Method and program product for determination of transient voltage drop in charged test of surge arrester

Abstract

The application discloses a method for determining transient voltage drop in live test of a lightning arrester, which comprises the following steps: collecting voltage data and current data of the lightning arrester at a preset frequency during switch operation by a sampling device to obtain a first voltage sequence and a first current sequence; determining a first correlation coefficient corresponding to the sampling device respectively, and calibrating the first voltage sequence and the first current sequence according to the first correlation coefficient respectively to obtain a second voltage sequence and a second current sequence; determining a second correlation coefficient corresponding to the lightning arrester, and determining resistance data of the lightning arrester according to the second voltage sequence, the second current sequence and the second correlation coefficient; and determining the transient voltage drop of the lightning arrester according to the second voltage sequence, the second current sequence, the resistance data and a third correlation coefficient corresponding to the lightning arrester, so as to accurately evaluate the performance of the lightning arrester.

Description

Technical Field

[0001] This invention relates to the field of electrical data processing technology, and in particular to a method and program product for determining transient voltage drop during live testing of surge arresters. Background Technology

[0002] With the continuous development of power systems and the gradual increase in voltage levels, as well as the increasingly complex operating environment of power equipment, surge arresters, as a crucial overvoltage protection device, have become increasingly critical in terms of the accurate evaluation of their transient voltage drop, which is one of the important parameters for measuring their performance.

[0003] However, in actual operating environments, surge arresters are affected by a combination of factors. The methods used in related technologies to determine the transient voltage drop of surge arresters during live-line testing are often limited to the collected voltage and current data themselves, failing to comprehensively and accurately assess the transient voltage drop, thus leading to inaccurate evaluation. Summary of the Invention

[0004] This invention provides a method and procedure for determining the transient voltage drop of a surge arrester during live testing, in order to solve the problem of inaccurate transient voltage drop determination of surge arresters by related technologies.

[0005] According to one aspect of the present invention, a method for determining the transient voltage drop during a live-line test of a surge arrester is provided, the method comprising:

[0006] During the switching operation, the voltage and current data of the surge arrester are collected at a preset frequency using a sampling device to obtain the first voltage sequence and the first current sequence.

[0007] First correlation coefficients corresponding to the sampling device are determined respectively. The first voltage sequence and the first current sequence are calibrated according to the first correlation coefficients to obtain the second voltage sequence and the second current sequence. The first correlation coefficients include preset calibration coefficients, temperature influence coefficients, aging influence coefficients, electromagnetic interference cumulative influence coefficients, and crosstalk influence coefficients between sampling channels.

[0008] A second correlation coefficient corresponding to the surge arrester is determined, and the resistance data of the surge arrester is determined based on the second voltage sequence, the second current sequence, and the second correlation coefficient. The second correlation coefficient includes the pollution influence coefficient, humidity influence coefficient, light intensity influence coefficient, and ground potential rise influence coefficient.

[0009] The transient voltage drop of the surge arrester is determined based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester. The third correlation coefficient includes the capacitive coupling coefficient, the mutual inductance coupling coefficient, the spatial electric field coupling coefficient, and the electrostatic induction coefficient.

[0010] According to another aspect of the present invention, an apparatus for determining transient voltage drop during live testing of a surge arrester is provided, the apparatus comprising:

[0011] The first sequence acquisition module is used to collect voltage and current data of the surge arrester at a preset frequency during the switching operation using a sampling device, so as to obtain a first voltage sequence and a first current sequence.

[0012] The second sequence acquisition module is used to determine the first correlation coefficient corresponding to the sampling device, and to calibrate the first voltage sequence and the first current sequence according to the first correlation coefficient to obtain the second voltage sequence and the second current sequence. The first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels.

[0013] The resistance data determination module is used to determine the second correlation coefficient corresponding to the surge arrester. The resistance data of the surge arrester is determined based on the second voltage sequence, the second current sequence and the second correlation coefficient. The second correlation coefficient includes the pollution influence coefficient, the humidity influence coefficient, the light intensity influence coefficient and the ground potential rise influence coefficient.

[0014] The transient voltage drop determination module is used to determine the transient voltage drop of the surge arrester based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester. The third correlation coefficient includes capacitive coupling coefficient, mutual inductance coupling coefficient, spatial electric field coupling coefficient, and electrostatic induction coefficient.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for determining transient voltage drop in live-line testing of surge arresters according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the method for determining transient voltage drop in live-line testing of a surge arrester as described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements a method for determining transient voltage drop during live-line testing of a surge arrester as described in any of the embodiments of this disclosure.

[0021] The technical solution of this invention firstly involves using a sampling device to collect voltage and current data of a surge arrester at a preset frequency during switching operations, obtaining a first voltage sequence and a first current sequence. The sampling based on the preset frequency ensures that the sampling device can accurately capture the complete transient signal when the surge arrester undergoes transient changes. Combined with subsequent data calibration and analysis methods, the relevant electrical parameters of the surge arrester can be calculated more accurately. Next, a first correlation coefficient corresponding to the sampling device is determined, and the first voltage sequence and the first current sequence are calibrated according to the first correlation coefficient to obtain a second voltage sequence and a second current sequence. Since the first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels, the calibration of the original voltage and current data comprehensively considers various factors affecting the sampling data, effectively eliminating errors caused by system errors, temperature drift, aging effects, electromagnetic interference, and crosstalk, making the calibrated data closer to the actual electrical parameters of the surge arrester. Finally, a second correlation coefficient corresponding to the surge arrester is determined, and the second voltage sequence, the second current sequence, and the second correlation coefficient are used to determine the... The resistance data of the surge arrester is described above. Since the second correlation coefficient includes pollution influence coefficient, humidity influence coefficient, light intensity influence coefficient, and ground potential rise influence coefficient, the introduction of corresponding influence coefficients can adapt to various complex operating environments and more accurately reflect the true resistance characteristics of the surge arrester under different environmental conditions and operating conditions, providing a more reliable basis for the performance evaluation of the surge arrester. Finally, the transient voltage drop of the surge arrester is determined based on the second voltage sequence, the second current sequence, the resistance data, and the corresponding third correlation coefficient of the surge arrester. Since the third correlation coefficient includes capacitive coupling coefficient, mutual inductance coupling coefficient, spatial electric field coupling coefficient, and electrostatic induction coefficient, it comprehensively considers the electromagnetic coupling relationship between electrical equipment and the influence of surrounding electric fields, making the calculation results more consistent with reality and accurately calculating the transient voltage drop of the surge arrester under various electromagnetic environments. This solves the problem of inaccurate transient voltage drop determination of the surge arrester by related technologies, enabling more accurate judgment of the operating status of the surge arrester under different operating conditions, providing a more reliable basis for the performance evaluation of the surge arrester, helping to promptly identify potential problems of the surge arrester, providing strong support for maintenance personnel to make reasonable maintenance decisions, and ensuring the safe and stable operation of the power system.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a method for determining the transient voltage drop during a live-line test of a surge arrester according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of a method for determining transient voltage drop during live testing of a surge arrester according to Embodiment 2 of the present invention;

[0026] Figure 3 This is a schematic diagram of a device for determining transient voltage drop during live testing of a surge arrester according to Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the method for determining transient voltage drop in the live-line test of a surge arrester according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0031] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0032] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0033] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0034] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0035] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0036] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0037] Example 1

[0038] Figure 1This invention provides a flowchart of a method for determining transient voltage drop during live-line testing of a surge arrester, as described in Embodiment 1. This embodiment is applicable to addressing situations in related technologies where the evaluation of surge arrester performance fails to adequately consider the impact of various factors in the actual operating environment, leading to inaccurate performance assessments. This method can be executed by a device for determining transient voltage drop during live-line testing of a surge arrester. This device can be implemented in hardware and / or software, optionally through electronic devices such as mobile terminals, PCs, or servers. Figure 1 As shown, the method may specifically include:

[0039] S110. During the switching operation, the voltage and current data of the surge arrester are collected at a preset frequency through the sampling device to obtain the first voltage sequence and the first current sequence.

