Abstract:In optimization dispatch of integrated energy systems, source?load uncertainty is always a difficult problem. Aiming at the source?load uncertainty fluctuation issue, a proximal policy optimization dispatching method is proposed based on data?depth reinforcement learning. Ensuring user requirements are satisfied, this method achieves the optimal cost for the integrated energy system while reducing the total carbon emissions under a tiered carbon trading framework. Firstly, with the objective of considering system overall cost, including carbon trading fees under a tiered carbon trading framework, a comprehensive demand response model for multiple types of flexible loads is established to enhance the responsiveness and scheduling flexibility of demand response. Then, within the framework of deep reinforcement learning, a Markov decision process (MDP) is defined for this model. Finally, to address the data variations caused by uncertainties, the proximal policy optimization (PPO) algorithm is employed to find solutions. This involves introducing mini?batch updates and importance sampling techniques, which limit the magnitude of policy updates within a certain range, ensuring the accuracy of policy updates in each iteration. The simulation results demonstrate that compared to the deep deterministic policy gradient (DDPG), this method can effectively mitigate the impact of source?load uncertainty while significantly reducing the total carbon emissions and the average daily operating cost of the system.

Abstract:Wind power and other clean energy sources are continuously increasing their share in the grid, posing significant challenges to peak load regulation. To promote the integration of wind power, a source?load side joint optimization peak regulation method is proposed, which combines heat and power generation (CHP) units including heat storage capabilities with deep adjustments in thermal power generation. On the source side, CHP units are equipped with thermal storage devices, combined with deep peak regulation technology from thermal power units, to increase the capacity for wind power integration. On the load side, considering user response, users are guided to proactively optimize their electricity consumption curves through dynamic time?of?use pricing, improving the phenomenon of wind power anti?peak regulation. The joint optimization model aims to minimize the overall operational scheduling cost of the system, taking into account source?side thermal storage constraints, thermal power unit lifespan loss costs, and load?side user response satisfaction constraints. Calculation example results demonstrate that this model is capable of maximizing the optimization of system peak regulation while ensuring user satisfaction with electricity consumption and minimizing scheduling operational costs, effectively reducing the curtailment of new energy sources.

Abstract:To address the bus voltage security problem of distribution network caused by the fluctuations of photovoltaic output, and improve the economy and maximum power supply capacity, a coordinated multi?objective optimal allocation model of energy storage and reactive power compensation devices in distribution network with distributed photovoltaic integration is proposed. The objectives are to minimize the investment and operation costs of energy storage and reactive power compensation devices, and to maximize the maximum power supply capacity of the distribution network. The decision variables are the installation location and capacity of energy storage and reactive power compensation devices. The original mixed integer nonlinear programming model is transformed into a mixed integer second?order cone programming model by using the second?order cone relaxation technique to reduce the difficulty of model solution and improve the computational efficiency. A search algorithm based on the parabolic approximation is proposed to directly search the compromise optimal solution of the multi?objective optimization model from its Pareto frontier. Taking an actual 180?bus distribution network with distributed photovoltaics as an example, the coordinated allocation calculation of energy storage and reactive power compensation devices is executed to demonstrate that the proposed method can efficiently obtain a decision scheme with a high optimization degree in economy and power supply capacity.

Abstract:The fluctuation in the output of new energy sources can increase the operational risks of the new type of power system. To address the issue of negative values that arise when using the A?type gram?charlier series expansion to fit probability density functions, in probabilistic power flow calculations for electrical systems with wind and photovoltaic integration, the Latin hypercube sampling (LHS) technique and the C?type Gram?Charlier (CGC) series expansion method are introduced. Then a power system probabilistic power flow calculation method is proposed based on the LHS?CGC approach. Taking the calculation results of the monte?carlo method as the reference, the probability density distribution of various system state variables and output variables is verified in three operational modes: summer peak load, winter off?peak load, and high penetration of new energy sources, using the improved IEEE 30 test system. This validates the accuracy and effectiveness of the method proposed in this paper, which also exhibited a high computational speed. Through comparative analysis of probabilistic power flow calculation results under different operational modes, it is found that the system branch currents and voltage exceedance risks increase during high penetration of new energy sources. Therefore, in grid planning and dispatching, special attention needs to be given to the impact of seasonal variations in wind and photovoltaic output on the operational risks of the system.

