Substation insulator pollution flashover monitoring method and device, electronic equipment and storage medium

Abstract

The application discloses a substation insulator pollution flashover monitoring method and device, electronic equipment and storage medium. Among them, the method comprises the following steps: collecting the pollution flashover sound wave data of the target monitoring insulator, filtering the pollution flashover sound wave data, and obtaining the target sound wave data; determining the target environment data of the environment where the target monitoring insulator is located, wherein the target environment data comprises at least one of equivalent salt density data, temperature and humidity data and air dust content data; the target sound wave data and the target environment data are input into the pollution flashover evaluation model for pollution flashover evaluation, and the target pollution flashover grade is obtained; the target pollution flashover grade is used as the pollution flashover monitoring result corresponding to the target monitoring insulator. The application monitors the pollution flashover of the insulator from multiple dimensions (sound wave and environment), and improves the accuracy of the substation insulator pollution flashover monitoring.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, and in particular to a method, device, electronic equipment, and storage medium for monitoring flashover of insulators in substations. Background Technology

[0002] With the expansion of power transmission capacity in substations, the issue of flashover due to pollution insulators is receiving increasing attention. Severe flashover can make reclosing difficult in substation power systems, leading to widespread and prolonged power outages and causing incalculable losses.

[0003] In related technologies, flashover detection is usually performed by analyzing insulator current or voltage to provide early warning when current or voltage is abnormal. However, false warnings often occur, so the accuracy of flashover detection for substation insulators is currently poor. Summary of the Invention

[0004] This invention provides a method, device, electronic equipment, and storage medium for monitoring flashover of insulators in substations, in order to solve the problem of poor accuracy in monitoring flashover of insulators in substations.

[0005] According to one aspect of the present invention, a method for monitoring flashover of insulators in substations is provided, wherein the method includes:

[0006] Collect pollution flashover acoustic wave data of the target monitoring insulator, and filter the pollution flashover acoustic wave data to obtain the target acoustic wave data;

[0007] Determine the target environmental data of the environment where the target monitoring insulator is located, wherein the target environmental data includes at least one of the following: equivalent salt density data, temperature and humidity data, and air dust content data;

[0008] The target pollution flashover level is obtained by evaluating the input target acoustic data and target environmental data using a pollution flashover assessment model, wherein the pollution flashover assessment model is obtained by training a support vector machine using a training sample set.

[0009] The target flashover level is used as the flashover monitoring result corresponding to the target monitoring insulator.

[0010] According to another aspect of the present invention, a substation insulator flashover monitoring device is provided, wherein the device comprises:

[0011] The flashover acoustic wave processing module is used to collect flashover acoustic wave data of the target monitoring insulator, and to filter the flashover acoustic wave data to obtain the target acoustic wave data.

[0012] An environmental data determination module is used to determine the target environmental data of the environment where the target monitoring insulator is located, wherein the target environmental data includes at least one of equivalent salt density data, temperature and humidity data, and air dust content data;

[0013] The pollution flashover assessment module is used to assess the pollution flashover level of the target acoustic wave data and the target environmental data by means of a pollution flashover assessment model, wherein the pollution flashover assessment model is obtained by training a support vector machine with a training sample set.

[0014] The monitoring result determination module is used to take the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator.

[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 substation insulator flashover monitoring method 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 for causing a processor to execute and implement the substation insulator flashover monitoring method according to any embodiment of the present invention.

[0020] The technical solution of this invention involves collecting flashover acoustic data from a target monitoring insulator, filtering the data to obtain target acoustic data, determining target environmental data for the environment where the target monitoring insulator is located, wherein the target environmental data includes at least one of equivalent salt density data, temperature and humidity data, and air dust content data; evaluating the input target acoustic data and target environmental data using a flashover assessment model to obtain a target flashover level, wherein the flashover assessment model is trained on a support vector machine using a training sample set; and using the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator. This invention provides multi-dimensional (acoustic and environmental) flashover monitoring of insulators, which can improve the accuracy of flashover monitoring of substation insulators.

[0021] 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

[0022] 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.

