Device, system, medium and equipment for evaluating operating overvoltage risk

Abstract

The invention discloses a device, a system, a medium and equipment for evaluating an operation overvoltage risk, and the device comprises a model building unit which builds a transformer substation model based on the actual configuration of a transformer substation; the simulation unit is used for simulating the transformer substation model so as to simulate overvoltage events under various operation situations; the data arrangement unit is used for summarizing the overvoltage data generated by simulation of the simulation unit and arranging the overvoltage data; and the risk evaluation and display unit is used for dynamically calculating and displaying the risk level in each operation scene based on the sorted overvoltage 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, and the time required by field operation overvoltage risk assessment can be greatly shortened.

Description

Technical Field

[0001] The present application belongs to the field of power system protection, and specifically relates to a device, system, medium and equipment for assessing operational overvoltage risks. Background Art

[0002] With the continuous expansion of the power system and the substantial increase in electrical equipment, the scale and complexity of substations are increasing day by day, accompanied by frequent expansion and renovation activities. Under this trend, the operation and management of substations are facing increasingly diverse challenges, especially the switching operations and related electrical switching activities have become more complex and frequent. In addition, during the commissioning and commissioning of the power transmission and transformation project, the commissioning of the newly built substation bus, transformers and transmission lines is often accompanied by potential multiple overvoltage risks, including but not limited to resonant overvoltage, operation-induced overvoltage, power frequency overvoltage caused by single-phase grounding, and induced overvoltage, all of which may pose a threat to the safety of the power grid.

[0003] In view of this, to ensure that power facilities are protected from overvoltage damage and to maintain the stability and safety of grid operation, a reliable overvoltage risk assessment process must be in place before any operation that may cause overvoltage is performed. However, the traditional risk assessment process often involves cumbersome calculations, which cover the electrical architecture of the grid, electrical parameters of equipment, operating modes, model construction, and simulation analysis under a variety of operating conditions. These pose a severe challenge to on-site instant assessment capabilities, because existing technical means are difficult to meet the needs of quickly calculating overvoltage risks.

[0004] Therefore, it is particularly important to develop an overvoltage risk assessment technology that can be quickly implemented on site. This will greatly help on-site operators make accurate judgments before performing switching operations, ensure operational safety, and at the same time protect the integrity of power equipment, thereby promoting the continued safe and stable operation of the entire power grid.

[0005] 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

[0006] In response to the deficiencies in the prior art, the purpose of this application is to provide a device, system, medium and equipment for assessing the risk of operational overvoltage. This application can provide substation operators with real-time and accurate risk prediction and control strategies to protect power equipment from overvoltage hazards and ensure safe and stable operation of the power grid.

[0007] To achieve the above objectives, this application provides the following technical solutions:

[0008] A device for evaluating operational overvoltage risks, the device comprising: a model building unit, for building a substation model based on the actual configuration of the substation; a simulation unit, for simulating the substation model to simulate overvoltage events under various operating scenarios; a data sorting unit, for summarizing and sorting overvoltage data generated by the simulation unit; and a risk assessment and display unit, for dynamically calculating and displaying the risk level under each operating scenario based on the sorted overvoltage data.

[0009] Optionally, the model building unit includes: a collection module for collecting parameter data of each component of the substation; a sub-model construction module for constructing models of each component of the substation based on the collected parameters of each component of the substation; and an integration module for integrating the models of each component of the substation to obtain a substation model.

[0010] Optionally, the simulation unit includes: a definition and classification module, used to define and classify operation scenarios; a setting module, used to set the operation procedures, preset states and initial conditions of each part of the substation based on the defined and classified operation scenarios; a simulation execution module, used to perform overvoltage simulation on the substation model according to the set operation procedures, equipment states and initial parameters, and record the overvoltage data during each simulation process; an analysis module, used to analyze the recorded overvoltage data.

[0011] Optionally, the overvoltage simulation performed on the substation model includes: power frequency overvoltage simulation, operation overvoltage simulation and induced overvoltage simulation.

