scholarly journals Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1385
Author(s):  
Sheng Wu ◽  
Kwok L. Lo
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Bp Neural Network ◽  
Distribution Curve ◽  
Electrical Characteristics ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Current Frequency ◽  
Operation Pattern ◽  
Load Monitoring

Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

scholarly journals An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Automatic Monitoring ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Slope Deformation ◽  
Open Pit ◽  
Gis Technology ◽  
Monitoring Method

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

Software Design and Implementation of Large-Scale Intelligent Traffic Monitoring System

2014 ◽  
Vol 644-650 ◽  
pp. 1351-1354
Author(s):  
Jun Ye Wang
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Large Scale ◽  
Rbf Neural Network ◽  
Traffic Monitoring ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Traffic State ◽  
Network Algorithm ◽  
Neural Network Algorithm

The design method of large-scale intelligent traffic monitoring system is studied. Traffic monitoring methods have become the core problem of intelligent transportation research field. To this end, this paper proposes an intelligent traffic monitoring method based on clustering RBF neural network algorithm. Fourier coefficient normalization method is used to extract the feature of traffic state, to be as the basis for intelligent traffic monitoring. Using clustering RBF neural network algorithm identify the traffic state effectively, thus to complete the state recognition of intelligent traffic monitoring. Experimental results show that the proposed algorithm performed in intelligent traffic monitoring, can greatly improve the accuracy of monitoring.

scholarly journals Insulation Monitoring of Dynamic Wireless Charging Network Based on BP Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 129
Author(s):  
Feng Wen ◽  
Wenjie Pei ◽  
Qiang Li ◽  
Zhoujian Chu ◽  
Wenhan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Power Conversion ◽  
Experimental Results ◽  
Calculation Error ◽  
Insulation Resistance ◽  
Wireless Charging ◽  
Monitoring Method ◽  
Insulation Monitoring ◽  
On Line

The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.

scholarly journals Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 704 ◽  
Author(s):  
Xin Wu ◽  
Dian Jiao ◽  
Yu Du
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Load Identification ◽  
Neural Network Models ◽  
Monitoring Method ◽  
Line Load ◽  
The Real ◽  
Load Monitoring ◽  
On Line

Non-intrusive load monitoring (NILM) is an effective way to achieve demand-side measurement and energy efficiency optimization. This paper studies a method of non-intrusive on-line load monitoring under a high-frequency mode of electric data acquisition, which enables the NILM to be automated and in real-time, including the short-term construction of a dynamic signature library and continuous on-line load identification. Firstly, in the short initial operation phase, load separation and category determination are carried out to construct the load waveform library of the monitoring user. Then, the continuous load monitoring phase begins. Based on the data of each user’s signature library, the decomposition waveforms are classified by convolutional neural network models that are constructed to be suitable for each signature library in order to realize load identification. The real-time power consumption status of the load can be obtained continuously. In this paper, the electricity data of actual users are collected and used to perform the experiments, which show that the proposed method can construct the load signature library adaptively for different users. Meanwhile, the classification of the convolutional neural network model based on a library constructed in actual operation ensures the real-time and accuracy of load monitoring.

Diagnosis of Stator Winding Inter-Turn Circuit Faults in Induction Motors Based on Wavelet Packet Analysis and Neural Network

2012 ◽  
Vol 529 ◽  
pp. 37-42 ◽  
Author(s):  
Jun Yong Sang ◽  
Chen Hao ◽  
Peng Chao Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Short Circuit ◽  
Stator Winding ◽  
Wavelet Packet Analysis ◽  
Current Frequency ◽  
Frequency Band Energy ◽  
Short Circuit Fault

Aiming at the problem of the traditional stator current frequency spectrum analysis method cannot completely guarantee the accurate identification of stator winding inter-turn faults,the diagnosis method of stator winding inter-turn based on wavelet packet analysis (WPA) and Back Propagation (BP) neural network is put forward. The finite element model of the three-phase asynchronous motor which is based on Magnet is established, and analysis the magnetic flux density and current of the motor through simulation in normal and in the situation of short-circuit fault of stator winding inter-turn, the current signal of stator is analysised by wavelet packet , and the feature vector of frequency band energy is extracted as the basis to judge the state of induction motor running, and the motor state is identified by BP neural network, and the mapping from feature vector to the motor state is established. Simulation results show that: The diagnosis system of inter-turn fault based on WPA and BP neural network can effectively identify short-circuit fault between ratios. This is to say that the method has a high fault diagnosis rate.

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