Early aging diagnosis of winding insulation is crucial for preventing insulation failures. However, early aging suffers from unclear mechanisms and weak changes of electrical parameters, which makes it difficult for mechanism and intelligent diagnosis approaches to achieve effective modeling. To address this issue, this article proposes a mechanism-guided intelligent enhancement method for early aging information based on the common-mode impedance spectrum. First, an effective mechanism knowledge focusing strategy is proposed, which can effectively alleviate coupling of aging mechanism features under different aging types, so as to provide the pure mechanism features for the dynamic knowledge matching. Then, a dynamic knowledge matching strategy is presented, by which aging mechanism features and early aging information are reliably matched, thereby supporting the strong generalization of the mechanism-guided reconstruction. Finally, a mechanism-guided reconstruction network is constructed to enhance the insulation early aging information, so as to achieve high accuracy and strong-generalization diagnosis modeling of insulation early aging. Notably, our method provides a promising solution for the diagnosis modeling of early aging. The experimental results on a 4-kW permanent magnet synchronous motor drive system demonstrate that our work outperforms the existing methods, which achieves the 60.74% reduction in the early aging diagnosis error of winding insulation.

The series-connected hybrid diode rectifier (SCH-DR) based high voltage direct current transmission system (HVdc) for integrating offshore wind farm (OWF) confronts challenges in startup, which comprises of a largecapacity DR and a small-capacity auxiliary modular multilevel converter (A-MMC) in series-connection. To achieve DR’s inputting onto OWF’s grid in zero dc current while maintaining energy balance of offshore system, this study proposes a startup strategy based on coordinated energy balance method. Based on this method, OWF needs to generate certain active power to meet consumption of offshore necessary auxiliary devices. To dissipate the surplus power, the proposed method parallels an energy-consuming resistor on AMMC’s dc side. Hence, through its adjustable dc voltage capability, A-MMC can actively and smoothly regulate the dissipated power, thus realizing the coordinated energy balance. The proposed startup strategy exhibits feasibility and engineering practicality, without much escalating investment cost or space requirements. The effectiveness of the proposed startup strategy is verified in MATLAB/Simulink using a scenario of a 2000 MW/±500 kV SCH-DR-HVdc system.

The thermal sensitive electrical parameter (TSEP) junction temperature estimating method is an important way to enhance the reliability of SiC MOSFETs, with the temperature-dependent model (TDM) serving as the fundamental basis. The drain-source turn-on current changing rate σds,on is a crucial TSEP, as it governs the turn-on characteristics and other associated transients TSEPs. However, the existing TDMs for σds,on are inaccurate and lack analytical rigor, thereby impeding the calibration process and limiting method generalization. To address this issue, we have developed an accurate nonlinear temperature-dependent analytical model (TDAM) for σds,on based on the gate turn-on transient model proposed in our previous study. First, the mechanism of carrier scattering is analyzed to determine the theoretical relationship between the mobility and the junction temperature. Furthermore, the relationship between the gate threshold voltage and the junction temperature is examined based on the principles of energy band theory. Third, the TDAM is established according to the obtained theoretical results. The proposed model is verified by the dual pulse experiment. Compared with existing models, it is more accurate and analytical, displaying the nonlinearity characteristics. The proposed model can pave the way for advancing transient TSEP methods and switching loss optimization strategies.

The rapid development of offshore wind power has led to widespread uses of high-voltage direct current (HVdc) transmission systems with long-distance dc cables. The detection and location of aged cable segment is critical for ensuring overall system reliability. As current researches mainly focus on offline location methods, a novel online detection and location scheme for modular multilevel converter HVdc systems based on admittance spectrum and compressed sensing is proposed in this article. The relationship between admittance spectrum and aged segment location is mathematically modeled and impacts of converters are revealed. Then, for online extraction of spectra, an injection scheme is proposed, requiring no additional equipment. Finally, the idea of compressed sensing is used to cope with nonuniformly spaced spectra obtained online. Extensive simulations in PSCAD/EMTDC are conducted and average location errors are under 2%. Compared with existing approaches, the novelty of the proposed scheme lies in online implementation and high location precision.