[0012] Optionally, the data sorting unit includes: a summary module, used to summarize and organize overvoltage data from various operating scenarios during the simulation process; an adjustment module, used to standardize and format the summarized and organized overvoltage data; a database design module, used to design a database table structure; an import module, used to import the standardized and formatted overvoltage data into the database table structure to build a data access and analysis platform.

[0013] Optionally, the risk assessment and display unit includes: an extraction module for extracting simulation data in real time; a calculation module for calculating operational risk coefficients of different operational behaviors based on the extracted simulation data; and a visualization module for visualizing the risk information based on the operational risk coefficients.

[0014] In addition, the present application also provides a system for assessing operational overvoltage risk, the system comprising any of the above-described devices.

[0015] In addition, the present application also provides a device, which includes the system as described above.

[0016] In addition, the present application also provides a computer-readable storage medium, which stores: the collected parameter data of each component of the substation, the recorded overvoltage data in each simulation process, and the summarized and sorted overvoltage data.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. By constructing a sophisticated substation model and performing multi-dimensional simulation, this application can accurately locate the overvoltage risks that may be caused in various operating scenarios, provide scientific support for the formulation of effective preventive measures, effectively avoid equipment damage and power grid accidents caused by overvoltage, and enhance the protection capabilities of the power system.

[0019] 2. This application can not only quickly calculate the risk factors under different operating behaviors, but also intuitively display these risk information through color coding or three-dimensional visual effects, so that operators can identify the level of operational risk at a glance and take corresponding level of response measures in time to ensure the timeliness and accuracy of operational decisions, thereby improving the stability and safety of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural schematic diagram of a device for assessing operational overvoltage risk provided by an embodiment of the present application;

[0021] Figure 2 This is a schematic diagram of the main electrical connection of a substation provided by another embodiment of the present application;

[0022] Figure 3 is a structural schematic diagram of a transformer model provided by another embodiment of the present application;

[0023] Figure 4 is a schematic diagram of operational risk levels provided by another embodiment of the present application;

[0024] Figure 5 This is a schematic diagram of operating overvoltage monitoring provided by another embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will refer to the attached Figures 1 to 5 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.

[0026] 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.

[0027] 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.

[0028] In one embodiment, if Figure 1 As shown, the present application provides a device for evaluating the risk of operational overvoltage, and the device includes: a model building unit 100, which builds a substation model based on the actual configuration of the substation; a simulation unit 200, which is used to simulate the substation model to simulate overvoltage events under various operating scenarios; a data sorting unit 300, which is used to summarize and sort the overvoltage data generated by the simulation unit; and a risk assessment and display unit 400, which is used to dynamically calculate and display the risk level under each operating scenario based on the sorted overvoltage 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 provides a set of fast and accurate risk warning devices for safe operation and maintenance of power systems through integrated simulation technology and intelligent analysis, which not only greatly shortens the risk assessment time, but also greatly enhances the flexibility and safety of on-site operations.

[0030] In another exemplary embodiment, the model building unit 100 includes:

[0031] A collection module is used to collect parameters of various components of the substation;

[0032] A sub-model building module, used for building models of each component of the substation based on the collected parameters of each component of the substation;

[0033] The integration module is used to integrate the models of the various components of the substation to obtain a substation model.

[0034] In this embodiment, it is first necessary to collect the parameters of each component of the substation, and based on the parameters, respectively construct a transformer model, a line model, a lightning arrester model, a mutual inductor model and a switch model constituting the substation;

[0035] Specifically, 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, parameters such as winding resistance, leakage inductance, core loss (Rm and Xm) and excitation impedance need to be considered to ensure that the model can reflect the electrical characteristics of the actual transformer.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] After constructing the above models, they need to be further imported into the EMTP software, and according to the electrical wiring diagram of the substation, the various models should be correctly connected to form a complete substation model.

