Permanent magnet synchronous motor has the advantages of high efficiency, high power factor, measurable rotor parameters and good control performance, for which it has been widely concerned and applied in academia and industry. The stator and rotor parameters of permanent magnet synchronous motor can be obtained in real time by online parameter identification technology, which can not only improve the control accuracy of the motor, but also monitor the health state of the motor online. In this paper, the research status of PMSM parameter on-line identification technology is summarized and summarized. On this basis, the key technical challenges of this technology are summarized and analyzed from the aspects of model equation rank shortage, cross saturation effect, inverter nonlinear effect and parameter identification under extreme conditions.

 Compared to power systems that use traditional RS485/CAN buses as transmission methods, the new power system has put forward new requirements for the radiation, efficiency, and power of information transmission methods Therefore, the article first introduces common information transmission methods, then elaborates on the principle of information power composite modulation from the essence of power electronics, analyzes the advantages and disadvantages of common digital modulation methods, and finally summarizes the current urgent problems and development prospects. The application of information composite modulation technology is quite extensive. When combined with the current flexible, efficient, and environmentally friendly distributed energy, it can not only improve energy utilization efficiency, reduce energy consumption and emissions, but also promote the development of new power systems and renewable energy, making important contributions to achieving sustainable global energy development.

 The permanent magnet synchronous motor （PMSM）drive system is often affected by multi-source disturbances under various operating conditions, leading to a decrease in its control performance. Linear Active Disturbance Rejection Controller （LADRC）has been widely used in PMSM drive systems due to its simple parameter tuning and independent mathematical model. The types of multi-source disturbances present in PMSM drive systems are first explained in the paper, and mathematical models for some disturbances are established. Then, the limitations of traditional LADRC are analyzed, and new types of LADRC proposed in recent years are summarized for different types of disturbances, while algorithm expressions for some of the proposed methods are provided. Finally, the development trend of PMSM drive systems based on LADRC in disturbance rejection control is summarized.

 Multi-energy virtual power plant （MEVPP）aggregates various forms of distributed energy and demand-side flexibility resources including electric and thermal energy. In order to realize the optimal scheduling of MEVPP, in this paper, a MEVPP model including power generation units, heating units, energy storage unit, air conditioning loads cluster, and demand response loads cluster is established, a deep reinforcement learning （DRL）-based optimal scheduling method is proposed for this model, corresponding reward function, state and action spaces are designed. The method is based on the proximal policy optimization （PPO）algorithm, which can regulate distributed energy and demand-side flexibility resources based on environmental information such as forecasted load, wind/light output, outdoor temperature, etc.，and obtain the strategy set of MEVPP optimal scheduling with the objective of minimizing the operating cost. The case study result proves the feasibility of DRL in MEVPP optimal scheduling and the extensibility of the strategy set.

The interconnection of multiple energy sources has gradually become the direction of future power system development, and the optimization and scheduling of multi energy interconnection systems is an important means to cope with the large-scale consumption of new energy connected to the grid. We have established a cost model for the wind solar water fire pumping and storage interconnected system that takes into account the cost of deep peak shaving for thermal power units, and a day ahead optimization scheduling model for the wind solar water fire pumping and storage interconnected system that takes into account the initiative of peak shaving. A hierarchical optimization scheduling model solving method is proposed for the complex structure of multi energy coupled systems. The upper level model aims to minimize the fluctuation of net load and obtain the equivalent load curve；The lower level model, based on the scheduling results of the upper level model, takes the economic optimization of peak shaving operation and the minimum amount of new energy waste as the objective function, taking into account the initiative of peak shaving, to solve the economic scheduling problem of a new energy system that considers the deep peak shaving coal consumption characteristics of thermal power units. The simulation examples show that the constructed model achieves complementary operation of multi energy interconnected systems, improves the deep peak shaving ability of thermal power units, reduces the abandonment rate of new energy and system operating costs.

The intermittency and volatility of high-proportion renewable energy access to the power grid have a significant impact on system stability. Therefore, it is crucial to evaluate and improve the stability of distribution networks with high proportions of renewable energy sources（RES）。This paper proposes a new power grid strength evaluation index, the integrated short circuit ratio （ISCR），which not only considers the interaction between RES, but also considers the impact of energy storage devices （ESD），and can more accurately identify weak links in the power grid. In addition, based on ISCR, a distribution network stability improvement method is proposed to allocate the location and capacity of RES and ESD through a two-level optimization method to enhance system strength and increase the capacity of RES and ESD. In the upper level, the location and capacity of RES are optimized to ensure the maximum ISCR value at the node. In the lower level, the location and capacity of ESD are optimized to ensure that the ISCR value is higher than the critical short circuit ratio （CSCR）to maintain system strength. The location optimization is determined by the ISCR value at the node. The capacity optimization is solved using linear programming methods. Finally, a case study verifies the effectiveness of the proposed optimization method in maintaining grid strength while increasing the capacity of RES and ESD.