[0023] Figure 1 This is a flowchart of a method for monitoring flashover of pollution insulators in a substation according to Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of a method for monitoring flashover of insulators in a substation according to Embodiment 2 of the present invention;

[0025] Figure 3 This is an architecture diagram of a substation insulator flashover monitoring system provided according to an embodiment of the present invention;

[0026] Figure 4 This is an overall flowchart of a method for monitoring flashover of insulators in substations according to an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of the structure of a substation insulator flashover monitoring device according to Embodiment 3 of the present invention;

[0028] Figure 6 This is a schematic diagram of the structure of an electronic device for implementing the substation insulator flashover monitoring method according to an embodiment of the present invention. Detailed Implementation

[0029] 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 of the present invention. 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.

[0030] 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 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.

[0031] Example 1

[0032] Figure 1 This invention provides a flowchart of a method for monitoring flashover of substation insulators based on pollution, according to Embodiment 1. This embodiment is applicable to situations where flashover monitoring of substation insulators is performed by assessing the flashover level. This method can be executed by a substation insulator flashover monitoring device, which can be implemented in hardware and / or software and can be configured in a computer. Figure 1 As shown, the method includes:

[0033] S110. Collect the flashover acoustic wave data of the target monitoring insulator, and filter the flashover acoustic wave data to obtain the target acoustic wave data.

[0034] The target monitoring insulator mentioned here can be understood as the target insulator for pollution flashover monitoring in a substation. It is important to understand that insulators in a substation have a probability of experiencing pollution flashover accidents, which can seriously affect the safe operation of the substation.

[0035] Specifically, the acquisition of flashover acoustic data of the target monitoring insulator may include: acquiring flashover acoustic data of the target monitoring insulator using an acoustic wave sensor when flashover occurs. The acoustic wave sensor can be used to acquire the acoustic waves generated when flashover occurs in the insulator.

[0036] The flashover acoustic data can be understood as the data of the acoustic waves generated when flashover occurs on the target monitoring insulator. Optionally, the flashover acoustic data may include at least one of the following: amplitude, period, frequency, phase, and wavelength of the flashover acoustic wave.

[0037] The target acoustic wave data can be understood as the filtered flashover acoustic wave data.

[0038] Optionally, the filtering process for the pollution flashover acoustic wave data includes:

[0039] The pollution flashover acoustic data is filtered using the Kalman filter algorithm.

[0040] The Kalman filter algorithm can be understood as a recursive filtering algorithm.

[0041] In this embodiment of the invention, the Kalman filtering algorithm can be used to clean the flashover sound wave data, removing sound data that is irrelevant to the flashover phenomenon. Compared with the unfiltered flashover sound wave data, the target sound wave data has better feature representation ability.

[0042] S120. Determine the target environmental data of the environment where the target monitoring insulator is located, wherein the target environmental data includes at least one of the following: equivalent salt density data, temperature and humidity data, and air dust content data.

[0043] The target environmental data can characterize the features of the environment in which the target monitoring insulator is located.

[0044] The equivalent salt density data can characterize the degree of pollution in the environment where the target monitoring insulator is located. In this embodiment of the invention, the equivalent salt density data can be an equivalent salt density value.

[0045] The temperature and humidity data can characterize the rate of change of temperature and humidity in the environment where the target monitoring insulator is located. In this embodiment of the invention, the temperature and humidity data can be a value characterizing the rate of change of temperature and humidity.

[0046] The air dust content data can characterize the amount of air dust in the environment where the target monitoring insulator is located. In this embodiment of the invention, the air dust content data can be a value characterizing the amount of air dust.

[0047] In this embodiment of the invention, the target environment data is collected by sensors.

[0048] Optionally, determining the target environmental data of the environment where the target monitoring insulator is located includes:

[0049] Determine the acoustic wave acquisition time period for the pollution flashover acoustic wave data, and determine the actual equivalent salt density of the environment where the target monitoring insulator is located during the acoustic wave acquisition time period;

[0050] A preset standard equivalent salt density is determined, and the equivalent salt density data is determined based on the sound wave acquisition time period, the actual equivalent salt density, and the standard equivalent salt density.

