Online verification and management scheme of gateway meter flow in the power system by machine learning
Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verif...
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| Veröffentlicht in: | PeerJ. Computer science Jg. 9; S. e1581 |
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| Abstract | Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system. |
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| AbstractList | Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system. Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system.Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system. |
| ArticleNumber | e1581 |
| Audience | Academic |
| Author | Wang, Yi Li, Qian Shen, Hongtao Yang, Peng Wang, Ruiming Li, Chuan Li, Chong Guo, Rongkun Li, Bing Wang, Hao |
| Author_xml | – sequence: 1 givenname: Chong surname: Li fullname: Li, Chong organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 2 givenname: Hao surname: Wang fullname: Wang, Hao organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 3 givenname: Hongtao surname: Shen fullname: Shen, Hongtao organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 4 givenname: Peng surname: Yang fullname: Yang, Peng organization: State Grid Hebei Electric Power Company, Shijiazhuang, HeBei, China – sequence: 5 givenname: Yi surname: Wang fullname: Wang, Yi organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 6 givenname: Qian surname: Li fullname: Li, Qian organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 7 givenname: Chuan surname: Li fullname: Li, Chuan organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 8 givenname: Bing surname: Li fullname: Li, Bing organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 9 givenname: Rongkun surname: Guo fullname: Guo, Rongkun organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China – sequence: 10 givenname: Ruiming surname: Wang fullname: Wang, Ruiming organization: State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38077539$$D View this record in MEDLINE/PubMed |
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| Keywords | Limited memory recursive least squares algorithm SSD model Faster-RCNN model Line loss Machine learning Online verification Dial reading identification Light load condition |
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| SubjectTerms | Detectors Dial reading identification Electric power systems Faster-RCNN model Inspection Investment analysis Limited memory recursive least squares algorithm Machine learning Neural networks Online verification SSD model |
| Title | Online verification and management scheme of gateway meter flow in the power system by machine learning |
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