[0040] In another exemplary embodiment, the simulation unit 200 includes:

[0041] Definition and classification module, used to define and classify operation scenarios;

[0042] A setting module, used to set the operation process, preset state and initial conditions of each part of the substation based on the defined and classified operation scenarios;

[0043] A simulation execution module, used to perform overvoltage simulation on the substation model according to the set operation process, equipment status and initial parameters, and record the overvoltage data during each simulation process;

[0044] The analysis module is used to analyze the recorded overvoltage data.

[0045] In this embodiment, it is necessary to first understand the actual operation of the power grid, including the basic information such as the main electrical connection form of the power grid, the configuration of electrical equipment, and the operation parameters of the power grid. Different operation scenarios are clarified and defined, such as closing the circuit, opening and closing operations, and closing the main transformer without load.

[0046] Secondly, in the EMTP software, the corresponding operation sequence, equipment status and initial conditions need to be set according to the defined operation scenario.

[0047] Exemplarily, the specific operation steps of the operation scenario may be: in scenario ①, with E1 as the power supply and E2 not in operation, lines 1-3-4-2 are closed in sequence; in scenario ②, with E2 as the power supply and E1 not in operation, lines 3-1-2-4 are closed in sequence; in scenario ③, with E1 as the power supply and line 2 in operation, the #2 main transformer is closed; in scenario ④, with E2 as the power supply and line 4 in operation, the #2 main transformer is closed without load; in scenario ⑤, with E1 as the power supply and line 2 in operation, line 1 performs three-phase opening and closing operations; in scenario ⑥, with E2 as the power supply and line 4 in operation, line 3 performs three-phase opening and closing operations.

[0048] The device status includes whether the switch is closed, whether the line is energized, whether the transformer is unloaded, etc.

[0049] 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.

[0050] Furthermore, 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:

[0051] 1. Power frequency overvoltage simulation

[0052] 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.

[0053] 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.

[0054] 2. Operation overvoltage simulation

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] Table 1 Overvoltage under scenario ① operation

[0061]

[0062] Table 2 Overvoltage under scenario ③ operation

[0063]

[0064] Table 3 Overvoltage under scenario ⑤ operation

[0065]

[0066] 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.

[0067] 3. Inductive overvoltage simulation

[0068] 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.

[0069] 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.

[0070] Finally, the analysis module can analyze the recorded overvoltage data by calling the following method.

[0071] 1. Preprocessing of overvoltage data, including:

[0072] Data cleaning: Remove invalid, erroneous or unreasonable data records, such as abnormal values ​​that exceed the sensor range.

[0073] Time synchronization: Ensure that the data timestamps of all monitoring points are consistent to facilitate subsequent timing analysis.

[0074] 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.

[0075] Normalization processing: standardize the data at different measurement points to eliminate the range differences and facilitate comparison and comprehensive analysis.

[0076] 2. Extract features from the pre-processed overvoltage data, including the following aspects:

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 3. Perform statistical analysis on the extracted features, including:

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] In another exemplary embodiment, the data sorting unit 300 includes:

[0088] The summary module is used to summarize and organize the overvoltage data from various operation scenarios during the simulation process to ensure the integrity and consistency of the data;

[0089] The adjustment module is used to standardize and format the aggregated overvoltage data to adapt to the storage requirements of the database and improve the compatibility and usability of the data;

[0090] Database design module, used to design database table structure;

[0091] The import module is used to import the standardized and formatted overvoltage data into the database table structure to build a data access and analysis platform.

[0092] In this embodiment, it is first necessary to summarize and 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.

[0093] Specifically, the overvoltage data may be sorted by the following exemplary method:

[0094] 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.

[0095] 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.

[0096] 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.

