Method for evaluating operating overvoltage

Abstract

The invention discloses a method for evaluating operation overvoltage. The method comprises the steps that S100, a transformer substation model is built; s200, carrying out simulation on the transformer substation model so as to analyze overvoltage conditions in different operation scenes; s300, arranging the simulation data into a database; and S400, displaying the operation risk in real time based on the database data. According to the method, the transformer substation model can be quickly built and simulated before switching operation, the overvoltage conditions in various operation scenes are analyzed in real time, timely risk early warning is provided for operators, the time required for risk assessment is greatly shortened, and the flexibility and safety of field operation are enhanced.

Description

Technical Field

[0001] The present application belongs to the field of power system protection, and in particular relates to a method for evaluating operating overvoltage. Background Art

[0002] As the capacity of the power grid becomes larger and the number of electrical equipment continues to grow, the construction and expansion of substations is imperative, which leads to many changes in the operation of substations, and frequent switching and switching. At the same time, during the startup and commissioning of the transmission and transformation project, the bus transformer and line operation of the newly built substation may have various overvoltage risks due to various reasons, such as resonant overvoltage risk, operational overvoltage risk, single-phase grounding power frequency overvoltage risk, and induced overvoltage risk. Therefore, it is necessary to conduct an overvoltage risk assessment before switching, but since the risk assessment involves a complex calculation process, including the main electrical connection form of the power grid, the main electrical parameters of each equipment, the system operation mode, the establishment of the electrical model, and the simulation operation lights under various working conditions, the site currently does not have the conditions for rapid risk calculation.

[0003] In view of this, a rapid on-site detection of the overvoltage risk of the substation is carried out before switching to determine whether the switching operation can be implemented, which is conducive to safe production operation on site and ensures the safety of electrical equipment. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of this application is to provide a method for evaluating operational overvoltage. This application aims to provide substation operators with real-time and accurate risk prediction and control strategies by integrating advanced simulation technology and intelligent algorithms, so as to protect power equipment from overvoltage hazards and ensure safe and stable operation of the power grid.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] A method for evaluating operational overvoltage comprises the following steps: S100: constructing a substation model; S200: simulating the substation model to analyze overvoltage conditions under different operational scenarios; S300: arranging simulation data into a database; S400: displaying operational risks in real time based on database data.

[0007] Optionally, step S100 includes the following steps: S101: constructing models of various components of the substation; S102: integrating the models of various components of the substation.

[0008] Optionally, step S200 includes the following steps: S201: define an operation scenario; S202: based on the defined operation scenario, set a corresponding operation sequence, define equipment status and set initial conditions; S203: perform overvoltage simulation on the substation model based on the set operation sequence, equipment status and initial conditions, and record the overvoltage data during each simulation process; S204: analyze the recorded overvoltage data.

[0009] Optionally, in step S203, overvoltage simulation is performed on the substation model including: power frequency overvoltage simulation, operation overvoltage simulation and induced overvoltage simulation.

[0010] Optionally, step S300 includes the following steps: S301: arranging overvoltage data under different operating scenarios output by simulation; S302: standardizing and formatting the arranged overvoltage data; S303: designing a database table structure; S304: importing the standardized and formatted overvoltage data into the database table structure.

[0011] Optionally, step S400 includes the following steps: S401: extracting simulation data in real time; S402: calculating various operational risk coefficients based on the simulation data in the database; S403: visualizing the risk information based on the risk coefficients.

[0012] In addition, the present application also provides a storage medium, which includes instructions, and when the instructions are executed on a computer, the computer executes the method as described in any of the preceding items.

[0013] In addition, the present application also provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the program.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. This application can accurately identify overvoltage risks in different operating scenarios, provide a scientific basis for preventive measures, and help avoid equipment damage or grid failures caused by overvoltage;

[0016] 2. This application can calculate the operational risk coefficient and visualize the risk information according to the risk coefficient, so that operators can intuitively understand the level of operational risk and take preventive measures of corresponding levels to ensure the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a method for evaluating operating overvoltage provided by an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of the main electrical connection of a substation provided by another embodiment of the present application;

[0019] Figure 3 is a structural schematic diagram of a transformer model provided by another embodiment of the present application;

[0020] Figure 4 This is a schematic diagram of operational risk levels provided by another embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will refer to the attached Figures 1 to 4 The specific embodiments of the present application are described in detail. Although the specific embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0022] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the implementation of the present application, but the description is based on the general principles of the specification and is not used to limit the scope of the present application. The scope of protection of the present application shall be determined by the attached claims.

