The impedance analysis method is common for understanding the small signal stability of new energy grid-connected systems. In impedance modeling works, the three-phase symmetry of the grid side AC system is assumed, and the port impedance characteristics of the converter can be described using a second-order matrix. However, under asymmetric conditions, the converter exhibits more complex frequency coupling characteristics, and the model is no longer applicable. This paper firstly considers the sideband effect of a pulse width modulation（PWM） modulation and the frequency aliasing effect of sampling link, phase-locked loop dynamics, DC voltage control, positive and negative sequence current inner loop control, and other influencing factors, and establishes a small signal model of grid-connected converter suitable for three-phase asymmetric conditions. Then, a method for equivalenting a multi-input-multi-output （MIMO） system to a single-input-single-output （SISO） system under asymmetric operating conditions is proposed, and the correctness of the theoretical analysis is verified through PSCAD/EMTDC. Furthermore, the asymmetry of the grid-connected converter system is defined and the influence of control parameters on the system asymmetry is analyzed. Finally, the stability of grid-tied systems under asymmetry operation condition is analyzed based on the maximum peak Nyquist criterion.

Focuses on the application of digital twin technology in ultra-high voltage DC system, this paper analyzes the challenges and problems of digital twin technology in solving the scale, data, and application of ultra-high voltage DC systems, and systematically demonstrates the application scenarios and functional characteristics of digital twin technology in basic management, equipment management, safety management, and business operations. Based on business needs, a digital twin scenario and the innovative application technology system of digital power grid with digital twin technology as the key node are constructed. Based on the digital twin transformation of the power grid, its application scenarios are explored from the perspectives of data perception, model construction, and model application, improving the system′s perception and prediction capabilities in the virtual world. At the same time, it supports the intelligent application of the substations and the digital service demand of the new power system, provides specific and effective paths and references for the digital transformation of the power industry, and enhances the interactivity and security of the power grid, gradually achieves automation, intelligence, and unmanned operation of power grid business.

With the increasing scale of long-distance and large-capacity HVDC transmission, the problem of transient voltage stability of DC receiving-end system is becoming more and more serious. The rapid development of measurement technology and artificial intelligence provides a new idea for solving the transient voltage assessment problem of DC receiving-end system. Therefore, this paper proposes a multi-binary table method based on intelligent enhancement to evaluate the transient voltage of DC system. Firstly, the feature dimension is reduced by extracting the transient time series features of the DC system and using the ReliefF method. Then, the back propagation （BP） neural network is used to perform regression fitting on the transient voltage stability margin index, and the improved particle swarm optimization （PSO） algorithm is used to solve the multi-binary table integral weights. Finally, a voltage collapse model is built and verified by simulation. The results show that the proposed intelligent enhanced multi-binary table transient voltage assessment method has good accuracy and adaptability to the DC receiving-end system, and has the potential for online application.

In the context of a high proportion of clean energy integration into the distribution network, the optimization and reconfiguration of the active distribution networks is investigated by integrating soft open points （SOP） and demand response （DR） into the networks. A strategy for reconfiguring the distribution network considering both SOP and DR is proposed. Firstly, models for SOP and DR within the distribution network are established. By comprehensively considering loss cost, operational cost, and load imbalance as optimization objectives, a multi-objective optimization model for active distribution network reconfiguration incorporating SOP and DR is formulated. Subsequently, an improved normalized normal constraint method （NNC） is employed to solve the three-objective optimization problem and obtain the complete Pareto frontier for the trade-off optimal solutions. Finally, simulation verification is carried out using the IEEE 33-node distribution system as an example. The results demonstrate that the reconfiguration strategy which considers both SOP and DR can effectively enhance the economic efficiency of the distribution network, improve the absorption capacity of clean energy, and reduce the load imbalance degree of the system lines.