Abstract:Disorderly charging of a large number of electric vehicles (EVs) can lead to issues such as traffic congestion on road networks and low voltage at distribution network nodes. To address this, an analysis of the EVs?Traffic?Distribution (ETD) interaction characteristics is conducted, leading to the development of a collaborative optimization framework for ETD. Firstly, travel and charging demand models are derived through analyzing the behavior characteristics of EVs. Next, the shortest path, optimal time, and minimal energy consumption routes that satisfy travel demand are proposed based on the Floyd algorithm. Then, node charging load is forcasted by rolling prediction and real?time electricity prices are formulated, both based on BP neural network models. Finally, an orderly charging guidance strategy is proposed to meet the charging demand based on three types of routes and real?time electricity prices. The simulation results show that the proposed guidance strategy can reduce EVs charging costs while effectively mitigating road congestion and low voltage problems.

Abstract:The transmission efficiency of the wireless power transfer system is affected by many factors， including the quality factor of the receiver. This research proposes a control model for determining and maintaining optimal values of the receiver’s quality factors to maintain maximum efficiency. Based on the LCC?S topology, the research first drew the relationship between the transmission efficiency and the quality factor of the receiver, and this paper presents an effective method to determine the optimal quality factor Q of the receiving coil. Then, a control strategy of the buck circuit is proposed to ensure that the system always runs at the maximum efficiency point. This strategy adopted phase shift regulation to adjust the phase angle of the voltage at the receiving end to ensure that the system always runs at the maximum efficiency point. Finally, the feasibility of the proposed method to determine the optimal value of quality factor Q at the receiving end is verified through simulation and a wireless charging experimental platform.

Abstract:Due to the unequal characteristic of equivalent positive and negative sequence impedance of the DC side system during the fault period, the traditional current mutation phase selector applied to the receiver AC line has some adaptability problems. In this paper, the problem of wrong phase selection of traditional current mutation phase selection element in AC/DC hybrid system is deeply analyzed, and an improved current mutation phase selector is proposed. The traditional phase current mutation is improved by current compensation method, and the phase selection coefficient is constructed by the improved phase current mutation, and the fault phase selection is carried out according to the amplitude relation of the phase selection coefficient, so as to compensate the effect of the unequal equivalent positive and negative sequence impedance on the DC side in principle. Simulation results show that the improved phase selector could quickly and accurately select the fault phase during the failure of the receiver AC line, and the phase selector would have good resistance to transition resistance.

Abstract:Conventional cascaded H?bridge power amplifier (CHB?PA) with N identical H?bridge power modules (HBPMs) could create 2N+1 levels of output voltage at most, and the output voltage level will directly affect the sinusoidal characteristic and fidelity performance of power amplifiers. With the same number of cascaded HBPMs, this paper firstly proposes an asymmetrical cascaded multilevel power amplifier (ACM?PA). Secondly, a virtual carrier phase shift pulse width modulation (VCPS?PWM) strategy is proposed for ACM?PA, which with the same HBPM number as conventional CHB?PA, to increase the output voltage level up to 6N-3. Comparative simulation and experimental results are included to validate that the proposed ACM?PA and VCPS?PWM could obviously increase the output voltage level and decrease the total harmonic distortion (THD) of load current, thus to improve the fidelity performance of power amplifiers.

Abstract:True bipolar multi?terminal flexible DC transmission voltage source converter based multi?terminal direct current(VSC?MTDC) new energy side converter station positive and negative converters can be controlled independently. In view of the failure of the converter with constant AC voltage amplitude and frequency control (U/f control), the control strategy of the converter with constant active and reactive power control (P/Q control) is converted into the AC and DC bus voltage under constant U/f control in this papers. Causing the problem that the fluctuation is large and the new energy station would be disconnected from the grid in severe cases. A unified switching control model is established, and a soft switching control strategy based on current following is introduced on the basis of the unified switching control model to realize the reference value of the inner loop current at the moment of switching synchronization. Based on PSCAD/EMTDC, a four?terminal true bipolar VSC?MTDC system with large?scale new energy access is built. Through the simulation under steady state, wind farm power fluctuation, and converter unipolar blocking operation, it is verified that the converter station has a voltage support of new energy access and the ability of smooth transition of the power system during the system switching process provide a reference for further improving the control flexibility of the true bipolar VSC?MTDC system.

