For multi-infeed HVDC systems, when analyzing the high-frequency resonance risk of voltage source converter-high voltage direct current （VSC-HVDC） and AC external systems, the influence of other DC in the near region on the AC external impedance characteristics is often overlooked. To assess the high-frequency resonance risk between a single VSC-HVDC and external system, this paper first introduces a calculation method of harmonic impedance of AC external system including multi-infeed DC and other power electronic equipment. Considering the problems of multiple DC operation modes, multiple parameters, complex acquisition of harmonic impedance characteristics, low efficiency and questionable necessity of detailed calculation in the system, the multi-infeed interaction factor （MIIF） index is introduced. The MIIF measures the strength of interaction between various converter stations in the multi-infeed HVDC system to evaluate the DC that needs to be considered in detail when calculating the harmonic impedance of VSC-HVDC external systems, This allows for fast and accurate calculations of the harmonic impedance of VSC-HVDC external systems. Finally, based on the actual data of a multi-infeed HVDC provincial power grid, the accuracy of the proposed method is verified. The proposed method provides a more accurate, fast and effective method for high-frequency risk analysis and avoidance of VSC-HVDC planning operation in multi-infeed HVDC system.

For Guangdong back-to-back volatage source converter - high voltage direct current (VSC-HVDC) project, there are only two outgoing lines on the AC side and only one lower level substation, the risk of the last circuit breaker appearing in the converter is high. In the project, the last circuit breaker protection will be configured to prevent equipment overvoltage damage. In order to reduce the overvoltage characteristics and improve the action time margin of the last breaker protection, based on the topology and control strategy of Guangdong back-to-back VSC-HVDC project, the overvoltage mechanism and characteristics under three-phase and single-phase tripping conditions are studied. Then the range of amplitude limit of the positive and negative sequence modulation signal is analyzed, and a method to reduce overvoltage characteristics by optimizing the amplitude limits is proposed, and EMTDC simulation is carried out. The results show that the overvoltage characteristics of back-to-back VSC-HVDC are strongly related to the positive and negative sequence control, and the overvoltage of single-phase tripping of the last breaker is more serious than that of three-phase tripping, the saturation characteristic of transformer will restrain overvoltage, but it will make the control response more complex. The method proposed in this paper can significantly improve the action time margin of the last breaker protection action.

The traditional time-domain reflection method is only applicable to more serious defects or faults because of its low content of high-frequency components, large signal attenuation in the transmission process in the cable, and the inability to compensate for the signal dispersion attenuation. It can not detect local weak defects well, and its positioning accuracy is low. Based on this, according to the characteristics of the reference signal, this paper proposes a positioning technology based on the waveform time-domain envelope. First, the reference signal and its reflected signal are enveloped by Hilbert transform in the time domain, then the envelope curve is logarithmized and normalized, and finally the eighth order Butterworth band-pass filter is used to filter and suppress noise. After that, 50 m and 500 m long power cables are actually tested, respectively. The results show that the time-domain envelope technology can effectively locate cable defects. At the same time, the type of cable defect can also be judged according to the change of time domain waveform.

The performance change and action mechanism of cross-linkable polyethylene material for cable insulation during storage are still unclear. This article is based on two storage methods commonly used in laboratories — tin sealant and plastic sealant. By characterising the dielectric properties and crystalline structure of XLPE samples prepared from the new cross-linkable polyethylene granules and the granules of same brand with two years’ storage by the above two storage methods, the effect and mechanism of storage on the properties of cross-linkable polyethylene material are studied. The experimental results show that the change in properties of the cross-linkable polyethylene material during storage is due to the decomposition of the cross-linking agent contained in granules. The cross-linking degree of the XLPE samples obtained from the granules after shelving for two years decreased significantly, the thermal extension properties deteriorated, the crystalline properties and crystalline structure are slightly improved, and the dielectric properties are improved. With the effect of light, the decomposition of the cross-linking agent is more serious, and the performance changes are the most obvious.

Digitization is the essential feature of a new round of informatization. The rapid development of digital technology has promoted the leap of social informatization, and promoted the wide application of information machines composed of computers and networks in the field of power grid. This paper creatively puts forward the π model theory for analyzing social information processing. On the basis, the theoretical model of digital power grid is analyzed. The four main characteristics of digital power grid are extracted, and the overall architecture of digital power grid is constructed based on physical system, information system and business system, which lays a solid foundation for the research and development of digital grid.

As the first regional electricity spot market, the rational use of electricity market clearing algorithm is of great significance for the electricity trading in southern China region electricity market. Therefore, the clearing price calculation system and calculation algorithm of electricity spot market are studied, and a electricity spot market clearing algorithm considering inter-provincial transmission is proposed. Taking the social welfare maximization as the goal function, a market clearing mathematical model is established considering the impact of inter-provincial transmission and distribution price on the optimal allocation of power generation resources in the five provinces. Based on the transmission and distribution price of inter-provincial transmission components, the power generation plan and inter-provincial transmission components within the region are determined in a unified manner. Finally based on the application example of IEEE 9-bus system, the results show that the clearing method proposed in this paper can meet the requirements of continuous production of power system.

