Anomaly Diagnosis Analysis for Running Meter Based on BP Neural Network

Wang, Zhengye; Gong, Gangjun; Wen, Yangping

doi:10.2991/cimns-16.2016.23

Cited by 3 publications

(5 citation statements)

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“…This process achieves a nonlinear fitting effect and exhibits good generalization capabilities for different data. Some researchers have proposed a neural network-based calibration method for energy meters [33][34][35][36][37], which partially validates the feasibility of this approach. It provides valuable insights for the large-scale, parallel remote verification of energy metering devices based on the electric PCP.…”

Section: Construction Of Simulation Standard Meter Based On Improved ...mentioning

confidence: 77%

“…The specific configuration of the hidden layer aims to strike a balance between computational efficiency and predictive performance. The basic structure of the BP network [33,36,37] is shown in Figure 8. The learning and training process is performed by minimizing the approximation error between the network output and the standard meter output values, and using them as the convergence criterion.…”

Section: Construction Of Simulation Standard Meter Based On Improved ...mentioning

confidence: 99%

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Research on Intelligent Verification System of High Voltage Electric Energy Metering Device Based on Power Cloud

et al. 2023

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To address the issues of low efficiency, poor security, insufficient compatibility, and difficulties in traceability associated with high-voltage electric energy metering (HVEEM) device verification methods, this paper proposes a design scheme for a remote verification system (RVS) of such devices based on a power cloud platform (PCP). The system adopts the concept of “high-precision local sampling + remote cloud verification” and develops a local acquisition device with compatibility and high precision to achieve fast acquisition of local electrical parameters. The IEC 61850 communication modeling is utilized to establish unified communication standards between the local device and the PCP. The PCP provides two verification methods: physical error verification based on a multi-channel standard and digital verification based on an improved Backpropagation (BP) neural network simulation model. Leveraging the advantages of power cloud technology, the system enables functions such as electrical energy calculation, remote intelligent error verification, cloud storage, condition monitoring, and early warning. Through testing and application, it has been demonstrated that the system achieves an integration accuracy level better than 0.02. It also exhibits good security, compatibility, and traceability of measurement values while attaining a high level of informatization and intelligence. Particularly, the system shows promising prospects for the remote and efficient verification of large-scale and multi-type high-voltage metering devices.

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Section: Construction Of Simulation Standard Meter Based On Improved ...mentioning

confidence: 77%

Section: Construction Of Simulation Standard Meter Based On Improved ...mentioning

confidence: 99%

Research on Intelligent Verification System of High Voltage Electric Energy Metering Device Based on Power Cloud

et al. 2023

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“…In order to further verify the performance of ML-ESN in large-scale AMI network flow, this paper selected a singlelayer ESN [34], BP [6], and DecisionTree [46] (true-positive rate) as the vertical axis. Generally speaking, ROC chart uses AUC (area under ROC curve) to judge the model performance.…”

Section: E Irdmentioning

confidence: 99%

“…Among them, researchers at home and abroad have applied network intrusion detection technology to anomaly detection of AMI network flow and proposed a variety of anomaly detection and analysis models, such as deep neural network [1], Markov [4], density statistics [5], BP neural network [6], attack graph-based information fusion [7], and principle component analysis [8].…”

Section: Introductionmentioning

confidence: 99%

“…At present, there are still some problems in the existing research on AMI network traffic anomaly detection; for example, the attack rules in [5] for AMI dynamic network environment must be updated regularly. In [6], the authors established a BP neural network training model based on 6 kinds of simple data of AMI and carried out simulation tests on Matlab software. However, the model is still a long way from real engineering applications.…”

Section: Introductionmentioning

confidence: 99%

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Network Traffic Anomaly Detection Based on ML-ESN for Power Metering System

Zhang

Lin

et al. 2020

Mathematical Problems in Engineering

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Due to the diversity and complexity of power network system platforms, some traditional network traffic detection methods work well for small sample datasets. However, the network data detection of complex power metering system platforms has problems of low accuracy and high false-positive rate. In this paper, through a combination of exploration and feedback, a solution for power network traffic anomaly detection based on multilayer echo state network (ML-ESN) is proposed. This method first relies on the Pearson and Gini coefficient method to calculate the statistical distribution and correlation of network flow characteristics and then uses the ML-ESN method to classify the network attacks abnormally. Because the ML-ESN method abandons the backpropagation mechanism, the nonlinear fitting ability of the model is solved. In order to verify the effectiveness of the proposed method, a simulation test was conducted on the UNSW_NB15 network security dataset. The test results show that the average accuracy of this method is more than 97%, which is significantly better than single-layer echo state network, shallow BP neural network, and some traditional machine learning methods.

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Detecting BGP Routing Anomalies Using Machine Learning: A Review

Muosa,

Ali

2024

Lecture Notes in Networks and Systems

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Anomaly Diagnosis Analysis for Running Meter Based on BP Neural Network

Cited by 3 publications

References 0 publications

Research on Intelligent Verification System of High Voltage Electric Energy Metering Device Based on Power Cloud

Research on Intelligent Verification System of High Voltage Electric Energy Metering Device Based on Power Cloud

Network Traffic Anomaly Detection Based on ML-ESN for Power Metering System

Detecting BGP Routing Anomalies Using Machine Learning: A Review

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