Noise Reduction Power Stealing Detection Model Based on Self-Balanced Data Set

Liu, Haiqing; Li, Zhiqiao; Li, Yuancheng

doi:10.3390/en13071763

Cited by 11 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [24], the authors proposed a model that performs conditional wasserstein generative adversarial network based data balancing to deal with unbalanced data problem, stacked convolution denoising autoencoder based feature extraction to deal with the curse of dimensionality issue, and light gradient boosting machine based detection of electricity theft. It is abbreviated as (CSL) classifier.…”

Section: Related Workmentioning

confidence: 99%

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

et al. 2022

View full text Add to dashboard Cite

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

show abstract

Section: Related Workmentioning

confidence: 99%

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

et al. 2022

View full text Add to dashboard Cite

show abstract

“…First, how to learn a general image feature extractor based on a cascade noise reduction autoencoder from massive auxiliary data in an unsupervised and off-line way is introduced in detail. A cascading noise reduction autoencoder is obtained by stacking several single-layer noise reduction autoencoder networks [ 12 ]. Figure 1 shows the general process of typical video processing.…”

Section: Construction and Application Analysis Of Moving Target Detec...mentioning

confidence: 99%

Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry

Zhang

Zheng

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The intelligent tracking and detection of athletes’ actions and the improvement of action standardization are of great practical significance to reducing the injury caused by sports in the sports industry. For the problems of nonstandard movement and single movement mode, this exploration takes the video of sports events as the object and combines it with the video general feature extraction of convolutional neural network (CNN) in the field of deep learning and the filtering detection algorithm of motion trajectory. Then, a target detection and tracking system model is proposed to track and detect targets in sports in real-time. Moreover, through experiments, the performance of the proposed system model is analyzed. After testing the detection quantity, response rate, data loss rate, and target detection accuracy of the model, the results show that the model can track and monitor 50 targets with a loss rate of 3%, a response speed of 4 s and a target detection accuracy of 80%. It can play an excellent role in sports events and postgame video analysis, and provide a good basis and certain design ideas for the goal tracking of the sports industry.

show abstract

“…Power theft is refers to the usage of power from utility company without their permission (Messinis et al, 2019;Thota et al, 2020). Power theft may be done through diversion of the distribution feeder, interferewith the energy meter and some physical manipulations to reduce that power bill (Liu et al, 2020a(Liu et al, , 2020b. It is one of the major problems in developed countries.…”

Section: Power Theft Detection Using Grf Optimization 1373mentioning

confidence: 99%

A machine learning approach-based power theft detection using GRF optimization

Prakash¹,

Joseph²,

Shanmugasundaram³

et al. 2021

JEDT

View full text Add to dashboard Cite

Purpose This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users. Design/methodology/approach In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm. Findings The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method. Originality/value The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

show abstract

Noise Reduction Power Stealing Detection Model Based on Self-Balanced Data Set

Cited by 11 publications

References 24 publications

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry

A machine learning approach-based power theft detection using GRF optimization

Contact Info

Product

Resources

About