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The application of virtual synchronous generator (VSG) control in flywheel energy storage systems (FESS) is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids. Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches, this paper proposes an adaptive VSG control strategy for a flywheel energy storage array (FESA). First, by leveraging the FESA model, a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge (SOC) equalization across units. Furthermore, energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit. The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation. In addition, VSG technology is applied for the grid-connected control of the FESA, and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback. This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation. Subsequently, a parameter design method is developed through a small-signal stability analysis. Consequently, considering the SOC of the FESA, an adaptive control strategy for the inertia damping and the P/ω droop coefficient of the VSG control is proposed to optimize the grid support services of the FESA. Finally, the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.

Abstract The explosion accident due to the internal arc fault in the metal‐enclosed switchgear cannot be ignored. Therefore, improving the ability of switchgear to resist the arc fault has important significance. In this paper, a passive protection measure by installing the buffer volume (BV) was proposed. The short‐circuit arcing tests were carried out in the small‐scale switchgear compartments, and the pressure rise was measured in the different models. The changes of pressure rise before and after installing BV were analyzed. Then, the calculation methods of pressure rise in the compartments and the mechanical response of the enclosure were put forward. By calculating the distributions of the pressure rise and temperature rise in the compartments, as well as the time‐varying displacement and stress of the enclosure, the pressure relief effect of the BV has been discussed. Results show that after installing the BV, the pressure rise, displacement, and stress are all decreased obviously in the initial of arcing. Hence, the arc fault protection effect for the switchgear can be improved by properly installing the BV.

Abstract In this article, the dynamic thermal rating assessment method of oil‐immersed power transformer with multiple operating conditions is proposed, considering the constraints of hot spot temperature (HST), top oil temperature, losses of life and the maximum allowable current of on‐load tap changer (OLTC) or bushing, which can determine the dynamic load curves under different operating conditions and give the most sensitive constraints to limit the dynamic load capacity. To improve the accuracy of HST estimation, the temperature estimation model is also improved and the thermal parameters are optimised using the HST measured by optical fibre. Finally, several application examples are studied for transformers in different scenarios. The results show that the normal cyclic dynamic transformer rating (DTR) is mainly limited by the losses of life when the ambient temperature is high, and the average load factor can be increased to 0.82 with a maximum load capacity of 1.23. The main limiting factor of short‐term DTR is the OLTC or bushing current constraint, and the average load factor can be increased to 1.00. The maximum load capacity of the transformer under both operating conditions is 23% and 50% higher than its rated load capacity, indicating that the transformer still has a large load potential available.

Abstract The trip mechanism is a weakness in circuit breakers. Traditional fault identification based on the coil current is difficult to report early mechanical defects such as coil‐plunger jam. Here, the vibration signal during the trip process was extracted. Based on the coil current signal and vibration signal, the characteristics of the trip mechanism are analyzed. The phase space reconstruction (PSR) method is used to extract features from the vibration signal. Combined with the features from the coil current waveform, the feature set representing the health condition of the trip mechanism is proposed. The fault simulation tests are carried out and the variation of current vibration characteristics under fault conditions is studied. The fault identification model based on a support vector machine (SVM) is proposed and compared with the identification results when features are extracted from a single signal. When the power supply voltage is dispersed, the prediction accuracy of fault identification is 83.3% considering only the features of the current waveform or vibration signal. And the identification accuracy rises to 96.7% while using the feature set of current and vibration signals. On basis of the current signal, the method further combines the vibration signal so that the robustness of defect identification improves.

Abstract Transformer overheating faults have an important impact on the transformer safe and stable operation. Taking 10 kV oil‐immersed transformer as the research object, using the indirect coupling analysis method of electromagnetic thermal fluid multi‐physical field, the transformer heating characteristics under overheating faults, such as winding inter‐turn short circuit and core multi‐point grounding, are analysed, and the transformer abnormal heating state identification method is proposed based on the inversion of transformer top oil temperature rise. By inverting the transformer normal top oil temperature rise under different load rates and comparing it with the actual measured value, taking into account the top oil temperature rise inversion error, it is determined that when the top oil temperature rise measured value is higher than the inversion value by 2.9 K, it indicates the transformer is in an abnormal heating state, and the higher the deviation degree is, the deeper the degree of abnormal heating fault. The accuracy of the transformer abnormal heating state identification method is verified by the multi‐working condition temperature rise test, which can provide a certain reference for the detection and maintenance of transformer abnormal internal heating faults.