[0040] In this embodiment of the invention, a surge arrester is a crucial overvoltage protection device in a power system. By rapidly conducting and releasing current when power equipment suffers from lightning strike overvoltage or operational overvoltage, the voltage is limited to a range that the equipment can withstand, thereby protecting the insulation of electrical equipment from damage and ensuring the safe and stable operation of the power system.

[0041] In live-line testing of surge arresters, voltage and current data are collected using sampling devices to ensure accurate capture of complete transient signals when the arrester experiences transient changes, such as lightning strikes or switching overvoltages. However, in actual operating environments, various influencing factors exist, including frequency fluctuations, environmental interference, and voltage sags. Therefore, it is necessary to set a sampling frequency to effectively cope with complex electromagnetic environments and power grid fluctuations, ensuring the quality of the collected surge arrester signals even under strong interference conditions.

[0042] For example, the sampling frequency of the sampling device is determined based on the sampling coefficient, the highest frequency of the expected transient signal, the frequency fluctuation coefficient, the environmental interference influence coefficient, and the voltage sag influence coefficient.

[0043] As an optional technical solution in this embodiment of the invention, the sampling frequency of the sampling device can be determined based on the following formula:

[0044] f s ≥k×2f max ×(1+ξ)×(1+φ euv )×(1+ω sag ),

[0045] Among them, f s The sampling frequency of the sampling device is represented by k; the sampling coefficient is represented by f. maxξ represents the highest frequency of the expected transient signal; φ represents the frequency fluctuation coefficient; euv ω represents the environmental interference impact coefficient; sag This represents the voltage sag influence coefficient.

[0046] The sampling coefficient k determines the additional sampling ratio relative to the Nyquist frequency, i.e., how many times the sampling frequency is greater than the highest frequency of the signal. It can be adjusted according to actual testing needs and accuracy requirements, and its value ranges from 1.5 to 3. A larger k value means higher sampling redundancy, enabling more accurate capture of signal details, but also significantly increases the amount of data processing; a smaller k value relatively reduces the data processing burden, but may slightly decrease the signal capture accuracy.

[0047] The highest frequency f of the expected transient signal max This is based on the estimation of the frequency range of transient signals generated by surge arresters under normal operation and possible fault conditions. The frequency characteristics of transient signals differ for different types of surge arresters and under different operating conditions. By analyzing the types of surge arresters currently in use and their possible operating states, the highest frequency f of the expected transient signal can be determined. max In order to set the sampling frequency reasonably.

[0048] The frequency fluctuation coefficient ξ is used to quantify the impact of electromagnetic environment fluctuations on the sampling frequency, ensuring that the acquisition device can completely and accurately acquire the required signal. This coefficient is set according to the stability of the actual electromagnetic environment on site, with a value range of 0-0.2. In a relatively stable electromagnetic environment, the frequency fluctuation coefficient is close to 0; however, in cases of strong electromagnetic interference or significant environmental changes, the frequency fluctuation coefficient will increase accordingly to compensate for the impact of electromagnetic fluctuations. By adjusting the frequency fluctuation coefficient, the sampling frequency can be controlled more precisely, enabling the acquisition device to effectively capture signal changes even in poor electromagnetic environments, ensuring the integrity and accuracy of the acquired signal.

[0049] Environmental interference influence coefficient φ euv This coefficient is used to quantify the impact of non-electromagnetic interference on the sampling frequency, such as radio frequency interference, mechanical vibration interference, and other types of interference that may exist in the field. It is obtained through prior analysis of the intensity and spectral characteristics of the interference signals in the field, and its value ranges from 0 to 0.1. When the field interference is small, the environmental interference influence coefficient is close to 0; however, when there are many different types of interference, this coefficient will increase accordingly to compensate for the impact of these interference factors. Environmental Interference Influence Coefficient φ euv It reflects the degree of impact of non-electromagnetic interference on the data acquisition system in complex environments and is used to adjust the sampling frequency to ensure that the acquisition device can accurately capture target signals in complex environments.

[0050] Voltage sag influence coefficient ω sag This coefficient, used to quantify the impact of voltage sag events in the power grid on the operating state of surge arresters and the transient signals they generate, is determined by monitoring the probability and amplitude of voltage sag events in the power grid, and its value ranges from 0 to 0.05. When the power grid experiences voltage sags, the operating state of the surge arrester may change, thus affecting the characteristics of the transient signals generated by the arrester. In a relatively stable power grid environment, the voltage sag impact coefficient is close to 0; however, when voltage sags occur frequently or are large in magnitude, this coefficient will increase accordingly to reflect the potential impact of this change. The voltage sag impact coefficient helps determine the adjustment requirements of the sampling frequency to ensure accurate capture and analysis of these transient signal changes.

[0051] Optionally, after determining the sampling frequency f s Then, through the formula The sampling time interval is calculated. The sampling time interval determines the number of times the surge arrester voltage and current data are collected per unit time. An appropriate sampling time interval can ensure that the collected data is neither too sparse, which would miss important signal information or features, nor too dense, which would result in excessive data volume, increase storage and processing costs, and potentially introduce more noise, thus complicating data analysis.

[0052] By comprehensively considering multiple influencing factors such as frequency fluctuation coefficient, environmental interference influence coefficient, and voltage sag influence coefficient, the sampling frequency is determined, reducing the impact of interference signals on test results and improving the anti-interference capability and stability of the test system. Compared with the fixed sampling frequency method, collecting voltage and current data of surge arresters according to the preset sampling frequency can adapt to changes in surge arrester signals under different operating conditions and environments, providing a more comprehensive and accurate data foundation for subsequent data analysis and performance evaluation. Accurately acquiring the transient signals of surge arresters, combined with subsequent data calibration and analysis methods, allows for more precise calculation of relevant electrical parameters of surge arresters, such as resistance and transient voltage drop, providing a strong basis for surge arrester fault diagnosis. This enables maintenance personnel to more accurately judge the performance status of surge arresters, promptly detect potential faults, and ensure the safe and stable operation of the power system.

[0053] S120. Determine the first correlation coefficient corresponding to the sampling device, and calibrate the first voltage sequence and the first current sequence according to the first correlation coefficient to obtain the second voltage sequence and the second current sequence.

[0054] The first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels.

[0055] Optionally, preset calibration coefficients, temperature influence coefficients, aging influence coefficients, electromagnetic interference cumulative influence coefficients, and crosstalk influence coefficients between sampling channels are determined respectively for the sampling device. The first voltage sequence and the first current sequence are calibrated according to the preset calibration coefficients, temperature influence coefficients, aging influence coefficients, electromagnetic interference cumulative influence coefficients, and crosstalk influence coefficients between sampling channels to obtain the second voltage sequence and the second current sequence.

