Evaluation and optimization of safety performance state of grounding grid

Lin, Hanliang; Liu, Hongshun; Zhi-yuan, Zhang; Ma, Xingguang; Zheng, Yuan; Chen, Wen; Lu, Yitao; Yan, Maohua; Huang, Yunyun

doi:10.1109/cieec50170.2021.9510929

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…lr Br (1) injected through point A and withdrawn at point C. The injected current size is 1A, L1 and L2 are two parallel measurement lines located at (y=2.5, z=0.8) and (y=7.5, z=0.8), respectively. Two types of data, Bx and ∂Bz/∂x, were extracted from the survey line to be used as grounding grid topology extraction, and the curve of the data along the x-axis is shown in Fig.…”

Section: Grounding Grid Topology Identification Errormentioning

confidence: 99%

“…Grounding grid is an important foundation to ensure the safe operation of the power system [1][2][3]. The grounding grid is always buried and underground, affected by the surrounding environment and corrosion, serious corrosion of the grounding grid will even produce a breakpoint, resulting in a reduction in the ability of the grounding grid to dissipate the current, and sometimes even produce an abnormal increase in the ground voltage and jeopardize the safety of the staff on inspection [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of corrosion detection error of grounding grid under amplitude characterization

Jinshan Yu

2024

jes

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Magnetic field amplitude detection is an important method for determining the positioning and corrosion degree of the grounding grid, and the conventional analysis only determines the grounding grid status by collecting single-component signals, which makes the diagnosis results of the grounding grid status susceptible to the influence of the collection process. By considering the single-component sensor device in the attitude deflection during the actual substation detection process, the analysis of the difference between the signals measured by the sensor under different attitude deflections is performed, and the degree of influence of the attitude deflection on the magnetic field amplitude characteristics is determined. Further, by analyzing the calculation error of the attitude angle on the corrosion degree of the ground grid, the correspondence between the sensor attitude and the calculation error of the corrosion degree was obtained. Finally, in order to reduce the influence of the sensor attitude on the detection signal, it is proposed that the detection scheme of measuring the three-component sensor signal and the attitude angle at the same time which could improve the accuracy of the detection system.

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Section: Grounding Grid Topology Identification Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of corrosion detection error of grounding grid under amplitude characterization

Jinshan Yu

2024

jes

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“…Long Short-Term Memory Network (LSTM) is a type of recurrent neural network that has been shown to be effective in addressing the issues of gradient explosion and gradient disappearance commonly encountered in traditional recurrent neural networks. This makes it an ideal candidate for processing data inputs [12] . The LSTM network is particularly wellsuited to identifying patterns in time series data, which allows it to effectively capture the complex, nonlinear, and timevarying power response of wind power systems [13] .…”

Section: System Identification Based On Lstmmentioning

confidence: 99%

Online evaluation of power system inertia based on LSTM deep-learning network

Cai¹

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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Conventional algorithms typically rely on system identification techniques to estimate the inertia of power systems online. However, selecting an appropriate model order can be challenging, and an incorrect choice can lead to significant errors. To address this issue, we propose an algorithm based on Long Short-Term Memory Network (LSTM) deep learning networks for power system inertia identification. In our approach, we preprocess and input frequency and power deviation data obtained from monitoring into the LSTM model for learning. Additionally, we utilize the multi-sampling point method to reduce errors introduced by approximation algorithms. Once we obtain the inertia time constant for each unit, we calculate the system's overall inertia. Finally, we build a simulation system using MATLAB/Simulink to demonstrate the effectiveness and accuracy of our proposed method.

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Optimum pattern of grounding grid by using closed-form solution based on optimized results

El-Refaie,

Abd Elrahman,

Mohamed

et al. 2023

Electr Eng

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Finding the optimum conductor’s pattern for unequally spaced grounding grids can be achieved by using empirical formulas or optimization techniques. Empirical formulas give an approximate solution depending on human experience and never cover the entire search space. On the other hand, the optimization techniques generally require a significant effort and time. To overcome the previous issues, this paper presents a closed-form solution based on optimized results. The main motivation behind this formula is the comprehensive study carried out by the authors to find optimum location of grounding grid conductors over different areas. In that work, particle swarm optimization technique (PSO) has been adapted with combined integration matrix method (CIMM) to facilitate the study. In this paper, the results obtained previously were summarized in the proposed formula. The proposed formula gives the optimum design of unequally grounding grids by specifying the conductor’s locations as percentage of the side length for square and rectangular shapes. As long as the side length and the number of conductors are given, the location of each conductor as a percentage of the side length can be obtained. The effect of adding driven rods and the stratified soil conditions on the results of the optimized design were investigated. The validity of the proposed closed-form solution was verified experimentally.

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Evaluation and optimization of safety performance state of grounding grid

Cited by 4 publications

References 3 publications

Analysis of corrosion detection error of grounding grid under amplitude characterization

Analysis of corrosion detection error of grounding grid under amplitude characterization

Online evaluation of power system inertia based on LSTM deep-learning network

Optimum pattern of grounding grid by using closed-form solution based on optimized results

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