Abstract 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.

The temperature of cable conductor is of great significance to improve the current carrying capacity, asset utilization and safe operation of cable lines. Aiming at the problems of slow calculation speed, low accuracy and weak anti-interference ability of the current temperature calculation methods, this paper proposes an inversion method based on improved sparrow search algorithm (ISSA) optimized back propagation neural network (BPNN). Tent mapping was used to increase the initial population diversity of sparrow. Modified sparrow optimization formula to improve convergence speed. Chaotic perturbation is applied to the optimal individual to improve the global and local search ability of SSA. The multi-physics simulation model of 110kv straight connector was established, and the temperature distribution data under three different working conditions were obtained. According to the simulation data and CEC2017 standard test experiments, the optimization ability of the improved model is compared with particle swarm optimization (PSO), whale optimization algorithm (WOA), SSA and MSWOA. To verify the generalization performance and migration ability of the proposed method, the thermal cycle test and inversion calculation of the 10kV cable straight-through joint were carried out. The results show that ISSA-BPNN has high accuracy, fast convergence speed, good robustness, and is less affected by cable joint type, load current and cable environment conditions. It has good engineering practicability.

The interactions between power quality in the AC-DC distribution network segments contribute to the distributed propagation of power quality anomalies throughout the entire network. Focusing on the photovoltaic multifunctional grid-connected inverter (PVMFGCI), this study deeply explores a collaborative governance strategy for optimizing regional power quality. Initially, the analysis dissects the DC ripple generation mechanism corresponding to harmonics and asymmetry in AC subnetwork voltages. Subsequently, a strategy is proposed for partitioning comprehensive control regions for AC-side power quality, taking into account photovoltaic governance resources based on insights from photovoltaic control realms and power quality classifications. Further, a collaborative allocation model for governance resources incorporating active optimization potentials of grid-connected converters is established based on the governance capabilities and residual capacities of PVMFGCI. Finally, the effectiveness of the proposed approach is validated through a MATLAB-based example analysis.

The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.

This paper proposes an inversion method to estimate a 10 kV oil-immersed transformer transient hot spot temperature (HST). A set of transient feature quantities which can reflect the load change are proposed, those quantities as well as the real time load rate and feature temperature points on the transformer iron shell are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the transformer transient HST. K-fold cross-validation training method and grid search (GS) parameters optimization method are used to find the optimal parameters of the SVR model, the HST inversion results agree well with the transformer temperature rise test data which are conducted with short circuit method, and the HST inversion results outperform the results obtained with GA-BPNN method. The mean absolute percentage error (MAPE) is 1.66 %, and the maximum temperature difference is 2.93 °C.

The inductance of degaussing windings is an important parameter for the dynamic current adjustment and overvoltage analysis of ship degaussing systems. This inductance depends on not only the shape, location, and turns of windings, but also on the distribution of the surrounding magnetic medium and its eddy current effect, leading to the difficulty of determining the self- and mutual inductances. To solve this problem, an AC inductance calculation method of degaussing windings considering the eddy current effect was first proposed. The relevant factors affecting calculation accuracy were then analyzed to provide a theoretical basis for the inductance calculation. Aiming at the accurate inductance calculation of a double-layer steel cylinder, a 3D numerical simulation model was established by ANSYS for frequency domain analysis at four different current frequencies (DC, 100 Hz, 1 kHz, and 10 kHz). The relationship between the current frequency and the mesh division in the shell thickness direction was analyzed. Effects of the outer shell on the inductance calculation and its mechanism were also analyzed. The comparative results of the measured and calculated inductance values show that the relative errors are less than 20%. Moreover, the maximum mutual inductance arrives at $21.6~\mu \text{H}$ , indicating a considerable value of the mutual inductance, which cannot be ignored at low-frequency equivalent circuit analysis.

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve’s future overtemperature state, which is characterized by the valve’s cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer–FCM–NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically.