[0056] For example, the first voltage sequence and the first current sequence can be calibrated based on the first correlation coefficient according to the following formula to obtain the second voltage sequence and the second current sequence:

[0057]

[0058]

[0059] Among them, V cal [n] represents the second voltage data at the nth sampling time, V[n] represents the first voltage data at the nth sampling time, and I cal [n] represents the second current data at the nth sampling time, I[n] represents the first current data at the nth sampling time, a V b represents the preset first calibration coefficient. V This represents the preset second calibration coefficient. η represents the temperature effect coefficient. age σ represents the aging effect coefficient. v τ represents the cumulative effect coefficient of electromagnetic interference. cro This represents the crosstalk effect coefficient between sampling channels. V represents the electromagnetic interference intensity at the i-th sampling time. adj [n] and I adj [n] represents the voltage and current sample values ​​of the adjacent channels at the nth sampling time, respectively.

[0060] Wherein, calibration coefficient a V and b VThis coefficient describes the systematic error of the surge arrester and corrects for systematic errors in the original data. It is obtained by performing multiple measurements on a standard signal source and fitting the data using weighted least squares combined with Bayesian estimation and Kalman filtering. Optionally, a series of measurements are obtained using a standard signal source with known characteristics as a reference. These measurements include systematic errors caused by factors such as equipment aging and environmental changes. First, weighted least squares is used to reduce the influence of measurement noise and ensure the accuracy of the results. Then, Bayesian estimation is combined with prior knowledge and current observation data to further improve estimation accuracy. Finally, Kalman filtering is used to correct the error in real time based on the dynamic characteristics of the system, thereby obtaining the optimal calibration coefficient.

[0061] Temperature influence coefficient Used to quantify and compensate for measurement data drift caused by changes in ambient temperature. Temperature effect coefficient. By conducting long-term measurements on a standard signal source under different temperature environments, the relationship between temperature changes and signal changes was recorded and analyzed in detail. This led to the determination of the degree of influence of temperature changes on the measurement data, and the acquisition of the temperature influence coefficient.

[0062] Aging effect coefficient η age This coefficient is used to correct measurement errors caused by the aging of the sampling device. It is determined by conducting accelerated aging tests on the sampling device to simulate its aging process over a long period. During the aging test, the performance of the sampling device is periodically tested and measurement data is recorded. The changes in the measurement data during the aging process are analyzed to determine the aging influence coefficient η. age By introducing an aging effect coefficient, measurement data can be corrected based on the service life of the sampling device. This not only compensates for errors caused by equipment aging and more accurately assesses the accuracy of current measurement data, but also provides a more scientific basis for evaluating the service life of the sampling device. Maintenance personnel can then rationally plan the maintenance and replacement of the sampling device based on the calibrated data and the aging effect, reducing the risk of equipment failure.

[0063] Electromagnetic interference cumulative influence coefficient σ v This coefficient is used to eliminate the adverse effects of electromagnetic interference on measurement data. It is calculated by simulating electromagnetic interference environments of varying intensities and frequencies, measuring a standard signal source under these environments, and then analyzing the cumulative impact of electromagnetic interference on the measurement data. v .

[0064] Sampling channel crosstalk coefficient τ croThis coefficient is used to correct the impact of crosstalk between sampling channels on measurement data. It is obtained by performing correlation analysis on signals from adjacent channels to determine the degree of mutual interference between them, thus yielding the sampling channel crosstalk coefficient τ. cro .

[0065] During the live-line testing of surge arresters, the voltage data sequence V[n] and current data sequence I[n] acquired by the sampling device are affected by various factors such as system errors, temperature drift, long-term aging effects, cumulative electromagnetic interference effects, and crosstalk between sampling channels, resulting in data errors. To obtain accurate and reliable data, this application comprehensively considers various factors affecting the sampling data and determines the corresponding calibration coefficients through scientific and reasonable methods to calibrate the original voltage and current data. This corrects various error factors of the sampling device under different operating conditions and environments, enabling the test system to output stable and accurate data even when facing temperature changes, long-term use, complex electromagnetic environments, and mutual interference between channels. This improves the anti-interference capability and adaptability of the test system, enhances the stability and reliability of the entire surge arrester live-line testing system, and makes the calibrated data closer to the actual electrical parameters of the surge arrester. Obtaining accurate voltage and current calibration data helps to more accurately calculate key parameters such as the surge arrester's resistance and transient voltage drop, providing an accurate and reliable data foundation for subsequent resistance calculation, transient voltage drop analysis, and surge arrester performance evaluation. This improves the accuracy of fault diagnosis and ensures the safe and stable operation of the power system.

[0066] S130. Determine the second correlation coefficient corresponding to the surge arrester, and determine the resistance data of the surge arrester based on the second voltage sequence, the second current sequence, and the second correlation coefficient.

[0067] The second correlation coefficient includes the pollution impact coefficient, humidity impact coefficient, light intensity impact coefficient, and ground potential rise impact coefficient.

[0068] Optionally, the pollution influence coefficient, humidity influence coefficient, light intensity influence coefficient, and ground potential rise influence coefficient corresponding to the surge arrester are determined, and the resistance data of the surge arrester is determined based on the calibrated second voltage sequence, the second current sequence, and the pollution influence coefficient, humidity influence coefficient, light intensity influence coefficient, and ground potential rise influence coefficient corresponding to the surge arrester.

[0069] For example, the resistance data of the surge arrester can be determined based on the second voltage sequence, the second current sequence, and the second correlation coefficient using the following formula:

[0070]

[0071] Among them, R d[n] represents the resistance data of the surge arrester at the nth sampling time, V cal [n] represents the second voltage data at the nth sampling time, I cal [n] represents the second current data at the nth sampling time, δ represents the dynamic adjustment coefficient, ρ represents the preset nonlinear higher-order influence coefficient, and τ S τ represents the pollution impact coefficient. H σ represents the humidity influence coefficient. light φ represents the influence coefficient of light intensity. gpr This represents the influence coefficient of ground potential rise.

[0072] The dynamic adjustment coefficient δ is used to dynamically correct the calculation results to adapt to the accuracy requirements of surge arrester resistance calculation under different operating conditions. This coefficient can be flexibly fine-tuned according to actual operating conditions and environmental changes, and its value range is 0.01-0.1.

[0073] The nonlinear higher-order influence coefficient ρ is used to quantify the higher-order influence of the nonlinear characteristics of the surge arrester under different operating conditions on the resistance calculation. This coefficient is obtained by accurately fitting the nonlinear characteristic curve of the surge arrester, so that the resistance calculation results are closer to the actual working state of the equipment.

[0074] Pollution and humidity can alter the conductivity of surge arrester surfaces, thus affecting their resistance. The pollution influence coefficient and humidity influence coefficient are key parameters used to quantify the impact of pollution and humidity on the conductivity and resistance of surge arrester surfaces. Optionally, the pollution influence coefficient τ... S To quantify the effect of dirt on resistivity, the humidity influence coefficient τ is used. H The effect of humidity on electrical resistance is quantified, where the contamination influence coefficient τ is included. S Humidity influence coefficient τ H The electrical characteristics of surge arresters were obtained by experimentally measuring them under different levels of pollution and humidity.

[0075] Light influence coefficient σ light This coefficient is used to quantify the effect of light intensity on the physical or chemical changes in the internal materials of a surge arrester, thereby affecting the arrester's resistance; it is obtained by testing the surge arrester under different light intensities.

