Power Electronic Systems Research Articles

Advancing maintenance, monitoring, and control techniques for the optimization of power electronic systems

This paper explores the innovative methodologies and technologies in predictive maintenance, real-time monitoring, fault detection, and advanced control strategies for power electronic devices. Initially, we delve into data acquisition and preprocessing techniques crucial for ensuring the quality and reliability of data used in predictive models. These models, leveraging machine learning and time-series analysis, predict the remaining useful life of devices, guiding proactive maintenance strategies. Furthermore, we discuss the significance of risk assessment and decision support systems in prioritizing maintenance tasks and allocating resources efficiently. The narrative then shifts to real-time monitoring and fault detection, emphasizing the role of sensor integration, data fusion, anomaly detection, and diagnostics in maintaining system integrity. Condition-based maintenance strategies, underscored by real-time data analytics, are presented as a means to optimize maintenance activities and enhance operational performance. The paper concludes with a detailed examination of advanced control strategies, including model predictive control, reinforcement learning, and distributed control and optimization techniques, highlighting their potential to improve system efficiency, reliability, and adaptability. Through comprehensive research and analysis, this work aims to provide valuable insights into the development of sophisticated maintenance and control mechanisms for power electronic systems, ultimately contributing to their longevity and operational efficacy.

Loss-Optimized Design of Magnetic Devices.

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss.

Design and Performance Investigation of a Series Compensated Inductive Wireless Power Transfer System for Supplying a Low Power DC Load

In this study, a series compensated WPT system is presented for low power DC load applications. Series LC resonant circuits are applied for both transmitter and receiver sides of WPT system to reduce the impedance at a specified operation frequency, and thus, ensure low power losses. The operation frequency is chosen as 109 kHz for the series compensated WPT system. Then, the series resonant LC filter is designed according to the operation frequency and WPT rating values. In addition, the power electronics systems with their controller and operation principle are demonstrated in depth. To investigate the performance of the proposed system, a 100 W series compensated WPT model is designed and constructed in Matlab/Simulink enviroment. Different simulation results are provided to illustrate the performance of the proposed WPT model. The simulation results show the stable operation of the proposed system under the designed system parameters.

Application of Digital Twin in Electric Vehicle Powertrain: A Review

Digital Twin (DT) is widely regarded as a highly promising technology with the potential to revolutionize various industries, making it a key trend in the Industry 4.0 era. In a cost-effective and risk-free setting, digital twins facilitate the interaction and merging of the physical and informational realms. The application of digital twins spans across different sectors, including aerospace, healthcare, smart manufacturing, and smart cities. As electric vehicles have experienced rapid growth, there is a growing demand for the development of innovative technologies. One potential area for digital twins application is within the automotive sector. The powertrain system of electric vehicles (EVs) consists of three parts, power source, power electronic system, and electric motor, which are considered as the core components of electric vehicles. The focus of this paper is to conduct a methodical review regarding the use of digital twins in the powertrain of electric vehicles (EVs). While reviewing the development of digital twin technology, its main application scenarios and its use in electric vehicle powertrains are analysed. Finally, the digital twins currently encounter several challenges that need to be addressed, and so the future development of their application to electric vehicles are summarized.

LTspice Modeling for GaN-GIT HEMT Including Cryogenic Temperature

Highly efficient electrically driven avionics have led to a renewed interest in cryogenic propulsion systems with the goal of reducing carbon emission footprint. Although cryogenic converters promise better efficiency and improved power density, the successful design is incumbent upon the appropriate switching device selection and simulation-based analyses prior to initial prototyping. In this work, a datasheet-driven compact model for a gallium nitride (GaN) Gate Injection Transistor (GIT) has been proposed and implemented in LTspice, a versatile, high performance, and free circuit simulator in order to investigate the merit of the chosen device in a power electronic system.

Neural Network Design for Impedance Modeling of Power Electronic Systems Based on Latent Features.

