A passive wireless intelligent on-line monitoring multi-module system for substation lightning arresters

Abstract

The present invention discloses a passive wireless intelligent on-line monitoring multi-module system for substation lightning arresters, which includes a hardware-based data sensing and acquisition technology module, an information-based data transmission network technology module, a software-based intelligent data processing technology module, and other subsequent expandable technology modules. A dedicated data processing model is constructed in the data center to perform digital and intelligent algorithm processing on the sensing data signals of the substation lightning arresters, and digitally monitor the operating status of the substation lightning arresters. The present invention can monitor the working and operating status of the lightning arresters in real time, realize efficient, accurate and real-time monitoring of the lightning arrester status, and is of great significance for improving the reliability and security of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of substation intelligentization, and in particular to a passive wireless intelligent substation lightning arrester online monitoring multi-module system and its application. Background Art

[0002] With the rapid development of the economy, the demand for electricity is increasing, resulting in a continuous growth of newly built substations in recent years. This has directly led to a rapid increase in the workload of monitoring lightning arresters in the station, and it is necessary to monitor and maintain numerous lightning arresters in multiple substations.

[0003] Metal oxide arresters (referred to as MOA) have been widely used in the power system due to their excellent protection performance, mature valve discs and manufacturing process quality. The health status of substation lightning arresters is crucial for ensuring the stable operation of the power grid. Especially for metal oxide arresters (MOA), they play an indispensable role by limiting overvoltage and protecting other power equipment from high voltage impacts such as lightning strikes and switching operations. However, the damage or performance degradation of lightning arresters may not be easily detected in a timely manner, thereby increasing the risk of power grid failures. For the safe operation of the power grid, a high-voltage insulation test needs to be carried out once a year according to the requirements of the high-voltage equipment operation regulations to facilitate the early fault diagnosis of MOA. In recent years, with the needs of economic and cultural development, the requirements for the power supply reliability and power quality of the power grid have been continuously improved. It is very difficult to carry out a power outage test once a year, especially for high-voltage lines, resulting in an increasingly prominent contradiction between the power supply reliability of the power grid and the safe operation of lightning arresters.

[0004] Currently, the method adopted for online monitoring of MOA in on-site operation is based on the installed leakage current meter and periodic tests according to the regulations. For this method, it is necessary for the on-duty operation personnel to check regularly, which can no longer meet the real-time requirements of power grid operation; at the same time, the existing inspection methods cannot fully and truly reflect the inherent characteristics of lightning arresters. Also, since periodic tests require an application for power outage, this will inevitably increase the non-fault power outage time of substation equipment.

[0005] It can be seen that various monitoring means of the prior art have certain limitations: on the one hand, the existing monitoring technical means are periodic, manual, on-site, subjective rather than data-based and intelligent; on the other hand, power outage maintenance is required, and real-time online reliable monitoring and early fault diagnosis of the operating state of lightning arresters cannot be achieved, the requirement of passive wireless remote transmission of lightning arresters cannot be achieved, and the needs and development of modern digital substations cannot be adapted.

[0006] On this basis, and also based on the development of informatization, dataization, and intelligent technologies in recent years, we seek to fully utilize modern monitoring means and artificial intelligence algorithms to develop an on-line monitoring system for arresters suitable for intelligent substations, realizing automatic, real-time, wireless, and passive on-line monitoring of all arresters. This is of great significance for improving production efficiency and ensuring the reliable operation of the power system. After successful development, it can be promoted and used throughout the system, converting preventive maintenance of arresters to condition-based maintenance, significantly improving the efficiency of substation maintenance and operation and maintenance work. At the same time, it is highly consistent with the development strategy and technical route of the State Grid regarding the intelligent construction of substations. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a passive wireless intelligent on-line monitoring multi-module system for arresters in substations. Through the multi-module technology of combining software and hardware, especially the development of a dedicated data processing model and its monitoring algorithm, the dataization and intelligent transformation of the monitoring and operation and maintenance of arresters in substations are carried out.