Focusing on the shortage of mechanical defect detection and diagnosis technology for disconnectors, a wireless monitoring method for the mechanical state of disconnectors is proposed. The split-core current sensors and improved voltage sensors are used to measure the motor currents and voltages of the disconnector under typical mechanical states at different working voltages. The wireless communication network is used to upload the acquisition data to the cloud server quickly, and the received data are processed by the software system. By comparing and analyzing the curves of current, input power, and output power under different states, it is concluded that the motor output power can adequately reflect the mechanical state of the disconnector. Twenty-three time-domain features of the output power time curve are extracted to form the original feature vector. Kernel principal component analysis (KPCA) method is used to reduce the dimension of the nonlinear features, and the Fisher's criterion function is constructed to determine the width parameter of the kernel function in the feature optimization. Grid search algorithm is used to optimize the kernel parameters of the support vector machine (SVM), and the trained SVM model is used to classify the mechanical state data whose working voltage part is known, and part is unknown, with a classification accuracy of 100%. The results show that the proposed wireless monitoring method can effectively diagnose the mechanical state of the disconnector and has a good generalization ability.

The low frequency section of frequency domain spectroscopy (FDS) of oil-paper insu- lation system can effectively reflect the ageing state and moisture of the oil-immersed paper. Meanwhile, steady-state insulation resistance is the one of indexes for the condition of transformer. In actual measurement, these two both are time-consuming and poor accessibility, thereby restraining their field applications. Aim at this problem, this paper analyses the polarization process of the oil-paper insulation in transformer, and adopts a low frequency equivalent circuit model for the oil-paper insulation to characterize the polarization process at low frequency. The simulated annealing particle swarm optimization (SAPSO) algorithm is proposed to identify the parameters in equivalent circuit, which are affected by change of insulation state. Compared with the experimental results, the errors of the steady-state insulation resistance and the tan δ below 10-3 Hz, which are calculated by the identified parameters, are less than 2.5%. This method can avoid the influence from external environment and reduce the measurement time. The time and frequency domain parameters calculated can provide reference for the ageing assessment of oil-immersed paper.

Crustal movement and land subsidence will cause tower foundation settlement, which seriously affects the safety of transmission line operation. This paper proposed a short-term failure warning method for transmission tower under land subsidence condition based on finite element method, strain monitoring and time series prediction. By simulating the stress distribution of the tower in actual foundation settlement, the weak components of the settlement tower were founded. Combined with the finite element simulation results in various conditions, the reliability of simulation results is verified, and the strain variation range of the corresponding measuring point is obtained. By installing the real-time strain monitoring system and establishing the autoregressive integrated moving average (ARIMA) time series forecast model, warning of the failure of the tower can be sounded in short time according to the strain monitoring and forecasting results. The field results show that the tower is in good condition and with no risk in short term. The method proposed by this paper is economical and easy to operate, which can be used to conduct the emergency treatment of the dangerous tower under land subsidence condition.

Due to the uncontrollable weather conditions, it is difficult to carry out the controllable prototype test to study fatigue damage of transmission tower and armour clamp and the effect evaluation of antigalloping device under actual transmission line galloping. Considering the geometric nonlinearity of the transmission line system, this study proposed an adaptive excitation method to establish the controllable transmission line galloping test system based on the Den Hartog vertical oscillation mechanism. It can skip the complicated process of nonlinear aerodynamic force simulation. An electromagnetic repulsion mechanism based on the eddy current principle was designed to provide periodic excitation for the conductor system according to the adaptive excitation method. The finite element model, including conductor, insulator string, and electromagnetic mechanism, was established. Newmark method and fourth-order Runge-Kutta algorithm were used to complete the integrated simulation calculation. By comparing with the measured data record of the actual transmission line galloping test, the results show that the proposed adaptive galloping excitation system can effectively reconstruct the key characteristics of the actual transmission line galloping, such as amplitude, frequency, galloping mode, and dynamic tension, and make the galloping state controllable. Thus, a series of research about transmission line galloping with practical engineering significance can be carried out.

The aging of the transformer oil-paper insulation distributes spatially, which results in changes in paper resistivity in different regions. This paper establishes an iterative inversion algorithm using the finite element method to calculate the oil-immersed paper resistivity in different regions of a transformer. This algorithm sets the transformer dielectric loss factor tan δ at low frequency, the dielectric parameters of the transformer oil as inputs and the oil-immersed paper resistivity as output. The resistivity obtained from inversion can be used as a reference to access the insulation state of the oil-paper insulation. This paper aims at achieving the nondestructive detection of the partial state of the oil-paper insulation.