 With the purpose of reducing the energy consumption of CO2 and promoting the efficiency of power generation, this paper proposes an optimization scheme of double reheat-carbon capture coupled with solar and ORC systems, using the ultra-supercritical double reheat-carbon capture system as the reference system. In this paper, an optimization scheme of double reheat-carbon capture coupled with solar and ORC systems is proposed. The optimized system model is constructed using Ebsilon software. The thermal performance, economics and operational performance of the system were analyzed. The results of the study showed that the thermal efficiency of the optimized system was increased by 4.78%，and the heat consumption was reduced by 1 005 kJ/（kW-h）and coal consumption by 36.50 g/（kW-h）compared to the reference system；the annual operating time of the solar auxiliary system is 60%，and the average coal saving can reach 2.0*104 t .The optimized scheme has better thermal economy and reduced coal consumption for power generation, which provides an optimization idea for CO2 capture and emission reduction.

For the future of high power electronics penetration, the stable operation of grid-forming inverters in the power system is crucial. Due to the high inertia and high damping generated by the mechanical rotor and damping winding of the traditional synchronous machine, the inverter may have obvious synchronous frequency resonance problems in the output power after grid connection due to the lack of inertia and damping and the negative damping introduced by coupling, which not only affects the waveform quality, but also leads to transient instability in serious cases. Four methods are proposed and analyzed for this problem, and finally the simulation verification is carried out by Matlab/Simulink 

Permanent magnet synchronous motor （PMSM）is often operates at high speed due to its inertia or external propulsion when the power supply is interrupted during operation. It is necessary to estimate the initial position/speed before flying start. The fixed action time and interval time of the traditional zero voltage vector pulse method often reduce the estimation accuracy and even cause overcurrent. Therefore, an adaptive triple zero voltage vector pulses method is studied in this paper, which can adaptively adjust the action time according to the amplitude of the current vector excited by the zero voltage vector pulses and adjust the interval time according to the initial speed estimation twice successively, so as to automatically apply the reasonable zero voltage vector pulses for different PMSM parameters or operating states. The simulation results show that the proposed adaptive triple zero voltage vector pulses method can adaptively adjust the reasonable action time and interval time, so as to improve the reliability of initial position/speed estimation and flying start.

 In the new power system, it is necessary for wind power to actively participate in power grid frequency regulation. How to evaluate the impact of wind power participation in frequency regulation on frequency dynamic characteristics has also become a research hotspot. In this paper, an improved wind turbine pitch control with constant load shedding pitch angle that can reduce the number of pitch actions before the rated wind speed is proposed, and the expression of the difference adjustment coefficient of the wind tur-bine participating in primary frequency regulation under this control is derived. Based on this, the frequency response model of the power system with constant load shedding pitch angle control is constructed, and the analytical expression of the frequency dynamic characteristic index is derived. The influence mechanism of wind speed and initial pitch angle on the adjustment coefficient and frequency dynamic characteristic of the wind turbine is revealed. The accuracy of the frequency equivalent model and the ef-fectiveness and feasibility of the proposed frequency modulation method are verified by the improved IEEE three machine nine-node-system.

With the transformation of the global energy structure and the improvement of environmental protection awareness, as an efficient and environmentally friendly way of energy utilization, the distributed generation system based on new energy is gradually occupying an important position in the power system. With the continuous improvement of the penetration rate of distributed generation system, the equivalent inertia of the power grid continues to decrease, which seriously affects the frequency stability of the power system. As a result, virtual synchronous control has emerged, which can compensate for inertia and equivalent damping of the grid by simulating the dynamic characteristics of traditional rotary synchronous generators. Although the small signal stability control of virtual synchronous machine （Virtual synchronous generators ,VSG）has been widely studied, the influence of its reactive power loop on the transient stability of the large signal of the system still needs to be further studied. In this paper, the influence of the reactive circuit of the grid-connected inverter on its transient stability is analyzed theoretically, and on this basis, three methods that can effectively improve the transient stability of the grid-connected inverter are listed. Finally, the simulation was verified by MATLAB/Simulink platform.