[0008] To solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0009] A passive wireless intelligent on-line monitoring multi-module system for arresters in substations includes a hardware-based data sensing and acquisition technology module, an informatization data transmission network technology module, a software-based intelligent data processing technology module, and other subsequent expandable technology modules; this data system uses a digital hardware data acquisition device module and an informatization data network to achieve passive wireless digital on-line real-time monitoring of arresters in substations, and transmits the monitored digital signals wirelessly to the data center for summary and structured data storage; at the same time, a dedicated data processing model is constructed in the data center to perform dataization and intelligent algorithm processing on the sensor data signals of arresters in substations, and to perform dataization monitoring on the operating status of arresters in substations.

[0010] As a preferred technical solution of the present invention, the digital hardware data acquisition device module includes a digital current sensor for measuring the static leakage current of the arrester, using a through-core high-precision current transformer, and cooperatively using a high-precision timing chip, with the high-precision I2C real-time clock RTC as the underlying technology and integrating a temperature-compensated crystal oscillator and a crystal; at the same time, an additional energy input terminal is constructed so that long-term accurate timing is still supported when the main power supply is disconnected.

[0011] As a preferred technical solution of the present invention, for the digital hardware data acquisition device module, a new power supply route bypass is constructed and the leakage current of the arrester is used as the main power supply of the hardware module, without using a storage battery or a storage battery as an auxiliary backup external power supply.

[0012] As a preferred technical solution of the present invention, the information data network in the information data transmission network technology module adopts a wireless long-distance communication architecture, and the digital signal of the hardware module is imported into the data center for storage and subsequent data algorithm processing through the Zigbee module of the communication repeater, the subsequent GPRS module and the remote host computer DTU module.

[0013] As a preferred technical solution of the present invention, in the information data transmission network technology module, an additional data path connected to the personal communication terminal is constructed and the mobile phone number is used as an identification mark to forward the lightning arrester and its sensor data or other data information in the form of text messages to the associated personal communication terminal; the information presentation form includes short messages or text push based on a dedicated App.

[0014] As a preferred technical solution of the present invention, the intelligent data processing technology module constructs a dedicated data processing model in a remote data center. The dedicated data processing model abandons the original resistive current separation data processing path based on phase difference, constructs a new linear time series data framework, and uses the time interval as the basic basis element of linear representation. At the same time, based on the harmonic characteristics of capacitive current and resistive current in the leakage current, the linear basis coefficient sequence of irrelevant current phase difference and resistive current separation is obtained to reconstruct the linear data representation of the full current time series data of the lightning arrester leakage.

[0015] As a preferred technical solution of the present invention, further, the digitized and intelligent algorithm processing is constructed based on the dedicated data processing model, and through the adjustment of the basic element in the linear representation deconstruction of the data processing model, namely the time interval, the total leakage current of the lightning arrester is fitted and a multi-sample data comparison based on threshold setting and over-threshold reminders and alarms are performed with the measured values, so as to carry out digitized monitoring of the real-time operating status of the substation lightning arrester.

[0016] As a preferred technical solution of the present invention, the main algorithm framework of the digitized and intelligent algorithm is: based on the linear data representation reconstruction of the arrester leakage full current time series data, the various coefficients of the linear representation of the arrester equipment leakage current in the fixed data processing model, including the numerical values of each coefficient and their arrangement order, and then transforming the basis of the linear representation and the time interval relative to the initial state, wherein the initial state corresponds to the initial measurement state of the newly installed substation arrester equipment or the newly repaired and maintained substation arrester equipment, and the equipment at this time is generally considered to be normal and accurate; the adjustment and transformation of the basis corresponds to being able to obtain a series of fitted full current data corresponding to different time intervals, based on the linear comparison and statistical comparison of the fitted data and the actual measurement data and the preset threshold standard, when the deviation between the two exceeds a certain range, such as exceeding the threshold, it indicates that there may be equipment abnormality.

[0017] As a preferred technical solution of the present invention, the lightning arrester is a metal oxide arrester MOA; the digital current sensor is designed to measure the static leakage current of the lightning arrester under operating conditions or the static leakage current of the lightning arrester under artificially set conditions, where the artificially set conditions include specific artificial-operation conditions constructed by artificial intervention under operating conditions.

[0018] As a preferred technical solution of the present invention, the leakage current includes passive leakage current and actively constructed leakage current environment; the actively constructed leakage current environment uses a standardized input source with a known pattern.

[0019] As a preferred technical solution of the present invention, the data system is compatible with adding an environmental electromagnetic interference shielding system as other subsequent expandable technical modules; thereby reducing environmental electromagnetic disturbances and improving the accuracy of data acquisition and subsequent data processing.