In order to meet the requirements of corrosion resistance of the grounding material and thermal stability of the grounding material of the transmission line towers, under the condition of power frequency current and high-frequency lightning current scattering into the ground in the case of short-circuit fault and lightning strikes, a kind of high-current capability flexible graphite grounding material was developed. The corrosion resistance of the flexible graphite grounding material and the steel type grounding material was compared by the soil simulation solution corrosion tests. The contact performance with different soils of the two materials was analyzed by the actual contact resistance measurement test. The thermal stability and the impulse current effective length of this grounding material were studied by means of simulation calculation. The results show that the high-current capability flexible graphite grounding material has a strong anticorrosion characteristic, whose reliable service life can exceed 30 years. Compared to the round steel grounding material, the contact resistance with the soil can be reduced by approximately 30%, and the impulse current effective length can be increased by about 30%.

Thermal-fluid coupled calculation is an effective way to simulate the temperature rise and heat dissipation process of switchgear. But there are still problems such as huge calculation and low accuracy need to be solved. This paper discusses the optimization methods of the thermal-fluid coupled field for the switchgear from grid controlling, boundary conditions, and heat source calculation. First, the mesh adaption method based on posterior error estimation is proposed to achieve efficient mesh refinement with less redundancy. The final mesh size is only 32.3% of that obtained with the traditional global-refining method. Then, an external flow model is built to obtain the convective heat transfer coefficient of the enclosure, replacing the process of artificial choosing. The results show that the convective heat transfer coefficient at the enclosure under natural convection is 0.4 W/(m2 ·°C) ~1.4 W/(m2·°C). Afterwards the eddy current field is used to solve the heat generation in the switchgear. The heat sources are coupled to the thermal-fluid calculation so that the influence of current non-uniformity, contact heat, and eddy loss is considered. At last, the methods are applied to the steady-state temperature rise simulation of KYN28A-12kV/630A switchgear and the results are compared with the test data. The maximum relative error between simulation and experimental results is 3.43%, which proves the validity of the mentioned methods.

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, form 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. The RDTC is to calculate the conductor temperatures from the surface temperatures of busbar in two different spots near the joint through the transient thermal network. The ADTC is to estimate the hotspot temperature from the conductor temperatures obtained in the RDTC using a fitted functional expression. The influence of solar radiation on the temperature distribution is greatly reduced by averaging the surface temperature in the RDTC and adjusting the functional form in the 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 a maximum error of only 3.8 K, indicating the high model precision and strong robustness to the solar radiation impact. Some factors in the applications 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.

The transformer bushing hot spot temperature (HST) 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 heat 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 is used as heat source. Next, the Finite Element Method (FEM) is used to determine a typical bushing's temperature distribution and specify its hot spot. According to the calculation results of hot spot temperature rise, the time interval is adjusted. Finally, the optimal time interval and the steady-state equivalent current are obtained by solving the hot spot temperature of the transformer bushing iteratively. In addition, the method is used to calculate the hot spot temperature of a 220kV Oil Impregnated Paper (OIP) bushing. Compared with the results of traditional transient calculation method, the validity of the proposed method is verified, and the computing time is greatly reduced.

This paper aims at presenting a novel approach for the analysis of vibration signals detected from circuit breaker (CB) opening or closing operations and to mechanical fault identification. An adaptive signal decomposition approach and chaotic nonlinear dynamic technique are used for extracting fault sensitive features of CB. The fault sensitive frequency band is derived using variational mode decomposition (VMD) combining with Hilbert marginal spectrum (HMS). The obtained sensitive frequency band(s) are employed for the reconstruction of the dynamical attractor (chaotic dynamics viewed from a phase space perspective, namely, the attractor-based perspective) following the Taken's embedding theorem. The invariant measures and ergodic quantities such as the largest Lyapunov exponent, correlation dimension, and Kolmogorov entropy which can be estimated on the reconstructed attractor are presented as the fault sensitive features. The CB fault is estimated in a chaotic space using these sensitive features. The effectiveness of the developed method is evaluated by using data sets recorded from several vacuum CBs with the same type.

This paper proposed a prediction method to predict a 10-kV oil-immersed transformer hot spot temperature (HST). A set of feature temperature points on the transformer iron shell is proposed based on fluid-thermal field calculation. These feature points, as well as transformer load rate, are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the HST. This model is trained by nine samples selected by L9(34) orthogonal array and applied to predict the HST of 20 test samples. The training samples are all obtained by simulation, and the test samples have consisted of simulation and transformer temperature rise test results. With effective parameter optimization of the SVR model, the predicted results agree well with the experimental and simulation data, the mean absolute percentage error (MAPE) is 1.55%, and the maximum temperature difference is less than 3 °C. The results validated the validity and the generalization performance of the prediction model.