[0023] To facilitate understanding of the embodiments of the present application, further explanation will be given below using specific embodiments as examples in conjunction with the accompanying drawings, and the various drawings do not constitute a limitation on the embodiments of the present application.

[0024] In one embodiment, if Figure 1 As shown, the present application provides a method for evaluating operating overvoltage, comprising the following steps:

[0025] S100: Build a substation model;

[0026] S200: simulating the substation model to analyze overvoltage conditions under different operation scenarios;

[0027] S300: arranging the simulation data into a database;

[0028] S400: Display operational risks in real time based on database data.

[0029] This embodiment can quickly build a substation model and perform simulation analysis before the switching operation, providing instant risk feedback for on-site operations. In addition, this embodiment can shorten the calculation time required for traditional overvoltage risk assessment, allowing operators to immediately complete the operational overvoltage risk assessment.

[0030] In another exemplary embodiment, in step S100, building a substation model includes the following steps:

[0031] S101: Collect data based on actual project requirements, and build transformer models, line models, lightning arrester models, transformer models, and switch models based on the collected data;

[0032] In this step, for the transformer model, it is necessary to collect the rated capacity, short-circuit impedance, winding parameters, etc. of the transformer. EMTP (Electromagnetic Transients Program) can be used to build the model. Specifically, in the EMTP software, the transformer model is established using the transformer module. During the construction process, it is necessary to consider parameters such as winding resistance, leakage inductance, core loss (Rm and Xm) and excitation impedance to ensure that the model can reflect the electrical characteristics of the actual transformer.

[0033] Specifically, this embodiment takes a newly built 500kV power transmission and transformation project as an example to conduct an analysis of operating overvoltage and risk assessment. Figure 2 As shown in the figure, the newly built substation 500kV bus adopts 3 / 2 connection mode, named bus III and bus IV respectively. Power 1 and power 2 are connected to the newly built substation through bus I and bus II. Four new lines 1~4 are built on the same pole. Among them, the length of line 1 and line 2 is 38km, and the length of line 3 and line 4 is 135km. No parallel high-voltage reactor is configured for the outgoing line. In addition, two 500kV main transformers are installed, with a capacity of 334MVA and a voltage level of 525KV / 230kV / 36KV. The transformer model is simulated using the transformer module, as shown in the figure. Figure 3 shown.

[0034] For the line model, it is necessary to collect the physical parameters of the line, including line length, conductor type, cross-sectional area, dielectric constant, line arrangement, etc. Some line parameters need to use measured data, and some use theoretical models, such as the π model, to simulate the inductance and resistance characteristics of the line. The π model is usually composed of series resistance (R), inductance (L) and capacitance to ground (C), which is used to simulate the distributed parameters of the line. For longer lines, multiple π models are used in series simulation to improve the adaptability and accuracy of the model. Each π model represents a small section of the line to ensure that the model can reflect the actual electrical characteristics of the line.

[0035] For the arrester model, it is necessary to collect the basic parameters of the arrester, such as rated voltage, residual voltage, energy absorption capacity, etc.; for the transformer model, it is necessary to collect the basic information of the transformer, such as rated voltage, rated current, ratio, accuracy level, etc.; for the switch model, it is necessary to collect the switch breaking time, contact speed, arc parameters, etc. In the EMTP software, the relevant parameters corresponding to the model are input to build the relevant model, which will not be repeated here.

[0036] S102: Integrate the transformer model, line model, arrester model, transformer model and switch model to form a full-scale substation model.

[0037] In this step, the above-mentioned single device models need to be imported into the EMTP software. According to the electrical wiring diagram of the substation, the device models are correctly connected to form a complete substation model.