[0020] As a preferred technical solution of the present invention, the data system is compatible with adding an environmental electromagnetic interference metering device system as other subsequent expandable technical modules. At this time, based on the dedicated data processing model in the original data system, the noise interference data of environmental electromagnetism is eliminated through a compensation algorithm, improving the accuracy of data acquisition and subsequent data processing.

[0021] As a preferred technical solution of the present invention, in the application scenario, according to the effectiveness (too sensitive or dull and missed detection) of the lightning arrester anomaly reminder or warning, and the change of environmental parameters, multi-segment interval sampling can be performed to construct multiple groups of initial data sets, and on different initial data sets (taking the total leakage current of MOA as the data object, see the design part of the data source structure), the above data processing model is run for multi-model parallel data monitoring, and then a secondary threshold is established based on the abnormal data ratio in the multi-model, and through the adjustment of the secondary threshold (when it is allergic, increase the abnormal ratio threshold of the multi-model, up to 100%; when it is dull, reduce the abnormal ratio, at least as the alarm data output by any model), thereby further improving the accuracy and precision of the abnormal detection data.

[0022] The beneficial effects produced by adopting the above technical solutions are as follows: The source wireless intelligent on-line monitoring multi-module system for substation lightning arresters developed by the present invention can monitor the working and operating states of lightning arresters in real time, realize efficient, accurate and real-time monitoring of the lightning arrester states, and is of great significance for improving the reliability and safety of the power grid.

[0023] The hardware-based data sensing and acquisition technology module of the present invention uses a through-core high-precision current transformer and a high-precision timing chip to achieve accurate measurement of the static leakage current of the lightning arrester; and the new technical attempt to build a new power supply route can use the leakage current of the lightning arrester as the main power source, which has innovation and practical value.

[0024] The information-based data transmission network technology module of the present invention adopts a wireless long-distance communication architecture, realizes the wireless transmission of lightning arrester digital sensing signals, and provides an information network foundation for the on-line intelligent monitoring of MOA devices. At the same time, we also innovatively propose to directly send the monitoring data to the associated personal communication terminal. This technological innovation has high convenience in practical aspects and can significantly improve the collaboration of different operators such as the internal information center of the power grid company and the substation site; it improves the efficiency of information transmission.

[0025] Particularly importantly, the software-based intelligent data processing technology module of the present invention constructs a dedicated data processing model in the remote data center. This model uses a linear time series data framework and constructs a linear basis and its coefficient sequence based on the harmonic characteristics of the leakage current and modelizes the operation data of the lightning arrester. And corresponding intelligent data algorithms are developed on the new data model structure, realizing the efficient and accurate processing of the time series data of the total leakage current of the lightning arrester. In terms of the intelligent data processing technology module, we introduce a dedicated data processing model and abandon the traditional data processing path for separating resistive current based on phase difference. Instead, a new linear time series data framework is constructed, which can more accurately reconstruct the time series data of the total leakage current of the lightning arrester. Based on this new data model and data processing method, on the basis of processing the time series data of the total leakage current of the lightning arrester, the real-time monitoring of the operation state of the lightning arrester and the reminder and early warning of possible faults are realized through algorithm processing.

[0026] At the same time, we also propose some subsequent expandable technology modules, such as cross-selection and comparison under different operating conditions, as well as environmental electromagnetic interference shielding systems and metering device systems, etc., all of which have certain additional technical application values. Brief Description of the Drawings

[0027] Figure 1 It is the principle framework diagram of the present invention; among them, the intelligent data processing technology module, as the core of the intelligent technology, includes the construction of a dedicated data processing model for the total current of the lightning arrester and the derivation of detection algorithms. Detailed Embodiments

[0028] The following embodiments illustrate the present invention in detail. The reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of this application. Thus, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having" and their variants mean "including but not limited to", unless otherwise specifically emphasized. In the description of the following embodiments, specific details such as specific system structures, technologies etc. are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of this application. However, those skilled in the art should clearly understand that this application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details from interfering with the description of this application. It should be understood that when used in the specification of this application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations. It should also be understood that the term " / and" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] Embodiment 1

[0030] The signal acquisition technology is an important technical unit of the intelligent on-line monitoring system of substation lightning arresters in this research. In terms of the hardware acquisition of the electrical signals of lightning arresters, since the leakage current of metal oxide arresters (MOA) is in the microampere level, how to acquire the signals is the key point of the acquisition technology. It is planned to use a through-core high-precision current transformer for acquisition; in addition, for such weak current signals, other relevant modules of the system also need to be matched with high precision.

