A remote monitoring method for high voltage switchgear

Abstract

The present invention proposes a remote monitoring method for high-voltage switchgear, which belongs to the technical field of intelligent detection of equipment. The method includes: obtaining a first state monitoring data set of the high-voltage switchgear at a first moment and a second state monitoring data set of the high-voltage switchgear at a second moment; determining the intersection of the first state monitoring data set and the second state monitoring data set; when the intersection is not empty, sending a data reporting signal; constructing a reporting data set based on the first state monitoring data set and the second state monitoring data set and reporting; based on the reporting data set, analyzing the current state of the high-voltage switchgear. The technical solution of the present invention can significantly reduce the delay phenomenon of reporting the state monitoring data of the switchgear, and can make timely predictions and take corresponding maintenance measures when possible abnormal situations occur, thereby improving the real-time state monitoring of the high-voltage switchgear and the integrity of data acquisition.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment intelligent detection, and in particular relates to a remote monitoring method for high-voltage switchgear. Background Art

[0002] The currently widely used intelligent power distribution equipment integrates modern power engineering technology. Through the integration of the Internet and the power industry, the platform carrying capacity and business application level have been greatly improved. High-voltage switchgear is an important part of intelligent power distribution equipment. For example, gas-insulated metal-enclosed switchgear, its online status monitoring data mainly includes SF6 gas status monitoring, partial discharge online monitoring, mechanical status online monitoring, infrared temperature measurement online monitoring and arrester insulation status online monitoring data. Through the intelligent operation and maintenance big data platform of high-voltage switchgear, remote access, multi-dimensional comprehensive analysis and health status assessment of online monitoring data, live detection data and other data of the whole life cycle of high-voltage switchgear are realized, potential defects of equipment are discovered in time, early warnings are issued, and defects are eliminated in time, so as to realize the status maintenance of equipment, avoid the occurrence of equipment malignant failures, and ensure the safe and reliable operation of the power grid.

[0003] However, the data in the intelligent power distribution system is huge in scale and complex in type. The sharp increase in intelligent distribution network equipment has led to data congestion when the equipment measurement points generate massive amounts of data for reporting, resulting in large delays in fault warning and status analysis. If the data reporting time interval is shortened, a large amount of irrelevant data will be uploaded, resulting in false positives. How to reduce the amount of status data reporting and reduce data warning delays while ensuring that valuable data can be uploaded in a complete and timely manner is a technical issue that needs to be considered in the process of remote status monitoring of high-voltage switchgear. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a remote monitoring method for high voltage switchgear, the method comprising the following steps:

[0005] Acquire a first state monitoring data set of the high-voltage switchgear at a first moment and a second state monitoring data set of the high-voltage switchgear at a second moment;

[0006] determining an intersection of a first condition monitoring data set and a second condition monitoring data set;

[0007] When the intersection is not empty, sending a data reporting signal;

[0008] Constructing a reporting data set based on the first condition monitoring data set and the second condition monitoring data set and reporting the data set;

[0009] Based on the reported data set, the current state of the high-voltage switchgear is analyzed.

[0010] The high-voltage switchgear obtains a variety of state monitoring data through a combined sensor unit according to a preset collection cycle, and each state monitoring data corresponds to a standard reference value;

[0011] When the difference between a certain state monitoring data obtained at a certain moment and the corresponding standard reference value exceeds a preset range, the state monitoring data is added to the state monitoring data set corresponding to the moment.

[0012] The high-voltage switchgear determines the intersection of the first state monitoring data set and the second state monitoring data set through the local edge data processing unit, and sends the data reporting signal to the remote data processing center, wherein the data reporting signal includes the number of elements K1 of the first state monitoring data set, the number of elements K2 of the second state monitoring data set, and the number of elements K of the intersection;

[0013] After receiving the data reporting signal, the remote data processing center reserves data reporting channel resources for the local edge data processing unit;

[0014] The local edge data processing unit reports the first status monitoring data set and the second status monitoring data set through the reserved data reporting channel resources.

[0015] After obtaining a first state monitoring data set of the high-voltage switchgear at a first moment and a second state monitoring data set at a second moment, and before determining an intersection of the first state monitoring data set and the second state monitoring data set, the method includes:

[0016] If both the first state monitoring data set and the second state monitoring data set are empty sets, increasing the preset acquisition period;

[0017] After obtaining a first state monitoring data set of the high-voltage switchgear at a first moment and a second state monitoring data set at a second moment, and before determining an intersection of the first state monitoring data set and the second state monitoring data set, the method includes:

[0018] If only one of the first state monitoring data set and the second state monitoring data set is an empty set, the preset collection period is reduced.