[0038] In another exemplary embodiment, in step S200, simulating the substation model includes the following steps:

[0039] S201: define the operation scenario;

[0040] In this step, it is necessary to understand the actual operation of the power grid, including the main electrical connection form of the power grid, the configuration of electrical equipment, the operation parameters of the power grid and other basic information. Different operation scenarios are clarified and defined, such as closing the circuit, opening and closing operations, and closing the main transformer without load.

[0041] S202: Based on the defined operation scenario, set the corresponding operation sequence, define the device status and set the initial conditions;

[0042] In this step, in the EMTP software, it is necessary to set the corresponding operation sequence, define the device status and set the initial conditions according to the defined operation scenario.

[0043] Examples of specific operation steps for the operation scenarios are as follows: Scenario ①: With E1 as the power source and E2 not in operation, lines 1-3-4-2 are closed in sequence; Scenario ②: With E2 as the power source and E1 not in operation, lines 3-1-2-4 are closed in sequence; Scenario ③: With E1 as the power source and line 2 in operation, main transformer #2 is closed; Scenario ④: With E2 as the power source and line 4 in operation, main transformer #2 is closed without load; Scenario ⑤: With E1 as the power source and line 2 in operation, line 1 performs three-phase splitting, splitting and splitting operations; Scenario ⑥: With E2 as the power source and line 4 in operation, line 3 performs three-phase splitting, splitting and splitting operations.

[0044] The device status includes whether the switch is closed, whether the line is energized, whether the transformer is unloaded, etc.

[0045] The setting of initial conditions includes determining the initial parameters of the power grid (such as the voltage level, power flow distribution, system impedance, etc. of the power grid), equipment parameters (such as the saturation characteristics of the transformer, the capacitive reactance and inductive reactance of the line, the closing characteristics of the circuit breaker, etc.) and environmental factors (such as temperature, humidity, etc.) before the simulation starts.

[0046] S203: performing overvoltage simulation on the substation model based on the set operation sequence, the defined equipment status and the set initial conditions, and recording overvoltage data during each simulation process;

[0047] In this step, it is necessary to simulate the power frequency overvoltage, operation overvoltage and induced overvoltage of the substation model. The specific simulation steps are as follows:

[0048] 1. Power frequency overvoltage simulation

[0049] First, based on the constructed model, different power frequency overvoltage scenarios are set, such as line closing operation, single-phase grounding fault, three-phase load shedding, etc. Special attention is paid to the no-load line closing operation, which is a common source of power frequency overvoltage.

[0050] Secondly, a large number of simulation runs were carried out to count the operation of the switch at different phases and different periods of time, and the amplitude of the power frequency overvoltage was calculated, including the maximum overvoltage and the 2% statistical overvoltage. According to the national standard GB / T50064-2014, the corresponding overvoltage limit was set.

[0051] 2. Operation overvoltage simulation

[0052] First, based on the constructed model, specific operation scenarios are defined, such as closing unloaded lines and three-phase closing and opening operations, which are likely to cause operational overvoltages.

[0053] Secondly, a statistical switch model is used in EMTP to simulate the operating switch, considering the closing effects of the main switch and auxiliary switch at different times to ensure that the simulation is close to the actual operation process.

[0054] Finally, the switch closing phase is randomly distributed, the different phase times of the three-phase switches are considered, and multiple simulations are performed to obtain the statistical characteristics of the overvoltage.

[0055] Specifically, the closing overvoltage of the line is related to the closing phase and the different phase time of the switch. When calculating the closing overvoltage of the line, this embodiment uses the statistical switch model in the software to simulate the operation of the switch. The closing phase of the main switch is evenly distributed in one cycle, and the closing phase of the auxiliary switch is randomly distributed near the closing phase of the main switch. The different phase time of the three-phase switch is considered to be less than or equal to 5ms. The statistical switch is randomly closed 360 times, and its maximum overvoltage and 2% statistical overvoltage are counted. According to the standard GB / T50064-2014 "Design Specification for Overvoltage Protection and Insulation Coordination of AC Electrical Equipment", when 252KV<Um≤800KV, the relative statistical overvoltage generated by closing and reclosing of the unloaded line should not be greater than 2.2, 2.0, and 1.8 pu for 330, 500, and 750kV systems, respectively.