With the development of distribution network and new energy, distributed generation (DG) is widely used. The strong intermittency of DG, the spatial distribution characteristics of the distribution network, and the temporal sequence of the distribution network electrical quantities lead to the difficulty of achieving optimal operation of the distribution network in reactive power optimization problems by the traditional optimization methods and optimization algorithms that consider a single dimension. In this context, the spatial distribution characteristics and time series characteristics of distribution networks are taken into consideration at the same time, and a multi-time scale distribution network reconstruction and reactive power collaborative optimization method based on adaptive graph attention network is proposed, spatial correlation modeling is performed based on the adaptive adjacency matrix and graph convolution network (GCN) with improved spatial attention mechanism, multi-time scale partitioning and bidirectional gated recurrent unit (Bi-GRU) optimization is adopted for time correlation modeling, and an overall optimization model is obtained; at the same time, the reconfiguration of the distribution network is considered containing distributed power sources in concert with reactive power collaborative optimization, and the traditional reactive power device and the soft open point(SOP) for collaborative optimization are utilizd. Finally, experiments and validation are carried out through the improved IEEE 33-bus distribution system, and the results show that the proposed method in this paper reduces the average loss by 55.83 % under different penetration rates, which is 10.1 % lower than the deep learning model, and ensures that the overall voltage level fluctuation per hour is less than 0.005 5 p.u., which verifies the validity and accuracy of the proposed model in reactive power optimization problems.

In the context of the coupling between the transportation network and the new distribution network, considering the impact of extreme disaster events on road damage, a multi period power restoration strategy for the transportation network and the new distribution network after extreme disasters is proposed. Firstly, considering the road repair status and traffic flow changes caused by disasters, a dynamic cell transmission model (DCTM) is established to obtain the travel time of distribution network repair vehicles and mobile energy storage vehicles (MESVs) through the historical decision-making behavior of travel vehicles. Secondly, the flow state factor is introduced to describe the flow transmission process of cells in the transportation network, the diversion factor value of road intersections is determined based on the distribution of path flow, and the driving path of repair vehicles and MESVs is optimized. Then, with the goal of minimizing load reduction and emergency resource scheduling costs, a mixed integer linear programming model for the distribution network considering changes in road flow, optimization of the driving paths of repair vehicles and MESVs is established, and a multi-period power supply recovery strategy for the transportation network new distribution network is proposed. Finally, the effectiveness of the proposed method in improving the restoration of power supply in the new distribution network is verified through numerical examples.

Under the background of high proportion of new energy access, the development of high frequency distribution data collection business is rapid, and the number of access terminals and business collection volume has surged, which puts higher requirements on the data collection, transmission, and processing capabilities of distribution networks. Therefore, a communication resource intelligent scheduling technology for high frequency collection in distribution networks is proposed. Firstly, a communication resource intelligent scheduling framework is constructed for high frequency collection in distribution networks. Secondly, based on considering the sensitivity of high frequency connection services to latency and energy consumption, an optimization model is established with the goal of meeting differentiated business requirements. Finally, in response to issues such as terminal decision coupling and gateway selection conflicts, a communication resource intelligent scheduling algorithm based on business differentiation awareness is proposed. Through improved unilateral matching, the differentiated demand guarantee of high frequency collection business in distribution networks is achieved, avoiding the waste of computing resources. The simulation results show that the proposed algorithm reduces the average energy consumption of terminals by 58.54% and 73.98% respectively, and reduces the average data processing latency by 51.99% and 69.93% respectively compared to the comparative algorithm.

Aggregating clean energy and energy storage access to the grid can effectively reduce the impact on the safe operation of power grids caused by decentralized access. However, the focus of future research on clean energy participating in market competition lies in improving the revenue of aggregated clean energy and energy storage. This paper aggregates clean energy and energy storage as an aggregated regulation generation unit （AGU）, and studies its bidding strategy on day-ahead power market. Firstly, a power market clearance model using queuing clearing method is established, and then the optimization model of bidding strategy based on evolutionary game theory is constructed. Secondly, the cost-benefit model of AGU is constructed to determine final bidding strategy. Finally, the arithmetic examples are analyzed using relevant data from the electricity market in Zhejiang Province and then a comparison is made with traditional bidding strategies, which verifies that the proposed strategy can improve the revenue of AGU.