[0019] When the intersection is empty, the method includes:

[0020] Obtaining factory test data, daily maintenance data, and routine test data of the high-voltage switchgear;

[0021] The elements in the first condition monitoring data set and / or the second condition monitoring data set are corrected based on the factory test data, the daily maintenance data, and the routine test data.

[0022] The combined sensor unit includes a current mutual induction device and a voltage mutual induction device;

[0023] The current mutual induction device and the voltage mutual induction device mutually measure the same state signal

[0024] The present invention also provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the aforementioned remote monitoring method for a high-voltage switch device.

[0025] The combined sensor unit includes an SF6 gas online monitoring unit, a partial discharge online monitoring unit, an infrared temperature measurement unit, a lightning arrester online monitoring unit and a mechanical state online monitoring unit

[0026] The present invention also provides a computer device. When a computer instruction is executed on the computer device, the electronic device executes the aforementioned remote monitoring method for a high-voltage switch device.

[0027] Specifically, the computer device can be various electronic devices, such as a portable terminal device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned remote monitoring method for high-voltage switchgear are implemented.

[0028] The technical solution of the present invention can significantly reduce the delay in reporting switchgear status monitoring data, and can timely predict and take corresponding maintenance measures when possible abnormal situations occur, thereby improving the real-time status monitoring of high-voltage switchgear and the integrity of data collection.

[0029] Further advantages of the present invention will be further reflected in detail in the specific embodiments section in conjunction with the drawings of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 This is a main flow chart of a remote monitoring method for high-voltage switchgear according to an embodiment of the present invention.

[0032] Figure 2 is realized Figure 1 Schematic diagram of the hardware composition system of the remote monitoring method of the high-voltage switchgear

[0033] Figure 3 and Figure 4 yes Figure 1 Schematic diagram of an embodiment of a computer flow chart of data acquisition and processing of a remote monitoring method for high-voltage switchgear DETAILED DESCRIPTION

[0034] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments that are the same as the present application. Instead, they are only examples of devices and methods that are the same as some aspects of the present application as detailed in the attached claims.

[0035] In addition, the present invention provides multiple embodiments, each of which can constitute an independent technical solution and may contribute to the prior art and solve corresponding technical problems. However, it should be pointed out that different embodiments can be combined with each other without violating logic; at the same time, each embodiment can solve at least one technical problem, but it is not required that each individual embodiment solves multiple or all technical problems.

[0036] It should also be noted that in the specific implementation of this application, if user-related data is involved, when the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0037] See first Figure 1 , Figure 1 The present invention is a flowchart of a remote monitoring method for a high-voltage switchgear according to an embodiment of the present invention.

[0038] The method comprises the following steps: Figure 1 Step numbers are omitted).

[0039] S100: Acquire a first state monitoring data set at a first moment and a second state monitoring data set at a second moment of a high-voltage switchgear;

[0040] S200: Determine an intersection of a first state monitoring data set and a second state monitoring data set;

[0041] S300: When the intersection is not empty, sending a data reporting signal;

[0042] S400: constructing a reporting data set based on the first condition monitoring data set and the second condition monitoring data set and reporting the data set;

[0043] S500: Analyze the current state of the high-voltage switchgear based on the reported data set.

[0044] Figure 2 is realized Figure 1 A schematic diagram of the hardware composition system of the remote monitoring method for high-voltage switchgear.

[0045] Combination Figure 2 , to introduce Figure 1 The implementation method of each step of the method.

[0046] S100: Acquire a first state monitoring data set of a high-voltage switchgear at a first moment and a second state monitoring data set of a high-voltage switchgear at a second moment.

[0047] In a specific implementation, the high-voltage switchgear acquires a variety of status monitoring data through a combined sensor unit according to a preset collection period, and each type of status monitoring data corresponds to a standard reference value.

[0048] The various status monitoring of high-voltage switchgear includes at least two of SF6 gas status monitoring, partial discharge online monitoring, mechanical status online monitoring, infrared temperature measurement online monitoring and arrester insulation status online monitoring data.