[0051] The acoustic wave acquisition time period can be understood as the acquisition time period of the pollution flashover acoustic wave data.

[0052] The actual equivalent salt density can be understood as the actual equivalent salt density of the environment where the target monitoring insulator is located. In this embodiment of the invention, the specific method for determining the actual equivalent salt density is not specifically limited.

[0053] The standard equivalent salt density can be understood as the standard equivalent salt density, the normal equivalent salt density of the environment where the target monitoring insulator is located, or a parameter used for calculating the equivalent salt density. In this embodiment of the invention, the standard equivalent salt density can be determined based on the historical equivalent salt density of the environment where the target monitoring insulator is located under historical conditions where no flashover has occurred.

[0054] Specifically, the determination of the equivalent salt density data based on the acoustic wave acquisition time period, the actual equivalent salt density, and the standard equivalent salt density can be achieved using the following formula:

[0055]

[0056] in, The data represents the equivalent salt density, ΔT represents the time period for acoustic wave acquisition, ρ0(T) represents the standard equivalent salt density, and ρ1(T) represents the actual equivalent salt density.

[0057] In this embodiment of the invention, the sound wave acquisition time period can also be the sound wave acquisition frequency.

[0058] Optionally, determining the target environmental data of the environment where the target monitoring insulator is located includes:

[0059] Determine the first and second temperature and humidity values ​​of the environment where the target monitoring insulator is located during the acoustic wave acquisition period;

[0060] The temperature and humidity data are determined based on the sound wave acquisition time period, the first temperature and humidity value, and the second temperature and humidity value.

[0061] The first temperature and humidity value can be the maximum value of temperature and humidity. The second temperature and humidity value can be the minimum value of temperature and humidity. In this embodiment of the invention, the first temperature and humidity value and the second temperature and humidity value can be different or the same.

[0062] Specifically, the determination of the temperature and humidity data based on the sound wave acquisition time period, the first temperature and humidity value, and the second temperature and humidity value can be achieved based on the following formula:

[0063]

[0064] in, The temperature and humidity data are represented by ΔT, which represents the time period for sound wave acquisition (h). max (T) represents the first temperature and humidity value, h min (T) represents the second temperature and humidity value.

[0065] Optionally, determining the target environmental data of the environment where the target monitoring insulator is located includes:

[0066] Determine the dust content of the target monitoring insulator at each target moment within the acoustic wave acquisition period;

[0067] The air dust content data is determined based on the content of multiple sub-dust particles.

[0068] The dust content can be understood as the air dust content of the environment where the target insulator is located at the target time.

[0069] Specifically, determining the air dust content data based on the content of multiple sub-dust particles can be achieved using the following formula:

[0070]

[0071] in, The data representing the airborne dust content, C i This represents the air dust content at target time i.

[0072] The above-described embodiment uses target environmental data (at least one of the following: equivalent salt density data, temperature and humidity data, and air dust content data) that affects the severity of flashover as reference data for assessing flashover of the target monitoring insulator, which can effectively improve the accuracy of flashover level assessment.

[0073] S130. The target acoustic data and the target environmental data are evaluated for pollution flashover using a pollution flashover evaluation model to obtain the target pollution flashover level. The pollution flashover evaluation model is obtained by training a support vector machine using a training sample set.

[0074] The target flashover level can be understood as the level of flashover phenomenon occurring on the target monitored insulator. Optionally, the target flashover level can characterize the severity of the flashover phenomenon. For example, the target flashover level may include mild level one, moderate level two, and severe level three, etc.

[0075] The training sample set can be understood as a collection of training samples.

[0076] The support vector machine can be understood as a binary classification model.

[0077] The pollution flashover assessment model can be understood as a model for assessing the specific pollution flashover level. Based on the pollution flashover acoustic data collected when the target monitoring insulator experiences a pollution flashover, and the target environmental data of the environment where the target monitoring insulator is located, the pollution flashover assessment model can evaluate the severity of the pollution flashover phenomenon and obtain the target pollution flashover level corresponding to the pollution flashover phenomenon.