[0097] Exemplarily, a three-dimensional thermal map of operation scenarios and overvoltage levels may be generated by executing the following procedure:

[0098] import numpy as np

[0099] import matplotlib.pyplot as plt

[0100] from mpl_toolkits.mplot3d import Axes3D

[0101] # Assume that the data structure is as follows: Each operation scenario corresponds to the overvoltage value of different devices

[0102] # Device ID list (example)

[0103] device_ids = ['QF1', 'QF2', 'QF3', 'QF4']

[0104] # Operation scenario list (example)

[0105] operation_scenarios = ['Scene1', 'Scene2', 'Scene3']

[0106] # Generate fake data

[0107] # Here we simplify it to 4 devices for each scenario, and the overvoltage value of each device in different scenarios

[0108] data = np.random.rand(len(device_ids), len(operation_scenarios))

[0109] # Map device IDs and operation scenarios to numerical indexes for easy drawing

[0110] device_to_index = {device: idx for idx, device in enumerate(device_ids)}

[0111] scenario_to_index = {scenario: idx for idx, scenario in enumerate(operation_scenarios)}

[0112] # Rearrange the data to fit a 3D plot

[0113] data_reshaped = data.T.reshape(len(device_ids), len(operation_scenarios), 1)

[0114] # Create a 3D coordinate system

[0115] fig = plt.figure(figsize=(10, 8))

[0116] ax = fig.add_subplot(111, projection='3d')

[0117] # Draw a 3D heat map

[0118] xpos, ypos = np.meshgrid(range(len(operation_scenarios)), range(len(device_ids)))

[0119] xpos = xpos.ravel()

[0120] ypos = ypos.ravel()

[0121] zpos = 0

[0122] dx = dy = 0.8 # width and height of bars

[0123] dz = data_reshaped.ravel()

[0124] ax.bar3d(xpos, ypos, zpos, dx, dy, dz, shade=True, cmap='viridis')

[0125] # Set axis labels

[0126] ax.set_xticks(range(len(operation_scenarios)))

[0127] ax.set_xticklabels(operation_scenarios)

[0128] ax.set_yticks(range(len(device_ids)))

[0129] ax.set_yticklabels(device_ids)

[0130] ax.set_zlabel('Overvoltage Level (p.u.)')

[0131] ax.set_xlabel('Operation Scenario')

[0132] ax.set_ylabel('Device ID')

[0133] # Add color bar

[0134] cbar = fig.colorbar(ax.plot_surface([], [], [], cmap='viridis'),shrink=0.5, aspect=5)

[0135] cbar.ax.set_ylabel('Overvoltage Level')

[0136] plt.title('3D Heatmap of Operation Scenarios vs. Overvoltage Levels by Device')

[0137] plt.show()

[0138] The above program uses the bar3d function to plot the overvoltage value corresponding to each operation scenario and device in three-dimensional space, forming a three-dimensional heat map effect, in which the depth of color represents the magnitude of the overvoltage.

[0139] Secondly, the collected overvoltage data needs to be standardized to ensure that all data follows a unified measurement unit and format. For example, the overvoltage values ​​should be uniformly converted to pu (per unit value) for easy comparison and analysis.

[0140] 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.

[0141] Secondly, it is necessary to design the database table structure, which includes but is not limited to "overvoltage record table", "operation risk table", "optimization suggestion table", etc. Each table contains corresponding fields, such as operation scenario ID, equipment ID, overvoltage type, overvoltage value, risk factor, optimization measures, etc.

[0142] Finally, you need to use database management tools or programming languages ​​(such as Python, Java) to write scripts to import the sorted data into the corresponding database tables in batches. This may include inserting data using SQL statements or using ORM (object-relational mapping) tools for data migration.

[0143] Exemplarily, the sorted database is shown in Table 4:

[0144] Table 4

[0145]

[0146] 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.

[0147] In another embodiment, the risk assessment and display unit 400 includes:

[0148] An extraction module, used for extracting simulation data in real time;

[0149] A calculation module, used to calculate the operational risk coefficients of different operational behaviors based on the extracted simulation data;

[0150] A visualization module is used to visualize the risk information based on the operational risk coefficient.