[0076] Ground potential rise influence coefficient φ gpr This coefficient is used to quantify the impact of changes in the potential distribution of a surge arrester caused by a rise in local potential, thereby affecting the resistance characteristics of the surge arrester; it is obtained by monitoring the rise in ground potential in the grounding system.

[0077] In actual power systems, surge arresters operate in complex and variable environments, influenced by a combination of factors such as pollution, humidity, sunlight, and ground potential rise. By introducing appropriate influence coefficients and indicators, the impact of various actual operating factors on surge arrester resistance is fully considered. This allows the system to adapt to various complex operating environments, ensuring accurate resistance calculations under different conditions. It more precisely reflects the true resistance characteristics of surge arresters under different environmental conditions and operating states, improving the adaptability and reliability of surge arrester testing. Accurate resistance calculation helps to promptly detect potential faults in surge arresters. For example, when the surface of a surge arrester is severely polluted or its interior is damp, its resistance value will change. Maintenance personnel can take proactive measures based on abnormal resistance readings to prevent the occurrence and escalation of faults, ensuring the safe and stable operation of the power system. For instance, for surge arresters with large resistance variations or operating in harsh environments, the frequency of inspections and maintenance can be increased; for surge arresters with good resistance performance, the maintenance cycle can be appropriately extended, thereby optimizing the allocation of maintenance resources and reducing maintenance costs.

[0078] S140. Determine the transient voltage drop of the surge arrester based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester.

[0079] The third correlation coefficient includes the capacitive coupling coefficient, the mutual inductance coupling coefficient, the spatial electric field coupling coefficient, and the electrostatic induction coefficient.

[0080] Optionally, the capacitive coupling coefficient, mutual inductance coupling coefficient, spatial electric field coupling coefficient, and electrostatic induction coefficient are obtained, and the transient voltage drop of the surge arrester is determined based on the calibrated second voltage sequence and second current sequence, the resistance data, and the capacitive coupling coefficient, mutual inductance coupling coefficient, spatial electric field coupling coefficient, and electrostatic induction coefficient corresponding to the surge arrester.

[0081] For example, the transient voltage drop of the surge arrester can be determined based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester, according to the following formula:

[0082] ΔV[n]=R d [n]×I cal [n]×(1+α+β+k+A),

[0083] Where ΔV[n] represents the transient voltage drop at the nth sampling time, I cal [n] represents the second current data at the nth sampling time, R d [n] represents the resistance data at the nth sampling time, α is the capacitive coupling coefficient, β is the mutual inductance coupling coefficient, k is the spatial electric field coupling coefficient, and A is the electrostatic induction coefficient.

[0084] Optionally, electrostatic inductance coefficient

[0085] I adj [n] represents the current sampling sequence of adjacent devices; E space (n) represents the electric field intensity in the surrounding space at the nth sampling time; E static (n) represents the electrostatic field strength generated by the surrounding conductor at the nth sampling time; α is the capacitive coupling coefficient, and β represents the mutual inductance coupling coefficient. The capacitive coupling coefficient α and the mutual inductance coupling coefficient β are obtained by performing multiple regression analysis on the standard test samples, combined with principal component analysis and partial least squares regression.

[0086] In actual power systems, surge arresters are surrounded by complex electromagnetic environments, influenced by current changes in adjacent equipment, spatial electric fields, and electrostatic fields. By introducing appropriate coupling coefficients and electric field-related parameters, this study comprehensively considers various physical effects and environmental factors during surge arrester operation, including electromagnetic coupling between electrical equipment, the influence of surrounding electric fields, and dynamic changes in the arrester's own electrical parameters. Compared to traditional calculation methods, this approach can accurately calculate the transient voltage drop of surge arresters under various electromagnetic environments, improving the applicability and reliability of the calculation results and providing reliable data support for a deeper understanding of the arrester's operating characteristics during transient processes. Accurate transient voltage drop calculation results can serve as an important basis for surge arrester fault early warning and diagnosis. For example, when a surge arrester experiences internal faults or performance degradation, its transient voltage drop will exhibit abnormal changes. By monitoring and analyzing the calculated transient voltage drop, maintenance personnel can promptly identify potential problems with the surge arrester, take preventative measures, prevent the escalation of faults, and ensure the safe and stable operation of the power system. Furthermore, for power system planners and designers, accurate surge arrester transient voltage drop data helps optimize the system's electrical layout and equipment selection. When designing new substations or transmission lines, the transient voltage drop of the surge arrester can be calculated based on this scheme, and the installation location and quantity of the surge arrester can be reasonably arranged to improve the overvoltage protection capability of the power system and reduce the risk of the system being struck by lightning or subjected to switching overvoltage.

[0087] As an optional technical solution of this invention, after determining the transient voltage drop of the surge arrester based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester, the method further includes: determining the simulated voltage drop based on the second current sequence, the resistance data, the ambient temperature influence coefficient, the heat conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient; determining error index data based on the difference data between the transient voltage drop and the simulated voltage drop; and determining the performance index data of the surge arrester based on the error index data.

[0088] Traditional methods for calculating voltage drop often only consider the electrical parameters of the surge arrester itself, neglecting the influence of environmental factors. This technical solution incorporates multiple environmental factors into the calculation, making the calculated simulated voltage drop more accurately reflect the operating status of the surge arrester in actual operating environments. Whether in special environments such as high temperature, high humidity, strong winds, or high altitudes, it can more accurately assess the voltage drop of the surge arrester, providing a more reliable basis for the performance analysis of the surge arrester. Because it comprehensively considers the impact of environmental factors on the voltage drop of the surge arrester, when environmental conditions change or anomalies occur in the environment surrounding the surge arrester, the calculated simulated voltage drop can promptly reflect these changes. Maintenance personnel can use the abnormal simulated voltage drop to identify potential risks to the surge arrester in advance, such as excessively high temperatures due to poor heat dissipation or decreased insulation performance due to humidity changes, and thus take corresponding preventive and handling measures to avoid failures.

[0089] The technical solution of this invention firstly involves using a sampling device to collect voltage and current data of a surge arrester at a preset frequency during switching operations, obtaining a first voltage sequence and a first current sequence. The sampling based on the preset frequency ensures that the sampling device can accurately capture the complete transient signal when the surge arrester undergoes transient changes. Combined with subsequent data calibration and analysis methods, the relevant electrical parameters of the surge arrester can be calculated more accurately. Next, a first correlation coefficient corresponding to the sampling device is determined, and the first voltage sequence and the first current sequence are calibrated according to the first correlation coefficient to obtain a second voltage sequence and a second current sequence. Since the first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels, the calibration of the original voltage and current data comprehensively considers various factors affecting the sampling data, effectively eliminating errors caused by system errors, temperature drift, aging effects, electromagnetic interference, and crosstalk, making the calibrated data closer to the actual electrical parameters of the surge arrester. Finally, a second correlation coefficient corresponding to the surge arrester is determined, and the second voltage sequence, the second current sequence, and the second correlation coefficient are used to determine the... The resistance data of the surge arrester is described above. Since the second correlation coefficient includes pollution influence coefficient, humidity influence coefficient, light intensity influence coefficient, and ground potential rise influence coefficient, the introduction of corresponding influence coefficients can adapt to various complex operating environments and more accurately reflect the true resistance characteristics of the surge arrester under different environmental conditions and operating conditions, providing a more reliable basis for the performance evaluation of the surge arrester. Finally, the transient voltage drop of the surge arrester is determined based on the second voltage sequence, the second current sequence, the resistance data, and the corresponding third correlation coefficient of the surge arrester. Since the third correlation coefficient includes capacitive coupling coefficient, mutual inductance coupling coefficient, spatial electric field coupling coefficient, and electrostatic induction coefficient, it comprehensively considers the electromagnetic coupling relationship between electrical equipment and the influence of surrounding electric fields, making the calculation results more consistent with reality. It can accurately calculate the transient voltage drop of the surge arrester under various electromagnetic environments, solving the problem of inaccurate transient voltage drop determination of the surge arrester by related technologies. It can more accurately judge the working state of the surge arrester under different operating conditions, providing a more reliable basis for the performance evaluation of the surge arrester, helping to promptly identify potential problems of the surge arrester, providing strong support for operation and maintenance personnel to make reasonable operation and maintenance decisions, and ensuring the safe and stable operation of the power system.

