The joint is the weakest link in three-core medium-voltage power cable systems and the temperature is an essential indicator to its insulation condition. Therefore, a model to estimate the temperature inside the three-core cable joint was built based on support vector regression (SVR) with two fixed cable surface temperatures as inputs. The samples for model training were obtained from 3-D transient thermal analyses through finite element method (FEM) under different single-step currents. A temperature-rise test of 15 kV three-core cable joint was carried out and the estimated temperature based on SVR agrees well with the measured result with a maximum error of about 4 °C. Besides, the proposed model could accurately estimate the joint temperature even though the thermal conductivity of armor wrap used in thermal analysis for model training differs from its real value. The accuracies and calculation speed of the proposed model were compared with those of FEM, showing a better generality of our model. A temperature-rise test under unbalanced three-phase currents was performed and the temperature estimation errors are within 6 °C, indicating the applicability of the method. The effect of contact resistance was briefly discussed in the end. This approach helps improve cable operation and maintenance.

The bushing hot spot temperature seriously affects its performance and design. In this paper, a novel method for calculating the hot spot temperature of bushing is proposed, in which the principle of constant joule heating is adopted to transform the fluctuating current into the steady-state current. Firstly, the fluctuating current based on time is segmented. Then, the fluctuating current in each period of time is transformed into steady-state current which used as heat source. Next, the Finite Element Method (FEM) is employed to determine a bushing’s temperature distribution and specify its hottest spot. According to the calculation results of hot spot temperature rise, the time interval is adjusted. Finally, the optimal time segment and the steady-state equivalent current are obtained by solving the hot spot temperature of transformer bushing iteratively. In addition, the method is used to calculate the hot spot temperature of 220kV Oil Impregnated Paper (OIP) bushing. Compared with the results of traditional transient calculation method, the validity of the method is verified, and the computing time is greatly reduced.

The fully insulated busbar system, which has been extensively used in the industry, contains a large number of joints due to its limited length, and thus it is prone to overheating failure caused by poor contact. Taking the uncertainty of contact resistance into account, this paper presents an indirect approach to monitor the conductor temperature for the fully insulated busbar prefabricated joint using the busbar surface temperatures. The model is composed of radial direction temperature calculation (RDTC) in the busbar and axial direction temperature calculation (ADTC) in the conductor. The equivalent thermal resistance of joint is defined to characterize the influence of contact resistance, based on which RDTC and ADTC are corrected by an iterative method. The temperature rise test of the prefabricated joint was carried out. When the contact state is normal, the transient and steady‐state error is within 4 and 1 K, respectively. When the contact state is abnormal, the steady‐state error under rated current is less than 5 K, and the transient error with the step load is about 10 K, which is thoroughly analyzed. From an engineering point of view, the proposed algorithm can effectively monitor the joint hotspot temperature and evaluate the contact state.

Autonomous Underwater Vehicles (AUVs) have become essential tools for underwater magnetic surveys. However, the magnetic interference from the AUV platform severely impacts measurement accuracy. To improve the accuracy of magnetic disturbance parameter identification and compensation for AUV platforms, a theoretical AUV magnetic disturbance model based on the rotating ellipsoidal shell and magnetic dipole model is established. The Success History-based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) algorithm is employed to optimize and identify model parameters for solving the magnetic field, and Back Propagation Neural Network (BPNN) is designed and optimized through training to enable the direct estimation of the true magnetic field from the measured magnetic field. To further improve training performance, a stacking ensemble learning (STACKING) model is introduced, with L-SHADE and BPNN as base learners and Convolutional Neural Network (CNN) as the meta-learner, integrating the advantages of both algorithms for optimization. Numerical simulations demonstrate that, under a 5° attitude error, the L-SHADE algorithm achieves mean decoding accuracies of 86.42%, 81.9%, and 86.15% for the three magnetic field components after training with low-noise data. Meanwhile, the BPNN achieves prediction accuracies of 86.58%, 83.14%, and 83.09%. By applying the stacking ensemble learning method, the maximum prediction accuracy improves by up to 25.77%. Compared to individual algorithms, the proposed fusion method significantly enhances anti-interference capability and decoding accuracy. This study provides a novel and efficient solution for AUV magnetic interference compensation and offers valuable insights for addressing similar problems.

The fully insulated busbar has been extensively used in power and shipboard applications due to its favorable economic efficiency and excellent performance. Because of contact resistance and larger insulation thermal resistance, the joint, in certain circumstances, forms a hotspot in the circuit. For hotspot temperature monitoring, therefore, this paper proposed an indirect approach which consists of radial direction temperature calculation (RDTC) in the busbar and axial direction temperature calculation (ADTC) in the conductor. RDTC is to calculate the conductor temperatures from the surface temperatures of busbar in two different spots near the joint through transient thermal network. ADTC is to estimate the hotspot temperature from the conductor temperatures obtained in RDTC using a fitted functional expression. The influence of solar radiation on the temperature distribution is greatly reduced by averaging the surface temperature in RDTC and adjusting the functional form in ADTC. A temperature-rise test on a practical insulated busbar taped joint was performed outdoors in hot summer to validate this approach. The calculated hotspot temperature agrees well with the measured result with maximum error of only 3.8 K, indicating the high model precision and strong robustness to the solar radiation impact. Some factors in the application of temperature monitoring are also discussed. This method is expected to be applied to the engineering application, thus improving the condition monitoring of the fully insulated busbar.