The fault of transmission main shaft often originates from cracks. Traditional condition monitoring for main shaft crack is often based on detecting the abnormal vibration and image caused by cracks, which cannot identify early slight shaft crack in time. In this paper, we use the inherent characteristic, resonant frequency of the main shaft, to monitor main shaft crack and propose an active condition monitoring scheme for monitoring the slight main shaft crack based on the chirp torque injection. First, we study the physical meaning of each order resonant frequency of the shaft system by a complex frequency domain-space shaft torsional vibration model, and determine the main shaft resonant frequency as monitoring indicator. Then, we obtain main shaft resonant frequency and monitor early slight cracks of the main shaft by chirp torque injection. Through theoretical derivation and experimental analysis, it is proved that our active monitoring scheme can be used to monitor early slight main shaft crack of the mechanical system in time to avoid the occurrence of severe faults.

Given the reverse distribution of energy supply and electricity demand in China, the Line-Commuted Converter High Voltage Direct Current (LCC-HVDC) delivery system has emerged as the primary solution to transmit renewable energy over long distances. The evaluation on potential damage of the wide-band oscillations to the grid stability requires the creation of a mathematical model of the LCC-HVDC delivery system to comprehensively analyze its interaction with renewable power stations. Traditionally, it has been assumed that the long DC transmission lines would separate the sending-end and receiving-end systems, leading to the simplification of the system into a single local rectifier station connected to a DC voltage source. This oversimplification, however, lacks a theoretical basis and hence, the scope of application of the equivalent model remains undefined. This study utilizes double Fourier transform to establish the impedance model of the LCC-HVDC delivery system, taking into account the receiving-end system which includes a remote inverter station and the AC grid. The internal harmonics distribution and propagation characteristics are then explored. Finally, the influence of the receiving-end AC grid on the complete system impedance is analyzed, providing insights into the dominant factors that determine the applicability of the equivalent model, which is limited to the rectifier station only.

The grid-connected photovoltaic (PV) power generation system which has multiple parallel DC modules is popularly applied in industry. In each DC-DC converter, the DC-link capacitor is the weak component which is subject to electrical property degradation. Most existing methods hardly achieve on-line capacitor diagnosis in economy and efficiency manners, considering the cost of measurement units and the effect of PV cables. In this paper, a high-efficiency diagnosis method for DC-link capacitors is proposed using two readily-available currents. The novelty lies in the linear relationship between the proposed current gain and the degradation degree at a certain characteristic frequency. The DC-link capacitor in each parallel branch can be diagnosed separately. Based on the built simulation model, the performance of the proposed DC-link capacitor diagnosis method is clarified with effectiveness and the effects of inherent harmonics and control strategies are investigated. The proposed current gain is not affected by voltage and current harmonics, as well as the varying load under the specified inverter control strategy. However, the proposed current gain is dependent on temperature and irradiance since the duty cycle of DC module is dynamically adjusted in the operation.

Defects that are formed during the cable manufacturing, transportation, and installation can cause cable failure after a relatively short period of operation. Locating cable insulation defects is essential to the reliability of the transmission system. Conventional methods mainly focus on locating insulation deterioration and are all verified on AC cables. Locating defects on submarine DC cables is becoming more challenging due to the long length and the small changes in electrical parameters caused by the defects. In this paper, the mathematical representation of the admittance spectrum of a cable with defects is first derived and a method for extracting the admittance spectrum using the inherent pre-charging conditions of the system is proposed. Finally, an approach for defect location based on the matrix pencil method is presented. This method not only enables the location of defects, but also yields qualitative estimations of the defect severity. Simulations in PSCAD/EMTDC prove the effectiveness of the proposed algorithm and comparison with other methods also supports the better performance of the proposed method.

