CN117805542B - Mining flameproof intrinsically safe frequency converter operation monitoring system - Google Patents
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Abstract
The invention relates to the technical field of power distribution monitoring, in particular to a mining flameproof intrinsically safe frequency converter operation monitoring system, which comprises: dividing the current data into a plurality of data segments by using maximum values in the current data, carrying out weighted filtering on the current values in each data segment in the current data according to the distribution condition of the current values in the data segments, obtaining filtered current values of data points in the data segments, obtaining the abnormality degree of each data point in each data segment according to the difference before and after the current value filtering of the data points in the data segments, the current value difference of different data points, the rated current and the temperature data of the frequency converter, and carrying out frequency converter operation abnormality alarm according to the abnormality degree of the data points. The invention improves the detection accuracy of the current abnormality output by the frequency converter, thereby improving the operation stability and reliability of the equipment.
Description
Technical Field
The invention relates to the technical field of power distribution monitoring, in particular to a mining flameproof intrinsically safe frequency converter operation monitoring system.
Background
The mining flameproof intrinsically safe frequency converter is a frequency converter which is specially applied to a mine environment, and in order to ensure that the frequency converter can safely and reliably operate in the dangerous mine environment, current data of the frequency converter are generally analyzed to monitor the operation of the frequency converter, an alarm is given when current abnormality occurs, and the safe operation of the frequency converter and other electrical equipment in the mine environment is ensured.
In the process of analyzing the current data of the frequency converter, the current data is subjected to severe fluctuation due to the influence of interference signals such as electromagnetic interference or power supply noise, so that the current data is usually required to be filtered, the instability of the current data is reduced, when the current data is filtered by using a traditional exponential weighted moving average method, the filtering effect of the current data is influenced by the selection of the weight coefficient, the accuracy of an abnormal detection result of the current data is further influenced, and the accuracy of judging the running state of the frequency converter is further influenced.
Disclosure of Invention
The invention provides a mining flameproof intrinsically safe frequency converter operation monitoring system which aims at solving the existing problems.
The invention relates to a mining flameproof intrinsically safe frequency converter operation monitoring system which adopts the following technical scheme:
The embodiment of the invention provides a mining explosion-proof intrinsically safe frequency converter operation monitoring system, which comprises the following modules:
the data acquisition module is used for acquiring rated current, current data and temperature data of the frequency converter;
The weighting filtering module is used for dividing the current data into a plurality of data segments through the maximum value point in the current data, and carrying out weighting filtering on the current values in each data segment in the current data according to the distribution condition of the current values in the data segments to obtain the filtered current values of the data points in the data segments;
the abnormal quantization module is used for obtaining the abnormal degree of each data point in each data section according to the difference between the current value of the data point in the data section and the filtered current value, the current value difference of different data points, the rated current of the frequency converter and the temperature data;
and the operation monitoring module is used for alarming abnormal operation of the frequency converter according to the abnormal degree of the data points.
Further, the current data is divided into a plurality of data segments by a maximum point in the current data, and the specific method comprises the following steps:
Obtaining a maximum point in the current data, and dividing the current data into the current data by utilizing the maximum point in the current data The segments are denoted as data segments, where/>Representing the number of maximum points in the current data.
Further, the method for weighting and filtering the current values in each data segment in the current data according to the distribution condition of the current values in the data segment to obtain the filtered current values of the data points in the data segment comprises the following specific steps:
Acquiring a time point of each data point in the current data;
the specific calculation method of the filtering weight of the data points in the data section of the current data comprises the following steps:
Wherein, First/>, representing current dataFirst/>, in data sectionFiltering weights for data points; /(I)First/>, representing current dataFirst/>, in data sectionTime points of the data points; /(I)First/>, representing current dataA time point maximum for all data points in the data segment; /(I)First/>, representing current dataStandard deviation of current values of all data points in the data segments; /(I)An exponential function based on a natural constant;
and carrying out filtering treatment on the data segment of the current data by using an EWMA algorithm according to the filtering weight of the data point in the data segment of the current data, and obtaining the filtered current value of the data point in the data segment.
Further, the specific calculation method includes the steps of:
Wherein, First/>, representing current dataFirst/>, in data sectionFiltered current values for the data points; /(I)First/>, representing current dataFirst/>, in data sectionA current value of the data point; /(I)First/>, representing current dataFirst/>, in data sectionCurrent values for data points.
