With the accelerated construction of new power system，the harmonic characteristics of power grid are becoming more and more complex. It is of great significance to study the effective statistical management of harmonic data for evaluating the power quality of power grid. A statistical method of harmonic evaluation index based on maximum entropy principle was proposed. By recording and saving the average value and center distance of harmonic data，the maximum entropy principle was used to fit the probability distribution of harmonics，so as to save and identify the harmonic characteristics and facilitate data storage. Two harmonic evaluation criteria of harmonic 95% probability value and 99% probability value were obtained by using the fitted probability distribution，which ensures the accuracy and consistency of the index. Finally，the effectiveness of the proposed method was verified by the example analysis of harmonic measured data.

In the grid load control and planning of distribution network，in order to optimize the grid structure of distribution network，such as line loss，power supply quality and load，a grid load control and planning technology based on differential evolution algorithm was proposed. For all kinds of power generation devices in the distribution grid，the power grid planning model was established，the initial population was set and constructed，and the appropriate differential initial vector was selected for mutation processing. On the premise of forming a new individual under the difference increment，in order to improve the diversity of the population of future generations，the differential individual was cross trained by using cross factors to form a new distribution individual. Finally，the greedy selection model was used to evaluate and retain the offspring population，control the active power of controllable load and uncontrollable load，and complete the grid planning. The experimental results show that the load control and planning of distribution grid using differential evolution algorithm are better，the robust performance is superior，and the iteration speed is faster.

Reasonable planning of the active distribution network is an important part to improve the wind energy accommodation capability，however，the overuse of the wind power output and load timing characteristics increase the difficulty of model solving and have adverse effects on the optimal results. The Latin hypercube sampling （LHS）combined with the K-means clustering was employed to reduce the number of samples，thus a typical wind power and load multi-scenario model with higher calculation efficiency can be obtained. Considering the interests of wind power operators and the State Grid Corporation，a bi-level planning model of active distribution network considering the wind power timing characteristics was established. The upper level determines the wind power planning scheme with the goal of maximizing benefit of wind power operators，and the lower level optimizes the system operation state with the minimum loss of distribution network. The effectiveness verification of the planning mode was conducted based on the IEEE 33-bus distribution system. The results show that the loss cost of the distribution system is 260 400￥after planning based on the GA-PSO joint optimization algorithm，which is 5.03% and 0.77% lower than that of single GA algorithm and PSO algorithm respectively，and the scenario cost is reduced by 40 000￥compared to that of the results calculated by GA algorithm and PSO algorithm. Therefore，the validity of the planning model proposed was verified.

The power transmission and transformation system is an important link of power transmission. In order to reduce the cost of operation and maintenance and improve the recognition effect，an application method of automatic identification and control of transmission and transformation vulnerable lines was proposed based on artificial intelligence （AI）technology. Using the greedy decision tree algorithm （ID3）of AI to measure the amount of information between different operation characteristics of the line，it got the line characteristics，introduced mutual information feature selection （MIFS）to optimize the decision tree，balanced the redundancy between the line operation characteristics by using the adjustment coefficient and penalty term，and introduced the weighted degree，measured the node weight and focused on the total active power of all connecting lines. Based on the comprehensive analysis of the permittivity and weighted degree，the comprehensive automatic identification index of vulnerable lines was obtained，the identification model was established，and finally the power flow transmission and load change of transmission and transformation lines were changed to achieve control. The experiment shows that the proposed method can accurately identify the location of vulnerable lines，which is effective and feasible，and its control application also effectively reduces the operation and maintenance costs.

In order to improve the debugging and fault monitoring and analysis capabilities of inverter products，the design scheme of network version process data acquisition and analysis software was proposed.The composition and implementation of the software system were discussed，and the technical means such as multi-thread，virtual memory，drawing class library and high-speed fiber synchronization were proposed to solve the technical problems of system operation. The field application of this software system shows that the system runs stably，has the characteristics of low cost，multi-channel transmission，low sampling period，high data accuracy，rich graphics functions，and strong scalability.