[0031] The research on the clock module with high-precision matching is also a sub-unit of the system development of this item. The design uses a high-precision timing chip DS3231. DS3231 is based on a high-precision I2C real-time clock (RTC) and has an integrated temperature-compensated crystal oscillator and crystal. The integrated crystal oscillator improves the long-term accuracy of the device and reduces the number of components on the production line. In addition, an additional input terminal is constructed and set on this device, and accurate timing can still be maintained when the main power supply is disconnected; in terms of parameter formulation, the commercial and industrial temperature ranges are defined.

[0032] The current sensor is designed to measure the static leakage current of the lightning arrester under operating conditions or the static leakage current of the lightning arrester under artificially set conditions, where the artificially set conditions include specific artificial - operating conditions constructed by artificial intervention under operating conditions. The leakage current includes passive leakage current and an actively constructed leakage current environment, and the actively constructed leakage current environment uses a standardized input source with a known pattern. In addition, this study also designs and installs an environmental electromagnetic interference shielding system and an environmental electromagnetic interference metering device, etc. as other subsequent expandable technologies. The former can reduce environmental electromagnetic disturbances and improve the accuracy of data acquisition and subsequent data processing, and the latter can eliminate the noise interference data of environmental electromagnetic through a compensation algorithm based on the dedicated data processing model in the original data system.

[0033] Example 2

[0034] Power - free power supply is an inspired technological innovation point we proposed. In fact, it utilizes the leakage current of the lightning arrester to provide energy, thus avoiding the maintenance problems in use caused by battery replacement required for battery - powered and external power - supply methods; external battery power supply is only used as an auxiliary backup. Although this technological innovation does not have a very high level of depth and difficulty and is a very simple technological improvement point, it has very good application value.

[0035] Example 3

[0036] Since the sensing and acquisition signals such as the leakage current of the lightning arrester are mainly in the form of text data and the data scale is not very large, in the research and development of this intelligent on - line monitoring system for substation lightning arresters, the design of the wireless long - distance communication architecture focuses on the stability of the communication network. The information of the lightning arrester (static and dynamic leakage current, number of operations and time, other environmental monitoring information, etc.) is transmitted to the Zigbee module of the communication repeater through the Zigbee module. The communication repeater sends the received data to the DTU module at the remote upper computer end through the GPRS module. The DTU transmits the data to the upper computer through the 485 signal line and then to the data processing center. In the data processing center, the data is structurally stored and the data model is built through the local server or cloud server, and the intelligent monitoring algorithm is constructed and embedded.

[0037] Example 4

[0038] As a simple and innovative sub-item of this research, we propose that by adding a specified mobile phone number to the online monitoring system of lightning arresters, the sensing monitoring data of lightning arresters and the result data processed by the subsequent data center can be timely and reliably forwarded to this mobile phone in the form of text messages. Through mobile phone text messages or the Lightning Arrester Online Monitoring System App constructed adaptively, the corresponding detailed data information can also be conveniently viewed. This technological innovation is not complex in terms of technical implementation, but it has practical convenience and can significantly improve the coordination among different operators such as the internal information center of the power grid company and the substation site.

[0039] Example 5

[0040] The intelligent technology core of the intelligent online monitoring system for substation lightning arresters in this research lies in the construction of the data center. In addition to relatively basic data storage, output and display of data processing results, etc., the key lies in the analysis and processing (modeling) of input data signals such as the leakage current of lightning arresters, and the intelligent detection algorithms further constructed based on the data processing model. Through the joint efforts of various technical resources and data expert resources related to the project, the highly data-driven and intelligent model development and algorithm implementation have been completed.

[0041] Among them, the developed data processing model abandons the original data processing path for separating resistive current based on phase difference, constructs a new linear time-series data framework, uses the time interval as the basic base element for linear representation, and at the same time obtains the linear base coefficient sequence for separating the irrelevant current phase difference and resistive current based on the harmonic characteristics of capacitive current and resistive current in the leakage current, and conducts the linear data representation reconstruction of the time-series data of the total leakage current of the lightning arrester.