Abstract:The mismatch of converter output line impedance reduces the power distribution accuracy of distributed energy storage units in an islanded DC microgrid and affects the balance of the state of charge, while the traditional droop control causes the drop of DC bus voltage. To address this problem, the proposed strategy firstly defines the SOC equalization factor and analyzes the effect of each factor change on it; secondly, the power distribution factor is combined with the converter output voltage as the information exchange factor, and the correction term of the sag control reference voltage is obtained through the integrator. At the system’s steady state, the proposed strategy achieves SOC equalization of each DESU, output power distribution according to capacity, and the average bus voltage is stabilized at a given value; finally, the simulation results based on the model verify the effectiveness of the proposed strategy.

Abstract:In recent years, the penetration rate of distributed photovoltaic (PV) in the distribution network has been continually increasing. When the output of PV and load are not matched, the voltage of the distribution network will easily exceed the limit, which will affect the safe and stable operation of the distribution network. In this paper, a voltage hierarchical control method for distribution network is proposed considering the participation of distributed PV’s reactive power and energy storage’s active power. First, the high/low voltage problem caused by the spatio?temporal mismatch between distributed PV and load is analyzed. Based on the distribution network topology and line parameters, the reactive power?voltage and active power?voltage approximate sensitivity matrices of the node voltage and injected power in the distribution network are calculated. Second, in the first layer of control, and a reactive power?voltage droop coefficient optimization control method is proposed for distributed PV to participate in voltage regulation. The optimal droop coefficient of distributed PV inverter is obtained by solving the voltage optimization model, then the reactive power output can be calculated correspondingly. When the reactive capacity of the PV inverter is insufficient, the second layer of control is proposed, that is, the active?voltage adaptive droop control strategy in which energy storage can participate in voltage regulation. The droop control coefficient of the energy storage is adjusted adaptively according to the state of charge (SOC) of energy storage and the local bus voltage, and the capacity of energy storage is also considered. In this way, the relative balance of energy storage power and SOC can be realized. Finally, a real 10?bus distribution network is used as an example to verify the effectiveness of the proposed method.

Abstract:The rapid recovery technology after fault clearing based on modular multilevel converter is an important research content of flexible DC power distribution technology. For the sub?module hybrid MMC, a bridge arm overcurrent suppression strategy is proposed to avoid the occurrence of secondary overcurrent of the bridge arm, so that the unlocking process could be carried out stably and reliably; at the same time, a fast sub?module capacitor voltage equalization method based on bridge arm current feedforward is studied. The recovery control could also ensure that the capacitor voltage of the sub?module of the bridge arm is quickly stabilized to the rated value when the converter is unlocked, which greatly improves the recovery speed of the converter. Finally, a single?ended hybrid MMC simulation model is built in MATLAB/Simulink to verify the effectiveness and feasibility of the proposed control method.

Abstract:The existing modal analysis method only judges the harmonic resonance degree according to the amplitude of the modal impedance, which may lead to the wrong analysis conclusion. In view of this limitation, an improved modal analysis method is proposed in this paper, and the half-power bandwidth method is introduced to further solve and improve the modal analysis results. Firstly, the principle of modal analysis method is introduced. Secondly, the resonance analysis is carried out based on the half-power bandwidth method, and the improved modal analysis method is proposed to introduce the performance index to assist in determining the degree of harmonic resonance. Then, construct the multiple grid-connected inverter model and build the node conduction matrix. Finally, the modified modal analysis method is applied to the harmonic resonance analysis, and a simulation is built in MATLAB/Simulink to verify the limitations of the existing modal analysis method and the effectiveness of this method. The results show that the harmonic resonance degree is affected by both the amplitude of the modal impedance and the damping ratio. The improved modal analysis method can be used to evaluate the harmonic resonance degree.

Abstract:For the distribution network connected to the inverter interfaced distributed generation (IIDG), The fault characteristics of IIDG is analyzed, and the fault equivalent model is proposed under the new grid connection regulations. And then, according to the ratio distribution of fault currents at both ends of the faulty line when the IIDG is connected to the distribution network, it is pointed out that the existing current longitudinal protection scheme cannot accurately distinguish between internal and external faults and high investment. A new current longitudinal protection scheme combining main protection with auxiliary protection is proposed, which is combined with the main and auxiliary protection, the auxiliary protection only needs to be configured when the main protection cannot correctly distinguish the faults, and its cost decreased. Finally, A standard IEEE 33?node circuit simulation is set up, and the simulation results verified that the new protection scheme is practical and feasible. It provides reliable protection for the distribution network connected with large number of IIDG.

