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Current sensors based on the tunneling magnetoresistive effect (TMR) are widely used for current measurement due to their high sensitivity, small size, and low power consumption. This paper proposes an effective error correction model to rectify the eccentricity of the transmission line, which can cause a significant measurement error in the ring‐array single‐axis TMR sensor. The model employs a convolutional neural network (CNN) to identify the relationship between the conductor eccentricity and the output of three sensors. The resulting correction factor is then fed back to eliminate the error associated with wire eccentricity. Concurrently, the Sparrow search algorithm (SSA) is employed to optimize the hyperparameters of the convolutional neural network (CNN) in order to enhance the model's performance. The experimental results demonstrate that the maximum error of the ring‐array single‐axis TMR current sensor, corrected by SSA‐CNN, is less than 0.42%, which markedly enhances the precision of the measurement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A distributed optical current transducer (DOCT) and a current measurement method are proposed to solve the problem that the straight-through optical current transducer is vulnerable to crosstalk magnetic field interference. We established a mathematical model of the magnetic interference degree of DOCT. Analyzing and summarizing the characteristics of the magnetic interference degree distribution of DOCT revealed that there are six magnetic interference zeros outside of DOCT. Then based on the background of three-phase common box current measurement in a gas-insulated substation, a current measurement method of anticrosstalk magnetic field interference is proposed, that is, adjusting the direction of DOCT to make the interference current at zero magnetic interference point, to achieve the effect of anticrosstalk magnetic field interference. The experiment shows that the measurement error of DOCT is <0.15 % , and the interference of the crosstalk magnetic field can be ignored. [ABSTRACT FROM AUTHOR]

Insulators have an extremely important role in transmission lines, and they are important components for ensuring the safe operation of transmission lines. In order to solve the difficult problem of insulator fault detection under complex background, IG‐YOLOv8 insulator fault detection algorithm is proposed in this paper. First, the Wise‐IoU (WIoU) loss function is introduced to mitigate the adverse impact of low‐quality images by employing a dynamic non‐monotonic focusing mechanism, thereby enhancing the detection performance of the entire model. Second, a novel C2f network is constructed by integrating the receptive field coordination attention (RFCA) convolutional module to address the parameter‐sharing issue associated with large convolutional kernels. Additionally, the data set has been reorganized using k‐fold cross‐validation to ensure that each subset undergoes training and testing, consequently reducing generalization errors. Finally, a deformable attention (DA) mechanism is employed to augment the feature extraction capability pertaining to insulator fault region information. In order to evaluate the detection performance of the improved IG‐YOLOv8 algorithm, this study constructed an insulator target detection data set containing four fault types: Normal, Defect, Dirty, and Aging. The experimental results show that the average accuracy of the improved model is increased from 89.7% to 96.9%, and the Recall value of the Aging type insulator is increased from 71.8% to 89.1%. The occurrence of missed detection is greatly reduced, and the accuracy of detection is improved. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]