[0056] Comparing scenario ① with scenario ②, the overvoltage in scenario ① is higher, as shown in Table 1. Comparing the excitation overvoltage level under the excitation inrush current state, the overvoltage level in scenario ③ is higher, as shown in Table 2. Comparing the three-phase separation and separation operation, the overvoltage level in scenario ⑤ is higher than that in scenario ⑥, as shown in Table 3.

[0057] Table 1 Overvoltage under scenario ① operation

[0058]

[0059] Table 2 Overvoltage under scenario ③ operation

[0060]

[0061] Table 3 Overvoltage under scenario ⑤ operation

[0062] From Tables 1 to 3, it can be seen that in scenario ①, when E1 is used as the power source and the line 1-3-4-2 is closed, the maximum overvoltage is 1.80pu, the 2% statistical overvoltage value is 1.67pu, the risk factor Kr=0.9, and the safety margin Ms=0.1. The simulation data shows that there is a certain risk in this case. In scenario ③, when 1 is used as the power source and the #2 main transformer is closed under no-load conditions under line 1, the maximum overvoltage is 1.36pu, the 2% statistical overvoltage value is 1.36pu, the risk factor Kr=0.68, and the safety margin Ms=0.32. At this time, the overvoltage risk is relatively low. In the three-phase split and split operation, due to the influence of residual charge, the overvoltage is relatively high, the maximum overvoltage is 1.95pu, the 2% statistical overvoltage value is 1.93pu, the risk factor Kr=0.975, and the safety margin Ms=0.025.

[0063] 3. Inductive overvoltage simulation

[0064] For induced overvoltage, the focus is on simulating the electromagnetic induction effect of the transmission line during operation, taking into account the coupling effect between lines and environmental impact.

[0065] Analyze different operating modes, such as the inductive impact of adjacent line operations on the target line, and evaluate its potential threat to equipment insulation.

[0066] S204: Analyze the recorded overvoltage data, specifically including the following steps:

[0067] 1. Preprocessing of overvoltage data, including:

[0068] Data cleaning: Remove invalid, erroneous or unreasonable data records, such as abnormal values ​​that exceed the sensor range.

[0069] Time synchronization: Ensure that the data timestamps of all monitoring points are consistent to facilitate subsequent timing analysis.

[0070] Filtering: Use appropriate digital filtering technology (such as low-pass filtering) to remove high-frequency noise, make the data smoother, and truly reflect the overvoltage characteristics.

[0071] Normalization processing: standardize the data at different measurement points to eliminate the range differences and facilitate comparison and comprehensive analysis.

[0072] 2. Feature extraction of pre-processed overvoltage data, including the following aspects:

[0073] Peak detection: Identify and record the peak value of overvoltage events, including the maximum voltage value and the time it occurred. This helps to directly assess the severity of the overvoltage, because the peak voltage is directly related to the instantaneous stress on the equipment. Specifically, the algorithm can automatically search for the maximum value points in the data, which can be a simple local maximum value search, or more complex statistical or signal processing methods can be used to ensure the accuracy of the identification.

[0074] Duration: Measure the duration of each overvoltage event to understand its impact. The duration is the time span from the start of the overvoltage rise to the recovery to normal levels. Usually, the start and end points of the event are defined by defining thresholds, and then calculating this time interval.

[0075] Frequency analysis: Analyzing the frequency of overvoltage events, especially those concentrated in specific time periods (such as system load peaks and severe weather), helps identify the regularity and potential causes of overvoltage. Spectral analysis techniques, such as fast Fourier transform (FFT), can be used to convert time domain signals into frequency domain, thereby revealing the periodicity and frequency components of overvoltage events.

[0076] Waveform analysis: Through waveform shape analysis, the characteristics of different types of overvoltage (such as switching overvoltage, lightning overvoltage, resonance overvoltage, etc.) can be distinguished. Specifically, the statistical distribution of overvoltage peaks can be described using histograms or probability density functions to understand the prevalence of overvoltage and the possibility of extreme events.

[0077] Through feature extraction, basic data can be provided for subsequent risk assessment, prediction and control strategy formulation. These features not only help to immediately identify and respond to overvoltage risks, but also optimize the operation and maintenance strategy of the power grid through long-term data analysis, and improve the overall safety and reliability of the system.

