State monitoring method and system for permanent magnet circuit breaker modular controller

Abstract

The application discloses a state monitoring method and system for a permanent magnet circuit breaker modular controller and relates to the technical field of equipment monitoring, which comprises the following steps: obtaining controller structure information, obtaining monitoring data type information based on data analysis requirements according to the controller structure information, obtaining controller historical monitoring data based on the monitoring data type information, and obtaining the data monitoring frequency corresponding to each kind of monitoring data according to the controller historical monitoring data.The application obtains the data monitoring frequency corresponding to each kind of monitoring data through the controller historical monitoring data, realizes the accurate adaptation of the monitoring frequency and the data fluctuation characteristics, provides accurate judgment basis for controller state abnormal early warning by quantitatively defining characteristic extreme points, determines the reference monitoring data and frequency by calculating the data significant coefficient and the fluctuation intensity coefficient, realizes the collaborative optimization of the monitoring frequencies of multiple types of monitoring data, and improves the systematicness and efficiency of the overall state monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device monitoring, in particular to a state monitoring method and system for a modular controller of a permanent magnet circuit breaker. BACKGROUND

[0002] As a key control core in the power system, the modular controller of the permanent magnet circuit breaker is widely used in low-voltage power distribution networks, industrial power supply systems and other scenarios. The controller integrates the permanent magnet drive structure, energy storage structure and drive coil control logic through modular design, realizes precise regulation and control of circuit breaker opening and closing operation, and undertakes functions such as equipment operation state feedback and fault preliminary diagnosis, which is an important link to ensure the continuity of power transmission and avoid fault expansion. State monitoring of the modular controller of the permanent magnet circuit breaker can capture the dynamic changes of electrical and mechanical data in real time, identify potential fault hazards in time, and provide early warning of equipment abnormalities, so as to avoid serious accidents such as circuit breaker failure and misoperation caused by controller failure, reduce economic losses caused by unplanned shutdown, provide data support for equipment life cycle maintenance, and reduce the cost and resource waste of blind maintenance.

[0003] At present, the state monitoring of the modular controller of the permanent magnet circuit breaker still has the problems that the historical monitoring data cannot be accurately analyzed, the state monitoring frequency of the modular controller of the permanent magnet circuit breaker cannot be accurately set according to the historical monitoring data, the existing technology mostly uses a preset fixed monitoring frequency, does not adjust the monitoring rhythm in combination with the fluctuation characteristics of the historical data, causes missing of key information due to too low frequency when the data fluctuates sharply, causes resource waste due to too high frequency when the data is stable, and mostly sets the monitoring frequency in a single dimension, which causes the monitoring rhythm of multiple types of monitoring data to be uncoordinated, affects the overall monitoring efficiency, reduces the intuitive features of the monitoring data, and affects the data mining efficiency. SUMMARY

[0004] To solve the above technical problems, the present application provides a state monitoring system for a modular controller of a permanent magnet circuit breaker, which solves the problems of the prior art that the historical monitoring data cannot be accurately analyzed, the state monitoring frequency of the modular controller of the permanent magnet circuit breaker cannot be accurately set according to the historical monitoring data, the existing technology mostly uses a preset fixed monitoring frequency, does not adjust the monitoring rhythm in combination with the fluctuation characteristics of the historical data, causes missing of key information due to too low frequency when the data fluctuates sharply, causes resource waste due to too high frequency when the data is stable, and mostly sets the monitoring frequency in a single dimension, which causes the monitoring rhythm of multiple types of monitoring data to be uncoordinated, affects the overall monitoring efficiency, reduces the intuitive features of the monitoring data, and affects the data mining efficiency.

[0005] To achieve the above purposes, the technical scheme adopted by the present application is as follows:

[0006] The state monitoring method for the modular controller of the permanent magnetic circuit breaker comprises the following steps:

[0007] Obtain the controller structure information, wherein the controller structure comprises a permanent magnetic drive structure, an energy storage structure and a drive coil;

[0008] According to the controller structure information, obtain the monitoring data type information based on the data analysis requirement, wherein the monitoring data type information comprises electrical data and mechanical data;

[0009] Obtain the controller historical monitoring data based on the monitoring data type information;

[0010] According to the controller historical monitoring data, obtain the data monitoring frequency corresponding to each kind of monitoring data;

[0011] According to the controller historical monitoring data, take the ratio of the number of the characteristic extreme points of each kind of controller historical monitoring data to the total number of the extreme points as the data significant coefficient of the controller historical monitoring data;

[0012] Take the ratio of the standard deviation to the mean value of each kind of controller historical monitoring data as the fluctuation intensity coefficient of the controller historical monitoring data;

[0013] Based on the data evaluation requirement, obtain the weight corresponding to the data significant coefficient and the fluctuation intensity coefficient;

[0014] According to the data significant coefficient and the fluctuation intensity coefficient, obtain the data monitoring requirement coefficient corresponding to each kind of controller historical monitoring data based on the weighted summation formula;

[0015] Take the monitoring data corresponding to the maximum data monitoring requirement coefficient as the reference monitoring data, and take the data monitoring frequency corresponding to the reference monitoring data as the reference monitoring frequency;

[0016] According to the data monitoring frequency corresponding to each kind of monitoring data and the reference monitoring frequency, obtain the data monitoring correction frequency corresponding to each kind of monitoring data based on the state monitoring requirement;

[0017] Take the data monitoring correction frequency corresponding to each kind of monitoring data as the reference to perform the state monitoring on the permanent magnetic circuit breaker modular controller.

[0018] Preferably, according to the controller historical monitoring data, the data monitoring frequency corresponding to each kind of monitoring data is obtained, specifically comprising the following steps:

[0019] According to the controller historical monitoring data, obtain the time stamp information corresponding to the controller historical monitoring data;

[0020] Take time as the horizontal coordinate, and take the controller historical monitoring data value as the vertical coordinate to construct a coordinate system, and obtain the historical monitoring data curve graph.

[0021] According to the historical monitoring data graph, the extreme point information corresponding to each historical monitoring data graph is obtained;

[0022] According to the controller historical monitoring data, the mean value and standard deviation corresponding to each kind of controller historical monitoring data are obtained;

[0023] According to the mean value and standard deviation corresponding to each kind of controller historical monitoring data, the data deviation threshold corresponding to each kind of controller historical monitoring data is obtained based on the Laplace criterion;

[0024] According to the data deviation threshold and the extreme point information, the extreme point corresponding to the value of the controller historical monitoring data of the extreme point in each historical monitoring data graph is taken as the characteristic extreme point of the controller historical monitoring data, which exceeds the data deviation threshold;

[0025] According to the characteristic extreme point, the data monitoring frequency corresponding to each kind of monitoring data is obtained.