Data-driven approaches are promising to address the modeling issues of modern power electronics-based power systems, due to the black-box feature. Frequency-domain analysis has been applied to address the emerging small-signal oscillation issues caused by converter control interactions. However, the frequency-domain model of a power electronic system is linearized around a specific operating condition. It thus requires measurement or identification of frequency-domain models repeatedly at many operating points (OPs) due to the wide operation range of the power systems, which brings significant computation and data burden. This article addresses this challenge by developing a deep learning approach using multilayer feedforward neural networks (FNNs) to train the frequency-domain impedance model of power electronic systems that is continuous of OP. Distinguished from the prior neural network designs relying on trial-and-error and sufficient data size, this article proposes to design the FNN based on latent features of power electronic systems, i.e., the number of system poles and zeros. To further investigate the impacts of data quantity and quality, learning procedures from a small dataset are developed, and K-medoids clustering based on dynamic time warping is used to reveal insights into multivariable sensitivity, which helps improve the data quality. The proposed approaches for the FNN design and learning have been proven simple, effective, and optimal based on case studies on a power electronic converter, and future prospects in its industrial applications are also discussed.

Design of adaptive protection coordination scheme using SVM for an AC microgrid

Microgrids have gathered a significant amount of attention within the past decade and becoming an essential asset in the energy industry. The ability to integrate sustainable renewable energy generation into the distribution network is one of the main reasons for microgrids' popularity. Besides technical advantages, renewable energy sources integration with the grid poses many technical challenges. Due to randomness in generation and different modes of operation, integrating renewable energy sources into power systems will be challenging. Reliable operation of renewable energy sources integrated microgrids even when faults occur, requires an adaptive protection scheme and relay coordination. In order to tackle those issues, this paper developed an adaptive protection coordination scheme using numerical overcurrent relays and support vector machines with a particle swarm optimization approach. Renewable energy sources based on microgrids use power electronics systems that require relays to operate in milliseconds, and the power system will be safe. particle swarm optimization's program execution time exceeds the primary relays. The machine learning algorithm is faster in computation time. This article proposes a method, particle swarm optimization, to estimate the optimum relay operating time. The machine learning model having a support vector machine is proposed to estimate the relay operating time. The proposed research is developed and tested on an IEEE12BUS and IEEE35BUS system. The support vector machine -based on adaptive protection coordination schemes estimates the operating time of directional overcurrent relays, with a lesser program execution time and a low percentage error rate. The derived result defined that the proposed approach is well-suitable for real-time applications of an AC microgrid system.

Lyapunov stability analysis and FIL implementation for boundary‐based hybrid controller in boost converter

AbstractThis paper presents a comprehensive stability analysis of the boundary‐based hybrid control (BBHC) algorithm designed for boost converter. The stability assessment is carried out utilizing multiple Lyapunov functions, addressing both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operation. The boost converter is modeled as a hybrid automaton to capture its dynamic behavior accurately. Through rigorous Lyapunov stability analysis, this study demonstrates the effectiveness of the BBHC algorithm in ensuring stable operation of the boost converter across various operating modes. Additionally, the proposed control algorithm's validation is conducted using the FPGA‐in‐the‐loop (FIL) technique, highlighting its efficiency and robustness in real‐world applications. This research contributes valuable insights into the design and implementation of stable control strategies for boost converter, emphasizing the practical utility of the BBHC algorithm with FIL for enhanced performance and reliability in power electronics systems.

A Comprehensive Investigation into Bidirectional DC-DC Converters with Soft Switching for Electric Vehicles

Electric vehicles (EVs) are one of the the promising solutions for the environmental issues such as carbon emission and global warming. The high voltage traction battery in EVs is charged from utility grid by an EV charger. The Society of Automotive Engineers (SAE) standardize EV charges into two categories on-board chargers and off-board chargers. Usually, the on-board chargers are located inside the vehicle whereas, off-board chargers are not mounted on the vehicle. For onboard chargers, size and power density of charger become a prior matter of concern. The only way to reduce the size of a converter and thereby increase the power density is to operate power electronic conversion system (PECS) at very high switching frequency (HSF) resulting in reduced size of magnetic component in the charger configuration. However, HSF operation may lead to a high electromagnetic interference (EMI), loss in switch and switch duty. The work in this paper is focused on the development of high frequency bi-directional DC-DC converter (BD2C) stage priory used in on-board chargers due to distributed sources of energy as well as to manage the power distribution in the configuration. The power electronic BDCs are derived from boost and buck unit. The boost derived converter are current fed converters and offers advantages in terms of inherent voltage gain, short circuit protection and lower input current ripple. The voltage spikes across switches, high rated device selection, hard switching, snubber and auxiliary circuit requirements are some of the undesirable factors. This occurrence happened due to unbalance power transfer by the power electronics converter. Similarly, voltage fed converter offers good voltage control during power transfer.