[0090] Example 2

[0091] Figure 2This is a flowchart illustrating a method for determining the transient voltage drop during live-line testing of a surge arrester, provided in Embodiment 2 of the present invention. It further describes the process of determining the simulated voltage drop based on the second current sequence, the resistance data, the environmental temperature influence coefficient, the heat conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient; determining error index data based on the difference between the transient voltage drop and the simulated voltage drop; and determining the performance index data of the surge arrester based on the error index data. Detailed implementation can be found in the description of this embodiment. Technical features that are the same as or similar to those in the foregoing embodiments will not be repeated here.

[0092] like Figure 2 As shown, the method may specifically include:

[0093] S210. During the switching operation, the voltage and current data of the surge arrester are collected at a preset frequency through the sampling device to obtain the first voltage sequence and the first current sequence.

[0094] S220. Determine the first correlation coefficient corresponding to the sampling device, and calibrate the first voltage sequence and the first current sequence according to the first correlation coefficient to obtain the second voltage sequence and the second current sequence.

[0095] The first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels.

[0096] S230. Determine the second correlation coefficient corresponding to the surge arrester, and determine the resistance data of the surge arrester based on the second voltage sequence, the second current sequence and the second correlation coefficient.

[0097] The second correlation coefficient includes the pollution impact coefficient, humidity impact coefficient, light intensity impact coefficient, and ground potential rise impact coefficient.

[0098] S240. Determine the transient voltage drop of the surge arrester based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester.

[0099] The third correlation coefficient includes the capacitive coupling coefficient, the mutual inductance coupling coefficient, the spatial electric field coupling coefficient, and the electrostatic induction coefficient.

[0100] S250. Determine the simulated voltage drop based on the second current sequence, the resistance data, the ambient temperature influence coefficient, the heat conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient.

[0101] The simulated voltage drop is used to simulate the working state of the surge arrester in an actual operating environment. Optionally, the simulated voltage drop is determined based on the calibrated second current sequence, the resistance data, the influence coefficients of ambient temperature, heat conduction, wind speed, air pressure, temperature gradient, and humidity gradient.

[0102] For example, the simulated voltage drop can be determined based on the second current sequence, the resistance data, the influence coefficients of ambient temperature, heat conduction, wind speed, air pressure, temperature gradient, and humidity gradient, according to the following formula:

[0103] ΔV sim [n] = I cal [n]×R d [n]×(1+a1+a2+a3+a4+a5+a6),

[0104] Where, ΔV sim [n] represents the simulated voltage drop at the nth sampling time, I cal [n] represents the second current data at the nth sampling time, R d [n] represents the resistance data at the nth sampling time, a1 represents the influence coefficient of ambient temperature, a2 represents the influence coefficient of heat conduction, a3 represents the influence coefficient of wind speed, a4 represents the influence coefficient of air pressure, a5 represents the influence coefficient of temperature gradient, and a6 represents the influence coefficient of humidity gradient.

[0105] Changes in ambient temperature can affect the electrical properties of surge arresters, such as the resistivity of the internal materials. The ambient temperature influence coefficient a1 is used to measure the impact of ambient temperature on the electrical performance of surge arresters, particularly the voltage drop. This coefficient is determined through experiments or data analysis. For example, when the temperature rises, the resistivity of the material may change, thus affecting the voltage drop.

[0106] Different thermal conductivity properties affect the temperature distribution and thermal balance of surge arresters during operation, thus indirectly affecting their electrical performance and voltage drop. The thermal conductivity influence coefficient α2 is used to quantify the ability of surge arrester materials to conduct heat, that is, the effect of thermal conductivity on voltage drop.

[0107] The wind speed influence coefficient a3 is used to quantify the indirect impact of wind speed on the performance of surge arresters. This coefficient reflects the extent to which wind speed affects the temperature and electrical characteristics (such as resistance and conductivity) of the surge arrester by changing the surface heat dissipation of the surge arrester, and may ultimately lead to changes in the voltage drop of the surge arrester. For example, when the wind speed is high, heat dissipation is accelerated, which may lead to a decrease in the temperature of the surge arrester, thereby affecting its electrical parameters and voltage drop.

[0108] The air pressure influence coefficient a4 is used to quantify the indirect impact of air pressure changes on surge arrester performance. This coefficient is determined by analyzing the electrical parameters of the surge arrester under different air pressure conditions to establish the relationship between air pressure changes and changes in electrical parameters, thus calculating the specific air pressure influence coefficient. Changes in air pressure affect the insulating properties of air and the gas discharge characteristics around the surge arrester. For example, in high-altitude areas, the lower air pressure reduces the insulating properties of air, which may affect the operation of the surge arrester.

[0109] The temperature gradient influence coefficient a5 is used to quantify the impact of temperature gradient changes on the voltage drop of a surge arrester. A temperature gradient refers to the temperature difference between different locations within an object. The existence of this temperature difference can cause thermal stress inside the surge arrester, potentially leading to material deformation or changes in performance, thereby affecting the voltage drop.

[0110] The humidity gradient influence coefficient a6 is used to quantify the impact of humidity gradient on voltage drop. Humidity gradient refers to the difference in humidity between different locations in the environment. This difference in humidity affects the wettability and conductivity of the surge arrester surface. The existence of humidity differences leads to varying degrees of wettability on the surge arrester surface, thus affecting its conductivity. When the humidity gradient is large, an uneven conductive layer may form on the surge arrester surface, thereby affecting the electrical characteristics of the surge arrester, especially the voltage drop.

[0111] Traditional methods for calculating voltage drop often only consider the electrical parameters of the surge arrester itself, neglecting the influence of environmental factors. This application's solution incorporates multiple environmental factors into the calculation, making the calculated simulated voltage drop more realistically reflect the operating state of the surge arrester in actual operating environments. Whether in special environments such as high temperature, high humidity, strong winds, or high altitudes, it can more accurately assess the voltage drop of the surge arrester, providing a more reliable basis for the performance analysis of the surge arrester. Furthermore, because it comprehensively considers the impact of environmental factors on the surge arrester's voltage drop, the calculated simulated voltage drop can promptly reflect changes in environmental conditions or anomalies in the environment surrounding the surge arrester. Maintenance personnel can use the abnormal simulated voltage drop to identify potential risks to the surge arrester in advance, such as excessively high temperatures due to poor heat dissipation or decreased insulation performance due to humidity changes, and thus take corresponding preventative and handling measures to avoid failures. Accurate simulated voltage drop calculation results help maintenance personnel formulate more reasonable maintenance strategies. For surge arresters operating in harsh environments, the frequency of inspections can be increased and their performance monitored more closely based on the simulated voltage drop calculations. For surge arresters operating in relatively favorable environments, the maintenance cycle can be appropriately extended. This optimizes the allocation of maintenance resources, improves maintenance efficiency, and reduces maintenance costs.