[0049] Preferably, the combined sensor unit includes an SF6 gas online monitoring unit, a partial discharge online monitoring unit, an infrared temperature measurement unit, a lightning arrester online monitoring unit and a mechanical state online monitoring unit.

[0050] The SF6 gas online monitoring unit is used to monitor SF6 gas pressure, gas density, gas water content and gas decomposition components;

[0051] The partial discharge online monitoring unit is used to monitor the discharge frequency and discharge amount;

[0052] Infrared temperature measurement unit is used to measure the temperature of cable components;

[0053] The arrester online monitoring unit performs leakage current monitoring, leakage full current detection, lightning strike time recording and lightning strike number recording;

[0054] The mechanical status online monitoring unit is used to detect the flow current of the opening and closing coils, the opening and closing time, the opening and closing stroke, and the energy storage motor current.

[0055] Since the status signals of high-voltage switchgear are diverse and the combined sensor may have a single abnormality, if only a certain signal abnormality is reported directly, it may lead to frequent data reporting and frequent "false" false positive signals.

[0056] To this end, step S100 is specifically improved as follows:

[0057] Each type of condition monitoring data corresponds to a standard reference value;

[0058] When the difference between a certain state monitoring data obtained at a certain moment and the corresponding standard reference value exceeds a preset range, the state monitoring data is added to the state monitoring data set corresponding to the moment.

[0059] Assume that at the first moment t1 (assuming the first moment t1 is the 0th second), the state monitoring data set obtained is {SF6 gas state monitoring parameter value, partial discharge online monitoring parameter value, mechanical state online monitoring parameter value, infrared temperature measurement online monitoring parameter value, arrester insulation state online monitoring data parameter value};

[0060] If the SF6 gas state monitoring parameter value (assuming the SF6 gas pressure value) and the partial discharge online monitoring parameter value (assuming the discharge frequency) are abnormal, {SF6 gas state monitoring parameter value, partial discharge online monitoring parameter value} are added to the state monitoring data set corresponding to the time t1 to obtain the first state monitoring data set of the high-voltage switchgear at the first time;

[0061] Similarly, the high-voltage switchgear continues to acquire various state monitoring data through the combined sensor unit according to the preset acquisition period T;

[0062] Then, at the second moment t2 (t2=T+t1), according to the same method, a second state monitoring data set of the high-voltage switchgear at the second moment can be obtained.

[0063] However, the result of only considering a single abnormality is not accurate enough, especially when the data of high-voltage switchgear in the intelligent distribution system is huge in scale and complex in type, and the number of intelligent distribution network devices increases dramatically.

[0064] To this end, proceed to step S200: determine the intersection of the first state monitoring data set and the second state monitoring data set.

[0065] The subsequent monitoring is carried out through the judgment of the intersection, that is, step S300: when the intersection is not empty, a data reporting signal is issued.

[0066] In the present application, in order to solve the problem that data congestion is easily generated when equipment measurement points generate massive amounts of data to report, resulting in large delays in fault warning and status analysis, it is first necessary to make a data reporting request, that is, to send a data reporting signal, and then within a predetermined time period after the data reporting signal is sent, execute step S400: construct a reporting data set based on the first status monitoring data set and the second status monitoring data set and report it.

[0067] Finally, step S500 analyzes the current state of the high-voltage switchgear based on the reported data set.

[0068] Reference Figure 2 The hardware combination diagram of the above process is implemented as follows:

[0069] The high-voltage switchgear determines the intersection of the first state monitoring data set and the second state monitoring data set through the local edge data processing unit, and sends the data reporting signal to the remote data processing center, wherein the data reporting signal includes the number of elements K1 of the first state monitoring data set, the number of elements K2 of the second state monitoring data set, and the number of elements K of the intersection;

[0070] After receiving the data reporting signal, the remote data processing center reserves data reporting channel resources for the local edge data processing unit;

[0071] The local edge data processing unit reports the first status monitoring data set and the second status monitoring data set through the reserved data reporting channel resources.

[0072] Further, after obtaining a first state monitoring data set at a first moment and a second state monitoring data set at a second moment of the high-voltage switchgear, and before determining an intersection of the first state monitoring data set and the second state monitoring data set, the method includes:

[0073] If both the first state monitoring data set and the second state monitoring data set are empty sets, the preset collection period is increased.