The reliability of underground power cables has become a critical issue in modern power systems. Due to the non-uniform operation stresses, segmental ageing will form in cable insulation, which will further result in insulation faults. Online detection of segmental ageing can lead to early and targeted maintenance, improving system reliability and save the operation cost, which is rarely studied in the existing researches. In this paper, a novel online segmental ageing detection method for underground power cables is proposed to fill the research gap. Instead of using conventional travelling wave signals, multi-frequency steady-state common-mode (CM) leakage current measurements are used to realize the online detection. The overall detection scheme is presented and the detection algorithm is also designed. Simulations and case studies prove the effectiveness of the proposed method. Accurate detection can be achieved with convenient algorithm implementation for cables of different lengths, structures and parameters.

SiC power devices have been increasingly used in industrial applications. Junction temperature estimating is the basis of high reliability operation for SiC devices, since thermal stress is one of the dominating failure inducing factors. Thermal sensitive electrical parameter (TSEP) methods have been widely used for junction temperature estimating for the advantage in low invasiveness and fast response speed. However, conventional TSEP methods merely focus on a single semiconductor device instead of a converter, which results in high cost and difficulty in integration. Low sensitivity is another limitation. To solve the problems, a high-sensitivity converter-level online junction temperature estimating method is proposed in this paper based on the current oscillation (Δi OS ) of DC and AC sides in a single-phase inverter. Theoretical analysis is provided to illustrate why Δi OS can serve as a converter-level junction temperature (T j ) indicator. The relationship between Δi OS and T j is obtained by experiment for calibration based on a dual pulse test. Online experiment for T j estimating based on a single-phase inverter is provided to verify the effectiveness of the proposed method.

In this article, an online adaptive optimal control algorithm based on adaptive dynamic programming is developed to solve the multiplayer nonzero-sum game (MP-NZSG) for discrete-time unknown nonlinear systems. First, a model-free coupled globalized dual-heuristic dynamic programming (GDHP) structure is designed to solve the MP-NZSG problem, in which there is no model network or identifier. Second, in order to relax the requirement of systems dynamics, an online adaptive learning algorithm is developed to solve the Hamilton-Jacobi equation using the system states of two adjacent time steps. Third, a series of critic networks and action networks are used to approximate value functions and optimal policies for all players. All the neural network (NN) weights are updated online based on real-time system states. Fourth, the uniformly ultimate boundedness analysis of the NN approximation errors is proved based on the Lyapunov approach. Finally, simulation results are given to demonstrate the effectiveness of the developed scheme.

Leakage current is an effective indicator for groundwall (GW) insulation condition of power transformers. The existing sensing technology based on magnetic shielding design and differential measurement principle enables on-line leakage current measurement from strong magnetic noises caused by load currents. The technology has been verified in the application of the traditional power system. In this paper, an improved method is proposed for harmonic leakage current measurement, with the goal of on-line GW insulation monitoring of converter-connected transformers in microgrids. First, the simulation model of modular multilevel converter system is built in MATLAB/ Simulink. The amplitude-frequency characteristics of the load current and the leakage current of the converter-connected transformer are analyzed. Second, the sensing performance of the improved method is investigated in converter harmonic frequency band, by considering the effect of the frequency-dependent core permeability. Third, the methodology is clarified to select the appropriate monitoring frequency with the highest sensitivity. Finally, a sensor unit with the dual-core structure is manufactured and its performance is verified by off-line and on-line experiments. On-line leakage current measurement is enabled with 1-mA sensitivity and 100-kHz bandwidth from strong noises caused by load currents up to 400-A.

On-line aging monitoring is the basis for high reliability operation of converters because many converter failures are related to aging defects. Conventional aging monitoring methods mainly focus on a single semiconductor device or module, and their industrial applications are limited. First, several aging monitoring circuits are required in a converter, which raises the cost. Second, the converter structure has to be modified, which is impractical. To solve this problem, a converter-level aging monitoring method is proposed in this paper, which is based on the on-line extraction of DC-link bus leakage current. The monitored aging mode of the proposed method is Si material degradation, which is one of the chip-related aging defects. First, the DC bus leakage current is analyzed theoretically as an indicator of the Si material degradation. A corresponding description parameter dSiconis proposed. Second, an on-line measuring method is proposed for the DC bus leakage current, which is called the state-selection method. Third, a converter-level Si material degradation monitoring strategy is proposed. On-line experiments based on a constant-voltage-constant-temperature (CVCT) accelerated aging test verify the effectiveness of the proposed method.

