The rapid development of modern power electronics technology promotes the insulated gate bipolar transistor （IGBT）wide range of applications in the AC motor drive，inverter，switching power supply and new energy industry. In the application process of IGBT，due to the complex and varied circuit topology and system conditions，the problem of gate waveform oscillation usually exists. How to understand the oscillation mechanism and suppress methods becomes the basis of IGBT security and stability application. According to the IGBT internal parasitic parameter structure and switching process，the IGBT gate turn-on oscillation，turn-off oscillation and short-circuit oscillation were introduced in detail，the mathematical model of the gate oscillation process and the oscillation of the radio frequency （RF）positive feedback oscillation （turn-off oscillation and short-circuit oscillation） were deduced. The corrective measures of adding negative feedback or decreasing the positive feedback gain were put forward. By improving the experiment of different oscillations，the effectiveness of the suppression measures was verified，and the stability and reliability of IGBT application were improved.

To address the supply-demand imbalance caused by renewable energy intermittency and load fluctuations in islanded microgrids，a two-layer optimization-based dynamic time-of-use（TOU）pricing scheduling model was proposed，aiming to enhance economic efficiency and operational stability.First，a comprehensive microgrid system model was established，integrating wind，photovoltaic，energy storage，marine energy，and diesel generators，while introducing a dynamic TOU pricing mechanism to guide user load behavior for peak shaving and valley filling.Subsequently，a two-layer optimization framework was constructed：the upper layer adjusts load distribution via dynamic pricing，and the lower layer optimizes generation dispatch and energy storage strategies to minimize operational costs，effectively resolving the limitations of traditional single-layer optimization in handling complex nonlinear constraints. Case studies demonstrate that the proposed model significantly reduces reliance on diesel generators，enhances adaptability to renewable energy fluctuations，and optimizes operational costs.The findings provide theoretical support for low-carbon scheduling of islanded microgrids，balancing economic and reliability objectives，and offer practical insights for advancing energy transition in island regions.

The micro-grid solves the problem that the intermittent and fluctuating power generation has adverse effects on the stable operation of the distribution network when the distributed generation is connected to the distribution network. In order to meet the economic operation of micro-grid under grid connected mode and improve power supply reliability，a micro-grid energy dispatching strategy based on peak-valley price and energy storage state of charge （SOC） was proposed. The strategy divided the whole day into three periods：peak，average and valley. During the real-time scheduling cycle，different scheduling strategies were applied based on different time interval and the SOC of energy storage. Reasonable energy storage charging and discharging penalty functions were designed in different time interval，and the maximum energy storage charging and discharging constraint factor was introduced to further improve the charging and discharging of the energy storage device. The minimum operating cost of micro-grid was took as the objective function and solved it through particle swarm optimization algorithm. The effectiveness of the strategy was verified by an example analysis.

The grid frequently exhibits weak grid characteristics because the impedance fluctuation range of the collector network is broad in high permeability distributed generation system. The coupling relationship between the phase-locked loop and the grid impedance leads to desynchronizing between the grid-following（GFL）converter and grid，which seriously threatens the stability of the system. However，the slow power response of grid-forming（GFM）converter is contradictory to the maximum power point tracking of source side，and the economy is poor. Consequently，in order to increase the grid-connected reliability of the system，some units are necessary to be configured flexibly，which will switch to the GFM mode. Concentrating on the grid-connected converters，a dual-mode adaptive flexible switching control strategy for distributed energy was proposed considering friendly interaction between grid and converters to maximize the utilization of new energy under the premise of system stability. The grid impedance identification algorithm based on non-characteristic harmonic injection was applied to sense the power grid strength，and the control strategy was adaptively switched according to the strength of the grid. Under the circumstance of robust grid，the constant power control method was adopted，which can quickly respond to the maximum power point instruction and improve the utilization rate of renewable energy. During the weak grid，converters flexibly switch to the virtual synchronous generator（VSG）control strategy to enhance inertia and damping support capabilities，realizing the friendly interaction between converters and grid. The proposed strategy enhances the robustness of grid-connected converters during the variation of grid strength，ensuring the stable operation of the system. The effectiveness of the proposed dual-mode control strategy was validated by PLECS simulation.

A brake unit based on IGCT was designed for different applications of frequency converter in industrial scenes，especially the medium voltage frequency conversion system without feedback function. IGCT was used as the main power device，and the parameters and characteristics of IGCT was analyzed. According to the common topology of NPC medium voltage inverter in the market，the matching circuit of brake unit was designed，and the working principle of the system was described. In terms of device loss，the junction temperature of the power device was evaluated，and the water cooling circuit and the press assembly structure were designed. In order not to affect the midpoint balance of the DC bus voltage，an independent software control method was designed.Finally，the medium voltage brake unit designed has been applied to the occasion of rolling metal composite material in the indμstrial field，which confirmed the feasibility of the design scheme.