Abstract:To evaluate the operation status of pole?mounted breakers in an intelligent and efficient manner, the random forest (RF) is em?ployed for feature optimization, and the annealing evolution algorithm (AEA) is applied to optimize the parameters of the re?sidual neural network (ResNet). Firstly, a database is constructed, encompassing 22?dimensional operational features of pole?mounted breakers, with their importance indices calculated using the RF. Through the reverse sequence search method, 11 features are determined as inputs. Subsequently, the AEA is employed to optimize the network parameters of ResNet. Sim?ulation results indicate that the RF effectively eliminates feature redundancy and improves the prediction performance of the model. In comparison to traditional prediction models, the proposed AEA?ResNet method significantly improves the accuracy, especially in the recall and precision of minority samples.

Abstract:Due to the low time?frequency resolution of S?transform (ST), it is difficult to accurately extract the characteristics of series fault arcs. In this paper, an improved incomplete S?transform (IIST) time?frequency analysis method was developed, where the center frequency measure is selected as the standard to screen the main frequency points in low and high frequency bands, and the Gaussian window coefficients corresponding to such low and high frequency bands is introduced, respectively. Firstly, a standard series arc fault test acquisition platform was built to collect current signals under different loads. Secondly, IIST was used for time?frequency analysis of the signals, and the corresponding features of low and high frequency bands were extracted to form the feature vector sample set. Finally, a fault arc diagnosis model was constructed to classify and identify the sample set. The results show that the recognition accuracy of this feature extraction method in support vector machine (SVM) is up to 98.29%, validating that the current fault features can be extracted effectively. By adding comparison experiments, the influence of different feature extraction methods and the SVMs with different kernel functions on diagnosis results was explored, which further verified the effectiveness of IIST and SVM fault diagnosis methods.

Abstract:Aiming at the problems of low detection accuracy and slow speed of traditional target detection algorithms, an improved YOLOv4 target detection model is proposed to detect four types of common power equipment and faults in overhead lines, such as poles, transformers, pole tilts, and insulators drop. Instead of the backbone network in the original YOLOv4, MobileNet which is designed for the embedded platform is deployed in this model, making this model lightweight. In order to further reduce the computational complexity and strengthen the learning ability of the convolutional neural network, a deep separable convolution and a CSP structure is introduced in the neck network. This improved model is used to conduct experiments on the overhead line image data set, and the experimental results show that this model can increase the detection speed to 1.68 times of the original model with a equivalent detection accuracy. It can be better applied to embedded devices, and thus achieves the real?time detection of common power equipment and faults in overhead lines by drones.

Abstract:The voltage prediction of the station area is the key to realize the timely management of the low voltage problem in distribution network. Most of the existing voltage prediction methods rely on the collection of power grid topology parameters and electricity information, which have the disadvantages of complicated required data, poor real?time performance, and large prediction errors. Therefore, this paper proposes a voltage prediction method based on the combined LSTM?BP model. Firstly, by analyzing the low?voltage formation mechanism in the station area, it is found that the dominant factor affecting the bus voltage is the users’ power consumption. Then the LSTM neural network is used to realize the short?term forecasting of load curves in the distribution station area, and the self?learning ability of BP neural network is used to establish the mapping relationship between users’ power and users’ voltage. By effectively combining the above two neural network models, the historical load data can be used to accurately and quickly predict the future low voltage situation in the station area. Finally, taking a station area as the research object and comparing the actual voltage data with the voltage data predicted in this paper, the results show that within the 1V error range, the prediction accuracy is 99.8%. Compared with the traditional voltage prediction method, during online implementation, the method proposed in this paper can realize real?time and accurate low voltage prediction without the need of information such as station topology, line parameters, users’ voltage, etc.

Abstract:To consider the impact of distributed power source integration and source?load uncertainty on the division of supply units in the distribution network, a method that takes into account fuzzy chance constraints is proposed for supply unit division in an active distribution network. Firstly, a supply unit division framework that takes into account the impact of distributed power source integration and its uncertainty is established. Secondly, with the goals of minimizing the number of supply units and optimizing load balance, a supply unit division model that considers fuzzy chance constraints is built. Thirdly, based on the clear equivalence treatment of fuzzy variables, an iterative solution method is proposed for supply unit division between and within stations. Finally, the proposed model and method are explained by calculation example simulation. And their effectiveness and practicality are verified.