Impact of Weight Functions on Performance of Three-Input Integrated Dc-Dc Converter in H ∞ Control

Many power electronic systems applications namely locomotives, hybrid electric vehicles, and all renewable energy sourced systems are shifting towards Multi-Input Multi-Output (MIMO) integrated DC-DC converters due to their reliability and flexible nature. Designing controllers for MIMO integrated DC-DC converter is complicated due to its integrated structure, presence of common elements, and interactions between the input and output variables of the converter. In this work, a Three-Input Integrated DC-DC (TIID) converter is modeled using state-space analysis, and a Transfer Function Matrix (TFM) is acquired from the small signal continuous time model. A robust controller based on the loop shaping method is designed for the TIID converter. In this loop-shaping method, the desired robustness and the performance of the controller are represented with weight functions i.e., loop-shaping filters. These weight functions are designed using TFM and are frequency-dependent. The robustness of the controller depends on the weight function parameters. The effect of varying the parameters of the weight functions on system dynamics, robustness, and performance are studied and plotted. TIID converter of 288 W, 24V-30V-36V to 48 V is considered and the impact of weight function on closed-loop system dynamics and sensitivity characteristics under varying parameter conditions are analyzed in MATLAB Environment.

Ultrahigh breakdown strength of NaNbO3‐based dielectric ceramics for high‐voltage capacitor application

AbstractDielectric ceramics have attracted significant attention in power electronic systems, owing to their exceptional charging and discharging speeds, as well as their high power density. However, simultaneously achieving a high recoverable energy density (Wrec) and high efficiency (η) in high‐voltage dielectric ceramics remains a challenge for applications where a high breakdown electric field (Eb) is required. In this study, high‐quality (1 – x)NaNbO3–xSr0.7Bi0.2(Mg1/3Nb2/3)O3 [(1 – x)NN–xSBMN] ceramics were prepared based on an optimization strategy combining phase structure with microstructural regulation. A quasilinear P–E loop with negligible hysteresis was realized in the ceramic with x = 0.4, excellent Wrec of 5.61 J/cm3, and high η of 85.1% obtained at a largely improved Eb of 710 kV/cm. To the best of our knowledge, the Eb of 710 kV/cm is one of the highest values achieved in dielectric ceramics to date. The noticeable reduction in grain size (∼0.95 µm) and increased bandgap improve the Eb to an ultra‐high level, which is a crucial factor in high energy storage density. The coexistence of a few antiferroelectric phases and the dominant paraelectric phase is the structural origin of the comprehensive energy‐storage performance improvement. Therefore, our research develops a unique approach to unleash the potential in NaNbO3‐based ceramics, holding great promise for application in high‐voltage dielectric capacitors.

A Brief Review of Single-Event Burnout Failure Mechanisms and Design Tolerances of Silicon Carbide Power MOSFETs

Radiation hardening of power MOSFETs (metal oxide semiconductor field effect transistors) is of the highest priority for sustaining high-power systems in the space radiation environment. Silicon carbide (SiC)-based power electronics are being investigated as a strong alternative for high power spaceborne power electronic systems. SiC MOSFETs have been shown to be most prone to single-event burnout (SEB) from space radiation. The current knowledge of SiC MOSFET device degradation and failure mechanisms are reviewed in this paper. Additionally, the viability of radiation tolerant SiC MOSFET designs and the modeling methods of SEB phenomena are evaluated. A merit system is proposed to consider the performance of radiation tolerance and nominal electrical performance. Criteria needed for high-fidelity SEB simulations are also reviewed. This paper stands as a necessary analytical review to intercede the development of radiation-hardened power devices for space and extreme environment applications.