[0151] In this embodiment, it is first necessary to extract simulation data from the database in real time; specifically, it is first necessary to establish a real-time connection with the database, and database connection technologies such as ODBC and JDBC can be used, or the latest overvoltage simulation data, equipment status, operation records and other information can be obtained from the database through the API interface. Furthermore, stream processing technologies (such as Apache Kafka and Flink) can be used to consume database change logs or real-time query results in real time to ensure the real-time and timeliness of the data.

[0152] Secondly, it is necessary to calculate various operational risk factors based on the extracted simulation data, such as the circuit breaker restrike risk factor, the resonance risk factor and the overvoltage risk factor; specifically, the circuit breaker restrike risk factor needs to be calculated based on factors such as the number of restrike times, the reignition current amplitude and duration; the resonance risk factor needs to be calculated based on the impedance characteristics, resonance frequency and duration of the system; 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.

[0153] For example, this embodiment can demonstrate how to calculate the above-mentioned operation risk coefficient by executing the following procedure:

[0154] import pandas as pd

[0155] from scipy.signal import find_peaks

[0156] from contextlib import contextmanager

[0157] @contextmanager

[0158] def database_connection():

[0159] """Context manager for automatically managing the opening and closing of database connections"""

[0160] conn = establish_database_connection() # Assume this function is defined

[0161] try:

[0162] yield conn

[0163] finally:

[0164] conn.close()

[0165] def fetch_simulation_data(connection):

[0166] """Get simulation data from the database"""

[0167] query = "SELECT * FROM simulation_results"

[0168] return pd.read_sql_query(query, connection)

[0169] def detect_reignitions(current_waveform, threshold=0.5):

[0170] """Detect the reignition event and return the index list of the reignition points"""

[0171] return [i for i in range(1, len(current_waveform))

[0172] if current_waveform[i] > threshold and current_waveform[i-1] <threshold]

[0173] def calculate_resonance_risk(frequency_responses, max_frequency_response):

[0174] """Calculate the resonance risk factor"""

[0175] peak_freqs = find_peaks(frequency_responses)[0]

[0176] if peak_freqs: # Ensure there is a peak

[0177] return max(frequency_responses[peak_freqs]) / max_frequency_response

[0178] return 0.0

[0179] def calculate_overvoltage_risk(voltage_peaks, standard_max_pu=2.0):

[0180] """Calculate the overvoltage risk factor, considering the case of no overvoltage"""

[0181] excess_pu=[peak - standard_max_pu for peak in voltage_peaks if peak >standard_max_pu]

[0182] return sum(excess_pu) / len(excess_pu) if excess_pu else 0.0

[0183] def main():

[0184] with database_connection() as db_conn:

[0185] sim_data = fetch_simulation_data(db_conn)

[0186] # Parameter Validation

[0187] if 'current_waveform' not in sim_data or 'frequency_responses' not insim_data or 'max_voltage_pu' not in sim_data:

[0188] raise ValueError("Simulation data missing required fields.")

[0189] # Extract data

[0190] current_data = sim_data['current_waveform']

[0191] frequency_data = sim_data['frequency_responses']

[0192] voltage_peaks = sim_data['max_voltage_pu']

[0193] # Calculate the risk factor

[0194] reignition_risk = calculate_reignition_risk(current_data)

[0195] resonance_risk = calculate_resonance_risk(frequency_data, max_frequency_response=maximum allowed frequency response value) # Need to specify the maximum frequency response value

[0196] overvoltage_risk= calculate_overvoltage_risk(voltage_peaks)

[0197] # Print results

[0198] print(f"Circuit breaker reignition risk factor: {reignition_risk}")

[0199] print(f"Resonance risk factor: {resonance_risk}")

[0200] print(f"Overvoltage risk factor: {overvoltage_risk}")

[0201] if __name__ == "__main__":

[0202] main()

[0203] After calculating the operational risk coefficients of different operational behaviors, the risk information can be visualized in the following ways:

[0204] Method 1: Develop an interactive web interface or application in which the operation risk factor is displayed in the form of a color-coded heat map. Different colors represent different risk levels, such as red for high risk, yellow for medium risk, and green for low risk. Operators can view the overvoltage risk distribution under different operation scenarios or equipment by clicking or sliding the interface, and adjust parameters in real time to observe risk changes. Specifically, Figure 4 and Figure 5 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.