[0078] S140. The target flashover level is used as the flashover monitoring result corresponding to the target monitoring insulator.

[0079] The technical solution of this invention can collect pollution flashover acoustic data of target monitoring insulators in real time based on a preset data acquisition frequency and determine the target environmental data of the environment where the target monitoring insulators are located in real time, so as to realize real-time pollution flashover monitoring of substation insulators.

[0080] The pollution flashover monitoring results can be understood as the results of pollution flashover monitoring of substation insulators.

[0081] Optionally, after setting the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator, the method further includes:

[0082] Determine the preset warning level for pollution flashover;

[0083] If the target flashover level exceeds the warning flashover level, a warning is issued by the target warning device based on the flashover monitoring results.

[0084] The aforementioned warning flashover level can be understood as a flashover level used for warning judgment. In this embodiment of the invention, the warning flashover level can be preset according to scenario requirements, and is not specifically limited here. Optionally, the warning flashover level can be moderate level two or severe level three, etc.

[0085] Specifically, taking the severe level 3 pollution flashover warning as an example: No warning is issued when the target pollution flashover level is mild level 1 or moderate level 2; a warning is issued when the target pollution flashover level is severe level 3.

[0086] The target warning device can be understood as a device with a warning function. In this embodiment of the invention, the target warning device can be preset according to scenario requirements, and no specific limitation is made here.

[0087] The technical solution of this invention involves collecting flashover acoustic data from a target monitoring insulator, filtering the data to obtain target acoustic data, determining target environmental data for the environment where the target monitoring insulator is located, wherein the target environmental data includes at least one of equivalent salt density data, temperature and humidity data, and air dust content data; evaluating the input target acoustic data and target environmental data using a flashover assessment model to obtain a target flashover level, wherein the flashover assessment model is trained on a support vector machine using a training sample set; and using the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator. This invention provides multi-dimensional (acoustic and environmental) flashover monitoring of insulators, which can improve the accuracy of flashover monitoring of substation insulators.

[0088] Example 2

[0089] Figure 2 This is a flowchart of a method for monitoring flashover of insulators in a substation according to Embodiment 2 of the present invention. This embodiment adds to the flashover assessment of the input target acoustic wave data and target environmental data using the flashover assessment model described in the above embodiments. Figure 2 As shown, the method includes:

[0090] S210. Obtain the training sample set, wherein the training sample set includes multiple training samples and sample labels corresponding to each training sample, and the training samples include pollution flashing sound wave samples and sample environment data corresponding to the pollution flashing sound wave samples.

[0091] The training samples can be understood as the samples used to train the support vector machine.

[0092] The sample label can be understood as the label of the training sample. The sample label can characterize the true level of pollution flashover of the training sample.

[0093] The pollution flashover acoustic wave sample can be understood as sample data of pollution flashover acoustic waves. The pollution flashover acoustic wave sample can be sample data of pollution flashover acoustic waves collected in history.

[0094] The sample environmental data can be understood as the environmental data corresponding to the pollution flashover acoustic wave sample.

[0095] Optionally, the sample environmental data includes at least one of the following: sample equivalent salt density, sample temperature and humidity, and sample dust content.

[0096] In this embodiment of the invention, the method for determining the sample environment data can be the same as the method for determining the target environment data described in the above embodiments, and will not be repeated here.

[0097] S220. For each training sample, the pollution flashover level is obtained by evaluating the input pollution flashover acoustic wave sample and the sample environmental data using the pre-constructed support vector machine.

[0098] The sample flashover level can be understood as the flashover level obtained by evaluating the training samples using the support vector machine.

[0099] S230. Determine the evaluation loss value based on the sample flashover level and the sample label, and adjust the model parameters of the support vector machine based on the evaluation loss value to obtain the flashover evaluation model.

[0100] The assessed loss value can be understood as the loss value between the sample flashover level and the sample label.