[0026] Preferably, the coordinate system is constructed based on the controller historical monitoring data value as the ordinate with time as the abscissa to obtain the historical monitoring data graph, specifically including:

[0027] According to the controller historical monitoring data, the historical data acquisition period information is obtained;

[0028] According to the time stamp information corresponding to the controller historical monitoring data, the time interval information of adjacent time stamps in each kind of controller historical monitoring data is obtained;

[0029] The time interval information is compared with the historical data acquisition period information, if the time interval exceeds 2 times of the historical data acquisition period of the corresponding data, it is determined that the time stamp is missing, the linear interpolation method is used to supplement the missing time stamp, and the time stamp continuous data time mapping table is obtained;

[0030] According to the data time mapping table, the abscissa basis information is obtained with time as the abscissa, the abscissa time range is the earliest time stamp to the latest time stamp of the historical monitoring data, and each 1 second is divided as a scale;

[0031] According to the time interval information of adjacent time stamps in each kind of controller historical monitoring data, the ordinate basis information is obtained, and the ordinate basis information includes data identification amplitude;

[0032] According to the abscissa basis information and the ordinate basis information, the data points in the data time mapping table are sequentially drawn in the coordinate system in time order based on the determined coordinate system range, and adjacent data points are connected by straight lines to form the original historical monitoring data graph of each kind of controller historical monitoring data;

[0033] According to the original historical monitoring data curve, taking the connecting straight line corresponding to any two adjacent data points as the basis, if the difference of the controller historical monitoring data values corresponding to the two adjacent data points exceeds the data identification amplitude, the two adjacent data points are taken as a smooth data point feature group;

[0034] Based on the Savitzky-Golay filtering algorithm, the original historical monitoring data curve corresponding to the smooth data point feature group is smoothed and optimized to obtain the historical monitoring data curve.

[0035] Preferably, the longitudinal coordinate basis information is obtained according to the time interval information of adjacent time stamps in each controller historical monitoring data, specifically including:

[0036] The controller historical monitoring data corresponding to any two adjacent time stamps is taken as a historical monitoring data group;

[0037] The absolute value of the difference of the two controller historical monitoring data in the historical monitoring data group is taken as the data fluctuation amplitude of the historical monitoring data group, and the difference of the data fluctuation amplitude and the time stamp is taken as the data fluctuation coefficient;

[0038] The historical monitoring data group corresponding to the maximum value of the data fluctuation coefficient is taken as the first historical monitoring data group, and the historical monitoring data group corresponding to the minimum value of the data fluctuation coefficient is taken as the second historical monitoring data group;

[0039] According to the first historical monitoring data group and the second historical monitoring data group, the ratio of the smaller value to the larger value in the corresponding two data fluctuation amplitudes is taken as the fluctuation sensitivity coefficient;

[0040] Based on the design analysis of the permanent magnet circuit breaker modular controller, the data rated value corresponding to each controller historical monitoring data is obtained;

[0041] The product of the data rated value and the fluctuation sensitivity coefficient is taken as the data identification amplitude;

[0042] Taking the controller historical monitoring data value as the longitudinal coordinate, the longitudinal coordinate data value range is the minimum value to the maximum value of each controller historical monitoring data, and the scale is divided according to the controller historical monitoring data value accuracy requirement to obtain the longitudinal coordinate basis information.

[0043] Preferably, the data monitoring frequency corresponding to each monitoring data is obtained according to the feature extreme point, specifically including:

[0044] According to the feature extreme point, the feature extreme point total information and the time stamp information corresponding to the feature extreme point of each controller historical monitoring data are obtained;

[0045] According to the time stamp information corresponding to the feature extreme point, the time interval information between adjacent feature extreme points is obtained based on the time sequence.

[0046] The time interval between adjacent feature extreme points is taken as an extreme value time interval, and a mean value of the extreme value time intervals is obtained;

[0047] The reciprocal of the mean value of the extreme value time intervals corresponding to each type of controller historical monitoring data is taken as the data monitoring frequency corresponding to the monitoring data.

[0048] Preferably, the data monitoring correction frequency corresponding to each type of monitoring data is obtained according to the data monitoring frequency corresponding to each type of monitoring data and the reference monitoring frequency, based on the state monitoring requirement, and specifically includes:

[0049] The data monitoring correction frequency corresponding to each type of monitoring data is obtained by adjusting the data monitoring frequency corresponding to each type of monitoring data according to the reference monitoring frequency;

[0050] If the data monitoring frequency corresponding to the monitoring data is greater than the reference monitoring frequency, the ratio of the data monitoring frequency to the reference monitoring frequency is taken as the correction coefficient;

[0051] The correction coefficient is adjusted to the nearest integer based on the rounding method;

[0052] The product of the adjusted correction coefficient and the reference monitoring frequency is taken as the data monitoring correction frequency corresponding to the monitoring data;

[0053] If the data monitoring frequency corresponding to the monitoring data is less than the reference monitoring frequency, the ratio of the reference monitoring frequency to the data monitoring frequency is taken as the correction coefficient;

[0054] The correction coefficient is adjusted to the nearest integer based on the rounding method;

[0055] The product of the reciprocal of the adjusted correction coefficient and the reference monitoring frequency is taken as the data monitoring correction frequency corresponding to the monitoring data.

[0056] Further, a state monitoring system for a modularized controller of a permanent magnet circuit breaker is proposed, which is used to implement the monitoring method as described above, and includes:

[0057] The control module is used to obtain the extreme point information corresponding to each historical monitoring data curve according to the historical monitoring data curve, obtain the feature extreme points according to the data deviation threshold and the extreme point information, obtain the data monitoring frequency corresponding to each type of monitoring data according to the feature extreme points, and obtain the data monitoring correction frequency corresponding to each type of monitoring data according to the data monitoring frequency corresponding to each type of monitoring data, based on the state monitoring requirement;

[0058] The information acquisition module is configured to acquire controller structure information, acquire monitoring data type information based on data analysis requirements according to the controller structure information, acquire controller historical monitoring data based on the monitoring data type information, and acquire timestamp information corresponding to the controller historical monitoring data according to the controller historical monitoring data.