Integrated Photovoltaic Power to Hydrogen and Refueling （IPp2HR）systems effectively utilize solar energy resources,providing green hydrogen for hydrogen-powered transportation and other industries.They are a promising pathway for green hydrogen demonstration.However,current research on IPp2HR systems either overlooks the operational constraints of purification or focuses solely on day-ahead scheduling.Traditional purification systems use fixed operational sequences to dry crude hydrogen,which conflicts with the flexible,variable-load operation required to accommodate renewable energy fluctuations.To address this,a bi-level energy management method is proposed to improve IPp2HR system efficiency.First,a comprehensive model covering power to hydrogen,purification,storage,and refueling is developed.The purification process is transformed into a Mixed-Integer Linear Programming （MILP）model using the Big-M method and integrated into the scheduling framework.Second,a bi-level energy management framework is designed,combining day-ahead and rolling scheduling with real-time control.The day-ahead and rolling stages determine the on/off of electrolyzers based on PV forecasts and hydrogen demand,while the real-time stage adjusts power deviations to enhance PV utilization and operational benefits.A case study based on a hydrogen refueling station in Northeast China validates the proposed method.Results show that considering the purification heating and cooling logic prevents high-cost hydrogen caused by the inability to shutdown at high temperatures.The bi-level framework effectively coordinates day-ahead,rolling,and real-time stages,improving both PV utilization and operational profitability.

Aiming at the problem of poor current loop immunity in the high-speed operation of permanent magnet synchronous motor （PMSM），a harmonic suppression strategy for flux weakening with voltage phase angle regulation is proposed in the paper. Firstly, the small-signal model of the control system is established through linearisation, and then the closed-loop transfer function of the system is obtained to analyse the stability of flux weakening. In order to overcome the problems of low damping coefficient and poor system stability during acceleration, a proportional differential compensation strategy is introduced into the straight-axis current regulator to realise the inner-loop feedback in order to improve the system damping ratio and suppress the system resonance peaks so as to reduce the current oscillations during high-speed operation. Finally, the key parameters of the compensator are determined based on the principle of equivalent internal mode controller and frequency domain analysis.The experimental results show that the method can effectively improve the stability of the current loop and the anti-interference performance of the control system, and significantly reduce the harmonic content of the motor current.

Renewable energy sources such as hydrogen, photovoltaics, and wind energy are important ways to achieve the dual carbon goals and carbon emission control goals. Due to the significant fluctuations in renewable energy generation, the dual risks of load loss and power abandonment are highlighted, which puts higher demands on the flexibility of energy systems. This article proposes a low-carbon scheduling model based on flexibility quantification for a high proportion renewable energy integrated energy system. Firstly, analyze the operational characteristics of flexible resources in multi energy coupled equipment, construct a comprehensive energy system flexible resource model, and combine the utilization and emission of carbon dioxide in system equipment to achieve deep utilization of carbon dioxide within the system；Secondly, considering the source load fluctuation of the system, combined with the quantitative difference in flexibility between the integrated energy system and the power system, the quantitative analysis of energy, power, and slope flexibility of the park system is achieved；Finally, combining the differentiated regulation characteristics of system flexibility resources with the supply-demand differences of system flexibility at different time scales, multi time scale scheduling is carried out to mitigate the flexibility requirements brought about by the uncertainty changes of renewable energy and small time scales of load. This article takes a comprehensive energy industrial park in the south as the experimental background, and uses the method proposed in this article to analyze and schedule, verifying the power and electricity balance of flexible resources at different time scales, which is conducive to improving the flexibility and low-carbon characteristics of the comprehensive energy electricity-gas-heat system.

In the process of achieving the goal of carbon peak and carbon neutrality, in order to achieve environmentally friendly and energy-saving development,electric vehicles have begun to take the stage.With the rapid growth of electric vehicle ownership in the market,the safety problems caused by electric vehicles have gradually begun to emerge.Spontaneous combustion and fire accidents of electric vehicles are common.They have caused serious economic losses and even life hazards to automobile manufacturers,consumers,and operators of charging equipment.The charging safety of electric vehicles has begun to restrict the development of the electric vehicle industry.In this paper, starting from the constant voltage and constant current charging method, through the analysis of the causes of electric vehicle charging faults, a data mining early warning method based on discrete point detection of Gaussian distribution is proposed.The characteristic of outlier detection method is that it can effectively distinguish the data with significant difference from a set of data sets.A large amount of data will be generated during the charging process of electric vehicles. The Gaussian distribution model of normal charging state is obtained by obtaining the data under normal charging.The outlier detection method is used to judge whether the charging of electric vehicles is in a dangerous state, and the charging fault is warned in time. The real-time monitoring and early warning of electric vehicle charging process are realized. The experiment proves that the outlier detection method has good feasibility and accuracy for electric vehicle charging safety early warning .