[0074] It can be seen that the embodiment further adopts an adaptive data acquisition cycle adjustment method, that is, if the first state monitoring data set and the second state monitoring data set are both empty sets, it means that the current state parameters of the high-voltage switchgear are normal and the overall state is normal, then the data acquisition cycle can be increased without affecting the monitoring safety.

[0075] In another aspect, after obtaining a first state monitoring data set of a high-voltage switchgear at a first moment and a second state monitoring data set at a second moment, and before determining an intersection of the first state monitoring data set and the second state monitoring data set, the method includes:

[0076] If only one of the first state monitoring data set and the second state monitoring data set is an empty set, the preset collection period is reduced.

[0077] When only one of the first state monitoring data set and the second state monitoring data set is an empty set, it means that there is at least one abnormal signal at present, but the other acquisition sequence does not have it. It is judged that the switching device may have an occasional failure but the acquisition cycle is too long and the acquisition is not complete. At this time, it is necessary to reduce the preset acquisition cycle for further judgment.

[0078] Excluding the above two situations, that is, the first state monitoring data set and the second state monitoring data set are both not empty, but the first state monitoring data set and the second state monitoring data set are an intersection, the method includes:

[0079] Obtaining factory test data, daily maintenance data, and routine test data of the high-voltage switchgear;

[0080] The elements in the first condition monitoring data set and / or the second condition monitoring data set are corrected based on the factory test data, the daily maintenance data, and the routine test data.

[0081] At this time, in order to avoid possible omissions and false alarms, the elements in the first state monitoring data set and / or the second state monitoring data set can be calibrated through factory test data, daily maintenance data, and routine test data before a comprehensive judgment is made.

[0082] As a further improvement, the combined sensor unit includes a current mutual induction device and a voltage mutual induction device;

[0083] The current mutual induction device and the voltage mutual induction device mutually measure the same state signal.

[0084] The present application adopts a combined sensor as a data collector to collect a variety of status monitoring data.

[0085] In the specific implementation, the main work is to complete the phase acquisition of voltage and current. FPGA is used to complete the control of the AD chip. After the FPGA receives the clock signal of the GPS, it triggers the AD chip through a series of processes such as frequency division. In order to ensure the data collection and transmission requirements, an SMV (sampled measurement value) interface is designed in the data collector. This interface can efficiently and quickly complete the collection and transmission of waveform data and reduce latency. Using SMV to quickly transmit the sampling points can solve the throughput capacity of big data. The data transmission of SMV messages occurs at the data link layer.

[0086] The collector is designed with 8 analog channels, each of which is compatible with current and voltage, and the input current range is set to 0-5A, and the input voltage range is set to 0-350V. The data collector of the power distribution equipment needs to have the function of phase measurement during operation, needs to have a higher sampling range, and make the data signal of the acquisition belt present a complete waveform. Therefore, the data collector needs to have the characteristics of high range and high precision, and needs to add current mutual inductance and voltage mutual inductance. The current mutual inductance device adopts an integrated mutual inductance, and the selected one is TS24D43-100A / 30mA. This type of mutual inductance device has a wide measurement range and high precision. The voltage transformer adopts ZWQ281A, which has a high conversion accuracy.

[0087] After the collected data is processed by the above two mutual inductors, a sampling value sequence is obtained. There are 8 channels in total, and each channel has 8 bytes, which represents the valid data and quality bits of the sampling value and determines the validity of the channel. The design of the data collector is now completed.

[0088] Step S500 analyzes the current state of the high-voltage switchgear based on the reported data set, specifically including:

[0089] The remote data processing center extracts the device status parameter feature vector based on the reported data set;

[0090] After normalizing the feature vector, the device status type is encoded;

[0091] Train the neural network initialized with the input vector and the corresponding state type encoding input;

[0092] When the confidence level of the state monitoring result output by the neural network is greater than a threshold, the current fault type code and the fault type of the high-voltage switchgear are obtained.

[0093] When the confidence of the state monitoring result output by the neural network is less than a threshold, after determining the fault type of the state monitoring result output by the neural network through the expert system, the state monitoring result output by the neural network is judged to be a new fault type;

[0094] Encoding the new fault type;

[0095] The new fault type is combined with the original sample to obtain a normalized vector, and the neural network is retrained until the confidence of the state monitoring result output by the neural network is greater than a threshold.