Abstract:In existing high?voltage scenarios, there are many harmonic data curves with similar CP95 values but significant differences in characteristics, which cannot be distinguished by the current single indicator of harmonic national standards. To solve this problem, a comprehensive evaluation method of high?voltage harmonics is proposed considering multi?dimensional probability and time?series characteristics of measured data. Firstly, based on the probability and statistics theory, fluctuation characteristic indices that characterize the data trend and local fluctuation conditions are proposed. Secondly, based on the wavelet transform theory, the time?series characteristics of the measured data are analyzed to construct an evaluation index system covering the data fluctuation characteristics and time?series energy characteristics. Then, in order to coordinate the objectivity of data and the subjectivity of human experience, a comprehensive evaluation model is established for high?voltage harmonics based on the weighted generalized Mahalanobis distance improved ideal solution combining the coefficient of variation method and the improved analytic hierarchy method. Finally, an calculation example is analyzed to verify the rationality of the proposed method. The analysis shows that the comprehensive evaluation model constructed can effectively characterize and evaluate the differences in the quality of high?voltage harmonics.

Abstract:Aiming at the problems of on?site verification difficulties and high periodic replacement costs of electricity metering devices, a combined electric meter error assessment model is proposed, which integrates the sparrow search algorithm (SSA), support vector regression (SVR), and fading memory recursive least squares algorithm (FMRLS). Firstly, this method utilizes an improved K?Means algorithm to classify platform areas, and imports the classified samples into an SVR model optimized by the SSA for training to build a platform area line loss rate prediction model. Then, the obtained line loss rate is taken into the improved line loss model to construct an equation for solving electricity meter errors. The FMRLS algorithm is subsequently used to solve the error equation and estimate electricity meter errors. By validating the data from a sample of low?voltage platform areas in Hebei Province, this method can effectively predict the line loss rate in low?voltage platform areas and estimate the errors in electricity meters during operation. This provides technical support for accelerating the transition of the smart electricity meter maintenance strategy from regular replacement to state rotation.

Abstract:With the gradual growth of the number and capacity of infeed DC, the characteristic of “strong DC and weak AC” in some receiving power grids have become prominent in China, and the operation patterns and stability attributes have become increasingly complex. This paper considers the adaptability of multi?in?feed HVDC power transmission systems under normal and fault operating conditions. It proposes a multi?dimensional evaluation index system for comprehensively evaluating the carrying capacity of main grid frame of a multi?in?feed HVDC power transmission system. This indicator system can be used to reveal the grid strength, anti?interference ability and resilience level of a multi?in?feed HVDC power transmission system under three operating scenarios: normal state, failure state and fault recovery process, to comprehensively evaluate the capacity to carry HVDC of main grid frame of multi?infeed HVDC power transmission system. Based on the Henan power grid planning data, an application empirical study was carried out, and the weak links that restricted the capacity to carry HVDC of Henan’s primary grid were evaluated. The countermeasures for improving the carrying ability of the primary grid were further given.

Abstract:Grid resilience indicates the ability of a system to withstand extreme disasters and return to normal operation. To improve the level of grid resilience assessment, a comprehensive evaluation method that takes into account the operation processes of the power grid is proposed. By decomposing the resilience curves into outage and restoration processes, this method reveals the process of extracting resilience indicators from standard power system data. Firstly, resilience curves are generated from historical grid data, which are decomposed into outage processes and recovery processes. These processes usually overlap in time. Next, the resilience events in the data are used to characterize the statistics of these processes. The resilience assessment indicators, such as restoration duration, customer outage hours, and outage and restoration rates, are derived from these statistical data. Then, the means of these indicators are expressed as functions of outage number. Subsequently, a variability model for the restoration duration is developed that can predict the maximum restoration duration with 95% confidence level. Finally, the performance of the proposed method is analyzed numerically. The results validate its effectiveness.