[0059] The curve construction module is configured to acquire time interval information of adjacent timestamps in each kind of controller historical monitoring data according to the timestamp information corresponding to the controller historical monitoring data, acquire horizontal coordinate basic information and vertical coordinate basic information according to the time interval information, acquire an original historical monitoring data curve diagram according to the horizontal coordinate basic information and the vertical coordinate basic information, and acquire a historical monitoring data curve diagram according to the original historical monitoring data curve diagram.

[0060] Optionally, the control module specifically comprises:

[0061] The control unit is configured to acquire data monitoring frequency corresponding to each kind of monitoring data according to the feature extreme point, and acquire data monitoring correction frequency corresponding to each kind of monitoring data based on state monitoring requirements according to the data monitoring frequency corresponding to each kind of monitoring data.

[0062] The information receiving unit is configured to receive data and transmit the data to the curve identification unit in interaction with the information acquisition module and the curve construction module.

[0063] The curve identification unit is configured to acquire extreme point information corresponding to each historical monitoring data curve diagram according to the historical monitoring data curve diagram, and acquire the feature extreme point according to the data deviation threshold and the extreme point information.

[0064] Optionally, the information acquisition module specifically comprises:

[0065] The first acquisition unit is configured to acquire controller structure information, and acquire monitoring data type information based on data analysis requirements according to the controller structure information.

[0066] The second acquisition unit is configured to acquire controller historical monitoring data based on the monitoring data type information, and acquire timestamp information corresponding to the controller historical monitoring data according to the controller historical monitoring data.

[0067] Optionally, the curve construction module specifically comprises:

[0068] The coordinate system construction unit is configured to obtain time interval information of adjacent time stamps in each kind of controller historical monitoring data according to time stamp information corresponding to the controller historical monitoring data, and obtain horizontal coordinate basic information and vertical coordinate basic information according to the time interval information;

[0069] The curve construction unit is configured to obtain an original historical monitoring data curve graph according to the horizontal coordinate basic information and the vertical coordinate basic information, and obtain a historical monitoring data curve graph according to the original historical monitoring data curve graph.

[0070] Compared with the prior art, the method has the advantages that:

[0071] The method and system for state monitoring of a modular controller of a permanent magnetic circuit breaker are proposed, the data monitoring frequency corresponding to each kind of monitoring data is obtained through controller historical monitoring data, accurate adaptation of monitoring frequency and data fluctuation characteristics is realized, characteristic extreme points are quantitatively defined, accurate judgment basis is provided for controller state abnormality early warning, the significant coefficient and the fluctuation intensity coefficient of data are calculated, the reference monitoring data and the frequency are determined, the monitoring frequency of multiple types of monitoring data is cooperatively optimized, and the systematicness and efficiency of overall state monitoring are improved. BRIEF DESCRIPTION OF DRAWINGS

[0072] Figure 1 A flow chart of the method for state monitoring of a modular controller of a permanent magnetic circuit breaker is proposed in the present application.

[0073] Figure 2 A flow chart of the method for obtaining data monitoring frequency is proposed in the present application.

[0074] Figure 3 A flow chart of the method for obtaining a historical monitoring data curve graph is proposed in the present application.

[0075] Figure 4 A flow chart of the method for obtaining vertical coordinate basic information is proposed in the present application.

[0076] Figure 5 A structure block diagram of the state monitoring system of a modular controller of a permanent magnetic circuit breaker is proposed in the present application. DETAILED DESCRIPTION

[0077] The following description is provided to enable any person skilled in the art to practice the present application. The preferred embodiments in the following description are only examples for illustration, and other obvious modifications can be made by those skilled in the art.

[0078] REFERENCE Figure 1 - Figure 4 As shown in the figure, the method for state monitoring of a modular controller of a permanent magnetic circuit breaker in the embodiment of the present application comprises:

[0079] Obtaining controller structure information, the controller structure including a permanent magnet drive structure, an energy storage structure and a drive coil;

[0080] According to the controller structure information, obtaining monitoring data type information based on data analysis requirements, the monitoring data type information including electrical data and mechanical data;

[0081] In this embodiment, the electrical data includes coil current, capacitor voltage, power supply output voltage, control signal amplitude, etc., and the mechanical data includes vibration amplitude, mechanical stroke, etc.

[0082] Based on the monitoring data type information, obtaining controller historical monitoring data;

[0083] According to the controller historical monitoring data, obtaining data monitoring frequency corresponding to each monitoring data;

[0084] Specifically, according to the controller historical monitoring data, obtaining data monitoring frequency corresponding to each monitoring data, specifically including:

[0085] According to the controller historical monitoring data, obtaining time stamp information corresponding to the controller historical monitoring data;

[0086] Taking time as the horizontal coordinate and the controller historical monitoring data value as the vertical coordinate, constructing a coordinate system to obtain a historical monitoring data curve graph;

[0087] According to the historical monitoring data curve graph, obtaining extreme point information corresponding to each historical monitoring data curve graph;

[0088] According to the controller historical monitoring data, obtaining mean value and standard deviation corresponding to each controller historical monitoring data;

[0089] According to the mean value and standard deviation corresponding to each controller historical monitoring data, obtaining data deviation threshold value corresponding to each controller historical monitoring data based on the Laplace criterion;

[0090] According to the data deviation threshold value and the extreme point information, taking the extreme point whose value of the controller historical monitoring data corresponding to the extreme point in each historical monitoring data curve graph exceeds the data deviation threshold value as the characteristic extreme point of the controller historical monitoring data of this kind;

[0091] According to the characteristic extreme point, obtaining data monitoring frequency corresponding to each monitoring data.

[0092] In the scheme, the time and space characteristics of the controller monitoring data are visualized by acquiring the time stamp and constructing the historical monitoring data curve, which provides an intuitive and traceable basis for subsequent extreme point and fluctuation rule analysis, solves the problem of traditional scattered and disordered data that is difficult to grasp the overall fluctuation trend, quantitatively defines the feature extreme point by calculating the mean, standard deviation and determining the data deviation threshold based on the Laplace criterion, upgrades the extreme point identification from "empirical subjective judgment" to "data-driven objective judgment", avoids misjudgment of normal fluctuations as abnormal or omission of real abnormal extreme values, greatly improves the accuracy of extreme point identification, and dynamically adapts the monitoring frequency to the data fluctuation intensity by determining the data monitoring frequency according to the feature extreme point - increases the monitoring frequency to capture key changes when the data fluctuates violently (feature extreme point is dense), and reduces the frequency to reduce redundant collection when the data is stable, solves the defects of traditional fixed frequency "missing key fluctuations" or "redundant collection waste resources", and provides an efficient and accurate collection mechanism for the state monitoring of the permanent magnet circuit breaker modular controller, ensuring the timeliness and reliability of subsequent state abnormal warning and fault diagnosis.