The permanent magnet synchronous generator (PMSG) connected to pulse width modulation (PWM) rectifier is widely used in the renewable energy applications, such as direct–drive wind turbines and marine current turbines. The reliability of the PMSG is crucial due to the high maintenance cost of the turbines. The stator winding insulation is one of the main reasons for the PMSG failures. However, the current technologies can only detect the insulation failure when the short-circuit failure has already happened, which cannot avoid the corresponding downtime losses. Therefore, the online monitoring method for stator winding insulation is performed on the PMSG-PWM rectifier system in this paper for the first time. The factors affecting the voltage harmonics on the stator winding insulation are studied and their effects on the monitoring results are analyzed. Based on the flow path of the insulation leakage current and the phase-to-ground voltage, the insulation impedance can be measured online. The effects of the rotor position and the working condition are also analyzed. The effectiveness of the online condition monitoring method for PMSG-PWM rectifier system under various working conditions is verified using offline and online tests.

Insulation monitoring is essential in maintaining distribution grid reliable operation and common-mode (CM) current is an effective indicator for insulation deterioration of electrical assets. Traditional CM current measurement relies on current transformers or sensors physically connected to transmission buses, which suffers from low accuracy due to accompanied load current disturbance. This article presents an innovative CM current sensor node for insulation monitoring framework of power distribution systems based on magnetic field analysis. A CM current measurement model is developed based on formal magnetic field solution and device design is characterized as a multiobjective optimization problem solved by Pareto envelop-based selection algorithm. A prototype is presented with tunneling magnetoresistive sensor and experiments on power cable CM current measurement are implemented. Compared with conventional measurement devices, proposed sensor node realizes high-accuracy CM current monitoring in a more flexible and cost-effective manner.

Aged cable segments in underground power distribution systems will gradually develop into severe faults. The location of aged cable segment is essential for ensuring system reliability and reducing maintenance cost, but is rarely studied by the existing research. In this article, a novel aged cable segment detection and location method based on wide frequency range transfer function (TF) measurement and deep learning approaches is proposed to fill the gap. The relationship between the measured TF and cable ageing parameters is analyzed, and two models with combined sparse autoencoder and convolutional neural network are trained to estimate the aging location and severity. A transfer learning approach is further developed to improve the model performance for various online situations. Experiments are conducted which prove the effectiveness and generalization ability of the proposed method. Compared with existing approaches, the proposed method shows better performance under different ageing situations, even smooth ageing scenarios.

Insulated Gate Bipolar Transistor (IGBT) is one of the most critical devices in converters. It is of great significance to provide online monitoring and over-temperature protection for IGBT for the reliable operation of converters. Leakage current is a suitable parameter for high-temperature warning. A novel leakage current online measurement technique for monitoring of IGBT modules is proposed in this paper. A circuit structure of the diode paralleling resistor is proposed based on the Volt-Ampere Characteristics of the diode and resistor. This method can provide online measurement of the leakage current of IGBT. Finally, a test circuitry is presented to verify the accuracy and feasibility of the proposed method. The experiment results show that the leakage current can be measured by the technique proposed, which offers an effective way to estimate high IGBT junction temperature in a non-invasive fashion. The advantages of the proposed method include simple structure and low cost for over-temperature protection.

Drive train failures are one of the common failure modes of wind turbines. Their early detection, including the generator bearing and gearbox defects, is considered difficult using the state-of-the-art monitoring techniques. In this paper, a novel electrical signature analysis-based drivetrain monitoring technique is proposed for wind turbines. A novel electrical signature tool, electrical multi-phase imbalance separation technique (eMIST), is proposed to improve the signal-to-noise ratio in electrical signature analysis. The theoretical basis of drivetrain defect detection is also presented in detail. The proposed approach is validated by experimental results obtained from a 25 HP wind drivetrain simulator, designed to simulate 1.5 MW wind turbines. The experimental results show that the proposed approach is capable of providing accurate detection of drivetrain defects at an early stage. The proposed approach is cost effective with high probability of detection (PoD) of drivetrain defects compared to existing techniques.