[0205] Method 2: Use 3D chart technology to build a 3D model, where the X-axis represents the operation scenario, the Y-axis represents the specific equipment or area, and the Z-axis represents the overvoltage risk factor. Different risk levels are represented by height or color intensity, allowing users to rotate and zoom the view to intuitively experience the spatial distribution of risks, which is particularly suitable for visual analysis of complex substation layouts.

[0206] Method 3: For each operation scenario or equipment, a bar chart is made to display different types of overvoltage risk factors, such as using different colored columns to represent restrike risk, resonance risk, and overvoltage risk, which highly reflects the degree of risk. This type of graph is simple and clear, and is suitable for quickly comparing the risk levels of different factors.

[0207] In another exemplary embodiment, the present application also provides a system for assessing operational overvoltage risk, the system comprising any of the above-described devices.

[0208] In another exemplary embodiment, the present application also provides a device, which includes the system as described above.

[0209] In another exemplary embodiment, the present application also provides a computer-readable storage medium, which includes but is not limited to a CD, a hard disk drive, a solid-state drive, a flash drive or a cloud storage space, which stores: the collected parameter data of each component of the substation, the recorded overvoltage data in each simulation process, and the summarized and sorted overvoltage data.

[0210] 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 device for assessing operational overvoltage risk, characterized in that: The device comprises: A model building unit builds a substation model based on the actual configuration of the substation; A simulation unit, used for simulating the substation model to simulate overvoltage events under various operation scenarios; A data sorting unit, used to collect and sort the overvoltage data generated by the simulation unit; The risk assessment and display unit is used to dynamically calculate and display the risk level in each operation scenario based on the sorted overvoltage data.

2. The method according to claim 1, characterized in that Preferably, the model building unit comprises: A collection module is used to collect parameter data of each component of the substation; A sub-model building module, used for building models of each component of the substation based on the collected parameters of each component of the substation; The integration module is used to integrate the models of the various components of the substation to obtain a substation model.

3. The method according to claim 1, characterized in that The simulation unit comprises: Definition and classification module, used to define and classify operation scenarios; A setting module, used to set the operation process, preset state and initial conditions of each part of the substation based on the defined and classified operation scenarios; A simulation execution module, used to perform overvoltage simulation on the substation model according to the set operation process, equipment status and initial parameters, and record the overvoltage data during each simulation process; The analysis module is used to analyze the recorded overvoltage data.

4. The method according to claim 3, characterized in that: The overvoltage simulation performed on the substation model includes: power frequency overvoltage simulation, operation overvoltage simulation and induction overvoltage simulation.

5. The method according to claim 1, characterized in that The data collating unit comprises: A summary module is used to summarize and organize overvoltage data from various operating scenarios during the simulation process; An adjustment module is used to standardize and format the aggregated overvoltage data; Database design module, used to design database table structure; The import module is used to import the standardized and formatted overvoltage data into the database table structure to build a data access and analysis platform.

6. The method according to claim 1, characterized in that The risk assessment and display unit comprises: An extraction module, used for extracting simulation data in real time; A calculation module, used to calculate the operational risk coefficients of different operational behaviors based on the extracted simulation data; A visualization module is used to visualize the risk information based on the operational risk coefficient.

7. A system for assessing operational overvoltage risk, characterized in that: The system comprises the device according to any one of claims 1 to 6.

8. A device, characterized in that: The device comprises the system of claim 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores: the collected parameter data of each component of the substation, the recorded overvoltage data in each simulation process, and the summarized and sorted overvoltage data.

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