[0112] Furthermore, the calculated simulated voltage drop can provide an important reference for the selection of surge arresters. By comparing the simulated voltage drops of different models of surge arresters under the same or different environmental conditions, it is possible to select surge arresters that are more suitable for the actual operating environment, thereby improving the safety and reliability of the power system.

[0113] S260. Determine error index data based on the difference data between the transient voltage drop and the simulated voltage drop.

[0114] As an optional technical solution in an embodiment of the present invention, the difference data may include voltage drop difference values. Further, determining error index data based on the difference data between the transient voltage drop and the simulated voltage drop may include: determining the voltage drop difference value between the transient voltage drop and the simulated voltage drop at each sampling time, and determining error index data based on the voltage drop difference values ​​at multiple sampling times. Even further, determining error index data based on the voltage drop difference values ​​at multiple sampling times may specifically involve weighted summation of the voltage drop difference values ​​at multiple sampling times to obtain the error index data.

[0115] As another optional technical solution of the present invention, the error index data can be determined based on the difference data between the transient voltage drop and the simulated voltage drop according to the following formula:

[0116]

[0117] Where, ε com This represents the error index data, where ΔV[n] represents the transient voltage drop at the nth sampling time. sim [n] represents the simulated voltage drop at the nth sampling time, N represents the total number of sampling times, ω represents the weighting coefficient, with a value range of 0.1-0.5, ψ represents the relative error comprehensive weighting coefficient, with a value range of 0.02-0.1, and ζ represents the transient rate of change error weighting coefficient, with a value range of 0.01-0.05. and These represent the rates of change of transient voltage drop obtained from actual measurement and simulation, respectively.

[0118] This application's technical solution comprehensively considers multiple aspects, including voltage drop differences, error stability, and transient rate of change differences, enabling it to fully reflect the difference between the actual performance of the surge arrester and the simulation expectations. Compared to single-dimensional evaluation methods, it can more accurately determine the operating status of the surge arrester under different operating conditions, providing a more reliable basis for surge arrester performance evaluation and helping to promptly identify potential problems. By adjusting the weighting coefficients, this application's technical solution can adapt to different evaluation needs and application scenarios. Whether focusing on static performance evaluation, dynamic performance evaluation, or having special requirements for error stability, these can all be achieved by reasonably setting the weighting coefficients, making this application's technical solution highly flexible and versatile, capable of meeting the diverse needs of different users and operating conditions in the power system for surge arrester performance evaluation. Accurate error index calculation can promptly detect abnormal changes in surge arrester performance. When the error index exceeds the preset threshold, it indicates that the actual performance of the surge arrester deviates significantly from expectations, potentially indicating internal faults or performance degradation. Maintenance personnel can take proactive measures for inspection and maintenance based on the changing trends of error indicators, enabling early warning and prediction of faults, avoiding power system accidents caused by surge arrester failures, and improving the safety and reliability of the power system. The calculation results of error indicators can also provide feedback for optimizing the simulation model and testing methods of surge arresters. If the error indicators remain large for a long period, it indicates that the simulation model may have defects or the testing methods are not accurate enough. By analyzing the sources and components of the errors, the simulation model can be improved in a targeted manner, the testing methods can be refined, and the accuracy and effectiveness of surge arrester performance evaluation can be enhanced.

[0119] S270. Determine the performance index data of the surge arrester based on the error index data.

[0120] The performance index data can be understood as index data used to indicate the performance quality of the surge arrester. For example, the performance index data specifically includes good performance and performance problems.

[0121] As an optional technical solution in an embodiment of the present invention, the step of determining the performance index data of the surge arrester based on the error index data includes: determining that the performance index data of the surge arrester is good when the error index data is less than a preset error index threshold; and determining that the performance index data of the surge arrester has a performance problem when the error index data is greater than or equal to the preset error index threshold.

[0122] This technical solution, by comparing error index data and threshold values, can quickly determine whether a surge arrester is performing well, eliminating the need for complex analysis and judgment processes. This significantly saves maintenance personnel time and effort, improves work efficiency, and makes regular performance evaluation of a large number of surge arresters in the power system more feasible. Simultaneously, it can accurately predict potential surge arrester faults. When error indicators exceed preset thresholds, maintenance personnel can promptly detect and take measures to prevent further development and expansion of the fault, ensuring the safe and stable operation of the power system and effectively reducing power outages and equipment damage caused by surge arrester failures. Furthermore, it optimizes the allocation of maintenance resources, providing strong support for maintenance personnel to make reasonable maintenance decisions. When the surge arrester is deemed to be performing well, the inspection cycle can be appropriately extended to reduce unnecessary maintenance work and lower maintenance costs. Conversely, when a problem is identified, detailed inspection and repair can be promptly arranged to determine the specific cause of the fault and perform repair or replacement, improving the scientific and rational nature of maintenance management.

[0123] The technical solution of this application firstly determines the simulated voltage drop based on the second current sequence, the resistance data, the environmental temperature influence coefficient, the heat conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient. This incorporates multiple environmental factors into the calculation, making the calculated simulated voltage drop more realistically reflect the working state of the surge arrester in the actual operating environment. Next, error index data is determined based on the difference between the transient voltage drop and the simulated voltage drop. Accurate calculation of the error index data enables timely detection of abnormal changes in surge arrester performance, improving the accuracy and effectiveness of surge arrester performance evaluation. Finally, the performance index data of the surge arrester is determined based on the error index data. Through simple comparison, a conclusion on whether the surge arrester's performance is good can be quickly reached, improving work efficiency. A standardized surge arrester performance evaluation system is established. The preset error index threshold provides a unified standard for surge arrester performance evaluation, ensuring consistency and comparability in performance judgments by different regions and maintenance teams. This helps promote the standardization and normalization of surge arrester operation and maintenance management in the power industry.

[0124] Example 3

[0125] Figure 3This is a schematic diagram of a device for determining transient voltage drop during live-line testing of a surge arrester, provided in Embodiment 3 of the present invention. This device is used to execute the method for determining transient voltage drop during live-line testing of a surge arrester provided in any of the above embodiments. This device and the method for determining transient voltage drop during live-line testing of a surge arrester in the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the device for determining transient voltage drop during live-line testing of a surge arrester can be found in the embodiments described above. Figure 3 As shown, the device includes: a first sequence acquisition module 310, a second sequence acquisition module 320, a resistance data determination module 330, and a transient voltage drop determination module 340.