[0101] The following further elaborates on the pollution flashover assessment model obtained by training a support vector machine using a training sample set:

[0102] The support vector machine (SVM) seeks optimal parameters between model complexity (i.e., learning accuracy on specific training samples) and learning ability (i.e., the ability to identify any training sample without errors) based on a limited set of training samples to achieve the best generalization capability. The training sample set can be as follows:

[0103] L={(x1,y1,s1),(x2,y2,s2),…,(x n ,y n ,s n )}

[0104] stx i ∈R N ,y i ∈{-1,1},(i=1,…,n)

[0105] Where, x i This represents the training samples (including filtered pollution flashover acoustic wave samples and quantitative index values ​​of the sample environmental data); y i Indicates the sample label (true flashover level); s i Indicates fuzzy membership degree (preset according to scenario requirements), s i Non-zero; ε≤s i ≤1, ε represents an arbitrarily small positive number, R N Let n represent the N-dimensional Gaussian feature space, where n represents the number of training samples (e.g., 30 days and 24 hours in a month).

[0106] The calculation formulas related to support vector machines can be summarized as follows:

[0107]

[0108] Where ω represents the classification interface vector; ξ i C represents the relaxation factor; b represents the penalty factor; and b represents the classification threshold. This represents a mapping from the input space to a higher-dimensional space. The relaxation factor, penalty factor, and classification threshold are set based on the actual model training conditions and are not specifically limited here. For example, the input quantification index values ​​can differ for different geographical locations.

[0109] For the test sample, we can calculate that:

[0110]

[0111] Here, α represents the Lagrange multiplier.

[0112] The weighted characteristic function is shown below:

[0113]

[0114] in, This represents the weight of the k-th feature, and this constant can be set according to geographical location.

[0115] S240. Collect the flashover acoustic wave data of the target monitoring insulator, and filter the flashover acoustic wave data to obtain the target acoustic wave data.

[0116] The following further elaborates on the filtering process of the pollution flashover acoustic wave data using the Kalman filter algorithm to obtain the target acoustic wave data:

[0117] The Kalman filter algorithm is used to process the acquired pollution flashover acoustic wave data to improve the data's feature representation capability. Kalman filtering is a recursive filter that uses the recursion of output and input values ​​to calculate and update the minimum mean square error estimate of the state. Its key process is the establishment of the state equation and measurement equation. The relevant calculation formulas of the Kalman filter algorithm are explained below:

[0118] The state estimation equation is as follows:

[0119]

[0120] in, c represents the state variables of the system. k-1 A represents the system input at time k-1 (i.e., the pollution flashover acoustic data); k-1 Represents the state transition matrix (based on different time steps). (The state transition matrix composed of values); B k-1This represents the control input matrix (a matrix input based on the actual situation on site; for example, if data needs to be collected for each day in winter, the value in the matrix is ​​1, and if data for a certain day does not need to be collected, the value in the matrix is ​​0).

[0121] The formula for calculating the error covariance matrix is ​​shown below:

[0122]

[0123] Among them, P k∣k-1 The error covariance matrix is ​​represented by the above A. k-1 Matrix Q can be obtained. k-1 The system noise is represented by Gaussian white noise (a constant set according to the actual site conditions).

[0124] The formula for calculating the Kalman gain is shown below:

[0125]

[0126] Among them, K k Indicates Kalman gain; C k This represents the system's observation matrix (which is c as described above). k-1 (a matrix composed of input quantities); R k This represents the observation noise (a constant set according to the actual field conditions).

[0127] The formula for calculating the measurement update of the state estimate is shown below:

[0128]

[0129] Where, D k This represents the control output matrix (compared to the control input matrix B mentioned above). k Correspondence, with a one-to-one correspondence between input and output.

[0130] The formula for calculating the measurement update of the error covariance matrix is ​​shown below:

[0131] P k =(IK k C k )P k∣k-1

[0132] Where I represents the identity matrix.

[0133] S250. Determine the target environmental data of the environment where the target monitoring insulator is located.

[0134] S260. The target acoustic data and the target environmental data are evaluated using a pollution flashover assessment model to obtain the target pollution flashover level.