The traditional three-phase pulse width modulation（PWM）rectifier requires six power switches.Due to the shoot-through problem between two switches on the same bridge arm，the control difficulty is increased and overall system design becomes more complicated. A half-controlled three-phase PWM rectifier topology was used to achieve a three-phase rectifier. Only three power switches were used. The three switches were controlled by the same driving signal. The driving signal was generated by a dedicated controller，and the control circuit was simple. An experimental prototype was built using SiC MOSFETs as power switches. The experimental results show that the rectifier could operate at a higher switching frequency and the inductor and the capacitor are greatly reduced，thus the volume of the rectifierand the product cost are reduced，the conversion efficiency is improved and total harmonic distortion is low.

Accurate and efficient multi-load forecasting is of great significance for the operation control and scheduling of integrated energy system（IES），in order to improve the load forecasting effect，a integrated energy system load prediction model based on least absolute shrinkage and selection operator（LASSO）and LSTM-GRU neural network was proposed. Firstly，in order to solve the problem of complex data caused by meteorological factors in the integrated energy system，a big data selection and analysis algorithm based on LASSO was studied to select and analyze the meteorological factors to obtain an effective data set. Secondly，the long short-term memory（LSTM）neural network was used to predict the system load，and the preliminary prediction value was obtained. Subsequently，the gated recurrent unit（GRU）was used to construct the error compensation model，and the compensation value of the prediction error was obtained through the training and learning of the prediction error. Finally，by reconstructing the output of the two，a more ideal prediction result was obtained. Through the simulation of the example，the proposed prediction model has higher prediction accuracy than the traditional LSTM neural network prediction model and the LSTM model optimized by particle swarm optimizer（PSO）.

The structure parameters of dual-stator permanent magnet synchronous motors（DSPMSM）were optimized with finite element method and Taguchi method at the rated point，maximum torque point，and maximum speed point，respectively，aiming at the problem of large torque ripple of DSPMSM. The influence on electromagnetic torque performance of each optimization variable was also analyzed. Then，comprehensively considering the influence degree of different variables under each operation point and the proportion of each variable's influence degree to the total influence degree，the optimal combination of variables that can balance the electromagnetic torque performance under three operation points was finally obtained. The results indicate that the comprehensive optimization design method based on Taguchi method for multiple operation points can significantly reduce torque ripple and improve the electromagnetic performance of DSPMSM.

A K-means clustering algorithm was proposed and a conditional Wasserstein generative adversarial network with gradient penalty（CWGAN-GP）to address the problem of imbalanced photovoltaic generation data caused by the low occurrence probability of extreme weather. A prediction approach combining bidirectional long short-term memory（BFLSTM）with convolutional neural network was introduced and incorporating channel attention mechanism to enhance the PV power prediction performance by integrating spatio-temporal features and dynamically adjusting the importance of feature channels. Firstly，correlation analysis and K-means algorithm were utilized to select and label various environmental factors. Then，extreme weather labels with fewer samples after clustering were selected，and CWGAN-GP was used for data augmentation.Finally，the augmented dataset was used to train the CNN-SE-BiLSTM prediction model for PV power prediction under extreme weather conditions.Simulation modeling was conducted using data from a certain PV power station，and the results demonstrate that augmenting the original extreme weather training set with CGAN-GP helps improve the prediction accuracy of the model. Moreover，CNN-SE-BiLSTM shows higher prediction accuracy among five weather categories compared to other traditional models，indicating that the proposed method is suitable for ultra-short-term photovoltaic power prediction.

As the penetration rate of household distributed PV in China's low-voltage distribution network continues to increase，the power flow of the distribution network has changed，leading to more serious problems such as node voltage overruns and three-phase imbalance，which poses a serious threat to the safe and stable operation of the distribution network. For situations where the distribution network topology and line parameters are unknown，the traditional voltage control strategy based on power flow calculation is no longer applicable， a data-driven voltage coordination control strategy was proposed for low-voltage distribution networks with high household photovoltaics penetration. First，a linear approximation model of the low-voltage distribution network was established，and the least squares method was applied to fit the relationship between node power and voltage based on the historical operation data of the low-voltage distribution network. Then，the photovoltaic inverter and energy storage system were used as regulation measures to minimize the degree of node voltage over-limit，minimize the three-phase unbalance and minimize the amount of equipment regulation as the objective function. The improved multi-objective particle swarm algorithm was utilized to achieve voltage optimization control. Finally，the effectiveness of the proposed voltage coordination control strategy was verified by simulation comparison and analysis with other control strategies and methods，taking the 21-node low-voltage station as an example.