Large-scale cross-season heat storage technology can effectively solve the problem of winter and summer heat imbalance of new energy heating.In this paper,a new type of zonal seasonal heat storage tank is proposed,and the heat dissipation effect and annual thermal efficiency of the heat storage tank under two different storage/release modes are calculated and compared by numerical simulation.The results show that the heat storage tank can maintain high annual thermal efficiency under both storage and release modes.Mode 2 adopts the strategy of "the last one goes first"， although the thermal efficiency of each zone is abandoned,it ensures that at least half of the zones are working under a higher heat utilization rate,and the annual thermal efficiency of the whole heat storage tank can reach 88.25%.Under mode 1，the annual thermal efficiency of the heat storage tank is 87.55%，which is slightly worse than that of mode 2， but it can ensure the stability of the hot water temperature throughout the heating season.

In the energy transition environment, the electric-gas integrated energy system has developed rapidly. However, the increase of complexity also challenges the safe and stable operation of the electric-gas integrated energy system. In order to better evaluate the operational risk between the two, this paper presents a risk assessment method that can reflect the risk of the electric-gas integrated energy system by using digital twin technology. By monitoring and simulating the operating state of the digital twin model, the operational risk of the electric-gas integrated energy system can be evaluated effectively. This method has the advantages of high precision and reliability, and has a wide prospect in practical application. In this paper, the original data of the system during normal operation are calculated by ox method to obtain the operating state of the system voltage, pressure and power flow. Then, the integrated energy system risk index is calculated to generate a database, and finally the calculated index data is evaluated and predicted using CNN-XGBoost technology. The example results show that the method used can improve the accuracy and efficiency of safety risk assessment and prediction of electric-gas system well.

With the increasing proportion of photovoltaic power generation connected to the distribution network, voltage out of limits and fluctuations have become increasingly serious. A voltage coordination control strategy for distribution network zoning is proposed to address this issue. In terms of distribution network zoning, based on the voltage sensitivity index information obtained from day-time power flow calculation, the voltage change index suitable for distribution network zoning is constructed by calculating the changes in the voltage of each node affected by photovoltaic active and reactive power regulation. The regional division of each node is determined based on the criterion that this index meets the voltage change threshold requirements；In terms of voltage coordination control of the zone, a voltage sensitivity control strategy within the zone is adopted to pair nodes and utilize the photovoltaic active and reactive power regulation ability to control all out of limit voltages within the zone to the normal range. Based on this, an interval distributed voltage coordination control strategy is adopted to achieve interval state variable equivalence and optimize the dynamic reactive power compensation of photovoltaic systems between adjacent zones based on the alternating direction method of multipliers （ADMM）algorithm, in order to achieve optimal suppression of expected voltage fluctuations across the entire network. Taking the IEEE 33 node system as an example, numerical analysis is conducted to verify the effectiveness of the proposed strategy.

 In order to further analyze the influence brought by the increasingly obvious characteristics of power network electronization, the monitoring of power network operation condition extends to the direction of interharmonics and higher harmonics, and puts forward higher requirements for synchronous wide-frequency measurement. The windowed interpolation spectrum analysis method widely used in synchronous wide-frequency measurement has spectrum leakage, which will lead to large errors when the fundamental frequency is shifted or there are neighboring signals in the signal. To solve this problem, an improved wide-frequency phasor measurement method based on Blackman window all-phase FFT bispectrum line correction is proposed in this paper. The spectrum of wide-frequency signals is analyzed by combining all-phase FFT and Blackman cosine window, and the parameters are corrected by using bimodal spectral lines. This method can effectively improve the suppression effect of spectrum leakage, reduce the interference of adjacent spectrum, and improve the measurement accuracy of wide-frequency signal parameters in complex operating conditions of fundamental wave frequency offset or adjacent spectrum.

The theoretical research of flexible DC distribution technology has entered a mature stage, and the imperfection of its line protection method has become an important factor restricting the development of this technology, especially the single-terminal protection with the advantage of fast action, which has weak resistance to high resistance and high sampling rate, still needs to be resolved. In order to solve the above problems, a new method of single-terminal protection for flexible DC distribution lines based on the current-limiting reactor voltage is proposed in this paper by analyzing the characteristics of the current-limiting reactor voltage and its negative rate of change in the time limit current reactor voltage when the metal bipolar short circuit fault occurs in the single-pole symmetrical connection system and the transit-resistance fault occurs. The simulation results show that the method can reliably identify faults in the region within 0.5ms,and can withstand the influence of transition resistance （20）and noise, and requires low sampling rate, which is of practical significance in engineering.

