To cope with the impact of electric vehicle（EV）charging loads on the power grid and achieve a balance of interests between the power grid，charging station and users，a customized charging strategy for EVs that takes dynamic electricity prices into consideration was proposed. Initially，the dynamic pricing mechanism was designed for charging station based on the needs of tripartite interests. Then，the optimization model with the lowest user charging cost and the lowest load fluctuation rate of power grid had been established to perfect the charging process of EV. Moreover，on the basis of the traditional artificial bee colony（ABC）algorithm，the adaptive normal attenuation coefficient was introduced to form the adaptive ABC algorithm and it was applied to solve the optimization model in order to obtain the customized ordered charging scheme. Furthermore，the charging process of each EV was optimized by combining the dynamic electricity pricing mechanism and EV customized charging method. Finally，based on Monte Carlo method，the charging situation of different number of electric vehicles under different charging modes was simulated. The simulation results show that the proposed method can significantly improve the load index of the power grid，ensure the revenue of charging station，reduce the charging costs of users，and achieve an all-win benefits between power grid，charging station and users.

In recent years，with the increasing shortage of energy，the ship power system is transitioning towards new energy upgrading. However，the uncertainty of new energy output has also brought new challenges to the economic and safe operation of the system. Therefore，traditional ship energy management is no longer applicable，and there is an urgent need for a comprehensive energy management system suitable for modern ships. In response to the above situation，a comprehensive energy management strategy using energy optimization scheduling was proposed，coordinated control at the upper level，and a combination of intelligent algorithms. A new energy ship microgrid system model was constructed，and four different operating conditions of the ship on the corresponding simulation platform were simulated，including accelerating navigation，normal navigation，decelerating navigation，and berthing. Finally， simulation models of various parts of the system on the Matlab/Simulink platform were built，and the simulation results verify that the strategy proposed can achieve an efficient balance of power supply and demand on both sides while maintaining the DC side bus voltage and system stability.

To improve the quality of power distribution network parameters，an abnormal parameter identification and localization method for distribution networks based on smart meter measurements was proposed. The method transformed the nonlinear identification equation solving problem in traditional identification algorithms into the inference problem of the optimal distribution of parameters. On the basis of parameter identification，probability statistics method was used to locate abnormal parameters. Firstly，given the initial distribution of line parameters，Markov Chain Monte Carlo method was used to generate parameter samples. The parameter distribution was updated through tree estimation method and loss function. The expectation of the parameter distribution when the loss function converges was taken as the identified value of the line parameters. Secondly，the relative deviation distances of line parameters were calculated，and probability statistics method was used to judge whether the identified data are bad data or abnormal parameters. The bad data were directly eliminated. Finally，the abnormal factors causing the incorrect feedback of line parameters were analyzed to locate the abnormal parameters of the line. The identification process of parameters was demonstrated through an actual 29-node 10 kV feeder. The abnormal parameter location was carried out through an actual 97-node 10 kV feeder，proving the feasibility and effectiveness of the proposed method.

Aiming at the low voltage ride through（LVRT）problem of the combined grid-connected system with new energy-energy storage under the control of the grid-forming converter，an additional voltage limiter control scheme based on the traditional LVRT was proposed. Firstly，the shortcomings of the traditional LVRT strategy based on current limitation was analyzed. When the fault duration is long，only setting current limitation would easily lead to synchronization instability. Then，an improved LVRT strategy with additional voltage limiter was proposed to suppress the fault current under a longer time scale and improve the synchronization stability under the fault. Finally，a simulation model of new energy-energy storage combined grid system was built in Matlab/Simulink to validate the proposed scheme. The experimental results show that the proposed scheme can ensure that the new energy-energy storage combined grid-connected system not be taken off-grid during the three-phase symmetrical sag fault，and effectively improved the synchronization stability of the system during the fault.