Optimize energy storage performance of NaNbO3 ceramics by multivariable control strategy

NaNbO3 ceramics have broad application prospects in ultra-high power electronic systems, electromagnetic pulses, and other applications. In this study, (1-y)[(1-x)NaNbO3-xSmMg0.5Zr0.5O3]-y(Bi0.5Na0.5)0.7Sr0.3TiO3 (x = 0.06, 0.08 and 0.10)(y = 0.30, 0.35, 0.40 and 0.45) ceramics were fabricated. The effects of the incorporation of SmMg0.5Zr0.5O3 (SMZ) and (Bi0.5Na0.5)0.7Sr0.3TiO3 (BNST) on the energy storage performance, impedance, actual charge and discharge capacity, phase structure and microstructure of the ceramic samples were analyzed. Finally, when x = 0.08, and y = 0.35, the recoverable energy storage density of the sample was 5.8 J/cm3 and a high energy storage efficiency of 88 % was obtained at 545 kV/cm. In addition, the ceramic maintains a stable actual charge and discharge capacity and an extremely fast discharge time (16.1 ns) in a wide temperature range (20–160 °C). These excellent energy-storage performances may be due to the fine grain size and large relaxation and activation energies.

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms.

In power electronics systems, system design and operation often involve multiple time and space scales, ranging from nanosecond switching dynamics to hour-level system operation behavior. Due to the complexity of these systems and the rise of wide-gap semiconductor technology, a series of multi-scale phenomena have emerged that are difficult to ignore. The high frequency of switching operations makes multi-scale effects particularly significant, including the fast dynamic response of the power loop, EMI, and heat conduction problems. They are key factors that must be considered in the design to ensure the efficient and reliable operation of power electronic devices. This study proposes the construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms to address the multi-scale phenomenon and limitations of single-scale power electronic converters. Firstly, a joint scale model for power electronic converters at both macro and micro-scales was established, targeting both single-scale models and simple combinations of multiple scale models for power electronic converters. The traditional single-scale model is sufficient to describe the average behavior of the converter, but it has serious limitations in capturing fast transient processes and high-frequency switching behavior in power electronic systems. These limitations often manifest themselves when there is a need to capture fine timescales of detail. By transforming between the time domain and the frequency domain, wavelet decomposition enables the model to capture both macroscopic average characteristics and microscopic transient dynamics. The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. Secondly, this algorithm is used to analyze BC in short time scale. Finally, the short time scale characteristics of power electronic converters are analyzed. Experimental results show that the fusion of wavelet decomposition and reconstruction algorithm enhances the accuracy of the power electronic converter model and improves the performance of the system. The model achieves an error reduction of nearly 3% in the calculation step size of 10-7s, which has a significant impact on the high precision requirements of high-frequency operations. In addition, the optimal calculation step size of 8×10-8s achieves an error reduction of more than 14%, making an important contribution to the transient analysis and fine structure simulation. The wavelet algorithm can improve the accuracy of multi-scale modeling in power electronic system and reduce the simulation time. The reduction of error not only shows the improvement of the accuracy of the model, but also shows its practical significance in the design and test of the actual power electronic system. The reduction in error reveals the ability to more accurately predict and mitigate potential performance problems in matching tests with actual hardware, as well as its ability to adapt to emerging wide bandgap semiconductor materials and structures.

Real-Time Digital Mapped Method for Sensorless Multitimescale Operation Condition Monitoring of Power Electronics Systems

Operation condition monitoring of power electronics systems can improve analysis visibility for device administrators and researchers. However, the current sensor-based monitoring methods have limitations in accurately monitoring variables at multi-timescales. This article presents a real-time digital mapped (RTDM) method for the sensorless multi-timescale operation condition monitoring in power electronics systems using FPGA simulators. The mathematic models of power converters are deployed in the simulator considering multi-timescale characteristics. Both the digital simulator and the physical power converter are controlled by the same control signals, allowing the simulator to map the accurate operation conditions of the power converters without additional physical sensors. The RTDM method can monitor variables on the switching period timescale to the system transient timescale due to the consideration for the ideal switch modeling of the semiconductor switch. The effectiveness of the proposed method is demonstrated using a 30kW DAB converter as an example, through the integration of an experimental prototype and a simulator to create an RTDM platform. This method offers an easy and sensorless way to monitor multi-timescale variables in power electronics systems, providing a more comprehensive and visual analysis of their operation.

