Journal of Power Electronics最新文献

The dual-active-bridge converter (DAB) is widely used in solid-state transformers. However, its efficiency improvement is always subject to phase shift modulation (PSM) since obtaining optimal results under PSM is difficult. Hybrid modulation provides a solution to this problem. However, present hybrid modulation has not been fully deduced. This paper proposes an improved hybrid modulation strategy. When compared to the traditional method, the proposed strategy has the characteristics of lower solution complexity and greater performance in terms of efficiency improvement. In this paper, the complete derivation process for the proposed hybrid modulation is given and the boundary conditions for different modes of the hybrid modulation are fully discussed. In addition, based on the published losses model, the numerical results of the current stress and total losses under the proposed hybrid modulation, the traditional single-phase-shift modulation, and a previously published triple-phase-shift modulation are compared. Finally, the correctness of the derivation process and efficiency improvement under the proposed hybrid modulation are verified on an experimental prototype.

How to improve the efficiency of IPT systems in the case of transceiver coil misalignment is a core problem that needs to be solved urgently. Compared with single-transmitter IPT systems, multi-transmitter ones exhibit greater power supply flexibility, which ensures stable operation despite misalignment. A topology of a multi-transmitter IPT system is proposed in this study. A grouping and switching control strategy of 3 × 3 multi-transmitter coils is designed on the basis of reconfigurable inverter. A method for charging area division is also put forward to improve the efficiency of the IPT system under the condition of power priority. Compared with the traditional multi-transmitter IPT system, the new multi-transmitter topology proposed in this study can realize the independent operation of each transmitter coil with the minimum number of passive devices.

The growing focus on clean energy is driving the extensive usage of grid-connected inverters (GCIs) and nonlinear loads. The adoption of these nonlinear components causes disturbances, such as harmonic injection, frequency variation, and DC offset, which reduce the power quality of the grid. This reduced power quality induces unstable operation and deterioration of sensitive equipment. The organizations of electrical engineers have defined some standards such as IEEE 519 and P1547 to maintain the power quality of GCIs by setting the limits for harmonics, phases, and frequency variations. Harmonic elimination with grid synchronization methods is used for GCIs to meet the standards. However, the performance of these methods degrades for distorted grid conditions such as DC offset and harmonics. To rectify the mentioned issues, this study proposes a harmonic compensation method using a frequency-adaptive digital lock-in amplifier-based phase-locked loop (DLIA–PLL). The aim is to provide accurate harmonic detection and elimination by performing frequency and phase tracking for grid synchronization. Simulation and experimental results for a 5 kW inverter in an environment with high-total harmonic distortion (THD) conditions are presented to validate the performance of the proposed harmonic compensation with DLIA–PLL. Yokogawa WT1600, a power analyzer, is used to provide reference THD.

The modular multilevel matrix converter can provide frequency control support for remote AC power grids and has good application prospects in high-voltage low-frequency AC transmission systems. However, the dynamic performance of the traditional outer-loop PI controller is susceptible to changes in its own parameters and external conditions, which in turn influences the control effect. Therefore, a variable universe fuzzy proportional-integral (PI) adaptive outer-loop control strategy is proposed in this paper. Using the automatic sensing and self-adaptive capability of variable universe fuzzy control, the self-adaptation and self-adjustment of the size of the input error of the outer loop is achieved. Meanwhile, a function-based scaling factor approach is utilized to develop scaling factors for the input and output domains to enhance the control accuracy. Simulation and experimental findings demonstrate that this control strategy advances system stability and reinforces the self-adaptive adjustment capability of outer-loop control when compared to preexisting approaches.

Distributed photovoltaic systems have encountered unprecedented opportunities for development given their environmentally friendly nature and flexible power generation characteristics. However, numerous connecting lines and taps within the distributed photovoltaic system can be subject to insulation issues, which will consequently cause direct current (DC) arc faults and severe electrical fire hazards. Moreover, the power semiconductor devices in the photovoltaic inverter can introduce common-mode noises to the DC current, resulting in unwanted tripping of the DC arc fault detector. The study proposes an arc fault detection method utilizing a deep residual shrinkage network (DRSN) to address this issue, thereby precisely detecting DC arc faults. A test platform for series arc faults in photovoltaic systems is built. The arc current data are collected for characteristic analysis in time and frequency domains to determine which bandwidth is preferred for the algorithm. The model’s depth is increased by introducing residual connections, enhancing its feature extraction, and improving noise reduction capabilities. The residual shrinkage network has been enhanced to prevent a computation increase from increased network depth. Introducing a convolutional auto-encoder for data dimension reduction has decreased neural network parameters, thereby improving training speed. A prototype for detecting photovoltaic DC arc faults was constructed using Raspberry Pi 4B, validating the practical application value of the proposed method. Experimental results demonstrate that the prototype for detecting photovoltaic DC arc faults successfully fulfills the real-time detection standard of the conduction test.