[0078] 3. Perform statistical analysis on the extracted features, including:

[0079] Trend analysis: Draw a time series graph to observe the trend of overvoltage over time, and analyze whether there is a clear upward or downward trend, and whether there is periodic or seasonal change. This helps to identify the relationship between overvoltage and time, such as whether it is associated with a specific season, time of day, or specific operating frequency. Statistical methods such as linear regression, moving average, trend line fitting, and periodic analysis techniques such as spectrum analysis can be used to determine and quantify these trends.

[0080] Correlation analysis: Study the correlation between overvoltage and other factors (such as weather, system load, and operational activities). Specifically, Pearson correlation coefficient, Spearman rank correlation, or more advanced multiple regression analysis can be applied to quantify the strength and direction of the relationship between these variables, thereby identifying the key factors that cause overvoltage.

[0081] Distribution characteristics: Analyze the distribution of overvoltage peaks to understand the prevalence of overvoltage events and the possibility of extreme events. Specifically, statistical charts such as histograms, probability density functions (PDFs), and cumulative distribution functions (CDFs) can be used to depict the distribution of overvoltage values. This helps identify concentrated areas of overvoltage and tail risks, that is, the probability of occurrence of extreme overvoltage events.

[0082] By conducting statistical analysis on the extracted features, not only can the inherent laws of overvoltage events be revealed, but also a scientific basis can be provided for formulating prevention strategies, optimizing grid operation modes and equipment configuration, thereby effectively reducing overvoltage risks in the power system.

[0083] In another exemplary embodiment, in step S300, arranging the simulation data into a database includes the following steps:

[0084] S301: sorting out overvoltage data under different operation scenarios output by simulation;

[0085] In this step, it is necessary to organize the overvoltage values, operational risk factors, detailed overvoltage data under different scenarios, etc. output by the simulation, including exporting all relevant simulation result data from the simulation software, including but not limited to the maximum overvoltage value, 2% statistical overvoltage value, risk factor (such as Kr, Ms), etc.

[0086] Specifically, the overvoltage data may be sorted by the following exemplary method:

[0087] 1. Dynamic visualization: Use interactive data visualization tools, such as D3.js or Tableau, to create dynamic dashboards. This kind of dashboard can respond to user queries in real time and dynamically display the overvoltage distribution, trends, and extreme values ​​under different operating scenarios. Operators can directly filter, sort, and compare different scenarios through the interface to intuitively understand the distribution of overvoltage risks.

[0088] 2. Apply clustering algorithms (such as K-means, DBSCAN) to automatically group overvoltage data and classify them according to similar operating scenarios and overvoltage characteristics. This can discover hidden patterns and provide reference templates for risk assessment of new scenarios.

[0089] 3. Generate a 2D or 3D heat map of the operation scenarios and overvoltage levels, with light and dark colors representing the level of overvoltage risk. The heat map intuitively shows which operation scenarios are the most dangerous, providing a basis for priority intervention and resource allocation.

[0090] Exemplarily, a two-dimensional thermal map of operation scenarios and overvoltage levels may be generated by executing the following procedure:

[0091] import seaborn as sns

[0092] import matplotlib.pyplot as plt

[0093] import pandas as pd

[0094] # Assume the data structure is as follows (with scene and device ID as index, overvoltage value as data)

[0095] data = {

[0096] 'Scene ①': [1.22, 1.80, 1.29, 1.36],

[0097] 'Scenario ②': [Data...], # Fill in the specific value according to the actual situation

[0098] 'Scene ③': [Data...],

[0099] # More scenes...

[0100] }

[0101] df = pd.DataFrame(data, index=['QF4', 'QF2', 'QF1', 'QF3']) # Device ID as column name

[0102] # Generate a 2D heat map

[0103] plt.figure(figsize=(10, 8))

[0104] sns.heatmap(df,annot=True,fmt=".1f",cmap="coolwarm", linewidths=.5)

[0105] plt.title("Two-dimensional heat map of operation scenarios and overvoltage levels")

[0106] plt.ylabel("Device ID")

[0107] plt.xlabel("Operation Scenario")

[0108] plt.show()

[0109] S302: Standardizing and formatting the sorted overvoltage data;

[0110] In this step, the overvoltage data needs to be standardized to ensure that all data follows a unified measurement unit and format. For example, the overvoltage values ​​are uniformly converted to pu (per unit value) for easy comparison and analysis.