Thermoionic converters and thermophotovoltaic converters are two primary solid-state thermoelectric converters capable of operating at extreme temperatures with the potential for high efficiency,making them suitable for ultra-high temperature thermal energy storage applications.Both rely on the transfer of fundamental energy carriers through highly non-isothermal junctions:electrons in thermoionic converters and photons in thermophotovoltaic converters.However,the performance of both converters is constrained by factors such as the Stefan-Boltzmann law and space charge effects,preventing further enhancement.Nevertheless,when the distance between the emitter and absorber is comparable to or less than the thermal radiation characteristic wavelength,the performance of both converters can be significantly enhanced due to the photon tunneling effect generated by evanescent waves and the mitigation of space charge effects.Therefore,it holds significant importance in fields such as waste heat recovery and renewable energy utilization.This paper reviews the research progress of domestic and foreign scholars in the two types of solid-state thermoelectric converters and their hybrid systems,summarizes and analyzes potential future directions and key challenges.

In complex operating conditions and motion states ,the AC motor system has nonlinear time-varying physical parameters due to factors such as magnetic field coupling and iron core saturation, which weakens the motor control performance and system robustness. To address the above issues, the model-free predictive control strategy utilizes the inherent relationship between variables of the AC motor to construct a data-driven model, thereby eliminating the dependence on physical parameters and eliminating the impact of parameter mismatches. In this paper, combined with the permanent magnet synchronous motordrives, an affine ultra-local model is designed, and a model-free predictive current control strategy is designed. This method uses the least squares algorithmto estimate affine operators online and designs a state compensation mechanism. State compensation gains are selected through the s-approximation consideration to reflect the system motion characteristics in real time. The stability of the method is verified through the analysis of system zeros and poles, and its effectiveness, model adaptability, current quality, and system robustness are verified through experiments.

In the context of facing the increasing number of stakeholders and the physical coupling of the regional integrated energy system, an innovative scheduling strategy is proposed, which takes into account the demand response of multiple parties and adopts a multi-subject game for optimization. First, the actual characteristics of different loads in each microgrid and the response behaviors of users are studied in depth, and a corresponding multi-load integrated demand response model is established. Then, a multi-subject master-slave game model is constructed by taking the system operator as the upper layer leader and the microgrid load aggregator, centralized energy storage plant and wind farm as the lower layer followers. In the upper layer model, the goal is to maximize the system operator's revenue from energy sales, and to determine the unit price of energy sales and compensation price of each microgrid through optimization. And in the lower layer model, it is committed to minimizing the comprehensive cost of all parties, and determines the energy supply of wind farms, the energy output between multiple devices in each microgrid, and the charging and discharging strategies of energy storage plants through optimization. In order to solve this complex problem, the particle swarm algorithm is finally used to solve the upper layer subject model and the Cplex solver is used to solve the lower layer slave model, which interact with each other to finally obtain the equilibrium strategy after the game.

This article proposes a short-term wind speed prediction method based on improved adaptive noise complete set empirical mode decomposition （ICEEMDAN）and particle swarm optimization （PSO）long and short term memory neural network （LSTM）models. Use ICEEMDAN algorithm to decompose daily wind speed data and calculate corresponding marginal spectra, and screen historical data based on spectral correlation；Using PSO algorithm to optimize LSTM neural network parameters, ICEEMDAN decomposition is performed on the input data, and multiple modal components obtained are predicted using PSO-LSTM. The wind speed prediction results are obtained by overlaying the predicted values of each component. Use the proposed method to predict the wind speed of a domestic wind farm, and verify the effectiveness of the proposed method through comparative analysis.

Carbon Capture, Utilization, and Storage （CCUS）technology is of great significance in global climate change mitigation and achieving carbon neutrality. However, its large-scale commercial implementation is restricted by low investment returns. This paper systematically reviews the progress of research on CCUS investment benefits from both domestic and international perspectives, analyzing the impacts of technological innovation, economic costs, and policy support on investment returns. It also assesses regional differences worldwide in terms of policies, technologies, and economic benefits. The study reveals that while CCUS technology shows great promise, its economic feasibility and insufficient policy incentives are still key barriers to its deployment. By summarizing the shortcomings and challenges in current research, this paper provides directions for future studies and theoretical and practical references for CCUS commercialization and policy-making.