[0042] Furthermore, based on the linear reconstruction data processing model of the total leakage current time series data, a data-driven and intelligent monitoring algorithm is further constructed. By adjusting the base element, i.e., the time interval, in the linear representation decomposition of the data processing model, the total leakage current of the lightning arrester is fitted and compared with the measured value through multi-sample data comparison and over-threshold reminder and alarm based on threshold setting, and the data-driven monitoring of the real-time operation status of the substation lightning arrester is carried out.

[0043] Example 6

[0044] Regarding the data processing model therein, it abandons the original data processing path for resistive current separation based on phase difference and constructs a brand-new linear time-series data framework; based on the harmonic characteristics of capacitive current and resistive current in the leakage current of metal oxide arrester (MOA), taking the time interval as the basic base element for linear representation, and at the same time obtaining the linear base coefficient sequence for separating the irrelevant current phase difference and resistive current based on the harmonic characteristics of capacitive current and resistive current in the leakage current, to perform the linear data representation reconstruction of the MOA leakage full-current time-series data. In the data processing model, the linear data representation reconstruction of the MOA leakage full-current time-series data includes two optional data paths: continuous or discrete; the two are combined to design a unified data structure framework, specifically including:

[0045] The data source structure of the data processing model: taking the leakage full current of MOA as the data object, and the initial full current data acquisition is limited to: newly installed MOA equipment in a substation or MOA equipment in a substation that has just been overhauled and calibrated. At this time, it is considered that the equipment is in an accurate normal operation state. Data is collected on these equipment through a leakage current meter. The discrete full current data table collected on the leakage current meter is used as the initial data set (in the discrete data path), or the discrete full current data collected on the leakage current meter is fitted by interpolation method as a function of time t, i.e., ia(t) (in the continuous data path). This ia(t) constitutes a one-dimensional function of a local time, and this function is considered as the representation function based on the full current data when the equipment is normal, and has predictability under data linear representation based on the harmonic characteristics of capacitive current and resistive current in the leakage current;

[0046] Taking the continuous data path as an example, in the data source structure: discrete full-current data on the leakage current meter of the substation MOA device is collected. For newly installed or recently overhauled MOA devices, data is collected under normal operating conditions. Then, the discrete data is interpolated and fitted into a continuous time function. Inside the data processing model, this function is named i(t) based on its foundation. Among them, for the leakage current function i(t) obtained from a single specific measurement and its continuous fitting, different functions are distinguished by letter identifiers inside the data processing model, such as ia(t) and ib(t). For specific functions obtained by fitting the data of the same measurement using different methods, they are distinguished by secondary letter identifiers, such as iaa(t) and iab(t). In addition, two independent parameters t1 and t2 can be set to respectively represent the time interval and sampling duration of the discrete full-current data collection on the leakage current meter of the substation MOA device. Within the optional value ranges of the independent parameters t1 and t2, t1 and t2 respectively pursue the goals of minimizing and maximizing data, and each corresponds to the detailing of the leakage current waveform and the expansion of the sample data scale. The satisfaction of the data pursuit goals is determined through comprehensive consideration according to the actual situation. Factors that can be considered include: the effectiveness of the final data output (such as the credibility of the arrester monitoring alarm data) and the consumption of system resources (not being too large), etc.

[0047] Algorithm Structure of Data Processing Model: Based on the data source structure, two data processing paths, namely direct discretization or fitting continuousization, can be adopted. Regardless of the data path, in order to realize the linear data representation reconstruction of predictable full-current time-series data, the following algorithm model is embedded in the data processing model: For the linear deconstruction and predictable linear representation of the obtained initial data set above, based on the time-series characteristics of the initial data set, which represents the data performance of the leakage current of the lightning arrester changing with time during normal operation, specifically a continuous current data curve or a discrete point broken line, collectively referred to as a curve, a new algorithm with a linear representation structure is used to describe this curve so that the leakage current data at any given time point can be obtained; For this purpose, the algorithm model first examines the change rate of the above current curve at a certain point, thereby obtaining a data representation parameter that shows how the leakage current curve of the initially measured MOA device is inclined at the inspection point; Progressively, continue to examine how this inclination changes, that is, the degree of bending of the measured normal MOA device leakage current curve, thereby obtaining a second data representation parameter that carries the degree of bending of the normal leakage current curve; And so on, perform several iterations to obtain successively progressive multi-level representation parameters; Then the combination of the multi-level representation parameters and the time interval contains all the information of the MOA device current leakage data, and becomes more and more refined as the number of representation parameters increases; At the same time, the multi-level representation parameters and the time interval are reconstructed into a linear combination data relationship (the time interval is constructed as the basis of the linear representation and the multi-level representation parameters are the coefficients of each basis), such a data structure directly leads to the linear data representation reconstruction of the MOA leakage full-current time-series data, and on the linear structure phenotype, the optimization of its basis and its coefficients corresponds to the data accuracy and data concentration of the leakage current data in the corresponding mapping section, and the adjustability of the linear basis corresponds to the predictability of the data.

