…Adaboost is formerly designed to solve highly non-linear tasks [24]. The main focus of Adaboost is to learn from the mistakes of previous models and boost the performance of the next model.…”

Section: Joint Training and Classification Modulementioning

confidence: 99%

“…Specifically, for the binary classification problem, the confusion matrix returns two rows and two columns, i.e., four possible outcomes. These four possible outcomes are described as follows: The following are the performance metrics given in Equations (11)- (16), as defined in [20,21,24]:…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…In contrast to the manual methods, supervised machine learning solutions have gained the interest of utilities and academia for performing ETD. Studies based on the supervised learning techniques [20][21][22][23][24][25] focus on ETD using the large and imbalanced datasets obtained through the smart meters. However, the performances of these techniques are still not sufficient for the practical applications in utilities.…”

Section: Introductionmentioning

confidence: 99%

“…Nelson et al [24] combined the maximal overlap discrete wavelet packet transform (MODWPT) with the random under-sampling boosting (RUSBoost) technique to obtain the most suitable features for the identification of NTL. Li et al [37] used a semi-supervised technique to perform the detection of NTL.…”

Section: Introductionmentioning

confidence: 99%

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A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

et al. 2020

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Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices.

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Section: Joint Training and Classification Modulementioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

et al. 2020

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“…Many feature extraction methods of EEG signals based on WPT have achieved good results. For example, Avila et al [6] calculated the autoregressive coefficient on the basis of wavelet packet transform, and used SVM classifier as the feature of EEG signal to obtain good recognition effect. Yong et al [39] used local wavelet packet coefficient to extract EEG signal feature, then BP Neural Network as classifier, and achieved 94% classification rate in EEG motion imagination classification.…”

Section: Introductionmentioning

confidence: 99%

Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

Zhang

Min²

2020

ITC

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Human Emotion Recognition is of vital importance to realize human-computer interaction (HCI), while multichannel electroencephalogram (EEG) signals gradually replace other physiological signals and become the main basis of emotional recognition research with the development of brain-computer interface (BCI). However, the accuracy of emotional classification based on EEG signals under video stimulation is not stable, which may be related to the characteristics of EEG signals before receiving stimulation. In this study, we extract the change of Differential Entropy (DE) before and after stimulation based on wavelet packet transform (WPT) to identify individual emotional state. Using the EEG emotion database DEAP, we divide the experimental EEG data in the database equally into 15 sets and extract their differential entropy on the basis of WPT. Then we calculate value of DE change of each separated EEG signal set. Finally, we divide the emotion into four categories in the two-dimensional valence-arousal emotional space by combining it with the integrated algorithm, Random Forest (RF). The simulation results show that the WPT-RF model established by this method greatly improves the recognition rate of EEG signal, with an average classification accuracy of 87.3%. In addition, we use WPT-RF model to train individual subjects, and the classification accuracy reached 97.7%.

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A Novel Framework forNTLDetection in Electric Distribution Systems

Chu

Avila²,

Figueroa³

et al. 2022

Intelligent Data Mining and Analysis in Power and Energy Systems

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NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting

Cited by 106 publications

References 31 publications

A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

A Novel Framework forNTLDetection in Electric Distribution Systems

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