[0126] The first sequence acquisition module 310 is used to collect voltage and current data of the surge arrester at a preset frequency during switching operation using a sampling device to obtain a first voltage sequence and a first current sequence. The second sequence acquisition module 320 is used to determine a first correlation coefficient corresponding to the sampling device, and calibrate the first voltage sequence and the first current sequence according to the first correlation coefficient to obtain a second voltage sequence and a second current sequence. The first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels. The resistance data determination module 33... 0, used to determine the second correlation coefficient corresponding to the surge arrester, and to determine the resistance data of the surge arrester based on the second voltage sequence, the second current sequence and the second correlation coefficient, wherein the second correlation coefficient includes the pollution influence coefficient, humidity influence coefficient, light intensity influence coefficient and ground potential rise influence coefficient; transient voltage drop determination module 340, used to determine the transient voltage drop of the surge arrester based on the second voltage sequence, the second current sequence, the resistance data and the third correlation coefficient corresponding to the surge arrester, wherein the third correlation coefficient includes the capacitive coupling coefficient, mutual inductance coupling coefficient, spatial electric field coupling coefficient and electrostatic induction coefficient.

[0127] The technical solution of this invention firstly involves a first sequence acquisition module collecting voltage and current data of a surge arrester at a preset frequency during switching operations using a sampling device. This yields a first voltage sequence and a first current sequence. The preset frequency ensures that the sampling device accurately captures the complete transient signal when the surge arrester experiences transient changes. Combined with subsequent data calibration and analysis methods, the relevant electrical parameters of the surge arrester can be calculated more precisely. Next, a second sequence acquisition module determines a first correlation coefficient corresponding to the sampling device. Based on these first correlation coefficients, the first voltage sequence and the first current sequence are then processed... The calibration process yields a second voltage sequence and a second current sequence. Since the first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels, it comprehensively considers various factors affecting the sampling data to calibrate the original voltage and current data. This effectively eliminates errors caused by system errors, temperature drift, aging effects, electromagnetic interference, and crosstalk, making the calibrated data closer to the actual electrical parameters of the surge arrester. Next, the second correlation coefficient corresponding to the surge arrester is determined through the resistance data determination module, based on the second voltage sequence, the second current sequence, and the... The second correlation coefficient determines the resistance data of the surge arrester. Since the second correlation coefficient includes pollution influence coefficients, humidity influence coefficients, light intensity influence coefficients, and ground potential rise influence coefficients, introducing these corresponding influence coefficients can adapt to various complex operating environments and more accurately reflect the true resistance characteristics of the surge arrester under different environmental conditions and operating conditions, providing a more reliable basis for the performance evaluation of the surge arrester. Finally, the transient voltage drop determination module determines the transient voltage drop of the surge arrester based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester. The third correlation coefficient includes capacitance... The coupling coefficient, mutual inductance coupling coefficient, spatial electric field coupling coefficient, and electrostatic induction coefficient comprehensively consider the electromagnetic coupling relationship between electrical equipment and the influence of surrounding electric fields, making the calculation results more consistent with reality. This allows for accurate calculation of the transient voltage drop of surge arresters under various electromagnetic environments. It solves the problem of inaccurate transient voltage drop determination of surge arresters by related technologies, enabling more accurate judgment of the operating status of surge arresters under different conditions. This provides a more reliable basis for surge arrester performance evaluation, helps to promptly identify potential problems with surge arresters, and provides strong support for maintenance personnel to make reasonable maintenance decisions, ensuring the safe and stable operation of the power system.

[0128] Based on the above scheme, optionally, the second sequence acquisition module 320 is used to calibrate the first voltage sequence and the first current sequence according to the first correlation coefficient based on the following formula to obtain the second voltage sequence and the second current sequence:

[0129]

[0130] Among them, V cal [n] represents the second voltage data at the nth sampling time, V[n] represents the first voltage data at the nth sampling time, and I cal [n] represents the second current data at the nth sampling time, I[n] represents the first current data at the nth sampling time, a V b represents the preset first calibration coefficient. V This represents the preset second calibration coefficient. η represents the temperature effect coefficient. age σ represents the aging effect coefficient. v τ represents the cumulative effect coefficient of electromagnetic interference. cro This represents the crosstalk effect coefficient between sampling channels. V represents the electromagnetic interference intensity at the i-th sampling time. adj [n] and I adj [n] represents the voltage and current sample values ​​of the adjacent channels at the nth sampling time, respectively.

[0131] Based on the above scheme, optionally, the resistance data determination module 330 is used to determine the resistance data of the surge arrester based on the second voltage sequence, the second current sequence, and the second correlation coefficient according to the following formula:

[0132]

[0133] Among them, R d [n] represents the resistance data of the surge arrester at the nth sampling time, V cal [n] represents the second voltage data at the nth sampling time, I cal [n] represents the second current data at the nth sampling time, δ represents the dynamic adjustment coefficient, and ρ represents the preset nonlinear higher-order influence coefficient; τ S τ represents the pollution impact coefficient. H σ represents the humidity influence coefficient. light φ represents the influence coefficient of light intensity. gpr This represents the influence coefficient of ground potential rise.

[0134] Based on the above scheme, optionally, the transient voltage drop determination module 340 is used to determine the transient voltage drop of the surge arrester based on the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester according to the following formula:

[0135] ΔV[n]=R d [n]×I cal[n]×(1+α+β+k+A),

[0136] Where ΔV[n] represents the transient voltage drop at the nth sampling time, I cal [n] represents the second current data at the nth sampling time, R d [n] represents the resistance data at the nth sampling time, α is the capacitive coupling coefficient, β is the mutual inductance coupling coefficient, k is the spatial electric field coupling coefficient, and A is the electrostatic induction coefficient.

[0137] Based on the above scheme, optionally, the sampling frequency of the sampling device satisfies:

[0138] f s ≥k×2f max ×(1+ξ)×(1+φ euv )×(1+ω sag ),

[0139] Among them, f s The sampling frequency of the sampling device is represented by k; the sampling coefficient is represented by f. max ξ represents the highest frequency of the expected transient signal; φ represents the frequency fluctuation coefficient; euv ω represents the environmental interference impact coefficient; sag This represents the voltage sag influence coefficient.

[0140] Optionally, based on the above scheme, the device for determining the transient voltage drop during the live-line test of the surge arrester further includes: a simulation voltage drop determination module and a performance index data determination module. The simulation voltage drop determination module is used to determine the simulation voltage drop based on the second current sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester, after determining the transient voltage drop of the surge arrester according to the second voltage sequence, the resistance data, the ambient temperature influence coefficient, the thermal conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient, after determining the transient voltage drop of the surge arrester according to the second current sequence, the resistance data, the ambient temperature influence coefficient, the thermal conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient, after determining the transient voltage drop of the surge arrester according to the third correlation coefficient. The performance index data determination module is used to determine error index data based on the difference data between the transient voltage drop and the simulation voltage drop, and to determine the performance index data of the surge arrester based on the error index data.

[0141] Based on the above scheme, optionally, the simulation voltage drop determination module is used to determine the simulation voltage drop based on the second current sequence, the resistance data, the ambient temperature influence coefficient, the heat conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient according to the following formula:

[0142] ΔV sim [n] = I cal [n]×R d[n]×(1+a1+a2+a3+a4+a5+a6),

[0143] Where, ΔV sim [n] represents the simulated voltage drop at the nth sampling time, I cal [n] represents the second current data at the nth sampling time, R d [n] represents the resistance data at the nth sampling time, a1 represents the influence coefficient of ambient temperature, a2 represents the influence coefficient of heat conduction, a3 represents the influence coefficient of wind speed, a4 represents the influence coefficient of air pressure, a5 represents the influence coefficient of temperature gradient, and a6 represents the influence coefficient of humidity gradient.