[0135] S270. The target flashover level is used as the flashover monitoring result corresponding to the target monitoring insulator.

[0136] The technical solution of this invention involves acquiring a training sample set, wherein the training sample set includes multiple training samples and a sample label corresponding to each training sample, and the training samples include pollution flashover acoustic wave samples and sample environment data corresponding to the pollution flashover acoustic wave samples; for each training sample, a pre-constructed support vector machine is used to perform pollution flashover assessment on the input pollution flashover acoustic wave sample and the sample environment data to obtain the sample pollution flashover level; an evaluation loss value is determined based on the sample pollution flashover level and the sample label, and the model parameters of the support vector machine are adjusted based on the evaluation loss value to obtain the pollution flashover assessment model. This achieves the effect of training a pollution flashover assessment model with high accuracy.

[0137] Figure 3 This is an architecture diagram of a substation insulator flashover monitoring system according to an embodiment of the present invention. Figure 3 As shown, in substations with severe electromagnetic interference, an optocoupler isolation chip is used to block external electromagnetic interference and protect the controller. A sound acquisition device (i.e., the acoustic sensor) automatically detects sound changes caused by flashover in relevant power equipment within the substation (i.e., flashover acoustic data), and transmits the collected data to the controller module. To ensure the collected sound data better reflects the flashover situation, multiple acquisition sensors can be installed near the power equipment. The collected flashover acoustic data can be transmitted wirelessly. The system has wireless communication capabilities, allowing real-time data to be transmitted to relevant personnel terminals. Key inputs can be used to adjust the system's parameters. Data can be sent back to the background and solved using relevant models, thereby providing early warning of flashover changes in power equipment and timely providing personnel with flashover sound information and warning signals. This invention uses solar power technology, directly converting solar radiation into electrical energy through solar panels to power the system.

[0138] Figure 4 This is an overall flowchart of a method for monitoring flashover of pollution insulators in substations according to an embodiment of the present invention. Figure 3 and Figure 4 As shown, specifically, the overall process of the substation insulator flashover monitoring method can be as follows:

[0139] Using sound acquisition devices and controllers as hardware foundations, the acquisition module collects pollution flashover information;

[0140] Analysis revealed that the severity of flashover is related to factors such as equivalent salt density, ambient temperature and humidity, and airborne dust, and corresponding quantitative indicators were constructed.

[0141] Analysis of the characteristics of the acoustic signal of insulator flashover discharge revealed a correlation between the acoustic signal and the insulator flashover.

[0142] The Kalman filtering algorithm is used to improve the anti-interference capability of the data acquisition device.

[0143] The processed audio signals are accurately classified by combining an improved support vector machine.

[0144] It enables accurate identification of the sound signal of flashover and precise assessment of the degree of flashover of insulators.

[0145] This invention can improve the reliability of substation equipment operation and the work efficiency of staff, and reduce the incidence of pollution flashover accidents.

[0146] Example 3

[0147] Figure 5 This is a schematic diagram of a substation insulator flashover monitoring device provided in Embodiment 3 of the present invention. Figure 5 As shown, the device includes: a flashover acoustic processing module 310, an environmental data determination module 320, a flashover assessment module 330, and a monitoring result determination module 340.

[0148] The system includes: a flashover acoustic processing module 310 for collecting flashover acoustic data of the target monitoring insulator and filtering the data to obtain target acoustic data; an environmental data determination module 320 for determining target environmental data of the environment where the target monitoring insulator is located, wherein the target environmental data includes at least one of equivalent salt density data, temperature and humidity data, and air dust content data; a flashover assessment module 330 for performing flashover assessment on the input target acoustic data and target environmental data using a flashover assessment model to obtain a target flashover level, wherein the flashover assessment model is trained on a support vector machine using a training sample set; and a monitoring result determination module 340 for using the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator.