[0096] Next, the model judgment process of possible different types of fault judgment processes is briefly explained through several different embodiments.

[0097] (1) Mechanical failure

[0098] By superimposing and comparing the collected data, conducting analogical analysis on a large amount of collected data according to different classifications such as manufacturer, model, production date, etc., organizing a large database of mechanical properties, studying related mechanical property analysis algorithms, evaluating the mechanical properties of high-voltage switchgear, and judging its state change trend.

[0099] Neural network algorithms can be used to diagnose circuit breaker faults. The radial basis function (RBF) neural network consists of three layers. The input layer nodes only play a role in signal transmission, that is, transmitting the input signal to the hidden layer. The hidden layer nodes are composed of action functions, and the output layer nodes are usually simple linear functions. The action function (basis function) in the hidden layer node of the radial basis function network will respond locally to the input signal.

[0100] (2) Partial discharge deep learning diagnosis,

[0101] Based on the accumulated massive data, a big data partial discharge deep learning diagnosis algorithm is developed. Deep learning uses multiple nonlinear transformations to perform multi-layer abstraction on data. It can learn to identify features, concepts and categories by itself and form a decision network. The deep learning method is used to effectively improve the analysis efficiency and diagnostic accuracy of unstructured image data. The system uses image pattern recognition technology to realize the time domain waveform and spectrum diagram of partial discharge number and the identification of fault type.

[0102] (3) Image recognition

[0103] By utilizing intelligent analysis algorithms and taking big data mining as the basis, deep learning image recognition technology is applied to partial discharge spectrum recognition, etc., to achieve automatic capture, comprehensive analysis, automatic early warning, and intelligent diagnosis of massive multi-source data, improve the intelligence level of equipment status evaluation and diagnosis, strengthen equipment status management and control, and optimize equipment operation and maintenance strategies.

[0104] The above principles can be found in the prior art documents as follows, which are introduced as part of the embodiments of this application:

[0105] [1] Zhang Junmin, Yin Junhua, Jiang Xiaoxu. Application of high voltage switchgear status assessment technology based on RBF network in intelligent GIS[J]. High Voltage Electrical Equipment, 2013, 49(3);

[0106] [2] Huang Jian, Hu Xiaoguang, Gong Yunan, et al. Design of expert system for mechanical fault diagnosis of high voltage circuit breakers[J]. Journal of Electric Machines and Control, 2011, 15(10): 43-49.

[0107] [3] Bai Shijun, Zeng Lincui, Zhang Yongqiang, et al. Intelligent GIS with remote expert self-diagnosis function[J]. Smart Grid, 2017, 5(01): 75-83.

[0108] The above method can be implemented by a pseudo code language process in the form of a computer program language, such as Figure 3 and Figure 4 A schematic diagram of an embodiment of a computer flow chart of data acquisition and processing of a remote monitoring method for high-voltage switchgear is provided, and the implementation process is as follows:

[0109] S11: Obtain n state monitoring data {d1, d2, ……, dn} at time t1 according to the preset acquisition period T; Let i = 1;

[0110] S12: Judge whether |d i - d i base | < Dd i holds, where d i is the value of the i-th state monitoring data, d ibase is the normal standard value of the i-th state monitoring data value, and Dd i is the preset comparison value of the i-th state monitoring data value; If so, go to step S13; Otherwise, go to step S14;

[0111] S13: Add d i to the state monitoring data set Sett1 corresponding to time t1;

[0112] S14: i = i + 1, judge whether i > n holds, if so, go to step S15; Otherwise, return to step S12;

[0113] S15: Obtain the state monitoring data set Sett1 corresponding to time t1;

[0114] When reaching time t2, that is, t2 = T + t1, execute the following steps:

[0115] S21: Obtain n state monitoring data {D1, D2, ……, Dn} at time t2 according to the preset acquisition period T; Let i = 1;

[0116] S22: Judge whether |D i - d i base | < Dd i holds, where D i is the value of the i-th state monitoring data, d ibase is the normal standard value of the i-th state monitoring data value, and Dd i is the preset comparison value of the i-th state monitoring data value; If so, go to step S23; Otherwise, go to step S24;

[0117] S23: Add D i to the state monitoring data set Sett2 corresponding to time t2;

[0118] S24: i = i + 1, judge whether i > n holds, if so, go to step S25; Otherwise, return to step S22;