On-line monitoring and the diagnosis of the high-voltage circuit breaker (HVCB) have been discussed and investigated significantly in the past few decades. The vibration analysis is a noninvasive and advanced diagnostic technique suitable for the detection of mechanical conditions during the HVCB operation, which plays an important role in improving the operating reliability of HVCB and reduces maintenance costs. However, due to the very complicated mechanical system and extremely short operation time of HVCB, the vibration signal has the characteristics of highly nonlinear, non-stationary, and corrupted by heavy garbage noise, which makes it very difficult to precisely extract effective features for machinery fault diagnosis. To address this issue, an energy entropy of Hilbert marginal spectrum (HMS) based on variational mode decomposition (VMD) is presented to analyze the vibration signals of HVCB in this paper. The VMD is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs) reflecting its local characteristics, and then, the energy entropy of IMF's HMS, which varies from different failure modes of HVCB, is obtained by Hilbert transform and entropy-information theory. The characteristics of IMF's HMS, which reveal the variation of vibration signals, under different failure modes of HVCB, are practically analyzed and examined to illustrate the advantage of the proposed method in feature extraction. The IMF that best reflects the mechanical anomaly information of HVCB is ascertained from IMF's HMS, and its Hilbert marginal spectrum energy entropy (HMSEE), namely, IMF-HMSEE, which synthetically reflects the variations of vibration signal's amplitude, phase, and frequency, is turned out to have excellent classification performance for some mechanical anomalies of HVCB. The effectiveness of the proposed approaches is substantiated by experiments carried out in a 12-kV vacuum HVCB.

Currently, there is no effective detection and diagnosis technology for the frequently happened mechanical defects of disconnectors. A porcelain column high-voltage disconnector is taken as the object to study the influence of mechanical defects, including bearing jamming, axle pin jamming, and three-phase position asynchrony on the operating torque through geometric mechanics deduction. An operating torque detection technology is proposed, which could accurately measure the operating torque of the disconnector without destroying its original shafting. Using this technology, the operating torque curves of the disconnector in normal state and typical mechanical defect simulation states are measured and compared. The results indicate that the operating torque changes with the mechanical state of disconnector, and these changes could be quantified through the proposed detection technology. And the types of mechanical defects can be distinguished according to their different effects on the operating torque curve. Based on this, a practical mechanical defects diagnosis method is summarized to provide reference for maintainers to diagnose the mechanical defects of disconnectors.

The surface temperature measurement is susceptible to the surrounding air for the cable or the insulated busbar laid in free air. Therefore, an approach for improving their surface temperature measurements by covering the temperature sensor with a heat insulated layer is put forward. Firstly, the surface temperatures of the cable and the insulated busbar attached by a platinum resistance thermometer with and without a heat insulated layer under rated current are obtained using the thermal analyses in Comsol. Subsequently, the temperature rise test of the insulated busbar was carried out for the indirect verification of the previous analyses. The measured surface temperatures were used to calculate the conductor temperature based on the transient thermal network. By comparison with the measured conductor temperature, it is found that the deviation of the surface temperature measurement without the heat insulated layer is about 4~7 K while that with the heat insulated layer is only ±1 K. Further, the generalization of the presented method to the distributed temperature sensing system is analyzed. This study demonstrates that the accuracy of the surface temperature measurement of the cable and the insulated busbar can be effectively improved by wrapping a suitable heat insulated layer around the sensor.

The hybrid high-voltage direct current (HVDC) breaker combines mechanical and power electronics switching that enables it to interrupt power flows within a few milliseconds. Mechanical switch is a key component of hybrid HVDC breaker and has a number of serially connected interrupter units which ideally would divide the voltage equally. The static voltage distribution characteristics and voltage sharing design of a multi-break mechanical switch were discussed in this study. A finite-element model was developed to study the static voltage distribution characteristics and capacitance parameters of multi-break mechanical switch (which actually consists of resistance and capacitance parameters under direct current) as a preliminary study. Comparisons were made under the simulation of vertical and U-shaped arrangement forms. The results indicate that the static voltage distribution of the high-voltage terminal is at least more than 65%, whereas the severe non-uniform voltage distribution can be well improved by means of the method proposed in this study.