[0144] Based on the above scheme, optionally, the performance index data determination module is used to determine error index data based on the difference data between the transient voltage drop and the simulated voltage drop according to the following formula:

[0145]

[0146] Where, ε com This represents the error index data, where ΔV[n] represents the transient voltage drop at the nth sampling time. sim [n] represents the simulated voltage drop at the nth sampling time, N represents the total number of sampling times, ω represents the weighting coefficient, ψ is the relative error comprehensive weighting coefficient, and ζ is the transient rate of change error weighting coefficient. and These represent the rates of change of transient voltage drop obtained from actual measurement and simulation, respectively.

[0147] The performance metrics data include those with good performance and those with performance problems.

[0148] Based on the above scheme, optionally, the performance index data determination module is specifically used to determine that the performance index data of the surge arrester is good when the error index data is less than a preset error index threshold; and to determine that the performance index data of the surge arrester has a performance problem when the error index data is greater than or equal to the preset error index threshold.

[0149] The device for determining transient voltage drop in live-line testing of surge arresters provided in this embodiment of the invention can execute the method for determining transient voltage drop in live-line testing of surge arresters provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0150] Example 4

[0151] Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0152] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0153] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0154] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method for determining transient voltage drop in a surge arrester live-line test.

[0155] In some embodiments, the method for determining the transient voltage drop during a live-line test of a surge arrester can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining the transient voltage drop during a live-line test of a surge arrester described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for determining the transient voltage drop during a live-line test of a surge arrester by any other suitable means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0159] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0160] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0161] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0162] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0163] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining a transient voltage drop in a live test of a surge arrester, characterized by, The method comprises the following steps: Collecting voltage data and current data of the surge arrester at a preset frequency during switching operation through a sampling device to obtain a first voltage sequence and a first current sequence; Determine a first correlation coefficient corresponding to the sampling device, respectively, and calibrate the first voltage sequence and the first current sequence according to the first correlation coefficient to obtain a second voltage sequence and a second current sequence, wherein the first correlation coefficient includes a preset calibration coefficient, a temperature influence coefficient, an aging influence coefficient, an electromagnetic interference cumulative influence coefficient, and a crosstalk influence coefficient between sampling channels; Determine a second correlation coefficient corresponding to the surge arrester, and determine the resistance data of the surge arrester according to the second voltage sequence, the second current sequence, and the second correlation coefficient, wherein the second correlation coefficient includes a contamination influence coefficient, a humidity influence coefficient, an illumination intensity influence coefficient, and a ground potential rise influence coefficient; Determine the transient voltage drop of the surge arrester according to the second voltage sequence, the second current sequence, the resistance data, and a third correlation coefficient corresponding to the surge arrester, wherein the third correlation coefficient includes a capacitance coupling coefficient, a mutual inductance coupling coefficient, a space electric field coupling coefficient, and an electrostatic induction coefficient; Wherein, the first voltage sequence and the first current sequence are calibrated according to the first correlation coefficient to obtain the second voltage sequence and the second current sequence based on the following formula: ， ， in, Indicates the first The second voltage data at each sampling time, Indicates the first The first voltage data at each sampling time. Indicates the first The second current data at each sampling time. Indicates the first The first current data at each sampling time. This represents the preset first calibration coefficient. This represents the preset second calibration coefficient. Indicates the temperature effect coefficient. Indicates the aging effect coefficient. This represents the cumulative effect coefficient of electromagnetic interference. This represents the crosstalk effect coefficient between sampling channels. Indicates the first Electromagnetic interference intensity at each sampling time, and They represent the first Voltage and current sampled values ​​of adjacent channels at each sampling time; Wherein, the resistance data of the surge arrester is determined according to the second voltage sequence, the second current sequence, and the second correlation coefficient based on the following formula: ， wherein, represents the resistance data of the surge arrester at the i-th sampling moment, represents the second voltage data at the i-th sampling moment, represents the second current data at the i-th sampling moment, represents a dynamic adjustment coefficient, represents a preset nonlinear high-order influence coefficient, represents a pollution influence coefficient, represents a humidity influence coefficient, represents an illumination intensity influence coefficient, represents a ground potential rise influence coefficient.​​​ 2. The method of claim 1, wherein, The transient voltage drop of the surge arrester is determined according to the second voltage sequence, the second current sequence, the resistance data, and a third correlation coefficient corresponding to the surge arrester based on the following formula: ， wherein, V(t) represents a transient voltage drop at a I(t) represents a second current data at a R(t) represents a resistance data at a is a capacitance coupling coefficient, is a mutual inductance coupling coefficient, is a spatial electric field coupling coefficient, is an electrostatic induction coefficient.​​​ 3. The method of claim 1, wherein the transient voltage drop is determined by: The sampling frequency of the sampling device satisfies: , wherein, represents a sampling frequency of the sampling device; represents a sampling coefficient; represents a highest frequency of the expected transient signal; represents a frequency fluctuation coefficient; represents an ambient interference influence coefficient; is a voltage sag influence coefficient.

4. The method of claim 1, wherein the transient voltage drop in the live test of the surge arrester is determined by: After determining the transient voltage drop of the surge arrester according to the second voltage sequence, the second current sequence, the resistance data, and the third correlation coefficient corresponding to the surge arrester, the method further comprises: Determine the simulation voltage drop according to the second current sequence, the resistance data, the environmental temperature influence coefficient, the heat conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient; Determine the error index data according to the difference between the transient voltage drop and the simulation voltage drop, and determine the performance index data of the surge arrester according to the error index data.

5. The method of claim 4, wherein the transient voltage drop is determined by: Determine the simulation voltage drop according to the second current sequence, the resistance data, the environmental temperature influence coefficient, the heat conduction influence coefficient, the wind speed influence coefficient, the air pressure influence coefficient, the temperature gradient influence coefficient, and the humidity gradient influence coefficient based on the following formula: ， wherein, represents a simulated voltage drop at the sample time, represents second current data at the sample time, represents resistance data at the sample time, represents an ambient temperature influence coefficient, represents a thermal conduction influence coefficient, represents a wind speed influence coefficient, represents a barometric pressure influence coefficient, represents a temperature gradient influence coefficient, represents a humidity gradient influence coefficient.

6. The method of claim 4, wherein the transient voltage drop is determined by: Determine the error index data according to the difference between the transient voltage drop and the simulation voltage drop based on the following formula: ， ， wherein, represents error index data, represents a transient voltage drop at the sampling time, represents a simulated voltage drop at the sampling time, represents the total number of sampling times, represents a weight coefficient, is a relative error comprehensive weight coefficient, is a transient change rate error weight coefficient, and are the change rates of the actual measured and simulated transient voltage drops, respectively.

7. The method of claim 4, wherein the transient voltage drop is determined by: ###0001### where V is the transient voltage drop, V is the voltage at the beginning of the test, V is the voltage at the end of the test, and t is the time of the test. The performance index data includes good performance and performance problems; determining the performance index data of the surge arrester according to the error index data comprises: In the case that the error index data is less than a preset error index threshold, it is determined that the performance index data of the surge arrester is good performance. In a case where the error index data is greater than or equal to a preset error index threshold value, it is determined that the performance index data of the lightning arrester has a performance problem.

8. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method for determining transient voltage drop in lightning arrester live-line testing according to any one of claims 1-7.

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