In order to solve the problem of Shenzhen power grid voltage stability during the construction of Guangdong target grid and improve Shenzhen power grid stability under serious AC fault, the mechanism of Shenzhen power grid voltage stability is analyzed with the operation of Guangzhou and Dongguan back-to-back HVDC projects, the proposed adaptive switching voltage dependent current order limiter (VDCOL) strategy is applied to Xindong UHVDC and Xing'an DC, and finally its effectiveness is verified by RTDS simulation. Shenzhen power grid voltage stability is caused by both the lack of dynamic reactive power support and the rapid increase of reactive power consumption demand of HVDC systems after AC fault. The adaptive switching VDCOL strategy can correctly change the optimized parameters in case of serious AC faults at inverter side, and has a great impact on the power recovery characteristics of Xindong DC, but the delay of power recovery characteristics will not cause Yunnan power grid frequency out of limit. Implementation of adaptive switching VDCOL strategy to both Xindong DC and Xing'an DC can well eliminate Shenzhen voltage stability problem of serious AC faults, and the effectiveness of the proposed adaptive switching VDCOL strategy is fully verified.

A complex and large AC-DC hybrid power grid has been built by China Southern Power Grid, which characterized by long distance, large capacity, AC-DC interconnected, and strong DC with weak AC. The problem of security and stability of power grid is becoming more and more serious, so it is necessary to build a large-scale power stability control system to ensure the safe and stable operation of the power grid. However, the existing power stability control system has the shortcomings of simple communication architecture, transparent communication protocol, low dependability and security, and weak anti-interference capability. Therefore, it cannot meet the requirements of high dependability and security in power stability control system for AC-DC hybrid power grid. In this paper, new technologies of communication frame recoding, unique authentication of communication identification code and verification of cyclic increasing counter are presented, which improve the reliability of communication between stations in power stability control system. A self-inspection technology based on online monitoring command is also presented, which effectively detects the hidden defects of large-scale stability control system and greatly improves the overall reliability of the whole system communication. Finally, a data encryption standard (DES) encryption algorithm is applied to encrypt interactive data between stations to ensure the communication security of the power stability control system. Experimental verification and field application show that the proposed new technologies significantly improve the reliability and security of the power stability control system.

The loop closing current is related to the bus voltage amplitude difference, phase angle difference, loop impedance and other factors, but the importance of each factor is not clear, and the closed loop current regulation lacks pertinence. For this reason, a method for evaluating the influencing factors of the power transfer from loop closing in the distribution network based on the extreme gradient boosting (XGBoost) algorithm is proposed to obtain the weight of each influencing factor and take targeted measures. Firstly, combined with the characteristics of the actual distribution network, the characteristic factor set that affects the size of the closed loop current is determined. Then, a closed loop model of the distribution network based on PSCAD/EMTDC is built, and the parameter values are changed to obtain a large number of sample data. Finally, the XGBoost algorithm is used to train the sample data to obtain the weight ranking of the factors affecting the loop closing current. The research results show that the priority of each factor is: phase angle difference>loop impedance>comprehensive load size> bus voltage amplitude difference>comprehensive load distribution. Based on this, corresponding loop closing current regulation strategies are formulated to achieve refined management of the distribution network, which has certain reference value for the dispatch and operation of the power grid.

Dynamic simulation package (DSP) is a power system analysis software developed by Electric Power Research Institute of China Southern Power Grid Co., Ltd., which is widely used in the planning and operation of China Southern Power Grid. It is found that the difference between the synchronous generator models of DSP and BPA is an important reason for the inconsistent transient stability calculation results of the two software. In this paper, the differences between the basic form synchronous generator model of DSP and the standard form synchronous generator model of BPA are analyzed and compared, the conversion relationship between the parameters of the two generator models is given, and the difference between the three-stage saturation curve used in DSP generator model and the secondary saturation curve used in BPA model is studied and the fitting method is proposed. The standard form synchronous generator model and the secondary saturation characteristics are developed in DSP, and the consistency between the model and BPA synchronous generator model is verified by examples. Compared with BPA, DSP provides both basic and standard form synchronous generator models, and both three-stage saturation and secondary saturation characteristics, which can meet a wider range of simulation model requirements.

