scholarly journals Using Grey Target Theory for Power Quality Evaluation Based on Power Quality Monitoring Data

2022 ◽  
Vol 119 (1) ◽  
pp. 359-369
Author(s):  
Qiang Yu ◽  
Xiankai Chen ◽  
Xiaoyue Li ◽  
Chaoqun Zhou ◽  
Zhichao Li
Keyword(s):  
Power Quality ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Power Quality Monitoring ◽  
Target Theory

scholarly journals Automatically detecting and correcting errors in power quality monitoring data

2017 ◽  
Author(s):  
Steven Blair ◽  
Campbell Booth ◽  
Gillian Williamson ◽  
Alexandros Poralis ◽  
Victoria Turnham
Keyword(s):  
Power Quality ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Power Quality Monitoring

A Power Quality Monitoring Data Management Scheme Based on Distributed Database

2013 ◽  
Vol 732-733 ◽  
pp. 1410-1414
Author(s):  
Qing Qing Zhang ◽  
Qing Yu Wang ◽  
Gao Feng Zhang ◽  
Yi Zhang ◽  
Lan Xu Wu
Keyword(s):  
Power Quality ◽  
Distributed Database ◽  
Database System ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Storage Space ◽  
Power Quality Monitoring ◽  
Management Scheme ◽  
Master Station ◽  
Distributed Database System

Currently the massive power quality monitoring data are stored in the centralized database of the monitoring master station, the problems such as large storage space; low query retrieval speed; low reliability and poor scalability will be caused. This paper proposes a data management scheme for massive power quality monitoring data based on the distributed database system. The monitoring data of different power quality indexes are stored in the distributed servers of the existing monitoring sub-stations; the server of monitoring master station is used for storing data characteristics value and data indexes, it is also used for unified management of distributed database system. The scheme takes full advantage of each servers storage space and network bandwidth, and saves the storage space and improves the access efficiency.

Fault Location Method in Complex Power Network Based on Power Quality Monitoring Data

2013 ◽  
Vol 732-733 ◽  
pp. 1320-1327
Author(s):  
Jian Lan ◽  
Dong Xu Wang ◽  
Xue Zheng ◽  
Kai Pei Liu ◽  
Cai Xia Yang
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Fault Location ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Fault Line ◽  
Location Method ◽  
Voltage Dip ◽  
Power Quality Monitoring ◽  
Trained Neural Network

In this paper, a fault location method using power quality monitoring data is proposed. The fault location is divided into two classes including fault line recognition and fault distance calculation. Along the each power line in the target network some fictitious fault points are set and the fault voltage of each monitoring bus is calculated. The voltage dip calculation results and the fault line data are matched to form the learning samples and then train the designed fault line recognition BP artificial neural network. The trained neural network searches the fault line when actual fault occurs. After it finishes fault line searching the fault position is calculated using the function of voltage dip amplitude and fault position. The correctness and effectiveness of the proposed method is proved by the simulation results in standard IEEE-14 bus system.

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