It is the most promising fault isolation method in flexible DC power network to remove faults by DC circuit breaker. At present, the traditional hybrid DC circuit breaker with a large number of fully controlled power electronic devices connected in series by main and branch has many problems such as high cost and technical difficulty. A Thyristor based Capacitor commutation hybrid DC circuit breaker topology （TCC-HDCCB）is proposed. The circuit breaker realizes the functions of two-way fault commutation, cut-off and adaptive reclosing through the combination of thyristor and capacitor, and uses the DC system itself to pre-charge the commutation capacitor, which greatly reduces the cost and control difficulty. In this paper, the topological structure and working principle of TCC-HDCCB are first introduced, and then the selection method of key device parameters is given. Finally, the simulation model of TCC-HDCCB is built in the software platform of PSCAD/EMTDC4.5，and the simulation verification is carried out in the single terminal and four terminal flexible DC power grid. The feasibility of the scheme is verified by comparing the performance and economy with the relevant schemes.

Based on the unpredictable AC-DC intrusion caused by human error or detection equipment failure in the terminal of secondary screen cabinet of substation relay protection, a new intrusion signal detection method based on generative adversarial network and principal component analysis is proposed to solve the problem of poor quality and small quantity of data samples. Gaussian kernel smoothing is used to preprocess the terminal data to reduce noise interference, and then the cleaned data is amplified through the generated adversarial network （GAN）to meet the subsequent principal component analysis processing and fault detection and identification of AC and DC intrusion signals. The identification accuracy of the proposed method reaches 95%，which realizes the accurate detection of the small sample fault data of the terminal of the secondary screen cabinet.

Short-term power load data are characterized by complexity and uncertainty, which often have an uncontrollable impact on the prediction results of the data. When short-term electricity load data are clustered and analyzed using traditional clustering methods, the prediction results will be biased due to characteristics such as the uncertainty of electricity load. In addition, considering that global regression forecasting methods are unable to use different modeling approaches for different parts of the data in the modeling stage, which limits the problem of adaptive capability for different distribution regions or different subsets of features. In this paper, we adopt the density peak clustering algorithm with K-nearest neighbor and weighted similarity to classify the features of short-term electricity load data, and propose a locally weighted linear regression model using K-nearest neighbor to forecast short-term electricity load. The advantages of this model are that it avoids the influence of Euclidean distance on the selection of cluster class centers, reduces the negative influence of global data on local data, avoids the centralized effect of cluster class division, and improves the generalization ability of the model. By comparing with fuzzy C-mean clustering and traditional global regression prediction methods, the model proposed in this paper is more superior for the prediction of real power data.

The high proportion of new energy grid connection leads to the reduction of power system inertia and the more complex spatial and temporal distribution characteristics of power grid frequency.It is urgent to clarify the frequency stability discrimination of power grid in new scenarios. In order to examine the frequency characteristics and frequency stability discrimination in the new scene, firstly, through the analysis of the new scene characteristics, the variables that can reflect the frequency time series characteristics are selected. After Pearson correlation analysis and feature hierarchical clustering dimension reduction, the feature quantities that are strongly correlated with the frequency and easy to measure are selected as the key response feature quantities. Through the correlation curve of its response characteristics with the frequency, the frequency is studied with the change of its feature quantities in the system stability and instability state, and the frequency criterion is constructed. The criterion can identify the fault unit while judging the stability. Finally, the effectiveness of the method is verified by an actual power grid example.

Under the background of "dual carbon"，the energy consumption in parks tends to be clean and low-carbon. The scaled development of distributed photovoltaic and energy storage can optimize the energy consumption structure and cost in parks. How to optimize resource allocation and achieve green energy consumption in parks is a current research hotspot.This paper will focus on the demand for optimizing the capacity configuration of distributed photovoltaic and energy storage in parks,and study the optimal configuration method of distributed photovoltaic and energy storage from the perspective of energy consumption cost.The goal is to minimize the annual energy consumption cost and the grid variability coefficient,and an improved adaptive weight particle swarm optimization algorithm is used to establish a two-level optimization model to propose a method for optimally configuring distributed photovoltaic and energy storage capacity for park energy consumption. An example application is carried out in a park energy consumption scenario to verify the feasibility and effectiveness of the model selection and optimization algorithm.

The use of electric hydrogen generation to consume the wind power abandoned in the integrated energy system with high percolation rate is an effective method to save energy and reduce carbon,but there exists the problem of improper power distribution of electric hydrogen generation array operation,which leads to the serious imbalance of the life span of each single tank,and greatly reduces the life span of the whole system of electric hydrogen generation, which needs to be solved urgently.The paper proposes a multi-timescale regulation strategy for the integrated energy system that takes into account the rotational start/stop of the electric hydrogen array.In the day-ahead phase,the rotational start/stop strategy of the electric hydrogen array is designed to equalise the system life depreciation；in the intra-day scheduling phase,the economic and low-carbon objective is to ensure the supply of the load demand；and in the real-time phase,the day-ahead real-time purchased power deviation is offset by the flexible use of the energy storage,so as to minimize the impact of the stochastic volatility of the lower-level integrated energy system on the power grid.The real-time phase,the energy storage is used to flexibly offset the day-ahead-real-time power purchase deviation to minimise the random volatility of the lower-level integrated energy system on the grid.Finally,engineering examples are presented to verify the economic,low-carbon and reliability advantages of the strategy.