In order to enhance the significance of numerical weather prediction (NWP) for short-term wind power prediction and take into account the influence of transitional weather processes on power prediction, a short-term wind power prediction method considering identification and testing of transitional weather processes is proposed. The samples with NWP interval 15 min of the time series are identified using a gated recurrent unit (GRU)based classifier for transitional weather processes. Based on the identification results, the wind speed series of transitional weather processes are tested by method for object-based on diagnostic evaluation (MODE), and the NWP forecasting regularity is explored. Based on the results of weather process identification for the time period to be predicted, matching weather processes, different models are selected for short-term wind power prediction. The proposed method is applied to a wind farm in Jilin, China, for arithmetic validation. The results show that the transitional weather process identification method has a high identification accuracy. The average reductions of RMSE value by 2.77% and MAE value by 2.46% for all types of weather process conditions prove the effectiveness of the method.

As a clean and renewable energy source, hydropower has significant environmental benefits. The development of hydropower microgrids is one of the important paths to achieve the " double carbon" goal. Based on the output characteristics of distributed hydropower, a coordinated operation strategy multi-distributed hydropower is proposed, and a microgrid reconfiguration method and model based on this strategy is put forward. In order to avoid a large number of infeasible solutions in the optimization process, a microgrid reconfiguration algorithm based on improved particle swarm optimization is proposed. The IEEE 33-node system is selected to simulate an example, which verifies the rationality and effectiveness of the proposed method, which can effectively improve the operation economy of the microgrid.

As the proportion of new energy in power systems gradually increases, new energy power prediction becomes a research focus. However, the power prediction of the new-built wind farm is faced with the problem of historical data insufficiency, and difficulty in feature transfer. A short-term wind power prediction approach based on the deviation compensation TCN-LSTM and step transfer strategies are proposed. First of all, the small amount of data of the target wind farm are divided into two groups according to the correlation with the source wind farm. Then the historical data of the source wind farm is used to train the hybrid model containing the error compensation module. Finally, the model is constructed with the step transfer strategy. The relevant case analysis of this paper exhibits that the prediction accuracy of the compensation step transfer learning model based on TCN-LSTM is increased by 1.23% compared to similar direct prediction models. The effectiveness of the proposed approach is proved by related cases.

With the popularization of user side distributed photovoltaic power generation equipment, higher requirements are put forward for distributed photovoltaic output forecasting and regulation technology. To solve the problems of low generalization ability and high sample dependence of traditional photovoltaic output forecasting methods, a user distributed short-term photovoltaic forecasting model based on meteorological coupling characteristics and improved XGBoost algorithm is proposed. Mutual information and principal component analysis are used to select characteristics and reduce dimensions to obtain highly correlated and decoupled meteorological characteristic variables. The parallel integration of XGBoost forecasting model based on Bagging algorithm improves the model generalization ability. An evaluation index of combined forecasting accuracy based on mean absolute error (MAE） and mean arctangent absolute percentage error (MAAPE） is proposed. In the example analysis, the mean MAE of the model in this paper is 6.934kW, and the mean MAAPE is 16.73%. The relative error is less than 10% in more than half of cases. Compared with the traditional BP neural network or random forest forecasting model, forecasting accuracy is greatly improved and has good practical application ability.

In order to solve the problems of difficult flexible peak regulation and renewable energy accommodation due to the obvious seasonal characteristics of power grid load, taking Chuxiong area of Yunnan Province as an example, a new solution of integrating the photovoltaic power generation and liquid air energy storage (LAES） system architecture is proposed, aiming to clarify the integrated planning concept of source-network-load-storage. Based on the typical climate conditions, photovoltaic output characteristics in different seasons and the historical data of electricity load in Yunnan, the models of short-term load peak shaving and long-term photovoltaic accommodation based on photovoltaic power generation and liquid air energy storage are established. Based on the simulation analysis of daily and seasonal power load peaking, the technical feasibility and economic benefit of short-term load peaking and long-term photovoltaic absorption are further verified. The evaluation results show that the photovoltaic power generation and liquid air energy storage system can reduce the power grid supply burden by 33.4% through daily load peak shaving, and the photovoltaic power discarding rate is expected to be reduced by 10.1% through seasonal long-term energy storage regulation. In the economic benefit evaluation of short-term load peak shaving and long-term photovoltaic accommodation, the average levelized cost of energy (LCOE） is about 0.47 yuan/kWh, and the static payback period (SPP） and dynamic payback period (DPP） can maintain at about 7.5 years and 10.5 years, which verify the economic feasibility of the proposed photovoltaic-LAES system effectively.

In order to fully exploit the flexible adjustable potentiality of decentralized electric heating load，an optimization control method for peak regulation of decentralized electric heating considering user satisfaction is proposed．Firstly，the response characteristics of the single electric heating load are analyzed based on the operating characteristics of decentralized electric heating．Secondly，the decentralized electric heating loads are clustered using affinity propagation (AP) algorithm，and a rotating control strategy for the electric heating load clusters is developed based on the equal area rule．Then，a multi-objective optimization model is established that simultaneously considers the participation of electric heating loads in peaking and customer satisfaction．Finally，the proposed control method is validated based on the decentralized electric heating load parameters in a region of Xinjiang．The simulation results show that the proposed method can guarantee the user satisfaction with certain peak regulation capability.