[0111] Furthermore, the standardized overvoltage data needs to be formatted (for example, exporting the data to CSV or Excel format) to ensure that the structure of the data table is clear, including but not limited to fields such as overvoltage type, operation type, and specific values, so as to facilitate import into the database.

[0112] S303: Design database table structure;

[0113] In this step, the database tables include but are not limited to "overvoltage record table", "operation risk table", "optimization suggestion table", etc., and each table contains corresponding fields, such as operation scenario ID, equipment ID, overvoltage type, overvoltage value, risk factor, optimization measures, etc.

[0114] S304: Importing the standardized and formatted overvoltage data into the database table structure.

[0115] In this step, you need to use database management tools or programming languages ​​(such as Python, Java) to write scripts to batch import the sorted data into the corresponding database tables. This may include inserting data using SQL statements or using ORM (object-relational mapping) tools for data migration.

[0116] Exemplarily, the sorted database is shown in Table 4:

[0117] Table 4

[0118]

[0119] Furthermore, after importing the data, data validation is required to check for errors or omissions to ensure the accuracy and completeness of the data. For outliers or missing data, appropriate processing is required, such as filling, deleting or correcting.

[0120] In another embodiment, in step S400, the real-time display of operational risks based on database data includes the following steps:

[0121] S401: extracting simulation data in real time;

[0122] In this step, you first need to establish a real-time connection with the database. You can use database connection technologies such as ODBC and JDBC, or obtain the latest overvoltage simulation data, equipment status, operation records and other information from the database through an API interface.

[0123] Secondly, stream processing technologies (such as Apache Kafka and Flink) can be used to consume database change logs or query results in real time to ensure the real-time and timeliness of data.

[0124] S402: Calculating various operation risk factors based on the extracted simulation data, for example, including a circuit breaker restrike risk factor, a resonance risk factor, and an overvoltage risk factor;

[0125] In this step, the circuit breaker restrike risk factor needs to be calculated based on factors such as the number of restrike times, restrike current amplitude and duration; the resonance risk factor needs to be calculated based on the impedance characteristics of the system, the resonance frequency and duration, etc.; the overvoltage risk factor needs to be calculated based on the comparison of the overvoltage with the standard allowable value. For example, if the maximum overvoltage is 1.8pu and the maximum value allowed by the standard is 2.0pu, the risk factor may be calculated based on the ratio of the two, taking into account the safety margin.

[0126] For example, this embodiment can demonstrate how to calculate the above-mentioned operation risk coefficient by executing the following procedure:

[0127] import pandas as pd

[0128] from scipy import stats

[0129] # Assume that simulation data is obtained from the database and stored as a DataFrame

[0130] def fetch_simulation_data(database_connection):

[0131] # pseudocode

[0132] data = pd.read_sql_query("SELECT * FROM simulation_results",database_connection)

[0133] return data

[0134] def calculate_reignition_risk(current_waveform):

[0135] """

[0136] Calculate the circuit breaker restrike risk factor.

[0137] Assume that current_waveform is a time series data containing current waveform.

[0138] """

[0139] # This example simplifies the logic. In practice, more complex analysis may be required to identify rekindling events.

[0140] reignitions = detect_reignitions(current_waveform)

[0141] risk_coefficient = len(reignitions) / len(current_waveform)

[0142] return risk_coefficient

[0143] def detect_reignitions(current_waveform):

[0144] # Simplified reignition detection logic

[0145] threshold = 0.5 # Reignition current threshold, can be adjusted according to actual situation reignition_points = [i for i in range(1, len(current_waveform)) if current_waveform[i] >threshold and current_waveform[i-1] < threshold]

[0146] return reignition_points

[0147] def calculate_resonance_risk(frequency_responses):

[0148] """

[0149] Calculate the resonance risk factor.

[0150] Assume frequency_responses is the frequency response data.