To suppress the torque ripple of synchronous reluctance motor （SynRM）and improve the robustness of the system, a compound control strategy based on multiple resonant velocity loop control and active disturbance rejection current loop is proposed. Because the reluctance of SynRM is not uniform, the inductance of the motor changes with the magnetic saturation and cross-coupling effect. On the one hand, the inductance parameter changes will affect the current loop control. On the other hand, the change of inductance parameter will cause torque pulsation；Therefore, a speed loop control strategy based on multi-proportional resonance is proposed to suppress the torque harmonics of SynRM. The beat free current predictive control based on the hyperlocal model uses the improved phase-locked loop observer to predict the current and disturbance of the next beat, which can track the current well when the motor parameters change, thus improving the anti-interference ability of the system.

In order to achieve high-precision ultra-short-term prediction of wind power,this study conducted cross-domain feature selection based on Wasserstein distance and Random Forest （RF），and combined it with Evolutionary Bagging （EvoBagging）。A new method for ultra-short term wind power prediction is proposed.Firstly,the Local Outlier Factor （LOF）algorithm is used for outlier detection,and K-Nearest Neighbors InterpolationK-NNI is used to replace outlier points in the original data.Secondly,the data after outliers were decomposed by Empirical Mode Decomposition （EMD）algorithm and statistically calculated to build the reconstructed data,and Wasserstein distance and RF cross-domain feature selection were used to reduce the feature dimension of the reconstructed data.Finally,in order to combine the advantages of each model to improve the prediction accuracy of the model,It is constructed with Deep Belief Network （DBN），Deep Neural Networks（DNN），Light Gradient Boosting Machine （LGBM）and eXtreme Gradient EvoBagging ensemble learning ultra-short term wind power prediction model based on Boosting（XGBoost）learner.It is proved that the prediction error of this model is reduced by 5%on average compared with that of a single model,and it can achieve high precision prediction of ultra-short term wind power.

A high proportion of new energy power sources connected to the grid will lead to power system voltage with volatility and randomness,thus reducing system voltage stability.As a key reactive power compensation device in new energy stations,distributed synchronous regulators play a significant role in enhancing system voltage stability.However,in the current new energy power system,how to configure these distributed regulators in an economically reasonable and reliable way needs to be explored in depth.To this end,a new distributed regulator siting and capacity-setting method focusing on voltage stability constraints is proposed in the paper.First,the static voltage stability index of the system is evaluated to guide the selection of the optimal installation location of the regulator to ensure that the selected location can most effectively improve the system voltage support capability.Subsequently,on the basis of site selection,the capacity configuration of the regulator is reasonably planned with the objectives of minimizing the investment cost and maximizing the operational reliability of the system.Finally,through the analysis of actual cases, this method is not only economical and efficient,but also provides a strong guarantee for the stable operation of the new energy power system.

The high proportion of renewable energy networking,represented by wind energy,has brought new challenges to the power system.Hydrogen energy storage technology is an effective way to smooth out fluctuations in renewable energy power and improve the economic and low-carbon performance of comprehensive energy systems.On the basis of analyzing the power regulation capability of a high proportion wind power interconnection system,it is pointed out that wind hydrogen coupling can reduce system wind abandonment and power shortage.The worst-case scenario cost is an important indicator for evaluating the operational status of a system under uncertain factors.Based on uncertain scenarios,a stochastic p-robust optimization method combining basic stochastic optimization and robust optimization is proposed to ensure stable operation of the system in the worst-case scenario.Taking into account both economic and environmental benefits,a unit commitment optimization model with dual objectives of expected cost and carbon trading cost was established under p-robust constraints.The results of the example show that the stochastic p-robust optimization method effectively reduces the expected cost of the system.The established unit combination optimization model can flexibly optimize the output of multi energy systems based on different objective weights,reduce abandoned wind power,and improve wind power utilization.

In view of the power grid blackout accident that may be caused by the outage of transmission lines under the typhoon disaster, a real-time optimal dispatching strategy to improve the resilience of the power grid is constructed. The relationship between wind speed and line fault rate is described by using the vulnerability curve, and the impact of line load rate on line fault is considered to obtain the transmission line fault probability under typhoon disaster；The fault scenario set is generated by Monte Carlo sampling, and the scenario reduction is performed by using the confidence set method；On this basis, a multi-objective real-time optimal dispatching model considering the power flow uniformity, the line load rate in the disaster area and the cost of power generation is built, and the optimal transmission switching（OTS）is introduced into the model to reduce the number of lines affected by typhoon disasters. In order to improve the calculation speed of the optimal scheduling model, Benders decomposition method is used to solve the problem. An IEEE 118-bus system is simulated to verify the effectiveness of the proposed method.

