System‐Level Temperature Compensation Method for the RLG‐IMU Based on HHO‐RVR

Liang, Hao; Tao, Y.; Wang, Meijiao; Guo, Yu; Zhao, Xiaofeng

doi:10.1155/2021/6613574

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…The number of nodes in the input layer is decided according to the actual problem to be solved by the temperature compensation model of the voltage transformer. In this paper, the temperature value of the environment and the voltage measurement of the transformer are taken as the input values [22][23], so the number of nodes in the input layer of the temperature compensation model is 2. The number of nodes in the output layer is decided according to the actual problem of this temperature compensation model itself.…”

Section: Temperature Compensation Based On Bp Neural Networkmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The metering accuracy of the voltage transformer is related to the normal operation of the power system, and the metering results can be optimized through the prediction of the error state. In this paper, according to the generation mechanism of the measurement error of the transformer, the maximum information coefficient is used to extract the error characteristic quantity, and the measurement perturbation model is constructed by combining the ambient temperature and the secondary load factor. Due to the specificity of the ambient temperature, a BP neural network is also used to compensate for the temperature of the perturbation model, which prepares for the improved BP neural network based on a genetic algorithm to recognize the error data. Finally, the simulated operation of the three-phase voltage transformer and the measured data of the wiring substation were utilized for validation, respectively. With the help of three-phase CVT simulation, the error change of A-phase simulated CVT amplitude information at the 4001st sampling point is 0.0962%, and the error change of phase information is -4.572′.GA-BP neural network also has high sensitivity to the difficult-to-detect asymptotic error and is able to realize the error calibration of voltage transformer.

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Section: Temperature Compensation Based On Bp Neural Networkmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…The ring laser gyro inertial measurement unit has many systematic error terms which seriously affects the stability time and accuracy. A system-level temperature modeling and compensation method is proposed based on the relevance vector regression method [52]. H. Xu et al [53] have proposed a new indoor people detection and tracking system using a millimeter-wave (mmWave) radar sensor.…”

Section: Different Sensing Techniques and Instrumentsmentioning

confidence: 99%