[0048] Similarly, taking the continuous data path as an example, perform sequential sub-data structure analysis in the algorithm structure. The following is a specific data process:

[0049] Select the starting point: In the time locally continuous function ia(t) obtained under the continuous data path in the data source structure, arbitrarily select a specific time point t0 as the starting point, and use this point as the reference point for the progressive expansion of each level of representation parameters;

[0050] Calculate the change: Calculate the change rate of the ia(t) function curve at the reference point to obtain a representation parameter that carries the inclination degree of the leakage current curve of the MOA device at this point;

[0051] Calculate the change of the change: Continue to calculate the change of the change rate of the ia(t) function curve at the starting point to obtain a representation parameter that shows how the degree of bending of the curve at this point changes;

[0052] Progressive iteration: Repeat the above data process to calculate higher-order characterization parameters, which successively carry more subtle information about the shape of the leakage current curve in the ia(t) function until the set number of iterations or the maximum possible accuracy of the system. Here, based on the design form of the data source structure: First, for a given initial data set, the above-mentioned characterization parameters at all levels can be directly valued. Second, the above-mentioned characterization parameters at all levels correspond to the same inspection point (generally set as the initial zero point moment when collecting and measuring the leakage current data of newly installed or newly overhauled MOA devices during the acquisition of the initial data set). Third, at the same time, the above progressive algorithm makes the characterization parameters at all levels at the same inspection point, such as the initial zero point moment, already contain the leakage current data information at a farther moment and have clear predictability based on the physical harmonic characteristics of the leakage current.

[0053] Linear combination: Set the basis as the time interval and its deformations, and the coefficients of each basis are constructed from the characterization parameters of each order of ia(t) and their deformations obtained by the above progressive iteration. On this data structure, an equivalent linear decomposition of ia(t) is performed.

[0054] Joint optimization of the characterization parameters and the basis of the linear combination: For the basis and coefficients of the linear representation, the most direct expectation is a global first-order linear form, such as all bases being equal and being the selected time interval, and the coefficients of each basis being the characterization parameters at all levels. However, if the original forms of the time interval and the characterization parameters are directly used, there are progressive deviations from the real data in both theoretical calculations and simulation verifications. Similarly, based on theoretical and simulation verifications, the following data pattern is designed, and the joint optimization of the characterization parameters and the basis in the linear combination is carried out according to it to achieve a linear combination representation without deviation: The deformation space of the basis is set to the nth power of the time interval, where n is a natural number and is consistent with the number of progressive iterations or, in other words, with the level of the characterization parameters. The coefficients are constructed from the characterization parameters at all levels, and their deformation space is n is a natural number and is consistent with the number of progressive iterations or, in other words, with the level of the characterization parameters. At this time, the optimized linear decomposition form makes the first few terms of the linear representation have both a simple linear structure and be the main part of the function value of ia(t) and be representative, so that the number of terms in the linear decomposition can be reduced without reducing the accuracy of numerical analysis.