[0119] S25: Obtain the state monitoring data set Sett2 corresponding to time t2;

[0120] Next, perform the set element judgment and the adjustment process of the preset acquisition period:

[0121] S3: If both the state monitoring data sets Sett1 and Sett2 are empty sets, increase the preset sampling period T, and return to step S11( Figure 3 also represents the branch of returning to step S21);

[0122] If the condition monitoring data sets Sett1 and Sett2 are not both empty sets, determine whether only one of Sett1 and Sett2 is an empty set (i.e., the other is not an empty set);

[0123] If yes, then reduce the preset acquisition period T and return to step S11 ( Figure 3 The branch returning to step S21 is also represented);

[0124] If both the condition monitoring data sets Sett1 and Sett2 are not empty sets, and more than one of them is an empty set, then proceed to step S4 (i.e., if both the condition monitoring data sets Sett1 and Sett2 are not non-empty sets, then proceed to step S4);

[0125] S4: determine the intersection of Sett1 and Sett2, Set12;

[0126] S5: If the intersection Set12 is empty, then obtain the factory test data, daily maintenance data, and routine test data of the high-voltage switchgear, calibrate the elements of Sett1 and / or Sett2, and then proceed to step S6;

[0127] Otherwise, go directly to step S6;

[0128] S6: Sending a data reporting signal;

[0129] S7: reserve data reporting channel resources;

[0130] S8: Report Sett1 and Sett2;

[0131] S9: Analyze the current status of high-voltage switchgear.

[0132] Figure 3 and Figure 4 Combined understanding, it can be seen that all the above steps are included. In addition, the specific implementation principles of the above steps can be referred to the above example introduction.

[0133] For example, step S6 specifically includes: determining the intersection of the first state monitoring data set and the second state monitoring data set, and sending the data reporting signal to the remote data processing center, wherein the data reporting signal includes the number of elements K1 of the first state monitoring data set, the number of elements K2 of the second state monitoring data set, and the number of elements K of the intersection;

[0134] Step S7 specifically includes: after receiving the data reporting signal, reserving data reporting channel resources;

[0135] Step S8 specifically includes: reporting the first state monitoring data set and the second state monitoring data set within a predetermined time period Yt after the data reporting signal is sent.

[0136] It can be seen that the reporting signal of this process does not include specific set element values, but only reports simple data such as the number of elements K1 of the first status monitoring data set, the number of elements K2 of the second status monitoring data set, and the number of elements K of the intersection. Therefore, it will not cause delay in uploading the reporting signal; based on the data included in the reporting signal, the remote data processing center determines the size of the reserved data reporting channel resources for the local edge data processing unit after receiving the data reporting signal.

[0137] For example, the broadband resources required for data uploading may be reserved based on the number K1 of elements in the first status monitoring data set, the number K2 of elements in the second status monitoring data set, and the number K of elements in the intersection.

[0138] The size of the broadband resource M may be determined based on the number K1 of elements of the first state monitoring data set, the number K2 of elements of the second state monitoring data set, and the number K of elements of the intersection.

[0139] For example, assuming that the average size of the elements of the condition monitoring data set is x bytes, and the predetermined time period Yt is set to y seconds, the minimum data upload speed V that the broadband resource M must ensure is determined as follows:

[0140]

[0141] Of course, when the broadband resources are sufficient, the size of the broadband resources M may be appropriately increased.

[0142] The following table lists the comparative data of the fault warning feedback time of the high-voltage equipment before and after the application of the technical solution of the present application in a certain distribution station:

[0143]

[0144]

[0145] It can be seen that in actual applications, the technical solution of the present invention can significantly reduce the delay in reporting of switchgear status monitoring data, and can make timely predictions and take corresponding maintenance measures when possible abnormal situations occur, thereby improving the real-time status monitoring of high-voltage switchgear and the integrity of data collection.

[0146] The foregoing has shown and described the method embodiments and system of the present invention, but it is understandable to those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

[0147] The above method of the present invention can be automatically executed by an electronic device through a computer program, the computer program is stored in a computer-readable storage medium, and the computer program can be executed by at least one processor to enable the at least one processor to perform the steps of the method.

[0148] Therefore, after the embodiment in the form of method flow is elaborated in detail, the corresponding embodiment in the form of system function architecture does not need to be repeated again, but those skilled in the art can directly and unambiguously determine that the embodiment in the form of system function architecture also corresponds to all the contents of the embodiment in the form of method flow. The same is true for the embodiment in the form of computer program code pseudo language.