Different types of faults on transmission lines seriously threaten the safe and stable operation of the system. At present, most transmission line fault diagnosis methods refer to a single fault information parameter, and often only consider the zero sequence current information in the fault record. Therefore, this paper proposes a fault type diagnosis method of transmission lines based on multi-source information fusion of operation inspection and control platform, constructs the actual waveform database and related information database under various fault causes of transmission line, uses wavelet packet analysis to extract the fault eigenvalues of different transmission line fault types, and uses machine learning algorithm to complete the fault diagnosis of multi-source information fusion.Based on the fault characteristic value of the historical tripping line in Hunan area, the simulation results show that the correctness and effectiveness of the method in this paper are verified by actual calculation examples.

The structure of the parallel gap is simple. However, after the lightning strike, the arc caused by the power frequency continuous flow cannot be extinguished quickly, so the tripping rate of the line rises. A new integrated lightning protection gap is proposed, which uses magnetic energy air blowing device to spray high-energy air flow acts on the gap arc, causing it to quickly extinguish the arc, and accelerate the dissipation of arc energy, while quickly thinning the gap arc, cutting off the power frequency current, and achieving the goal of rapid arc extinguishment. Based on the theory of magnetohydrodynamics (MHD), a finite element simulation analysis software is used to construct the theoretical model, and AC arc extinguishing experiments are conducted. The research results show that the integrated lightning protection gap based on the principle of air blowing can reduce the arc column temperature of the arc to 1/5 of the original in a short time, blow the arc to 30% of the original, and make the arc extinguishing time no more than 5 ms, which is only 6%~8% of the traditional protection gap extinguishing time. The proposed method greatly shortens the extinguishing time of gap power frequency continuous current arc, and ensures the power supply reliability of the power system.

Transmission wire plays an important part of the power system, and defects in transmission wire will affect the operation of the power system. A transmission wire defect detection method based on the improved YOLOv7 algorithm is proposed. First, a method of automatically expanding the data set is proposed in this paper, which can use a small number of pictures to establish a data set of transmission wire defects. Then, a lightweight self-attention backbone network is proposed, which replaces the YOLOv7’s backbone network, and uses the BiFPN to perform feature fusions. The experimental results show that the improved YOLOv7 algorithm proposed improves the accuracy from the original 89.4% to 97.5%. At the same time, the detection speed is increased by about 60.49%, and the FPS is improved from the original 52.36 frames to 84.03 frames; real-time detection of transmission lines can be achieved, which reduces the misdetection and omission rate of transmission wire defects, as well as improves the detection speed and enhances the inspection efficiency.

The intelligent perception of overheating of insulated jumper clamp under high temperature and high load conditions in summer is taken as the research object, and the three-dimensional finite element model of electrical thermal multi-physical field coupling of insulated jumper clamp under typical working conditions is established. The validity of the model is verified by experiments, and the temperature field distribution data under different current load, sunlight intensity, environmental temperature and wind speed factors are obtained as jumper clamp overheating perception model sample. The reverse learning strategy is introduced to improve the ability of sparrow search algorithm (SSA)in global search and improved sparrow search algorithm(ISSA) is established. ISSA optimized BP neural network (ISSA-BPNN) is used to establish the temperature prediction model of insulated jumper clamp, and the prediction accuracies of ISSA-BPNN, particle swarm optimization BP neural network (PSO-BPNN), genetic algorithm optimized BP neural network (GA-BPNN), sparrow search algorithm BP neural network (SSA-BPNN) and BP neural network are evaluated using the mean square value and determination coefficient. The results show that compared with the prediction models of the other four algorithms, the ISSA-BPNN model can control the average prediction error within 0.71%, with higher prediction accuracy and faster convergence speed. It can more accurately predict insulated jumper clamp temperature rise, providing a basis for the state detection and evaluation of the insulated jumper clamp.

At present, in the theoretical calculations and researches of coupled lightning overvoltage of transmission lines, most of them consider the situation of single return stroke current source and ignore that lightning mainly exists in the form of multi-pulse in reality. Many studies ignore the influence of conductor corona discharge effect on overvoltage suppression. Therefore, this paper improves the double Heidler return stroke current source, establishes a multi-pulse current source with three peak-currents in one discharge process, and optimizes the corona discharge model under single pulse to meet the corona discharge effect under multi-pulse. At the same time, the leap-frog algorithm is used to make the second-order high-precision difference of Maxwell equations to improve its model stability. The results show that the relative error between the overvoltage peak calculated in this paper and the IEEE standard result under the action of single pulse is only 2.11%. At the same time, the line coupling overvoltage peak is 17.08% higher than that of single pulse; the corona effect suppresses the peak overvoltage of transmission line under multi-pulse action by 7.29%; the peak value of overvoltage coupled with the phase conductor far from the lightning point is 17.5% different from the overvoltage of the phase conductor nearest to the lightning point. The conclusions of this paper can provide more accurate theoretical support for differential lightning protection design of transmission lines.