[0055] The above mainly takes the continuous path as an example. For the discretized data path, in fact, it can implement data based on the same data structure framework as that under the continuous path. The continuous data path constructs a continuous function i(t) through interpolation fitting, and then linearly deconstructs i(t) based on various levels of characterization parameters. At the application end, it realizes intelligent monitoring by comparing the predictability of the linear characterization model with the measured full leakage current data of the MOA device at each sampling time point. Among them, during the data process, it continuousizes the discrete leakage current data of the MOA device, and finally still uses it for comparison with discrete data. Therefore, the discretized data path uses discrete data from the beginning, omits data processes such as interpolation fitting, and maintains the data accuracy at the application end based on equivalent linear basis coefficients. Thus, under the discretized data path, the continuous function i(t) becomes a discrete data table j(t). In the discrete data table j(t), different discrete t values respectively correspond to different leakage current values jt of the MOA device. For the analysis of the current curve of the continuous function i(t), although it cannot be directly carried out on the discrete data table, it can be based on its progressive analysis and data processing mode of curve change and use differential operation to replace differential operation, that is, obtain a similar linear expansion and linear characterization for the discrete data table j(t). And in the linear structure of the linear characterization, its basis is also the time interval, and the coefficients of each basis are composed of similar characterization parameters based on differences. In terms of data structure, data predictability, data concentration, and subsequent application end (data comparison and data monitoring between the predicted current value based on the adjustability of the linear basis and the measured leakage current value of the MOA device), the discrete data processing model of this architecture is equivalent to the data processing model under the continuous path. Both theory and data fitting experiments show that the discretized data processing model is not only feasible, but also has complete equivalence with the above-mentioned continuous data processing model, but usually consumes less system resources.

[0056] Example 7

[0057] Furthermore, regarding the construction of the arrester monitoring algorithm, in fact, based on the foundation of the data processing model, through relatively simple data linking and evolution derivation, the framework of the above intelligent monitoring algorithm and its algorithm path can be obtained. Generally speaking, the main algorithm framework of the data-based and intelligent algorithm is as follows: based on the linear data representation reconstruction of the full leakage current time series data of the arrester, fix each coefficient of the linear representation of the leakage current of the arrester device in the data processing model, including the numerical value and arrangement order of each coefficient, and then transform the basis of the linear representation and the time interval relative to the initial state, where the initial state corresponds to the initial measurement state of the newly installed substation arrester device or the newly repaired and maintained substation arrester device. At this time, the device is usually considered normal and accurate; the adjustment and transformation of the basis can obtain a series of fitted full current data corresponding to different time intervals. Based on the linear comparison, statistical comparison between the fitted data and the actual measurement data, and the preset threshold standard, when the deviation between the two exceeds a certain range, such as exceeding the threshold, it is prompted that there may be equipment abnormalities.

[0058] In the application scenario, according to the effectiveness of the abnormal reminder or warning (too sensitive or insensitive and missed detection), as well as the changes in environmental parameters, multi-segment interval sampling can be performed to construct multiple groups of initial data sets (taking the full leakage current of the MOA as the data object, see the design part of the data source structure), and the above data processing model can be run on different initial data sets for multi-model parallel data monitoring. Then, a secondary threshold is established based on the proportion of abnormal data in multiple models, and the secondary threshold is adjusted (increase the abnormal proportion threshold of multiple models when it is too sensitive, up to 100%; reduce the abnormal proportion when it is insensitive, down to the alarm data output by any model). Thereby, the accuracy and precision of the abnormal detection data are further improved.

[0059] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. In each embodiment, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, personal computers, smart phones, handheld single devices, floor-standing single devices, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memories, calculators, and controllers, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS networks, the World Wide Web, etc. In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms. The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A passive wireless intelligent on-line monitoring multi-module system for substation lightning arresters, comprising a hardware-based data sensing and acquisition technology module, an information-based data transmission network technology module, a software-based intelligent data processing technology module, and other subsequent expandable technology modules; characterized in that: The system uses data sensing acquisition technology modules and information data transmission network technology modules to realize passive wireless digital online real-time monitoring of substation lightning arresters, and transmits the monitored digital signals to the data center in a wireless manner for aggregation and structured data storage; at the same time, a data processing model is constructed in the data center, and its main algorithm framework is: based on the linear data representation reconstruction of the arrester leakage full current time series data, the various coefficients of the linear representation of the arrester equipment leakage current are fixed, including the values of each coefficient and their arrangement order, and then the basis of the linear representation and the time interval relative to the initial state are transformed, wherein the basis is the time interval, and the initial state corresponds to the initial measurement state of the newly installed substation lightning arrester equipment or the newly repaired and maintained substation lightning arrester equipment, and the equipment at this time is considered to be normal and accurate; the adjustment and transformation of the basis correspond to a series of fitted full current data corresponding to different time intervals, based on the linear comparison and statistical comparison of the fitted data and the real measurement data and the preset threshold standard, when the deviation between the two exceeds a certain range, it is prompted that there may be equipment abnormality; Further algorithm improvements are made in application scenarios. According to the effectiveness of abnormal reminders or warnings, including over-sensitivity or passivation missed detection, and changes in environmental parameters, multi-segment interval sampling is performed to construct multiple groups of initial data sets; the total leakage current of the lightning arrester is used as the data object, and the above data processing model is run on different initial data sets to perform multi-model parallel data monitoring, and then a secondary threshold is established based on the proportion of abnormal data in the multiple models, and the secondary threshold is adjusted; when sensitive, the abnormal proportion threshold of the multiple models is increased, up to 100%; when passivated, the abnormal proportion is reduced, and the minimum is the alarm data output by any model, thereby further improving the precision and accuracy of the abnormal detection data.