To improve the accuracy of low-voltage DC cable location,an improved empirical wavelet transform（IEWT）method and an improved double-end traveling wave criterion method are proposed.This method first uses the double-end traveling wave method to obtain the current signals at both ends of the faulty cable,and then decomposes and reconstructs the current signal through IEWT to obtain processed intrinsic mode function （IMF）components, which are screened to effectively avoid mode mixing in the decomposition of complex noise signals.Finally,the selected IMF components are detected using kurtosis rules,and the position of the fault point is determined by using the improved double-end traveling wave fault criterion.Simulation results show that this method has high accuracy and meets the requirements of engineering practice for location.

The research aims to explore the new construction and renovation strategies for energy-saving transmission conductors.By comparing and analyzing the application of five types of conductors in practical engineering,including aluminum-clad steel-cored （high-conductivity）aluminum stranded wire,aluminum alloy-cored （high-conductivity）aluminum stranded wire,medium-strength aluminum alloy stranded wire,steel-cored high-conductivity aluminum stranded wire,and aluminum alloy-cored aluminum stranded wire,combined with the principle of energy-saving conductors,the investment and operating costs of transmission conductors under different voltage levels （+800kV DC，500kV AC，220kV AC，110kV AC）and their energy-saving effects are evaluated.The research results show that the aluminum alloy core -type aluminum stranded wire has the best economic performance,and can cover the reconstruction cost and new construction cost through energy-saving benefits in a relatively short period of time.Further analysis shows that line length,annual utilization hours,and carbon price are key factors affecting the cost-benefit balance point.Therefore,using aluminum alloy core-type aluminum stranded wire for energy-saving transmission transformation can not only effectively reduce power loss and carbon emissions,but also bring significant economic benefits and promote the sustainable development of the transmission system.

Aiming at the lack of system operation flexibility caused by the low utilization rate of flexibility resources during the operation of virtual power plant cluster （VPPC），this paper proposes an optimal scheduling strategy for VPPC that takes into account the mutual benefit of flexibility resources.Firstly,based on the trading relationship between VPPC and the electricity spot trading market,a VPPC trading model is constructed in which VPPC counts and flexibility resources are mutually beneficial.Under this trading model,the supply-demand relationship between VPPC operating flexibility under the source-load uncertainty scenario is further analyzed with respect to the flexibility shortage phenomenon,and the VPPC flexibility mutual aid service fee settlement model is established.Secondly,the impact of participation in flexibility mutual aid service on the energy interaction between the lower virtual power plants and the economic operation of the upper cluster is studied in depth,and a two-layer optimization model of master-slave game is constructed to take into account the interests of multiple subjects,and a master-slave game equilibrium algorithm based on the Kriging meta-model is used to simulate the operation of each virtual power plant and reasonably formulate the price of mutual aid for the energy of virtual power plant clusters；lastly,the proposed solution can not only enable the cluster operator to earn a profit but also provide the cluster operator with a reasonable price for the energy of the cluster.Finally,it is verified through case studies that the proposed scheme not only enables the cluster operator to earn profits from the mutual aid service,but also reduces the power supply pressure on the power grid,lowers the operating costs of each virtual power plant,significantly improves the overall operational flexibility of the system and reduces the operating costs of all parties involved in the service.

How to achieve efficient and precise control of multi-band radiation properties is a common scientific challenge in military camouflage,aerospace,solar energy and other fields.Conventional radiation property control often uses inefficient trial-and-error optimisation of functional groups or micro-nanostructures,which is time-consuming, laborious and difficult to obtain the best radiation properties.The emergence of machine learning has overturned the traditional optimisation methods and greatly improved the efficiency of radiation property optimisation and design by simulating the brain's learning and thinking.In this paper,machine learning algorithms in radiation property regulation are discussed in detail,and their advantages and challenges in terms of accuracy,scalability and efficiency are evaluated；the advanced results of the fusion of machine learning and radiation property directional regulation are summarised in a systematic way,including forward radiation response prediction and material directional optimal design；and finally,the hot spots of the research on the combination of radiation property regulation and machine learning and the direction of future development are explored.By reviewing the existing literature,this paper provides a reference for the design and application of radiation property directional regulation and machine learning algorithms, and makes suggestions for further optimisation and innovation of radiation property directional regulation.