It is necessary to achieve current matching for GaN-based CMOS-like inverters. However, due to the low hole mobility of GaN p-FET devices, the weak output capacity of GaN p-FET devices makes it difficult to obtain current matching with n-FET devices in the off-state, which hinders the development of GaN-based CMOS-like inverters. In this study, a GaN-based CMOS-like device with an AlGaN back barrier layer is designed and its off-state leakage current is compared with that without an AlGaN back-barrier layer. The results show that the 2DEG confinement in the GaN-based n-FET device with an AlGaN back barrier layer can be enhanced and the leakage current is reduced from 10^–3 A to 10^–6 A in the off-state. This is accomplished without influencing the current of the GaN-based p-FET device in the off-state, resulting in a good current consistency between the n-FET device and the p-FET device in the off-state. The static power consumption is 4.5 µW for GaN-based CMOS-like inverters with an AlGaN back barrier structure when it is operated at V_dd = 5 V. The rise time (t_r) and fall time (t_f) of the GaN-based CMOS-like inverters are 4 μs and 0.12 μs, respectively. The low noise margin (NM_L) is 1.90 V and the high noise margin (NM_H) is 2.55 V. This work lays a foundation for the development of the future of GaN-based integrated ICs.

This paper proposes a diagnostic and fault-tolerant method for an open-fault condition in dual inverters with open-end winding interior permanent magnet synchronous motors (OEW-IPMSMs). Certain switch pairs show similar behavior in the output current in an open-fault condition due to the structural characteristics of the two-level dual inverter. This work presents a method for detecting open-fault conditions and ensuring a fault-tolerant operation. The proposed method is applied in dual inverters with isolated voltage sources for driving OEW-IPMSM. In the event of an open-fault condition, a pair of switches containing the faulty switch is identified based on the stator current. Subsequently, the switch with the open-fault condition is determined, and fault-tolerant control for a faulty switch is immediately performed through a single-mode operation using zero-voltage vectors. The effectiveness of the proposed detection and tolerant control method is confirmed through simulation and experimental results.

Solar energy is clean and pollution free. However, the evident intermittency and volatility of illumination make power systems uncertain. Therefore, establishing a photovoltaic prediction model to enhance prediction precision is conducive to lessening the uncertainty of photovoltaic (PV) power generation and to ensuring the safe and stable operation of power grid scheduling. The radiation from the sun to the Earth has a certain regularity, which can be estimated under ideal weather conditions. However, the radiation is affected by climate, cloud cover, and other reasons. Therefore, this paper puts forward a PV prediction model combining a physical model and a neural network that can modify solar radiation in complex weather through the neural network to enhance the accuracy of PV power prediction. First, a solar radiation model (SRM) is established by using the solar radiation mechanism to estimate the sum radiation value on the horizontal plane. Then the slope radiation value received by the PV panel is calculated by the slope irradiance conversion method. Second, the main factors that greatly influence PV power are screened out by the Pearson method. The calculated slope radiation and the main influencing factors are taken as inputs. The long short-term memory network (LSTM) is selected to set up the SRM-LSTM PV power prediction method. The significance of the suggested method is verified by the true data from Alice Springs, Australia. The results show that when compared with the backpropagation (BP) prediction method, the MAE and RMSE were reduced by 22.18% and 33.89% under complex weather conditions, respectively. When compared with the LSTM prediction method, the MAE and RMSE were reduced by 15.99% and 21.73%, respectively. These results demonstrate high accuracy.

In inverter technology for hybrid and electric vehicles, some properties, such as low weight, compactness, small size, high power density, and high efficiency, are highly required because they affect vehicle production costs and fuel economy. Bringing reliable and cheap devices with high response rates into being, which is in close relation with circuit design, miniaturization, and appropriate selection of components, have become one of the main topics of scientific research in electronics. One of the main obstacles to achieving these goals is the bulkiness of inductors and capacitors. These essential building blocks of the converter topology are used to reduce input current and output voltage ripples, which are closely related to thermal stress in batteries, affecting their lifespan. This study proposes a GaN-based multidevice interleaved boost converter (MDIBC) topology for hybrid vehicles. The topology is investigated in terms of power loss, efficiency, current and voltage ripples, and size of passive components under two salient case studies at various switching frequencies. In both cases, current and voltage are reduced by smaller values of passive components without sacrificing efficiency. Efficiencies ranging between 97.34 and 97.83%, are achieved with passive components remaining in the benchmark converter.

Conventional multi-level inverters such as neutral point clamped and flying capacitor inverters do not have boosting capability and self-balanced capacitor voltage. Thus, in this paper, we propose a novel three-level inverter based on switched capacitors to solve the above problems. The proposed inverter has lower voltage stress than the general switched-capacitor multi-level inverter. Meanwhile, an improved modulation strategy is adopted in the proposed inverter. The three-level space vector diagram is converted into a two-level one, and the space vector modulation is simplified. Then, we analyze the power loss and design guidelines for the device in the inverter. Finally, a corresponding simulation and an experimental platform will be established to verify the feasibility of the proposed inverter. The results prove that the proposed three-level inverter has good performance and dynamic response that meets the requirements.