The accurate and reasonable calculation of the carbon emission factors on the electricity-consumption side is of great significance for the accurate assessment of the carbon emissions caused by electricity consumption of various regions, industries and enterprises. In this paper, a calculation model and method of carbon emission factors of corporate electricity consumption is constructed by combining power flow tracing and green power trading. Based on the power production and consumption data, power flow distribution data and green power trading data of power generation and consumption enterprises, the relationship among the carbon emissions on the electricity consumption side and the enterprise location, power consumption period and green power trading is researched to achieve real-time update of carbon emission factors on the electricity consumption side. Finally, the cases calculations on both IEEE 30-bus and an actual provincial power grid verify the effectiveness of the proposed method.

In the context of cyber-physical fusion, the impact of information system failures on system reliability has become a research hotspot. In order to improve the reliability of power supply in distribution network, a multi-objective planning method for switch and distribution automation terminals considering information system faults is proposed in this paper. First of all, a distribution network reliability calculation method is proposed considering information system faults. This method can adapt to different topologies and can quickly calculate the distribution network reliability. Then, a multi-objective programming model for switch and distribution automation terminals is established, which takes the investment cost and the average power supply shortage of the system as the goal. The improved non dominated sorting genetic algorithm II algorithm is used to solve the above model. The configuration schemes in the scenario of the IEEE-33 system are compared to verify the effectiveness of the proposed method.

The large-scale use of natural gas units intensifies the interactions between power grid (PG) and natural gas network (NGN), which puts forward new requirements for the expansion planning for PG and NGN. Existing planning methods have high redundancy to cope with the uncertainty in the long-range year. To solve it, this paper proposes a joint dynamic planning method for grid and gas network with the integration goals of economic, reliable and low-carbon from the lifecycle perspective. A lifecycle cost management model for electricity lines and natural gas pipelines is established using a lifecycle management approach, including construction, operation, retirement, environmental, and load curt costs. A joint planning model of PG and NGN is proposed, in which the reliability constraints of PG and NGN are formulated based on the multi-scenarios of failure events, and the carbon emission constraints of PG and NGN are introduced by lifecycle carbon emission. A dynamic programming framework based on multi-objective integration is proposed, which can realize rolling planning across time scales to significantly reduce the redundancy of planning results to uncertainty. For multi-objective planning, fuzzy optimization method is used to determine the optimal solution, and the trade-off among economy, reliability and low carbon is achieved. The case study verifies the validity and practicality of the proposed model, and the results show that the proposed multi-objective integrated dynamic programming scheme can improve social welfare more than the planning scheme that only considers the economy or the multi-objective open-loop planning scheme.

It is important to study the fluctuation of wind speed in numerical weather prediction to improve the prediction accuracy of wind power. At first, a deep self-adaptive filtering framework is proposed. For numerical weather prediction of wind speed, the variational mode decomposition algorithm with kullback-leibler divergence is adopted. After multiple modal components are generated by decomposition, the noise components are filtered based on the non-local means algorithm. Then, the de-noised wind speed sequence is obtained by reconstruction with the effective component. On the basis, the example data is classified by season, and the wind speed sequence of the numerical weather prediction after denoising is taken as the input. In the alternative model base, the most suitable wind speed-power conversion model in this season is selected by the verification set, and the wind power prediction is carried out for the test set. A wind farm in northeast China is selected for example analysis, compared with other decomposition algorithms, the prediction accuracy of the proposed method in different seasons can be increased by 0.25% to 1.58%, that means, the depth adaptive filtering framework under seasonal typing can effectively improve the prediction accuracy of wind power.

In order to meet the requirements of users for thermal and electric energy and solve the problem of poor coordination performance of existing multi-energy coordination technologies, a multi energy coordination optimization technology considering the demand response of combined thermoelectricity is proposed. The mathematical model of combined heat and power supply is constructed by integrating energy production modes such as photovoltaic power generation, wind power generation, hydropower generation and gas-fired boiler heat generation. In this model, the thermal power demand is predicted according to the heating area and power consumption law of users, and the demand response mode is determined. The energy hub device is used to realize the conversion between energy and adjust the peak value of energy fluctuation. Under the constraints of lines, purchase and sale of electricity and other conditions, genetic algorithm is used to solve the mathematical model, expand the energy storage capacity and realize multi-energy coordinated optimization. It can be seen from the experimental results that after the application of this technology, it can meet the requirements of users from two aspects of thermal energy and electric energy, effectively reduce energy consumption, improve energy utilization, and have good coordination performance.