[0093] Specifically, a coordinate system is constructed with time as the horizontal coordinate and the historical monitoring data value of the controller as the vertical coordinate, and a historical monitoring data curve is obtained, specifically including:

[0094] According to the historical monitoring data of the controller, historical data acquisition period information is obtained;

[0095] According to the time stamp information corresponding to the historical monitoring data of the controller, the time interval information of adjacent time stamps in each kind of controller historical monitoring data is obtained;

[0096] The time interval information is compared with the historical data acquisition period information, if the time interval exceeds 2 times of the historical data acquisition period of the corresponding data, it is determined that the time stamp is missing, the linear interpolation method is used to supplement the missing time stamp, and a time stamp continuous data time mapping table is obtained;

[0097] According to the data time mapping table, the horizontal coordinate basis information is obtained, with time as the horizontal coordinate, the horizontal coordinate time range is the earliest time stamp to the latest time stamp of the historical monitoring data, and each 1 second is divided as a scale;

[0098] According to the time interval information of adjacent time stamps in each kind of controller historical monitoring data, the vertical coordinate basis information is obtained, and the vertical coordinate basis information includes data identification amplitude;

[0099] According to the horizontal coordinate basis information and the vertical coordinate basis information, the data points in the data time mapping table are sequentially drawn in the coordinate system in time order based on the determined coordinate system range, and adjacent data points are connected by straight lines to form the original historical monitoring data curve of each kind of controller historical monitoring data.

[0100] According to the original historical monitoring data graph, if the difference between the values of the controller historical monitoring data corresponding to any two adjacent data points exceeds the data recognition amplitude, the two adjacent data points are taken as a smooth data point feature group based on the connecting straight line corresponding to the two adjacent data points;

[0101] Based on the Savitzky-Golay filtering algorithm, the original historical monitoring data curve corresponding to the smooth data point feature group is smoothed and optimized to obtain the historical monitoring data graph.

[0102] In the scheme, the continuity of the monitoring data time sequence is realized by supplementing the missing time stamp through the linear interpolation method, the problems of curve fault and analysis logic confusion caused by asynchronous time stamp in traditional data are solved, a unified time reference is laid for subsequent synchronous analysis and feature extraction of multiple types of data, the spatio-temporal visualization of the controller historical monitoring data is realized by constructing a coordinate system according to the time scale and data amplitude and drawing the original curve, the fluctuation trend and change law of the data are intuitive and traceable, which facilitates subsequent operations such as extreme value point identification and fluctuation feature analysis, and the intuitiveness and efficiency of data processing are improved, the balance between "noise filtering" and "key feature retention" is realized by screening the smooth data point feature group based on the data recognition amplitude and using the Savitzky-Golay filtering algorithm for targeted optimization of the curve, that is, the curve burr caused by electromagnetic interference, acquisition error, etc. (such as the instantaneous false fluctuation of the driving coil current) is eliminated, and the real abnormal extreme value point (such as the sudden change of the opening and closing time caused by the mechanism jam) is completely retained, which greatly improves the authenticity and readability of the monitoring curve and provides a high-quality data basis for the accurate identification of feature extreme points and the accurate determination of state abnormalities.

[0103] Specifically, according to the time interval information of adjacent time stamps in each controller historical monitoring data, the vertical coordinate basic information is obtained, specifically including:

[0104] Taking the controller historical monitoring data corresponding to any two adjacent time stamps as a historical monitoring data group;

[0105] Taking the absolute value of the difference between the two controller historical monitoring data in the historical monitoring data group as the data fluctuation amplitude of the historical monitoring data group, and taking the difference between the data fluctuation amplitude and the time stamp as the data fluctuation coefficient;

[0106] Taking the historical monitoring data group corresponding to the maximum value of the data fluctuation coefficient as the first historical monitoring data group, and taking the historical monitoring data group corresponding to the minimum value of the data fluctuation coefficient as the second historical monitoring data group;

[0107] According to the first historical monitoring data set and the second historical monitoring data set, a ratio of a smaller value to a larger value in the corresponding two data fluctuation amplitudes is taken as a fluctuation sensitivity coefficient;

[0108] Based on the design analysis of the permanent magnet circuit breaker modular controller, the data rating value corresponding to the historical monitoring data of each controller is obtained;

[0109] The product of the data rating value and the fluctuation sensitivity coefficient is taken as the data identification amplitude;

[0110] Taking the controller historical monitoring data value as the ordinate, the ordinate data value range is the minimum value to the maximum value of each controller historical monitoring data, and the scale is divided according to the accuracy requirement of the controller historical monitoring data value, to obtain the ordinate basic information.