With the continuous increase of photovoltaic power generation installed capacity, accurate analysis and calculation of photovoltaic fault output characteristics have become the key to the safe operation of power systems. The control of inverters directly affects the fault behavior of photovoltaic power generation systems. When an asymmetric short-circuit fault occurs in the power grid, the existing photovoltaic short-circuit current calculation methods do not consider the transient process of the phase-locked loop and ignore the impact of phase-locked deviation on the short-circuit current, which may result in significant errors. To this end, the transient response of photovoltaic positive and negative sequence control under power grid faults is analyzed, the expression of positive and negative sequence AC and DC axis currents is derived, the tracking characteristics of currents are analyzed, and a calculation method for phase locking deviation under asymmetric power grid faults is further proposed. The influence of phase locking deviation on photovoltaic short-circuit current in the positive and negative sequence control process is analyzed, and the expression of photovoltaic positive and negative sequence current in the positive and negative sequence rotating coordinate system is derived. The calculation method of photovoltaic short-circuit current is obtained, and the correctness of the proposed photovoltaic short-circuit current calculation method is verified through simulation analysis.

In order to solve the problem that the protection of high voltage direct current （DC） transmission lines is susceptible to lightning interferences, a new method for the identification of lightning interferences and faults on DC transmission lines based on the quadratic polynomial fitting of the multifractal spectrum is proposed, which is the first time that the multifractal algorithm is applied to distinguish between lightning strikes and in-area faults. The method achieves the identification of lightning interference and fault conditions by calculating the multifractal spectrum of the voltage transient signals and using the coefficients of the highest terms of its quadratic polynomial fitting function to characterise fluctuation of the voltage signals. Simulation results show that the method can not only accurately identify lightning interferences and faults, but also identify them quickly. In addition, the polynomial fitting coefficients can better characterise the fluctuation differences of different signals. Therefore, the method can be used as an auxiliary criterion for high voltage DC transmission lines protection, which is of great significance for further improving the reliability of DC transmission lines protection.

In order to assess the safety state of superconducting cables in real-time, this paper proposes a safety state evaluation method for superconducting cables based on system dynamics theory. Firstly, the key non electrical indicators that affect the safety state of the system are selected and a system dynamics flow chart for them is built; Secondly, health indicators related to equipment parameters are obtained and weights are assigned to them; Then, the relationship between equipment health index and failure probability is obtained to conduct real-time safety state assessment of the equipment; Finally, the series system model is used to obtain the fault probability of the superconducting system, and the rationality of the proposed model and technical method is verified through simulation examples. The results show that the proposed method can evaluate the safety state of superconducting cable operation in real time.

Carbon fiber composite core soft aluminum conductors have the advantages of large capacity, small sag, high conductivity, and obvious energy-saving effects, making them one of the ideal solutions to improve the capacity of transmission corridors. However, the bending resistance of carbon fiber composite cores is weak, and they are prone to damage and fracture during installation and construction process, lacking effective detection techniques. In order to solve the above problems, a magnetic leakage detection method of is designed based on the theory of carbon fiber composite core magnetic leakage detection theory. Based on a three-dimensional finite element simulation model of magnetic film coated carbon fiber core, crimping fittings, and excitation device, the influences of key parameters such as permanent magnet size, magnetic pole spacing, and radial air gap distance on magnetic leakage signals are analyzed. The optimized design parameters are: permanent magnet size L₁ is 15 mm, the pole distance L₂ is 50 mm, and the gap distance M is 2 mm. Finally, the influence of the fracture scale of carbon fiber composite material cores on magnetic flux leakage signals is analyzed, confirming the technical feasibility of magnetic leakage detection for local fracture defects in carbon fiber composite cores. The axial component of magnetic induction intensity first increases and then decreases, reaching its maximum value when the fracture scale d=4 mm, while the radial component of magnetic induction intensity increases with the increase of fracture scale.