Abstract:The power grid engineering data varies frequently due to the complex geographical location, long span distance and long cycle, and the external environment, market and human stochastic factors, which makes the cost control of power grid engineering difficult. The data chain construction of power grid engineering based on blockchain technology can realize the management of data integrity, process supervision, and traceability. The probabilistic analysis model of power grid engineering data is established, and the sensitivity calculation model of the different random factors influence on the engineering data changes is studied. Based on the improved differential evolution algorithm, the optimal risk level of power grid engineering is calculated, and a differentiated risk management process of power grid engineering cost data based on blockchain is proposed. The analysis of engineering examples shows that the proposed method can improve the reliability of the cost data, proactively respond to the risk of cost changes, and realize the differentiated management of different engineering data.

Abstract:Composite insulators are increasingly employed in EHV/UHV transmission lines due to their light weight and excellent mechanical properties. However, with prolonged service life, composite insulators are susceptible to hidden defects and heating, leading to serious consequences. In this paper, the multiphysical field finite element model for real?size 500 kV transmission line composite insulators is established, and the distribution of electric field and temperature field under conductive faults at high voltage end, low voltage end and middle position are calculated. The research findings yield the following conclusions: There are substantial changes in the electric field and thermal field of the insulator strings under conductive faults. Specifically, when conductive faults are introduced at the high?voltage end, low?voltage end and middle position, the maximum electric field at the tip of insulator umbrella skirt can reach 17.45, 15.95 and 5.62 kV/cm respectively, and the corresponding temperature rise can reach 9.3, 4.4 and 1.8 ℃ respectively, representing a maximum temperature rise 10.6 times higher than that under normal working conditions. In addition, the temperature distribution along the insulation interface exhibits distinctive characteristics under conductive fault conditions at different positions. For example, in the case of a fault at the middle end, two obvious temperature peaks are observed. Finally, an infrared thermal image test is carried out, and the results are in good agreement with the simulations presented in this paper.

Abstract:In order to improve the safety and stability of transformer operation, a transformer insulation aging state evaluation model based on improved whale optimization algorithm?back propagation (IWOA?BP) algorithm is proposed. An oil?paper insulation model of transformer for polarization and depolarization current (PDC) method experiment is established. In order to avoid the influence of moisture content on the evaluation results, the comprehensive conductivity is extracted as the aging characteristic quantity. An evaluate algorithm model for the aging state of oil?paper insulation basing on BP neural network is established, in which PDC current data is the input and comprehensive conductivity is the output. In order to promote the evaluation effect of the algorithm model, the whale optimization algorithm (WOA) is improved by adding nonlinear convergence factors and adaptive weights. And IWOA is used to optimize the BP neural network to obtain better BP weights and threshold initial values. Thus, the convergence speed and accuracy of the BP neural network are improved. The final result proves that the proposed IWOA?BP evaluation model has certain accuracy and practicability, and has some development space in the direction of oil?paper insulation life evaluation.

Abstract:Although the supercapacitor has the characteristics of fast charging?discharging speed and high power density, its charging?discharging system is vulnerable to the inherent nonlinear characteristic of converter and the impact of charging?discharging voltage disturbance. The main circuit of the charging?discharging system for supercapacitor is constructed with a bi?directional DC?DC converter, and the inductance and capacitance are designed under the conditions of current continuity and DC?bus voltage fluctuation in the allowed range. In order to enhance the robustness of the charging?discharging system, a control strategy with constant current is adopted and PI controller parameters are adjusted under the constant current mode. Simulation results show that the charging?discharging system for supercapacitor not only achieve the output current stably flowing through with small ripple, but also has strong robustness.

Abstract:To mitigate the problems of long working time, low efficiency and large manpower investment in the test of one key sequence control, the one key sequence control specification of the new?generation autonomous and controllable substation proposes the non?outage verification method. The sequence control operation ticket test can be accomplished by the simulation of the main and auxiliary integrated monitoring host, which can solve the above problems. A one key sequence control test method for new?generation autonomous and controllable substation is proposed. The configuration information points required by the one key sequence control test library are formed by constructing an intermediate database based on the signal definition in the standard one key sequence control operation ticket. Based on the standard one key sequence control operation ticket and the interval definition under typical wiring mode, the one key sequence control test library is constructed by using the information points in the intermediate database. Based on the optimized TF?IDF weighted text semantic similarity algorithm, the instantiation of the test library is realized. The test library is applied to the one key sequence control simulation test in specific projects to solve the one key sequence control simulation test problem. The effectiveness of the proposed method is verified by an actual simulation test example.