Full-scale discharge tests of transmission line–tower air gaps are costly and time-consuming, and they cannot exhaustively simulate all the gap configurations in practical engineering. In this paper, a machine learning model established by support vector machine is introduced to predict the switching impulse discharge voltages of the ultra-high-voltage (UHV) AC transmission line–tower air gaps. The three-dimensional finite element models of a UHV cup-type tower and a UHV compact transmission line were established for electric field calculation, and some features were extracted from the hypothetic discharge channel and the shortest path between the bundled conductor and the tower. These features under a given voltage were normalised and input to the SVM model, while the output is two binary values, respectively, representing gap withstanding or breakdown. Trained by experimental data of one type of the UHV transmission line–tower gaps, the SVM model is able to predict the discharge voltages of another gap type. The mean absolute percentage errors of the two engineering gap types, under different gap distances, are 8.31 and 4.86%, respectively, which are acceptable for engineering applications. The results provide a possible way to obtain the discharge voltages of complicated engineering gaps by mathematical calculations.

The conductor temperature of cable in operation is the basis for determining whether the cable reaches the ampacity. For the three core cable, it is difficult to calculate the thermal resistance of the filling layer accurately due to its irregularity shape, resulting in a large error in the temperature calculation of the cable conductor using the thermal circuit model. Therefore, it is proposed to calculate the thermal resistance of the filling layer by temperature field, and then calculate the conductor temperature by thermal circuit model. The conductor temperature of three core cable in steady state and transient state is calculated using the thermal circuit model, and the results are compared with the finite element simulation results. The results show that the maximum temperature difference of the two methods is about 2°C, showing a high accuracy of the proposed method. Finally, the effectiveness of the method is verified by the experimental data, the results show that the relative error between the calculated temperature of cable conductor and the experimental data is within 10%, which meets the needs of engineering application. The research results of this paper contribute to improving the operation and maintenance of three core cables.

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.

This paper presents a fluid-thermal coupled analysis method to compute the temperature distribution in a 31.5 MVA/110 kV oil natural air natural (ONAN) three-phase three-limb transformer. The power losses of windings and core are measured by load-loss test and no-load test respectively. The convective heat transfer process, radiation and oil flow inside the transformer are investigated by finite volume method (FVM). In order to validate the feasibility and accuracy of the presented method, the temperature measuring system based on fiber Brag grating (FBG) sensor is constructed for the temperature rise test of the 31.5 MVA/110 kV ONAN transformer. The simulation results deduced from the proposed method agree well with experimental data. This model can be applied to optimizing design and load scheduling.

The resistivity of oil impregnated paper will decrease during its aging process. This paper takes paper resistivity as an assessment index to evaluate the insulation condition of oil impregnated paper in power transformer. The feasibility of this method are discussed in two aspects: reliability and sensitivity. Iterative inversion of paper resistivity was combined with finite element simulation. Both the bisection method and Newton’s method were used as iterative methods. After the analysis and comparison, Newton’s method was selected as the first option of paper resistivity iteration for its faster convergence. In order to consider the spatial distribution characteristic of paper aging and enhance the calculation accuracy, the resistivity calculation is expanded to a multivariate iteration based on Newton’s method, in order to consider the spatial distribution characteristic of paper aging and improve the calculation accuracy. This paper presents an exploratory research on condition assessment of oil impregnated paper insulation, and provides some reference to the security and economy operation of power transformers.

When an arc fault occurs in a medium-voltage (MV) metal enclosed switchgear, the arc heats the filling gas, resulting in a pressure rise, which may seriously damage the switchgear, the building it is contained in, or even endanger maintenance personnel. A pressure rise calculation method based on computational fluid dynamics (CFD) has been put forward in this paper. The pressure rise was calculated and the arc tests between the copper electrodes were performed in the container under different gap lengths by the current source. The results show that the calculated pressure rise agrees well with the measurement, and the relative error of the average pressure rise is about 2%. Arc volume has less effect on the pressure distribution in the container. Arc voltage Root-Mean-Square (RMS) has significant randomness with the change of arc current, and increases with the increase of gap length. The average arc voltage gradients measure at about 26, 20 and 16 V/cm when the gap lengths are 5, 10 and 15 cm, respectively. The proportion (thermal transfer coefficient kp) of the arc energy leading to the pressure rise in the container is about 44.9%. The pressure is symmetrically distributed in the container before the pressure wave reaches the walls and the process of the energy release is similar to an explosion. The maximum overpressure in the corner is increased under the reflection and superimposition effects of the pressure wave, but the pressure waves will be of no importance any longer than a few milliseconds in the closed container.