In order to further improve the accuracy of fault identification of local abnormal factors in distribution network，a digital twin based fault identification simulation of local abnormal factors in distribution network was proposed. Through real-time acquisition of electrical parameters of distribution network operation and preprocessing，the fault feature matrix based on time series was extracted，and the multidimensional scaling （MDS）method was used to detect the abnormal physical nodes of distribution network from the reduced dimension fault features. Then，the fault section corresponding to the abnormal physical nodes was obtained according to the distribution network topology. Finally，the local abnormal factor value corresponding to each physical node was calculated with local outlier factor （LOF）algorithm，so as to obtain the fault diagnosis results and complete the accurate identification of distribution network faults. The simulation results show that the proposed method can achieve accurate identification of distribution network faults.

In order to solve the problems of intermittent and fluctuating distributed energy，took three major physical entities in the demand response project，namely system operator，load aggregator and load end user，as the research object，the transactions between supply and demand within the power system composed by them were analyzed，and the objective conditions of the benefits of the whole system load user were considered. The factors affecting the income of load aggregators，system operators and users at the load end were proposed to combined，and an optimal bidding model with multiple types of physical entities under the market environment with the income of load aggregators as the optimization objective was constructed. In the model，the surplus and shortage of system scheduled electricity were directly connected with the power market. A typical case study shows that the optimal bidding model can effectively improve the benefits of load aggregators，and to some extent increase the benefits of load users，and indirectly improve the stability of power system.

Accurately and quickly identifying the fault types of traction transformers is a key technology for intelligent operation and maintenance. Aiming at the problems of single model deviation in the current traditional algorithm and the constraints between the iteration rate of complex models and the deployment of computing resources，a traction transformer fault diagnosis model based on the Stacking ensemble learning framework was proposed，and incorporated knowledge distillation technology to compress model iteration time to improve the computational performance of the model. First，an evaluation feature vector composed of gas indicators in transformer oil was constructed，and then the single Bagging and Boosting framework algorithm were combined based on the Stacking integrated learning framework，and knowledge distillation technology was incorporated to realize the effective mapping of feature vectors and fault types. The actual generalization effect in the DGA data sample shows that this method solves the problem of bias and variance in the traditional integrated model，accelerates the iteration speed of the integrated model，and proves the engineering application value of the model.

An adaptive active arc suppression method based on S-transform correlation degree for distribution network unidirectional grounding was proposed to effectively eliminate single-phase grounding fault arc in distribution network and improve power system stability. The time series data of zero-sequence current of distribution network lines were collected，the time-frequency information matrix of zero-sequence current through S-transformation was obtained，the correlation matrix of time-frequency information matrix between each line was calculated and obtained. Input the convolution neural network model，and output the single-phase grounding fault line selection results；the double closed-loop adaptive control method based on fuzzy quasi-PR and PI control was adopted in the fault line to inject zero-sequence current into the neutral point of the fault line of the distribution network，effectively control the phase and amplitude of the injected current，force the voltage value of the phase of the fault line of the distribution network to be 0，and complete the adaptive active arc suppression. The experimental results show that this method can effectively identify the single-phase grounding fault lines in the distribution network，realize the single-phase grounding adaptive active arc suppression，and complete the single-phase grounding adaptive active arc suppression work as soon as possible to ensure the safe and stable operation of the distribution network.

Combined with the current software features and the requirements for function expansion of the current test equipment engineering project，a set of test data analysis system was designed. The system includes the functions of remote test data monitoring，historical data playback，test data analysis，data prediction simulation，and test data collaborative sharing，etc. It realized the improvement of the functions of the current test and control system which transform process-oriented data management into analysis-oriented data management. The system has been applied in many test projects and provided reliable data management and analysis tools for the test. Finally，it helps users to improve the test efficiency.

Maximum torque per ampere （MTPA）control can effectively improve system efficiency through reducing copper loss，which is suitable for air conditioning permanent magnet compressor system. However，due to the inverter non-ideal characteristics，the tracking accuracy of the optimal current vector angle decreases.The negative influence of inverter non-ideal characteristics on virtual direct signal injection based MTPA control and the compensation method were studied. Aiming at the digital delay issue of the control system，the voltage information in the process of current vector angle tracking was corrected according to the system sampling time. In addition，the saturation function based dead-time compensation method was utilized to reduce the voltage error caused by the inverter nonlinearity. Experimental results show that the compensation method of inverter non-ideal characteristics improves the tracking accuracy of the optimal current vector angle.