Claims

1. A remote monitoring method for high voltage switchgear, It is characterized in that The method comprises the following steps: Acquire a first state monitoring data set of the high-voltage switchgear at a first moment and a second state monitoring data set of the high-voltage switchgear at a second moment; determining an intersection of a first condition monitoring data set and a second condition monitoring data set; When the intersection is not empty, sending a data reporting signal; Constructing a reporting data set based on the first condition monitoring data set and the second condition monitoring data set and reporting the data set; Analyzing the current state of the high-voltage switchgear based on the reported data set; The high-voltage switchgear obtains a variety of state monitoring data through a combined sensor unit according to a preset collection cycle, and each state monitoring data corresponds to a standard reference value; When the difference between a certain state monitoring data obtained at a certain moment and the corresponding standard reference value exceeds a preset range, the state monitoring data is added to the state monitoring data set corresponding to the moment; The high-voltage switchgear determines the intersection of the first state monitoring data set and the second state monitoring data set through the local edge data processing unit, and sends the data reporting signal to the remote data processing center, wherein the data reporting signal includes the number of elements K1 of the first state monitoring data set, the number of elements K2 of the second state monitoring data set, and the number of elements K of the intersection; After receiving the data reporting signal, the remote data processing center reserves data reporting channel resources for the local edge data processing unit; Based on the number of elements K1 of the first state monitoring data set, the number of elements K2 of the second state monitoring data set, and the number of elements K of the intersection, the broadband resources required for data upload are reserved. The size of the broadband resource M is determined based on the number K1 of elements of the first state monitoring data set, the number K2 of elements of the second state monitoring data set, and the number K of elements of the intersection; The minimum data upload speed V that the broadband resource M must ensure is determined as follows: ； Wherein, the average size of an element of the condition monitoring data set is x bytes, and the predetermined time period Yt is set to y seconds; The local edge data processing unit reports the first status monitoring data set and the second status monitoring data set through the reserved data reporting channel resources; After obtaining a first state monitoring data set of the high-voltage switchgear at a first moment and a second state monitoring data set at a second moment, and before determining an intersection of the first state monitoring data set and the second state monitoring data set, the method includes: If both the first state monitoring data set and the second state monitoring data set are empty sets, increasing the preset acquisition period; If only one of the first state monitoring data set and the second state monitoring data set is an empty set, reducing the preset collection period; When both the first state monitoring data set and the second state monitoring data set are not empty, but the intersection of the first state monitoring data set and the second state monitoring data set is empty, the method includes: obtaining factory test data, daily maintenance data, and routine test data of the high-voltage switchgear; The elements in the first condition monitoring data set and / or the second condition monitoring data set are corrected based on the factory test data, the daily maintenance data, and the routine test data.

2. A remote monitoring method for high voltage switchgear according to claim 1, Features: The combined sensor unit includes a current mutual induction device and a voltage mutual induction device; The current mutual induction device and the voltage mutual induction device mutually measure the same state signal.

3. A remote monitoring method for high voltage switchgear according to claim 2, Features: The combined sensor unit comprises an SF6 gas online monitoring unit, a partial discharge online monitoring unit, an infrared temperature measurement unit, a lightning arrester online monitoring unit and a mechanical state online monitoring unit.

4. A remote monitoring method for high voltage switchgear according to claim 2, Features: Based on the reported data set, the current status of the high-voltage switchgear is analyzed, specifically including: The remote data processing center extracts the device status parameter feature vector based on the reported data set; After normalizing the feature vector, the device status type is encoded; Train the neural network initialized with the input vector and the corresponding state type encoding input; When the confidence level of the state monitoring result output by the neural network is greater than a threshold, the current fault type code and the fault type of the high-voltage switchgear are obtained.

5. A remote monitoring method for high voltage switchgear according to claim 4, Features: When the confidence of the state monitoring result output by the neural network is less than a threshold, after determining the fault type of the state monitoring result output by the neural network through the expert system, the state monitoring result output by the neural network is judged to be a new fault type; Encoding the new fault type; The new fault type is combined with the original sample to obtain a normalized vector, and the neural network is retrained until the confidence of the state monitoring result output by the neural network is greater than a threshold.

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