[0149] The technical solution of this invention involves collecting flashover acoustic data from a target monitoring insulator, filtering the data to obtain target acoustic data, determining target environmental data for the environment where the target monitoring insulator is located, wherein the target environmental data includes at least one of equivalent salt density data, temperature and humidity data, and air dust content data; evaluating the input target acoustic data and target environmental data using a flashover assessment model to obtain a target flashover level, wherein the flashover assessment model is trained on a support vector machine using a training sample set; and using the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator. This invention provides multi-dimensional (acoustic and environmental) flashover monitoring of insulators, which can improve the accuracy of flashover monitoring of substation insulators.

[0150] Optionally, the pollution flashing acoustic wave processing module includes: a filtering processing unit, specifically used to filter the pollution flashing acoustic wave data using a Kalman filtering algorithm.

[0151] Optionally, the substation insulator flashover monitoring device further includes: a warning level determination module and a flashover warning module;

[0152] The warning level determination module is used to determine a preset warning level after the target flashover level is used as the flashover monitoring result corresponding to the target monitoring insulator;

[0153] The flashover warning module is used to issue a warning on the flashover monitoring results via a target warning device when the target flashover level exceeds the warning flashover level.

[0154] Optionally, the substation insulator flashover monitoring device further includes: a sample acquisition module, a sample evaluation module, and a model parameter adjustment module;

[0155] The sample acquisition module is used to acquire the training sample set before performing pollution flashover assessment on the input target acoustic wave data and target environmental data through the pollution flashover assessment model. The training sample set includes multiple training samples and sample labels corresponding to each training sample. The training samples include pollution flashover acoustic wave samples and sample environmental data corresponding to the pollution flashover acoustic wave samples.

[0156] The sample evaluation module is used to evaluate the pollution flashover level of each training sample by using the pre-built support vector machine to evaluate the input pollution flashover acoustic wave sample and the sample environmental data.

[0157] The model parameter adjustment module is used to determine the evaluation loss value based on the sample flashover level and the sample label, and adjust the model parameters of the support vector machine based on the evaluation loss value to obtain the flashover evaluation model.

[0158] Optionally, the substation insulator flashover monitoring device further includes: a sample determination module, used to determine the flashover acoustic wave sample and the sample environmental data corresponding to the flashover acoustic wave sample before acquiring the training sample set, wherein the sample environmental data includes at least one of the sample equivalent salt density, sample temperature and humidity, and sample dust content.

[0159] Optionally, the sample determination module is specifically used for:

[0160] Determine the sampling time period for the pollution flashover acoustic wave sample, and determine the actual equivalent salt density of the environment where the target monitoring insulator is located during the sampling time period;

[0161] A preset standard equivalent salt density is determined, and the sample equivalent salt density is determined based on the sample collection time period, the actual equivalent salt density, and the standard equivalent salt density.

[0162] Optionally, the sample determination module is specifically used for:

[0163] Determine the first and second temperature and humidity values ​​of the environment where the target monitoring insulator is located during the sample collection period;

[0164] The sample temperature and humidity are determined based on the sample collection time period, the first temperature and humidity value, and the second temperature and humidity value.

[0165] Optionally, the sample determination module is specifically used for:

[0166] Determine the dust content of the target monitoring insulator at each target moment within the sample collection period;

[0167] The sample dust content is determined based on the content of multiple sub-dust particles.

[0168] The substation insulator flashover monitoring device provided in this embodiment of the invention can execute the substation insulator flashover monitoring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0169] Example 4

[0170] Figure 6A 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.

[0171] like Figure 6 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.

[0172] 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.

[0173] 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, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the substation insulator flashover monitoring method.