[0151] """

[0152] # Example logic, looking for peaks close to the resonant frequency

[0153] peak_freqs = find_peaks(frequency_responses)

[0154] risk_coefficient = max(frequency_responses[peak_freqs]) / max_frequency_response # Assume max_frequency_response is the maximum allowed frequency response

[0155] return risk_coefficient

[0156] def calculate_overvoltage_risk(voltage_peaks, standard_max_pu=2.0):

[0157] """

[0158] Calculate the overvoltage risk factor.

[0159] voltage_peaks is a list of maximum overvoltage values ​​obtained from the simulation.

[0160] """

[0161] #Calculate the degree to which the overvoltage exceeds the standard

[0162] excess_pu = [peak - standard_max_pu for peak in voltage_peaks if peak> standard_max_pu]

[0163] # If there is no overvoltage, the risk factor is 0

[0164] risk_coefficient = sum(excess_pu) / len(excess_pu) if excess_pu else0

[0165] return risk_coefficient

[0166] # Main program

[0167] if __name__ == "__main__":

[0168] # Assume you are already connected to the database

[0169] db_conn = establish_database_connection()

[0170] # Get simulation data

[0171] sim_data = fetch_simulation_data(db_conn)

[0172] # Extract the required data for risk factor calculation

[0173] current_data = sim_data['current_waveform']

[0174] frequency_data = sim_data['frequency_responses']

[0175] voltage_peaks = sim_data['max_voltage_pu']

[0176] # Calculate each risk factor

[0177] reignition_risk = calculate_reignition_risk(current_data)

[0178] resonance_risk = calculate_resonance_risk(frequency_data)

[0179] overvoltage_risk = calculate_overvoltage_risk(voltage_peaks)

[0180] print(f"Circuit breaker reignition risk factor: {reignition_risk}")

[0181] print(f"Resonance risk factor: {resonance_risk}")

[0182] print(f"Overvoltage risk factor: {overvoltage_risk}")

[0183] # Close the database connection

[0184] db_conn.close()

[0185] S403: Visualize the risk information based on the risk coefficient.

[0186] In this step, risk information under different operation modes, such as operation overvoltage risk, reignition risk, resonance risk, etc., can be displayed through an interactive interface, such as a topology diagram. Figure 4 As shown, the interface can intuitively display the operational risk level, for example, using color coding (red, yellow, green) to indicate high and low risks.

[0187] In another exemplary embodiment, the present application further provides a storage medium, which includes instructions, and when the instructions are executed on a computer, the computer executes the method as described in any of the preceding items.

[0188] In another exemplary embodiment, the application further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the program.

[0189] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for evaluating switching overvoltage, characterized in that: The method comprises the following steps: S100: Build a substation model; S200: simulating the substation model to analyze overvoltage conditions under different operation scenarios; S300: arranging the simulation data into a database; S400: Display operational risks in real time based on database data.

2. The method according to claim 1, characterized in that Preferably, step S100 includes the following steps: S101: Construct models of each component of the substation; S102: Integrate the models of the various components of the substation.

3. The method according to claim 1, characterized in that Step S200 includes the following steps: S201: define the operation scenario; S202: Based on the defined operation scenario, set the corresponding operation sequence, define the device status and set the initial conditions; S203: performing overvoltage simulation on the substation model based on the set operation sequence, the defined equipment status and the set initial conditions, and recording overvoltage data during each simulation process; S204: Analyze the recorded overvoltage data.

4. The method according to claim 3, characterized in that In step S203, overvoltage simulation is performed on the substation model, including power frequency overvoltage simulation, operation overvoltage simulation and induced overvoltage simulation.

5. The method according to claim 1, characterized in that Step S300 includes the following steps: S301: collating overvoltage data under different operation scenarios output by simulation; S302: Standardizing and formatting the sorted overvoltage data; S303: Design database table structure; S304: Importing the standardized and formatted overvoltage data into the database table structure.

6. The method according to claim 1, characterized in that Step S400 includes the following steps: S401: extracting simulation data in real time; S402: Calculate various operational risk coefficients according to the simulation data in the database; S403: Visualize the risk information based on the risk coefficient.

7. A storage medium, characterized in that: The method comprises instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 6.

8. An electronic device, characterized in that: The electronic device comprises: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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