[0111] In the scheme, by dividing the adjacent timestamp data into historical monitoring data groups, the "data fluctuation amplitude (absolute value of adjacent data difference)" and "data fluctuation coefficient (fluctuation amplitude / time stamp difference)" are calculated, realizing the quantitative characterization of the change rule of the controller monitoring data. Compared with the subjectivity of "judging the size of data fluctuation by experience" in the traditional method, this quantitative process changes the data fluctuation from "fuzzy description" to "calculable index", for example, it can accurately distinguish the fluctuation difference between "1A / 0.5s" and "0.5A / 1s" of the drive coil current, providing objective data support for the calculation of the subsequent fluctuation sensitive coefficient and data recognition amplitude, and ensuring the rigor of the whole monitoring analysis logic. Capturing the exclusive fluctuation extreme value of the data and adapting the individualized characteristics By screening the "first data group corresponding to the maximum fluctuation coefficient" and the "second data group corresponding to the minimum fluctuation coefficient", and calculating the ratio of the fluctuation amplitudes of the two as the "fluctuation sensitive coefficient", the exclusive fluctuation extreme value range of this kind of monitoring data (such as energy storage pressure, closing and opening time) can be accurately captured. The fluctuation characteristics of different monitoring data are significantly different (such as the drive coil current fluctuation is violent, and the energy storage pressure fluctuation is gentle), and this step avoids the problem of insufficient adaptability caused by using "uniform sensitive coefficient" - for example, for current data, its fluctuation sensitive coefficient can reflect the difference multiple of "large fluctuation and small fluctuation", and for pressure data, it reflects the difference multiple of "gentle fluctuation", making the subsequent recognition standard more in line with the true fluctuation rule of single data. Combined with the design benchmark of the equipment, the recognition amplitude is accurately adapted Based on the controller design parameters, the "data rated value" (such as the rated current of the drive coil is 10A, and the rated pressure of the energy storage mechanism is 5MPa) is obtained, and multiplied by the fluctuation sensitive coefficient to get the "data recognition amplitude", so that the recognition amplitude is anchored to the equipment design benchmark and the actual fluctuation characteristics of the data. In the traditional method, "setting a uniform recognition amplitude" (such as all data are set with ±2 as the recognition threshold) may cause deviation: for current data, it may be too wide (missing small amplitude anomalies), and for pressure data, it may be too narrow (misjudging normal fluctuations). And this step through "rated value x sensitive coefficient", the recognition amplitude of current data is adapted to its rated range and fluctuation intensity, and the recognition amplitude of pressure data is adapted to its low fluctuation characteristics, ensuring that the abnormal fluctuation recognition standard of different types of monitoring data (electrical / mechanical data) conforms to the equipment operation logic, greatly improving the recognition accuracy. Optimizing the vertical coordinate visualization parameters ensures the integrity of the curve information By determining the "vertical coordinate range covering the minimum and maximum values of the data" and "the scale matching the accuracy requirement", the information loss problem caused by unreasonable vertical coordinate parameters of traditional monitoring curves is solved: on the one hand, it avoids the "cut-off" of data caused by too narrow range (such as the maximum value of the energy storage pressure cannot be displayed due to the upper limit of the vertical coordinate), and on the other hand, it avoids the loss of subtle fluctuations caused by too coarse scale (such as the 0.1s difference of closing and opening time is ignored).For example, for the driving coil current (range 0-15A, accuracy requirement 0.1A), the ordinate can be set to 0-16A, and each 0.1A has a scale, which ensures that the curve can completely and clearly present the subtle changes of the current from start to stability, provides an accurate visual carrier for subsequent curve-based smoothing processing and feature extreme point identification, and avoids affecting subsequent monitoring and judgment due to curve information distortion.

[0112] Specifically, according to the feature extreme point, the data monitoring frequency corresponding to each kind of monitoring data is obtained, specifically including:

[0113] According to the feature extreme point, the feature extreme point total information corresponding to each kind of controller historical monitoring data and the timestamp information corresponding to the feature extreme point are obtained;

[0114] According to the timestamp information corresponding to the feature extreme point, the time interval information between adjacent feature extreme points is obtained based on time sequence;

[0115] The time interval between adjacent feature extreme points is taken as the extreme time interval, and the mean value of the extreme time interval is obtained;

[0116] The reciprocal of the mean value of the extreme time interval corresponding to each kind of controller historical monitoring data is taken as the data monitoring frequency corresponding to the monitoring data.

[0117] In this scheme, the monitoring frequency is dynamically adapted, and the balance between monitoring accuracy and efficiency is achieved. The reciprocal of the mean value of the extreme time interval is taken as the data monitoring frequency (for example, the interval mean value is 10 minutes, and the frequency is 1 / 10 times / minute, that is, it is collected once every 10 minutes; the interval mean value is 5 minutes, and the frequency is 1 / 5 times / minute, that is, it is collected once every 5 minutes), and the essence is to dynamically bind the monitoring frequency and the fluctuation intensity of the feature extreme point: when the device data fluctuates frequently (such as the mechanism card stagnation causes the extreme point interval to be reduced), the monitoring frequency is automatically increased to ensure that no key abnormal signal is missed; when the data fluctuates gently (such as the extreme point interval is expanded when the device runs stably), the monitoring frequency is automatically reduced to avoid redundant collection of controller storage and computing resources. This dynamic adaptation mechanism solves the defects of traditional fixed frequency "frequent fluctuations and missed detection, and gentle fluctuations and waste", and achieves the balance between monitoring accuracy and resource efficiency.

[0118] According to the controller historical monitoring data, the ratio of the number of feature extreme points corresponding to each kind of controller historical monitoring data to the total number of extreme points is taken as the data significance coefficient of the controller historical monitoring data;

[0119] The ratio of the standard deviation to the mean value corresponding to each kind of controller historical monitoring data is taken as the fluctuation intensity coefficient of the controller historical monitoring data;

[0120] Based on the data evaluation requirement, the weight corresponding to the data significance coefficient and the fluctuation intensity coefficient is obtained;

[0121] According to the data significance coefficient and the fluctuation intensity coefficient, the data monitoring requirement coefficient corresponding to the historical monitoring data of each controller is obtained based on the weighted sum formula;

[0122] The monitoring data corresponding to the maximum data monitoring requirement coefficient is taken as the reference monitoring data, and the data monitoring frequency corresponding to the reference monitoring data is taken as the reference monitoring frequency;

[0123] According to the data monitoring frequency corresponding to each monitoring data and the reference monitoring frequency, the data monitoring correction frequency corresponding to each monitoring data is obtained based on the state monitoring requirement;

[0124] Specifically, according to the data monitoring frequency corresponding to each monitoring data and the reference monitoring frequency, the data monitoring correction frequency corresponding to each monitoring data is obtained based on the state monitoring requirement, which specifically includes:

[0125] According to the reference monitoring frequency, the data monitoring frequency corresponding to each monitoring data is adjusted to obtain the data monitoring correction frequency corresponding to each monitoring data;

[0126] If the data monitoring frequency corresponding to the monitoring data is greater than the reference monitoring frequency, the ratio of the data monitoring frequency to the reference monitoring frequency is taken as the correction coefficient;

[0127] Based on the rounding method, the correction coefficient is adjusted to the nearest integer;

[0128] The product of the adjusted correction coefficient and the reference monitoring frequency is taken as the data monitoring correction frequency corresponding to the monitoring data;

[0129] If the data monitoring frequency corresponding to the monitoring data is less than the reference monitoring frequency, the ratio of the reference monitoring frequency to the data monitoring frequency is taken as the correction coefficient;

[0130] Based on the rounding method, the correction coefficient is adjusted to the nearest integer;

[0131] The product of the reciprocal of the adjusted correction coefficient and the reference monitoring frequency is taken as the data monitoring correction frequency corresponding to the monitoring data.