In order to study the DC bias mechanism of AC power grid caused by metro traction system, this paper takes the ground potential as the object, and carries out time-domain simulation about the influence of metro traction system on the adjacent ground potential. Firstly, the time domain simulation model of ground potential including rail, rail-ground transition resistance, tunnel, structural reinforcement and drainage network is established. Then, the time domain waveforms of ground potential under different conditions are simulated which include metro departure, metro arrival, metros running in the same direction and metros running in the opposite direction. Results show that the time variation characteristics of DC bias current and surface potential originate from the time variation of traction current and stray current, and the magnitude, polarity and peak time of surface potential are affected by the relative position among metro, observation location, and traction station; When multiple metros are considered, the superposition effect of ground potential generated by each metro decreases as the running interval increases, and the time domain waveform of ground potential shows intermittent oscillation. On the contrary, the superposition effect of ground potential generated by each train increases when the running interval decreases leading to continuous oscillation of ground potential. Moreover, large pulses of surface potential may appear if two metros enter or leave the station simultaneously. In conclusion, the time-varying traction current, train position, operation interval, quantity, and operation state of metros are the main causes for the change of polarity, magnitude, and oscillation frequency of DC bias current and potential. Finally, the simulation model and results are verified by measurement.

Due to the insufficient anti-short-circuit ability of dry-type transformer, the interturn short-circuit fault of the dry-type transformer windings caused by the load side outlet short circuit in operation still occurs. In order to improve the sensitivity of real-time monitoring faults, it is urgent to study the transient characteristics of the dry-type transformer windings when the interturn short circuit fault occurs. The principle of "field-circuit" coupling is analyzed in this paper. Secondly, a two-dimensional "field-circuit" coupling simulation model is established with finite element software ANSYS that is consistent with the physical structure of the dry-type transformer, and the accuracy of the model under the rated operating conditions is verified. Moreover, the interturn short circuit fault occurs at different locations set by the external circuit of the coupling model and distribution characteristics of electromagnetic parameters is analysed when a single interturn short circuit fault occurs in the winding of dry-type transformer. Finally, the transient characteristics of electrical parameters such as the power loss factor and change rate of active power loss are obtained as sensitive characteristic state quantities, and the conclusion provides a theoretical basis for the real-time online monitoring of interturn short circuit faults in dry-type transformers and the improvement of winding anti-short circuit capability.

Aiming at the problems of low accuracy and long running time of support vector machines(SVM) transformer fault diagnosis model, a transformer fault diagnosis model based on bald eagle search algorithm (BES) is proposed. Firstly, four test functions are selected to test the performance of BES algorithm, and compared with cuckoo algorithm (CS), artificial bee colony algorithm (ABC) and firefly algorithm (FA). The results show that BES algorithm has better optimization performance both in convergence speed and generalization ability. Then, the BES algorithm is used to optimize the kernel function parameters g and c of SVM, and the BES-SVM transformer fault diagnosis model based on dissolved gas analysis (DGA) in oil is established. The simulation experiments are compared with ELM, SVM, CS-SVM, ABC-SVM, FA-SVM diagnosis models. The results show that the comprehensive accuracy of the BES-SVM model is 98.67%, which is 22.67%, 20%, 13.34%, 12%, 10.67% higher than the above comparison fault diagnosis models, and the running time is the shortest. The proposed BES-SVM transformer fault diagnosis model has better fault diagnosis effect.

Different ice types of overhead transmission lines have different harm to the power grid, which effect the decision-making of anti-icing and de-icing. Aiming at the problems of poor image quality, low utilization rate and weak generalization ability in ice type image recognition, this paper studies ice types identification and prediction of overhead transmission lines driven by micro-meteorological data of three consecutive days icing. Based on the shooting time and terminal number data of the icing images, the icing image, micro-meteorological, terminal and tower data from the transmission line icing monitoring system of China Southern Power Grid during 2014 to 2018 are merged, so that a micro-meteorological dataset of ice types is constructed. The micro-meteorological characteristics and geographical distribution characteristics of ice types such as glaze, rime, mixed rime and wet snow are statistically analyzed. The k-nearest neighbors (KNN) classification method is proposed to identify and predict the ice types of 1 269 samples in test sets by using 5 075 samples training sets of 9 micro-meteorological parameters and ice types in three consecutive days. The identification accuracy can reach more than 80%, and the percision(P), racall(R)and accuracy(A) are 86.7%, 86.6% and 92.2% respectively. The results show that the proposed method can greatly improve the utilization rate, analysis efficiency and generalization ability of overhead transmission line icing data.