To solve the problem of the accuracy and generalization of the transmission line loss prediction, a new method for transmission line loss prediction based on cross-validation (CV) and gradient boosting decision tree (GBDT) is proposed. In this method, time granularity matching and statistical feature extraction are firstly carried out to improve the information content of the data. Then the data is divided into a training set and test set by CV method to train the GBDT model. Finally, the actual transmission line loss verification test is conducted. The result shows that for 500kV transmission lines with an average daily supply of 7990MWh, the average line loss error of this model is 35MWh, and the average line loss rate error is 0.093%, which verifies the effectiveness of the method in this paper.

This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reactive power. This decomposition process can compress the high frequency of reactive power and benefits the following separate forecasting. Long short-term memory is proposed for the high-frequency feature of reactive power to deal with the forecasting difficulty caused by strong signal disturbance and randomness. In contrast, random forest regression is applied to the low-frequency part in order to speed up the forecasting. Four classical algorithms and four hybrid algorithms based on different signal decompositions are compared with the proposed algorithm, and the results show that the proposed algorithm outperforms those algorithms. The predicting index RMSE decreases to 0.687, while the fitting degree R² gradually approaches 1 with a step-by-step superposition of high-frequency signals, indicating that the proposed decomposition-predicting reconstruction strategy is effective.

Under the action of strong DC electric field, the resistance of the insulating cylinder of high-voltage DC voltage divider shows a nonlinear decrease with the increase in electric field strength, which leads to the decrease in measurement accuracy. It is shown that the effect can be suppressed to a certain extent by applying a semi-conductive coating on the surface of the insulating outer barrel. However, due to the inherent properties of the conductive nano-fillers, it is difficult to achieve uniform dispersion in the polymer matrix, which limits the electric field modulation ability of the coating and makes it difficult to fundamentally solve the nonlinear effect of the insulating outer barrel resistance. In this study, based on the multi-walled carbon nanotubes/polyaniline (MWCNTs/PANI) epoxy semiconducting coating system, the effects of dispersant type and dosage on the variation in the coating equivalent resistance with voltage were investigated. The semiconducting coatings with different dispersion states of conductive fillers were prepared by regulating the types and amounts of alkyl ammonium salt dispersants, polymer urethane dispersants, and low molecular weight unsaturated polycarboxylic acid polymer dispersants, and analyzed in combination with the equivalent resistance test, SEM microscopic morphology characterization, and coating adhesion test. The results show that the dispersion of the conductive filler has a significant effect on the suppression of the resistance nonlinear effect, and the polymer polyurethane dispersant can make the filler uniformly dispersed by virtue of the strong spatial site resistance, and the equivalent resistance change rate is lower than 28% under the additive amount of 0.5–1.5%, which has an excellent suppression of the nonlinear effect of the insulation resistance, and the adhesion of the coating is significantly improved to grade 0. The study provides theoretical basis and experimental support for the optimization of the preparation process of high-performance MWCNTs/PANI semiconducting coatings, which can help to improve the measurement accuracy and operational stability of high-voltage DC voltage dividers.

Accurate measurement of the state of charge (SOC) in lithium-ion batteries is crucial for battery management systems in electric vehicles and fair transactions at battery swapping stations. However, battery aging leads to nonlinear variations in capacity and internal resistance, posing challenges for precise SOC estimation. To address this issue, this paper proposes a high-precision measurement method based on aging trajectory transfer and hardware-in-the-loop (HIL) simulation. Firstly, a two-stage aging trajectory transfer prediction model is established, utilizing aging data from standard batteries to predict the capacity degradation trajectory of the target battery. Factors such as temperature and current are incorporated to enhance prediction accuracy. Subsequently, the predicted aging trajectory is introduced into a PHIL simulation platform to update battery model parameters in real-time, simulating the charging and discharging characteristics of the battery under different aging states. Experimental results demonstrate that this method can effectively predict battery aging trajectories and achieve high-precision SOC measurement. The model accurately quantifies the capacity degradation from 734.63 mAh to 558.24 mAh, a decrease of 24.01%, along with the decline rate of open-circuit voltage and charging time, contributing to high-precision measurement and fair transactions for lithium-ion batteries.

Current measurement in Power and Energy Systems must accommodate direct-current components, converter harmonics, fast transients, wide dynamic ranges, and strong electromagnetic interference. Tunneling magnetoresistance (TMR) sensors offer high field sensitivity, broad intrinsic bandwidth, compact size, and low power consumption, but these device-level advantages do not directly translate into system-level accuracy or usable bandwidth. The principal contribution of this review is a unified framework organized around sensor architecture that connects magnetic tunnel junction transfer characteristics and Wheatstone-bridge behavior to complete current-sensing systems. Unlike previous reviews organized mainly by materials, operating principles, or applications, this work compares surface-mounted, linear-array, ring-array, and magnetic-core open- or closed-loop architectures across sensor-element, circuit, external-field, manufacturing, and installation errors, together with compensation strategies, test conditions, implementation burden, and application evidence. It further distinguishes device metrics from complete-system performance and laboratory feasibility from deployment evidence. The comparative analysis identifies current-to-field geometry, bridge matching, thermal drift, external interference, signal conditioning, and feedback dynamics as the coupled factors that determine usable range, accuracy, and bandwidth. Their relative importance changes with architecture: surface-mounted layouts are especially sensitive to placement and crosstalk; linear and ring arrays introduce channel-matching and spatial-calibration requirements; and magnetic-core designs are additionally constrained by core behavior and, in closed-loop systems, feedback dynamics. These relationships provide a requirements-driven basis for selecting an architecture according to waveform, current range, installation geometry, interference environment, and uncertainty requirements. Meaningful comparison further requires common-condition evaluation of amplitude and phase error, overload recovery, environmental stability, and calibration transfer across devices and installations.