2. The on-line monitoring multi-module system for passive wireless intelligent substation lightning arresters according to claim 1, characterized in that: in, The construction path of the arrester leakage full current time series data is as follows: abandoning the original resistive current separation data processing path based on phase difference, constructing a new linear time series data framework, taking the time interval as the basic basis element of linear representation, and obtaining the linear basis coefficient sequence of irrelevant current phase difference and resistive current separation based on the harmonic characteristics of capacitive current and resistive current in the leakage current, and reconstructing the linear data representation of the arrester leakage full current time series data; and the linear data representation reconstruction of the arrester leakage full current time series data includes two optional data paths of continuous or discretization, and the two data paths adopt a consistent data structure framework when designing the data structure.

3. A passive wireless intelligent on-line monitoring multi-module system for substation lightning arresters according to claim 1, characterized in that: The data sensing acquisition technology module includes a digital current sensor for measuring the static leakage current of the lightning arrester, adopts a through-core high-precision current transformer, and collaboratively adopts a high-precision timing chip. It uses a high-precision I2C real-time clock RTC as the underlying technology and integrates a temperature-compensated crystal oscillator and a crystal; at the same time, an additional energy input terminal is constructed to support long-term accurate timing when the main power supply is disconnected.

4. The multi-module system for on-line monitoring of passive wireless intelligent substation lightning arresters according to claim 1, wherein: For the data sensing acquisition technology module, a new energy supply route bypass is constructed and the leakage current of the lightning arrester is used as the main power supply of the data sensing acquisition technology module. No battery is used or the battery is used as an auxiliary backup external power supply.

5. The multi-module system for on-line monitoring of a passive wireless intelligent substation lightning arrester according to claim 1, characterized in that: The information data network in the information data transmission network technology module adopts a wireless long-distance communication architecture. The digital signal of the data sensor acquisition technology module is fed into the data center for storage and subsequent data algorithm processing via the Zigbee module of the communication repeater and the subsequent GPRS module and the remote host computer DTU module.

6. The multi-module system for on-line monitoring of a passive wireless intelligent substation lightning arrester according to claim 1, wherein: In the information data transmission network technology module, an additional data channel connected to the personal communication terminal is constructed and the mobile phone number is used as an identification mark to forward the lightning arrester and its sensor data or other data information in the form of text messages to the associated personal communication terminal; The information is presented in the form of short messages or text push based on dedicated apps.

7. A passive wireless intelligent substation lightning arrester on-line monitoring multi-module system according to claim 3, characterized in that: The arrester is a metal oxide arrester MOA; the digital current sensor is designed to measure the static leakage current of the arrester under operating conditions or the static leakage current of the arrester under artificially set conditions, wherein the artificially set conditions include specific artificial operating conditions constructed by human intervention under operating conditions.

8. The passive wireless intelligent on-line monitoring multi-module system for substation lightning arresters according to claim 7, characterized in that: The leakage current includes passive leakage current and an actively constructed leakage current environment; the actively constructed leakage current environment adopts a standardized input source with a known pattern; the system is compatible with the installation of an environmental electromagnetic interference shielding system as another subsequent expandable technical module; the system is compatible with the installation of an environmental electromagnetic interference metering device system as another subsequent expandable technical module, and at this time, the environmental electromagnetic noise interference data is eliminated through a compensation algorithm.

Images