[0132] In this scheme, by calculating the "data significance coefficient" (feature extreme point proportion, reflecting the data's indication value for equipment abnormalities) and the "fluctuation intensity coefficient" (standard deviation / mean, reflecting data stability), the monitoring importance of different monitoring data (such as drive coil current, energy storage pressure) is converted into quantifiable indicators. For example, the feature extreme point proportion of the drive coil current is high (significant coefficient is large), and the fluctuation is frequent (fluctuation intensity coefficient is large), indicating that its value for fault warning is higher, and the monitoring density needs to be prioritized; while the energy storage pressure fluctuates smoothly and the significant coefficient is small, the monitoring priority can be relatively reduced. This process replaces the traditional "subjective way of dividing priorities by experience", ensuring that monitoring resources are tilted towards high-value data, and avoiding the problem of "insufficient monitoring of key data and occupation of resources by redundant data" caused by indiscriminate allocation of resources to all data. Establish a unified monitoring benchmark to solve the problem of uncoordinated frequency of multiple data By selecting the "data monitoring demand coefficient maximum" monitoring data as the benchmark data (such as drive coil current), and taking its frequency as the benchmark monitoring frequency, a unified frequency adjustment reference is provided for all types of monitoring data (electrical and mechanical data). For example, if the benchmark frequency is 5Hz, the original frequency of the energy storage pressure is 3Hz (lower than the benchmark), and the original frequency of the opening and closing current is 9Hz (higher than the benchmark), the monitoring frequency of the energy storage pressure can be adjusted to 2.5Hz, and the monitoring frequency of the opening and closing current can be adjusted to 10Hz around the benchmark frequency. Through the adjustment logic of "rounding off the correction coefficient + operating on the benchmark frequency", the final data monitoring correction frequency is ensured to be an integer frequency that meets the hardware acquisition capability (such as avoiding non-integer frequencies such as "once every 3.2 minutes", which adapt to the timing logic of the controller acquisition module), while the frequency adjustment amplitude is accurately controlled, the data-driven dynamic adjustment is adapted to the changes in the controller operating state. The entire correction frequency calculation process is completely based on the objective characteristics (significant coefficient, fluctuation intensity coefficient) of the historical monitoring data of the controller, rather than fixed preset values. When the controller operating state changes (such as aging leading to increased mechanical data fluctuation, unstable power grid leading to increased electrical data significant coefficient), the data monitoring demand coefficient will be updated, and the benchmark monitoring frequency and the correction frequency will also be dynamically adjusted. For example, after the controller ages, the fluctuation intensity coefficient of the energy storage pressure increases, and its monitoring demand coefficient may exceed the original benchmark data, becoming a new benchmark, driving other data frequency to adapt and adjust, ensuring that the monitoring scheme always matches the actual operating state of the controller, breaking through the limitations of traditional fixed frequency "unable to respond to changes in device state", and improving the effectiveness of long-term monitoring.

[0133] In this embodiment, the weight of the data significance coefficient is 0.4, and the weight of the fluctuation intensity coefficient is 0.6.

[0134] Take the data monitoring correction frequency corresponding to each monitoring data as the benchmark to conduct state monitoring on the permanent magnet circuit breaker modular controller.

[0135] Refer toFigure 5 As shown, further, in combination with the above-mentioned state monitoring method for the modular controller of the permanent magnetic circuit breaker, a state monitoring system for the modular controller of the permanent magnetic circuit breaker is proposed, comprising:

[0136] The control module is configured to obtain extreme point information corresponding to each historical monitoring data curve based on the historical monitoring data curve, obtain a characteristic extreme point based on the data deviation threshold and the extreme point information, obtain a data monitoring frequency corresponding to each kind of monitoring data based on the characteristic extreme point, and obtain a data monitoring correction frequency corresponding to each kind of monitoring data based on the data monitoring frequency and a state monitoring requirement.

[0137] The information acquisition module is configured to obtain controller structure information, obtain monitoring data type information based on a data analysis requirement and the controller structure information, obtain controller historical monitoring data based on the monitoring data type information, and obtain timestamp information corresponding to the controller historical monitoring data based on the controller historical monitoring data.

[0138] The curve construction module is configured to obtain time interval information of adjacent timestamps in each kind of controller historical monitoring data based on the timestamp information corresponding to the controller historical monitoring data, obtain horizontal coordinate basic information and vertical coordinate basic information based on the time interval information, obtain an original historical monitoring data curve based on the horizontal coordinate basic information and the vertical coordinate basic information, and obtain a historical monitoring data curve based on the original historical monitoring data curve.

[0139] The control module specifically comprises:

[0140] The control unit is configured to obtain a data monitoring frequency corresponding to each kind of monitoring data based on the characteristic extreme point, and obtain a data monitoring correction frequency corresponding to each kind of monitoring data based on the data monitoring frequency and a state monitoring requirement.

[0141] The information receiving unit is in interaction with the information acquisition module and the curve construction module, and is configured to receive data and transmit the data to the curve recognition unit.

[0142] The curve recognition unit is configured to obtain extreme point information corresponding to each historical monitoring data curve based on the historical monitoring data curve, and obtain a characteristic extreme point based on the data deviation threshold and the extreme point information.

[0143] The information acquisition module specifically comprises:

[0144] The first acquisition unit is configured to obtain controller structure information, and obtain monitoring data type information based on a data analysis requirement and the controller structure information.

[0145] The second acquisition unit is configured to acquire controller historical monitoring data based on the monitoring data type information, and acquire timestamp information corresponding to the controller historical monitoring data according to the controller historical monitoring data.

[0146] The curve construction module specifically comprises:

[0147] The coordinate system construction unit is configured to acquire time interval information of adjacent timestamps in each kind of controller historical monitoring data according to the timestamp information corresponding to the controller historical monitoring data, and acquire horizontal coordinate basic information and vertical coordinate basic information according to the time interval information.

[0148] The curve construction unit is configured to acquire an original historical monitoring data curve graph according to the horizontal coordinate basic information and the vertical coordinate basic information, and acquire a historical monitoring data curve graph according to the original historical monitoring data curve graph.

