scholarly journals Research on Transmission Line Voltage Measurement Method of D-Dot Sensor Based on Gaussian Integral

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2455 ◽  
Author(s):  
Jingang Wang ◽  
Yanhang Zhao ◽  
Wenjiang Li ◽  
Xianglong Zeng ◽  
Juan Tang ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Electric Field ◽  
Transmission Line ◽  
Measurement System ◽  
Measurement Method ◽  
Voltage Measurement ◽  
Line Voltage ◽  
Voltage Sensors ◽  
Gaussian Integral ◽  
Sensor Voltage

D-dot sensors meet the development trend towards the downsizing, automation and digitalization of voltage sensors and is one of research hotspots for new voltage sensors at present. The traditional voltage measurement system of D-dot sensors makes possible the reverse solving of wire potentials according to the computational principles of the electric field inverse problem by measuring electric field values beneath the transmission line. Nevertheless, as it is limited by the solving method of the electric field inverse problem, the D-dot sensor voltage measurement system is struggling with solving difficulties and poor accuracy. To solve these problems, this paper suggests introducing a Gaussian integral into the D-dot sensor voltage measurement system to accurately measure the voltage of transmission lines. Based on studies of D-dot sensors, a transmission line voltage measurement method based on Gaussian integrals is proposed and used for the simulation of the electric field of a 220 kV and a 20 kV transmission line. The feasibility of the introduction of the Gaussian integral to solve transmission line voltage was verified by the simulation results. Finally, the performance of the Gaussian integral was verified by an experiment using the transmission line voltage measurement platform. The experimental results demonstrated that the D-dot sensor measurement system based on a Gaussian integral achieves high accuracy and the relative error is lower than 0.5%.

scholarly journals Research on Transmission Line Voltage Measurement Method Based on Improved Gaussian Integral

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73711-73718 ◽  
Author(s):  
Jingang Wang ◽  
Xiang Li ◽  
Shucheng Ou ◽  
Ruiqiang Zhang
Keyword(s):  
Transmission Line ◽  
Measurement Method ◽  
Voltage Measurement ◽  
Line Voltage ◽  
Gaussian Integral

scholarly journals Evaluation of Electric Field Integral Voltage Measurement Method of Transmission Line Based on Error Transmission and Uncertainty Analysis

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4340
Author(s):  
Jiarui Fan ◽  
Cheng Ai ◽  
Aofei Guo ◽  
Xiaojun Yan ◽  
Jingang Wang
Keyword(s):  
Electric Field ◽  
Transmission Line ◽  
Numerical Integration ◽  
Error Propagation ◽  
Evaluation Criteria ◽  
Propagation Model ◽  
Voltage Measurement ◽  
Line Voltage ◽  
Integration Algorithms ◽  
Accuracy And Stability

Electric field numerical integration algorithms can realize the non-contact measurement of transmission line voltage effectively. Although there are many electric field numerical integration algorithms, lack of a comprehensive comparison of accuracy and stability among various algorithms results in difficulties in evaluating the measurement results of various algorithms. Therefore, this paper presents the G-L (Gauss–Legendre) algorithm, the I-G-L (improved Gauss–Legendre) algorithm, and the I-G-C (improved Gauss–Chebyshev) algorithm and proposes a unified error propagation model of the derived algorithms to assess the accuracy of each integration method by considering multiple error sources. Moreover, evaluation criteria for the uncertainty of transmission line voltage measurement are proposed to analyze the stability and reliability of these algorithms. A simulation model and experiment platform were then constructed to conduct error propagation and uncertainty analyses. The results show that the G-L algorithm had the highest accuracy and stability in the scheme with five integral nodes, for which the simulation error was 0.603% and the relative uncertainty was 2.130%. The I-G-L algorithm was more applicable due to the smaller number of integral nodes required, yet the algorithm was less stable in achieving the same accuracy as the G-L algorithm. In addition, the I-G-C algorithm was relatively less accurate and stable in voltage measurement.

Research on transmission line voltage measurement method based on Gauss–Kronrod integral algorithm

2020 ◽  
Vol 31 (8) ◽  
pp. 085103
Author(s):  
Jingang Wang ◽  
Xiang Li ◽  
Qian Wang ◽  
Lu Zhong ◽  
Xiaobao Zhu
Keyword(s):  
Transmission Line ◽  
Measurement Method ◽  
Voltage Measurement ◽  
Line Voltage

scholarly journals Optimization method of electric field inverse problem based on intelligent algorithm

2021 ◽  
Vol 260 ◽  
pp. 02009
Author(s):  
Qiang Zhao ◽  
Shenglan Yang ◽  
Li Lu ◽  
Zefeng Jiang ◽  
Wei Yang ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Electric Field ◽  
Transmission Line ◽  
High Voltage ◽  
Fitness Function ◽  
Optimization Method ◽  
High Voltage Transmission Line ◽  
Intelligent Algorithms ◽  
Field Operation ◽  
High Voltage Transmission

With the expansion of China's power grid, the scale, transmission distance and load capacity of transmission projects are all growing rapidly. Meanwhile, the problem of transmission line running state detection is also attracting more and more attention. Electric field under high voltage transmission line is taken as the research object in this paper, and the principle of electric field inverse operation is analyzed, two kinds of calculation objects and methods of nondestructive examination are are described in detail. There are two kinds of intelligent algorithms in this paper in order to solve the problem of several unknown solutions in inverse electric field operation. Correspondingly, the fitness function based on voltage value and result value is established, and the inverse electric field operation algorithm with global optimization function is proposed. A calculation example of high-voltage transmission line is also used to verify the inverse problem optimization algorithm. It is respectively proved by calculation results that the effectiveness and accuracy of the optimization method based on the two kinds of intelligent algorithms.

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