Real-Time Rotor Ground Fault Protection Based on the Convolution Integral for Synchronous Motors With Static Excitation

The detection of motor failures is a very active research topic within the literature. In particular, ground faults in wound rotors of synchronous motors have a relatively high failure rate since the rotor winding is exposed concomitantly to electrical, mechanical, and thermal stress cycles. Its detection depends on the grounding type of the electrical power system, and, in its early stages, it may present significant resistance. This manuscript provides a new rotor ground fault protection method based on the convolution integral of the neutral voltage that is measured by a single extra sensor. In addition, it is able to fill out the gaps of the current state-of-the-art, such as: provides a larger detection frontier for incipient faults; has low sensitivity for dynamic variances of the firing angle, allowing its utilization in cyclical applications; and can be implemented in power electronic controller systems with limited bandwidth. In this sense, mathematical modeling, simulations and experimental data are presented to support to the proposed method. Furthermore, it was implemented in the controller of static excitation converters for real-time protection and successfully tested with synchronous motors (2.4MW/3.3kV) of a cold rolling mill plant owned by ArcelorMittal Group.

A Critical Review of Recent Industrial Developments, Trends, and Future Perspectives of Power Electronic Systems: Fuel Cell Electric Vehicles

A Critical Review of Recent Industrial Developments, Trends, and Future Perspectives of Power Electronic Systems: Fuel Cell Electric Vehicles

WAYS TO REDUCE THE IMPACT OF PULSATIONS IN LOCAL POWER SUPPLY ROBOTIC SYSTEMS

An analysis of the changes in the flow of pulsation transforming the energy-dynamic indicators of on-board power electronics systems has shown that there are several options for solving this problem.
 The article outlines the main options for the current problem of pulsation in the energy systems of autonomous electrical systems, such as UAVs, robotic complexes, and other systems. The effectiveness of changes in pulsation is examined, in addition to directly assessing the effectiveness of stagnation strategies and methods. Also in the statistics, the optimal technical solutions for specific operating minds were identified.
 The prospects and future directives have been predicted based on the proposals for further research in the sphere of changes in pulsation in local life systems.
 In the future, it is predicted and supported by the development of technology and innovation in this country.
 To sum up, we can say that the statistics have analyzed the changes in pulsation in robotic local power supply systems, which is one of the key directions in the field of electrical engineering and electronics. Increased attention to this problem is due to the need for assured stability, reliability and greater energy efficiency of electronic systems. Research in this area is aimed at developing more comprehensive methods and strategies for reducing noise and pulsation in electrical systems.
 Assessing the effectiveness of stagnant methods, promoting the development of new technologies and innovation, as well as developing promising directions in this field opens the way to creating more stable and adaptive living systems. Replacement of advanced electronic filters, voltage stabilizers, selection of new materials and components, integration of intelligent energy management systems and optimization of essential energy resources local power supply systems – the whole purpose is to directly conduct further investigations.
 The growing demand for stable and efficient electronic systems is driven by current research and the introduction of new technologies to change pulsation. Technological developments and scientific research in this area open up broad prospects for reducing the power supply and ensuring the stability of the functioning of modern electronic devices.

Ultrahigh energy storage performance in AN-based superparaelectric ceramics

Ceramics capacitors, especially featuring antiferroelectric (AFE) structure, are widely used in pulsed power electronic systems due to distinctive high-power density and external field stability. Lead-free AFE material AgNbO3 has seized substantial research attention owing to its unique temperature driven multi-level phase transitions, and many works have greatly improved the energy storage properties by engineering these temperature-dependent phase boundaries. In this work, an ever-unadopted strategy was proposed to modulate the relaxor structure in AgNbO3 by designing room-temperature (RT) superparaelectric phase via Bi incorporation into AN lattice matrix. Structural analysis shows M phase (space group Pbcm) evolves into O phase (space group Cmcm) with 12 mol.% Bi-substitution, breaking the long-range AFE order into polar nanoregions. Hence, an ultra-high recoverable energy density (7.6 J/cm3) and a high efficiency (79 %) are simultaneously achieved in the Ag0.64Bi0.12NbO3 ceramics under 52.2 kV/mm. Moreover, the excellent energy storage properties are accompanied with good temperature and frequency stability, with the variation of Wrec less than ± 15% (over 25–120 °C) and ± 10% (over 1–200 Hz) under 25 kV/mm, respectively. The novel approach reported herein provides a new guidance for designing AgNbO3-based and other AFE materials for high-performance energy storage applications.

Lightweight Machine-Learning-Based Diagnosis for Power Electronic Systems Subject to Imbalanced Data

Lightweight Machine-Learning-Based Diagnosis for Power Electronic Systems Subject to Imbalanced Data