Three-level neutral-point-clamped （3L-NPC） interconnected converters have been widely used in the AC-DC hybrid distribution grids due to their superiorities of large capacity and high power quality. However，their working conditions are always with high power，varying load，and limited heat dissipation，etc.，with a high open-circuit failure rate of power switches. Meanwhile，existing fault diagnosis methods are mostly single mechanism-based or data-based，unable to overcome the problems of complex system model structure and changing operating conditions，resulting in low diagnostic accuracy and speed. To this end，a mechanism-data-fusion-driven fault diagnosis method for interconnected conversion systems was proposed. Firstly，a mechanism-data-fusion model was constructed using a neural network observer to improve the fault diagnosis accuracy. Subsequently，the trajectories of current residuals after open-circuit faults of different devices were analyzed，and a current residual table was summarized，based on which a fast and accurate open-circuit fault diagnosis method was formed. Finally，the experimental and hardware-in-the-loop results verify the effectiveness of the proposed method.

Flexible multi-state switch （FMSS） is a new type of power electronic device that replaces traditional tie switches and is applied to modern distribution networks. It can optimize the consumption and regulation of distributed power sources in modern distribution networks. The FMSS controlled by VSG is not affected by the deterioration of phase-locked loop performance in weak power grid environments，it can provide frequency support to the grid at low grid strength，thus enhancing the stability of the system. As a device connected to the distribution network，FMSS，when there is three-phase imbalance in the grid voltage，the output current of FMSS under traditional VSG control will become unstable，and the power will also fluctuate significantly，greatly reducing the efficiency of the grid connection point. To address this issue，based on the modular multilevel converter （MMC） structure and the traditional VSG control strategy，positive and negative sequence current compensation was introduced to achieve the stability of FMSS output current，active power，and reactive power under unbalanced power grids. Finally，a four terminal FMSS model was built using Matlab/Simulink，and the effectiveness of the proposed control strategy was verified through the simulation.

With the rapid development of the society and economy and the accelerated construction of new power systems，flexible interconnection has gradually become an important technical means for upgrading the structure and enhancing the flexible regulation ability of distribution network. A power coordinated control strategy for engineering applications was proposed to address the power control requirements of multi-terminal flexible interconnection systems，including heavy-load limiting control for the rational power distribution when some feeder lines were heavy-loaded，and power balance control for power flow optimization distribution when all feeders were heavy-loaded. A flexible interconnection coordinated control device was developed based on the proposed strategy and applied to practical engineering. The case analysis based on the real load data and the measured data of the project verify that the proposed strategy can deal with different flexible interconnection scenarios with different load/power characteristics，effectively solve the problems of unbalanced feeder load and reverse PV power flow in the distribution network，and improve the power supply efficiency and security.

With the continuous promotion of the "double carbon" strategy，the reformation of China's electricity market has entered a new stage，but the overall construction of the electricity market is still in the primary stage，and there is an urgent need to develop a scientific and feasible regulatory prevention strategy. An iterative model of "evolution game—optimized clearing" was established between regulators and risky subjects. A library of market risk and profit indicators was formed. And the risk prevention strategy was quantitatively constructed for the electricity market. The evolutionary stabilization strategy and market risk changes in the process of continuous game interaction between the two participants was explored. Iterative simulations base on real data，the simulation results show that when the market risk is high，after limited rounds of evolutionary game，the regulatory prevention strategy can reduce the degree of transaction risk，which verifies the effectiveness of the regulatory prevention strategy. The proposed method can provide theoretical support for the formulation of electricity market regulatory strategy.

Aiming at the problem that the ripple content of current and voltage in cycloconverter speed control system is large and the ripple period is time-varying， the real-time sampling value is not suited for feeding back to the speed control system directly，a variable period mean sampling algorithm was proposed. Firstly，the algorithm took the interval time of trigger pulse of cycloconverter power device as the mean period to calculate the meanvalue of actual current and voltage. Secondly，the phase lag angle caused by the mean period was calculated according to the real-time frequency and mean period of current and voltage. Finally，the lag compensation and interpolation operation were carried out according to the phase lag angle. More ideal voltage and current feedback values were obtained for speed control system. Compared with the average sampling method，this algorithm is more suitable for the time-varying ripple period of cycloconverter conversion，and it eliminate the sampling lag and improve the performance and stability of the speed control system.