[0174] In some embodiments, the substation insulator flashover monitoring method 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 substation insulator flashover monitoring method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the substation insulator flashover monitoring method by any other suitable means (e.g., by means of firmware).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 monitoring flashover of pollution insulators in substations, characterized in that, include: Collect pollution flashover acoustic wave data of the target monitoring insulator, and filter the pollution flashover acoustic wave data to obtain the target acoustic wave data; Determine the target environmental data of the environment where the target monitoring insulator is located, wherein the target environmental data includes equivalent salt density data, temperature and humidity data, and air dust content data; The target pollution flashover level is obtained by evaluating the input target acoustic data and target environmental data using a pollution flashover assessment model, wherein the pollution flashover assessment model is obtained by training a support vector machine using a training sample set. The target flashover level is taken as the flashover monitoring result corresponding to the target monitoring insulator; The determination of target environmental data for the environment where the target monitoring insulator is located includes: Determine the acoustic wave acquisition time period for the pollution flashover acoustic wave data, and determine the actual equivalent salt density of the environment where the target monitoring insulator is located during the acoustic wave acquisition time period; A preset standard equivalent salt density is determined, and the equivalent salt density data is determined based on the sound wave acquisition time period, the actual equivalent salt density, and the standard equivalent salt density; Determine the first and second temperature and humidity values ​​of the environment where the target monitoring insulator is located during the acoustic wave acquisition period; the first temperature and humidity value is the maximum value of temperature and humidity, and the second temperature and humidity value is the minimum value of temperature and humidity. The temperature and humidity data are determined based on the sound wave acquisition time period, the first temperature and humidity value, and the second temperature and humidity value. Determine the dust content of the target monitoring insulator at each target moment within the acoustic wave acquisition period; The air dust content data is determined based on the content of multiple sub-dust particles.

2. The method according to claim 1, characterized in that, The filtering process for the flashover acoustic wave data includes: The pollution flashover acoustic data is filtered using the Kalman filter algorithm.

3. The method according to claim 1, characterized in that, After setting the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator, the method further includes: Determine the preset warning level for pollution flashover; If the target flashover level exceeds the warning flashover level, a warning is issued by the target warning device based on the flashover monitoring results.

4. The method according to claim 1, characterized in that, Before performing the pollution flashover assessment on the input target acoustic data and target environmental data using the pollution flashover assessment model, the method further includes: Obtain the training sample set, wherein the training sample set includes multiple training samples and sample labels corresponding to each training sample, and the training samples include pollution flashing sound wave samples and sample environmental data corresponding to the pollution flashing sound wave samples; For each training sample, the pollution flashover level is obtained by evaluating the input pollution flashover acoustic wave sample and the sample environmental data using the pre-constructed support vector machine. The evaluation loss value is determined based on the sample flashover level and the sample label. The model parameters of the support vector machine are adjusted based on the evaluation loss value to obtain the flashover evaluation model.

5. A substation insulator flashover monitoring device, characterized in that, include: The flashover acoustic wave processing module is used to collect flashover acoustic wave data of the target monitoring insulator, and to filter the flashover acoustic wave data to obtain the target acoustic wave data. An environmental data determination module is used to determine the target environmental data of the environment where the target monitoring insulator is located, wherein the target environmental data includes equivalent salt density data, temperature and humidity data, and air dust content data; The environmental data determination module is specifically used to determine the acoustic wave acquisition time period of the pollution flashover acoustic wave data; determine the actual equivalent salt density of the environment where the target monitoring insulator is located within the acoustic wave acquisition time period; determine a preset standard equivalent salt density; determine the equivalent salt density data based on the acoustic wave acquisition time period, the actual equivalent salt density, and the standard equivalent salt density; determine a first temperature and humidity value and a second temperature and humidity value of the environment where the target monitoring insulator is located within the acoustic wave acquisition time period; the first temperature and humidity value is the maximum value of temperature and humidity, and the second temperature and humidity value is the minimum value of temperature and humidity; determine the temperature and humidity data based on the acoustic wave acquisition time period, the first temperature and humidity value, and the second temperature and humidity value; determine the sub-dust content of the environment where the target monitoring insulator is located at each target moment within the acoustic wave acquisition time period; and determine the air dust content data based on multiple sub-dust contents. The pollution flashover assessment module is used to assess the pollution flashover level of the target acoustic wave data and the target environmental data by means of a pollution flashover assessment model, wherein the pollution flashover assessment model is obtained by training a support vector machine with a training sample set. The monitoring result determination module is used to take the target flashover level as the flashover monitoring result corresponding to the target monitoring insulator.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the substation insulator flashover monitoring method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the substation insulator flashover monitoring method according to any one of claims 1-4.

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