At present, there is a huge demand for simulation resources for large-scale new energy stations and county-level ten thousand nodes level power systems in China. However, physical simulation platforms have problems such as high cost, slow compilation speed, lack of evaluation methods for key performance indicators, and bottleneck in key simulation core technologies. In view of this, a new domestic real-time simulator UREP300 has been independently developed. Its composition principle and parallel iterative compilation principle are elaborated in detail, and key indicators and evaluation methods of the simulation platform are proposed to measure the performance of the simulator. Based on this, a detailed comparison is made with leading foreign real-time simulation equipment. The results have proven that the performance of UREP300 in the simulation field is comparable to or even exceeds that of leading foreign devices, especially in terms of compile speed, which has achieved breakthroughs, greatly shortened compilation time, solved the problem of missing key indicators and evaluation methods in simulation platforms, and contributed to the localization research process.

In response to the current problem of only single standard to evaluate the meter in China and a lower accuracy in its life prediction under complex environment stress, a deep research on the life prediction of smart meters under complex environment stress is conducted. The failure mechanism of smart meters is studied through the analysis of 6 stresses existing in environment which affect smart meter’s life. In addition, a smart meter’s life prediction solution based on T-S（Takagi-Sugeno） fuzzy model with particle swarm optimization is proposed to address the difficulty of predicting lifespan due to the small number of fault samples and long lifespan of intelligent energy meters. Then stress and lifespan data of smart meters under four typical climatic conditions in China are collected for experimental verification of lifespan prediction, and experiments about the life prediction are made through these data. The experimental results show that the proposed approach is an effective way to predict the life of smart meters under different environments and various environmental parameters, with higher accuracy than the original T-S fuzzy model algorithm.

The zero-sequence impedance or impedances of the transformers is an important basis for the selection and setting value calculation of relay protection components, and is an important parameter of power systems. It can be used to accurately calculate the zero-sequence component of short-circuit current in power system accident states. The zero-sequence impedance of transformers is mainly affected by the structure of the iron core and the connection group of the windings. Analytical methods are difficult to accurately calculate the zero-sequence impedance, while finite element methods can better solve this problem. Taking YNyn0d11 3-phase 3-leg transformer as the research object, a finite element field circuit coupling calculation model for zero-sequence impedance is established in Ansys Maxwell, and three calculation methods for zero sequence impedance are studied: energy method, volt-ampere method, and inductance matrix method. Based on the above model, the influence of fuel tank and magnetic shielding modeling methods on the accuracy of zero sequence impedance calculation is studied. The error between the calculation results and the transformer test data is within 5%, which meets the engineering calculation accuracy requirements and verifies the effectiveness of the three proposed calculation methods.

Because of its superior characteristics, inverse-time zero-sequence overcurrent relays have been put into use in more and more areas. Many scholars propose to set its setting value through optimization method. However, the commonly used protection setting optimization algorithms often suffer from complex and time-consuming iterative processes, as well as unstable results, and there is still room for further optimization. Therefore, a quadratic optimization method of inverse-time zero-sequence overcurrent relay settings in extra-high voltage power grid is proposed. Firstly, the inverse-time zero-sequence overcurrent relay setting optimization model is established. When the time setting value T_p is certain, the relationship between action time t and the starting current I_p is analyzed, and then the linear fitting is carried out to transform the complex nonlinear non-convex optimization model that is difficult to optimize into a quadratic objective quadratic constraint model. Then, based on the MATLAB software platform, the problem is modeled according to the modeling tool YALMIP, and the optimization solving tool Gurobi is used to solve it. Finally, the IEEE 39-bus system is taken as an example to verify that the proposed method is effective and superior.