[0149] In summary, the application has the following advantages: the data monitoring frequency corresponding to each kind of monitoring data is acquired through the controller historical monitoring data, the accurate adaptation of the monitoring frequency and the data fluctuation characteristics is realized, the fixed frequency is avoided to miss key fluctuations or to collect redundantly, an efficient data collection mechanism is provided for the permanent magnet circuit breaker modular controller state monitoring, the timestamp alignment and smoothing processing of the historical monitoring data are performed, a high-quality data basis is provided for subsequent extreme value point identification, the extreme value point identification is changed from an experience threshold to data-driven through quantitative definition of characteristic extreme value points, accurate determination basis is provided for controller state abnormality early warning, the reference monitoring data and the frequency are determined through calculation of the data significant coefficient and the fluctuation intensity coefficient, the monitoring frequency of multiple types of monitoring data is cooperatively optimized, the problem of uncoordinated monitoring rhythm caused by single dimension adjustment is solved, and the systematicness and efficiency of the overall state monitoring are improved.

[0150] The basic principles, main features and advantages of the application are shown and described above. It should be understood by those skilled in the art that the application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only the principles of the application. Without departing from the spirit and scope of the application, various changes and improvements can be made to the application, and these changes and improvements all fall within the scope of the claimed application. The scope of protection claimed by the application is defined by the appended claims and their equivalents.

Claims

1. A method for condition monitoring of a modular controller for a permanent magnet circuit breaker, characterized in that, include: Obtain controller structure information, wherein the controller structure includes a permanent magnet drive structure, an energy storage structure, and a drive coil; Based on the controller structure information and data analysis requirements, the monitoring data type information is obtained, including electrical data and mechanical data. Based on the type of monitoring data, obtain the controller's historical monitoring data; Based on the controller's historical monitoring data, obtain the data monitoring frequency corresponding to each type of monitoring data; Based on the historical monitoring data of the controller, the ratio of the number of characteristic extreme points to the total number of extreme points corresponding to the historical monitoring data of each type of controller is used as the data significance coefficient of the historical monitoring data of that type of controller. The ratio of the standard deviation to the mean of the historical monitoring data for each type of controller is used as the fluctuation intensity coefficient of the historical monitoring data for that type of controller. Based on data evaluation needs, obtain the weights corresponding to the data significance coefficient and volatility coefficient; Based on the significance coefficient and fluctuation intensity coefficient of the data, and using a weighted summation formula, the data monitoring demand coefficient corresponding to the historical monitoring data of each controller is obtained; The type of monitoring data corresponding to the maximum value of the data monitoring demand coefficient is used as the benchmark monitoring data, and the data monitoring frequency corresponding to the benchmark monitoring data is used as the benchmark monitoring frequency. Based on the data monitoring frequency and baseline monitoring frequency corresponding to each type of monitoring data, and based on the status monitoring requirements, obtain the data monitoring correction frequency corresponding to each type of monitoring data; The status of the modular controller for permanent magnet circuit breakers is monitored based on the data monitoring correction frequency corresponding to each type of monitoring data.

2. The method of condition monitoring of a modular controller for a permanent magnet circuit breaker according to claim 1, characterized in that, The step of obtaining the data monitoring frequency corresponding to each type of monitoring data based on the controller's historical monitoring data specifically includes: Based on the controller's historical monitoring data, obtain the timestamp information corresponding to the controller's historical monitoring data; A coordinate system is constructed with time as the horizontal axis and historical monitoring data values ​​of the controller as the vertical axis to obtain a historical monitoring data curve. Based on the historical monitoring data curves, obtain the extreme point information corresponding to each historical monitoring data curve; Based on the historical monitoring data of the controllers, obtain the mean and standard deviation of the historical monitoring data for each type of controller; Based on the mean and standard deviation of the historical monitoring data for each controller, and using the Raida criterion, the data deviation threshold for the historical monitoring data of each controller is obtained. Based on the data deviation threshold and extreme point information, the extreme point where the value of the controller's historical monitoring data exceeds the data deviation threshold corresponding to the extreme point in each historical monitoring data curve is taken as the characteristic extreme point of the controller's historical monitoring data. Based on the characteristic extreme points, obtain the data monitoring frequency corresponding to each type of monitoring data.

3. The method of condition monitoring of a modular controller for a permanent magnet circuit breaker according to claim 2, characterized in that, The process of constructing a coordinate system with time as the horizontal axis and historical monitoring data values ​​of the controller as the vertical axis to obtain historical monitoring data curves specifically includes: Based on the controller's historical monitoring data, obtain historical data collection cycle information; Based on the timestamp information corresponding to the historical monitoring data of the controller, obtain the time interval information between adjacent timestamps in the historical monitoring data of each type of controller; The time interval information is compared with the historical data collection cycle information. If the time interval exceeds twice the historical data collection cycle of the corresponding data, it is determined that the timestamp is missing. The missing timestamp is supplemented by linear interpolation to obtain a data time mapping table with continuous timestamps. Based on the data time mapping table, with time as the horizontal axis and the time range of the horizontal axis being the earliest to the latest timestamp of the historical monitoring data, the basic information of the horizontal axis is obtained by dividing it into 1-second increments. Based on the time interval information of adjacent timestamps in the historical monitoring data of each controller, the basic information of the vertical axis is obtained, and the basic information of the vertical axis includes the data identification amplitude; Based on the basic information of the horizontal and vertical axes, and within the defined coordinate system range, the data points in the data time mapping table are plotted sequentially in the coordinate system according to time order. Adjacent data points are connected by straight lines to form the original historical monitoring data curve of each controller. Based on the original historical monitoring data curve, and taking the connecting line corresponding to any two adjacent data points as the basis, if the difference between the controller historical monitoring data values ​​corresponding to these two adjacent data points exceeds the data identification range, then these two adjacent data points are taken as a smooth data point feature group. Based on the Savitzky-Golay filtering algorithm, the original historical monitoring data curves corresponding to the feature groups of smooth data points are smoothed and optimized to obtain historical monitoring data curves.

