Grid searching method in spherical coordinate for PD location in a substation

Xue, Feng; Wei, Dong; Wang, Zhi; Li, Tong; Hu, Yue; Huang, Hong-Yi

doi:10.1109/cmd.2018.8535972

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Finally, Grid Search will be used to estimate equations ( 3) and ( 12), with a step size of 0.5, traversing the entire region. The performance of Grid Search will be used as a lower bound for the proposed method, although such a boundary is impractical in reality 11 .…”

Section: Simulation and Analysismentioning

confidence: 99%

Cooperative localization of wireless sensor networks in non-ideal conditions

Sun,

Liu

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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The localization problem plays a vital role in the services provided by wireless sensor network (WSN). Under non-ideal conditions, the transmit power is different and the clock is not synchronized. The measured received signal strength (RSS) and time of arrival (TOA) are subjected to deviation. In this paper, a novel algorithm was proposed to alleviate these impact on localization accuracy and reduce computing consumption of localization. The algorithm called generalized trust region subproblem with improved weights (GTRS-IW) transforms the original non-convex problem into a framework of generalized trust region subproblem and solves it through a bisection process. Additionally, a critical distance was introduced to improve the weights. The proposed GTRS-IW achieves precise localization accuracy with lower computation time and was verified through simulations.

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Section: Simulation and Analysismentioning

confidence: 99%

Cooperative localization of wireless sensor networks in non-ideal conditions

Sun,

Liu

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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“…The grid search method is to map the penalty coefficient and kernel function parameters that need to be optimized to the XOY two-dimensional plane, and at the same time specify the search range and step size of the penalty coefficient and kernel function parameters to divide the plane into two-dimensional grids. During the training process, a set of penalty coefficients and kernel function parameters mapped by each grid node are substituted into the SVR model, and the combination with the highest accuracy will become the best parameters of the inversion model [27], [28].…”

Section: ) Parameter Optimization and Evaluation Indexmentioning

confidence: 99%

Research on Inversion Model of Winding Hot Spot Temperature of 10-kV Oil-Immersed Three-Dimensional Coiled Core Transformer

Chen¹,

Chen²,

Huang³

et al. 2024

IEEE Access

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Realizing online monitoring of the hot spot temperature of grid mounted transformer windings is a challenge for the power industry. The heat exchange process between the internal heat source area and the external field area of the transformer is the direct cause of the temperature increase of the transformer shell. In this paper, based on the numerical coupling calculation of temperature -fluid field of 10kV oil immersed solid coiled iron core transformer, the typical heat flow streamline is extracted, and the measurable temperature characteristic points strongly correlated to the winding hot spot temperature are selected in the heat dissipation area of the shell, and the inversion model of the transformer winding hot spot temperature is established. This article verifies the effectiveness of the simulation results and the accuracy of the inversion model through the multi condition temperature rise test of transformer. The maximum error between the calculated steady-state temperature rise of each temperature measurement point in the simulation model and the test value is no more than 3°C, the simulation calculation results are in good agreement with the test results, and the average absolute error of the inversion model winding hot spot temperature inversion is 2.67 %, the maximum temperature difference is less than 3°C. This research provides an effective method for online monitoring of the hot spot temperature of the winding of a 10kV oil-immersed three-dimensional coil iron core transformer, and provides a reference for the state detection of grid mounted transformers.INDEX TERMS oil-immersed transformer; temperature fluid field; streamline extraction; hot spot temperature inversion; power equipment status monitoring

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