4. The method of condition monitoring of a modular controller for a permanent magnet circuit breaker according to claim 3, characterized in that, The step of obtaining basic information for the vertical axis based on the time interval information of adjacent timestamps in the historical monitoring data of each controller specifically includes: Take the controller's historical monitoring data corresponding to any two adjacent timestamps as a historical monitoring data group; The absolute value of the difference between the historical monitoring data of the two controllers in the historical monitoring data group is taken as the data fluctuation amplitude of the historical monitoring data group, and the difference between the data fluctuation amplitude and the timestamp is taken as the data fluctuation coefficient. The historical monitoring data group corresponding to the maximum value of the data fluctuation coefficient is taken as the first historical monitoring data group, and the historical monitoring data group corresponding to the minimum value of the data fluctuation coefficient is taken as the second historical monitoring data group. Based on the first and second historical monitoring data sets, the ratio of the smaller to the larger value of the corresponding two data fluctuation amplitudes is used as the fluctuation sensitivity coefficient. Based on the design and analysis of modular controllers for permanent magnet circuit breakers, the rated data values ​​corresponding to the historical monitoring data of each controller are obtained. The product of the data rating and the fluctuation sensitivity coefficient is used as the data identification amplitude; Using the historical monitoring data of the controller as the vertical axis, with the range of the vertical axis data values ​​from the minimum to the maximum value of the historical monitoring data for each type of controller, the scale is divided according to the accuracy requirements of the historical monitoring data values ​​of the controller to obtain the basic information of the vertical axis.

5. The method of condition monitoring of a modular controller for a permanent magnet circuit breaker according to claim 4, characterized in that, The step of obtaining the data monitoring frequency corresponding to each type of monitoring data based on the characteristic extreme points specifically includes: Based on the characteristic extreme points, obtain the total number of characteristic extreme points and the timestamp information corresponding to the characteristic extreme points in the historical monitoring data of each type of controller; Based on the timestamp information corresponding to the feature extreme points, and based on the time order, the time interval information between adjacent feature extreme points is obtained; The time interval between adjacent feature extreme points is taken as the extreme value time interval, and the mean of the extreme value time interval is obtained. The reciprocal of the mean of the extreme value time intervals corresponding to the historical monitoring data of each controller is taken as the data monitoring frequency corresponding to that type of monitoring data.

6. The method of condition monitoring of a modular controller for a permanent magnet circuit breaker according to claim 5, characterized in that, The step of obtaining the data monitoring correction frequency corresponding to each type of monitoring data based on the data monitoring frequency and baseline monitoring frequency corresponding to each type of monitoring data, and based on the status monitoring requirements, specifically includes: Based on the baseline monitoring frequency, the data monitoring frequency corresponding to each type of monitoring data is adjusted to obtain the data monitoring correction frequency corresponding to each type of monitoring data; If the data monitoring frequency corresponding to the monitoring data is greater than the benchmark monitoring frequency, the ratio of the data monitoring frequency to the benchmark monitoring frequency will be used as a correction coefficient. Based on the rounding method, the correction factor is adjusted to the nearest integer; The product of the adjusted correction factor and the baseline monitoring frequency is used as the data monitoring correction frequency corresponding to the monitoring data. If the data monitoring frequency corresponding to the monitoring data is less than the benchmark monitoring frequency, the ratio of the benchmark monitoring frequency to the data monitoring frequency will be used as a correction factor. Based on the rounding method, the correction factor is adjusted to the nearest integer; The product of the reciprocal of the adjusted correction factor and the baseline monitoring frequency is used as the data monitoring correction frequency corresponding to the monitoring data.

7. A condition monitoring system for a modular controller of a permanent magnet circuit breaker, used to implement the monitoring method as described in any one of claims 1-6, characterized in that, include: The control module is used to obtain the extreme point information corresponding to each historical monitoring data curve based on the historical monitoring data curve, obtain the characteristic extreme point based on the data deviation threshold and extreme point information, obtain the data monitoring frequency corresponding to each type of monitoring data based on the characteristic extreme point, and obtain the data monitoring correction frequency corresponding to each type of monitoring data based on the data monitoring frequency corresponding to each type of monitoring data and the status monitoring requirements. The information acquisition module is used to acquire controller structure information, acquire monitoring data type information based on the controller structure information and data analysis requirements, acquire historical monitoring data of the controller based on the monitoring data type information, and acquire the timestamp information corresponding to the historical monitoring data of the controller based on the historical monitoring data of the controller. The curve construction module is used to obtain the time interval information between adjacent timestamps in the historical monitoring data of each controller based on the timestamp information corresponding to the historical monitoring data of the controller. Based on the time interval information, it obtains the basic information of the horizontal axis and the basic information of the vertical axis. Based on the basic information of the horizontal axis and the basic information of the vertical axis, it obtains the original historical monitoring data curve. Based on the original historical monitoring data curve, it obtains the historical monitoring data curve.

8. The condition monitoring system for modular controllers of permanent magnet circuit breakers according to claim 7, characterized in that, The control module specifically includes: The control unit is used to obtain the data monitoring frequency corresponding to each type of monitoring data based on the characteristic extreme point, and to obtain the data monitoring correction frequency corresponding to each type of monitoring data based on the state monitoring requirements according to the data monitoring frequency corresponding to each type of monitoring data. An information receiving unit interacts with an information acquisition module and a curve construction module to receive data and transmit it to a curve recognition unit. The curve recognition unit is used to obtain the extreme point information corresponding to each historical monitoring data curve based on the historical monitoring data curve, and to obtain the characteristic extreme points based on the data deviation threshold and the extreme point information.

9. The condition monitoring system for modular controllers of permanent magnet circuit breakers according to claim 7, characterized in that, The information acquisition module specifically includes: The first acquisition unit is used to acquire controller structure information and, based on the controller structure information and data analysis requirements, acquire monitoring data type information. The second acquisition unit is used to acquire historical monitoring data of the controller based on the monitoring data type information, and to acquire the timestamp information corresponding to the historical monitoring data of the controller.

10. The condition monitoring system for modular controllers of permanent magnet circuit breakers according to claim 7, characterized in that, The curve construction module specifically includes: The coordinate system construction unit is used to obtain the time interval information of adjacent timestamps in the historical monitoring data of each controller according to the timestamp information corresponding to the historical monitoring data of the controller, and obtain the basic information of the horizontal axis and the basic information of the vertical axis according to the time interval information. A curve construction unit is used to obtain the original historical monitoring data curve based on the basic information of the horizontal axis and the basic information of the vertical axis, and to obtain the historical monitoring data curve based on the original historical monitoring data curve.

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