Further, the method for obtaining the abnormality degree of each data point in each data segment according to the difference between the current value of the data point in the data segment and the filtered current value, the current value difference of different data points, the rated current of the frequency converter and the temperature data comprises the following specific steps:
Will be the first in the current data Maximum point and No./>The absolute value of the difference value of the current values of the maximum points is recorded as an extremum difference parameter of the current data; let/>Maximum point and No./>Absolute value of difference between time points corresponding to the maximum points is recorded as the/>Maximum point and No./>The time intervals of the maximum points are recorded as time difference sequences of the current data points, wherein the time intervals of all adjacent maximum points in the current data form sequences;
Obtaining a period parameter of the current data according to the extremum difference parameter and the time difference sequence of the current data;
The DTW algorithm is utilized to acquire the DTW distance between any two data segments in the current data, and the difference parameters of the data segments in the current data are acquired according to the DTW distances of different data segments;
Any data segment of current data Data points and/>The absolute value of the difference in current values of the data points is recorded as the/>The current change degree of the data points is used for obtaining the abnormal mutation degree of the data points in the data section of the current data according to the current change degree of the data points in the data section;
obtaining the mutation confidence level of the data points in the data section of the current data according to the rated current of the frequency converter, the difference parameter of the data section in the current data, the current value of the data points and the abnormal mutation degree of the data points;
dividing the temperature data into the following points according to the time points corresponding to the maximum value points in the current data A data segment for the temperature dataData points and/>Absolute difference of temperature values of data points, recorded as the/>, of the data segment of the temperature dataThe degree of temperature change of the data points;
Recording the difference value between the current value of the data point in the data section of the current data and the current value after filtering as the residual value of the data point; the first to the current data First/>, in data sectionAbrupt failure of data points, the first/>, of current dataFirst/>, in data sectionAbsolute value of residual value of data point and the/>, of temperature dataFirst/>, in data sectionThe cumulative result of the temperature change degree of the data points is recorded as the first/>, of the current dataFirst/>, in data sectionAnd (3) carrying out linear normalization on the first numerical value of the data point, and marking the first numerical value of the data point after the linear normalization as the abnormality degree of the data point.
Further, the method for obtaining the cycle parameter of the current data according to the extremum difference parameter and the time difference sequence of the current data comprises the following specific calculation methods:
Wherein, A cycle parameter representing current data; /(I)First/>, representing current dataA extremum difference parameter; /(I)The number of extremum difference parameters representing the current data; /(I)A/>, in a time-series of differences representing current dataThe numerical value of the individual elements; /(I)A/>, in a time-series of differences representing current dataThe numerical value of the individual elements; /(I)Representing the number of elements in the time-differential sequence of current data; /(I)Representing an absolute value function.
Further, the method for obtaining the difference parameters of the data segments in the current data according to the DTW distances of the different data segments comprises the following specific steps:
the specific calculation method of the difference parameters of the data segments in the current data comprises the following steps:
Wherein the method comprises the steps of ，/>First/>, representing current dataA difference parameter for each data segment; /(I)Representing the number of data segments in the current data; /(I)First/>, representing current dataData segment and/>DTW distance of individual data segments.
Further, the method for obtaining the abnormal mutation degree of the data point in the data section of the current data according to the current change degree of the data point in the data section comprises the following specific steps:
recording the average value of the current change degree of all data points in any data segment in the current data as a second numerical value of the data segment;
Will be the first in any data segment The absolute value of the difference between the current change degree of the data point and the second value of the corresponding data segment is recorded as the first/>A third value for a data point;
And carrying out linear normalization processing on the third numerical value of all the data points in any data segment, and recording the linear normalization processing result of the third numerical value of the data point as the abnormal mutation degree of the data point.
Further, the method for obtaining the mutation confidence level of the data points in the data section of the current data according to the rated current of the frequency converter, the difference parameter of the data section in the current data, the current value of the data points and the abnormal mutation degree of the data points comprises the following specific calculation methods:
Wherein, First/>, representing current dataFirst/>, in data sectionMutation confidence of data points; /(I)A cycle parameter representing current data; /(I)First/>, representing current dataA difference parameter for each data segment; /(I)Representing the rated current of the frequency converter; /(I)First/>, representing current dataFirst/>, in data sectionA current value of the data point; /(I)First/>, representing current dataFirst/>, in data sectionAbnormal mutation degree of data points; /(I)Representing a preset first super parameter; /(I)Representing an absolute value function.
Further, the method for alarming abnormal operation of the frequency converter according to the abnormal degree of the data points comprises the following specific steps:
When the abnormality degree of the data points in the data section of the current data is larger than a preset abnormality degree threshold, the data points are marked as target points, and when the number of the target points in the data section is larger than Y times of the number of all the data points in the data section, the frequency converter triggers an alarm device, wherein Y is a preset second super parameter.
The technical scheme of the invention has the beneficial effects that: the current data is divided into a plurality of data segments, the difference of maximum points in the current data is combined to reflect the periodic characteristics of the current data, the nearest data points are given higher weight in the weighted filtering process by utilizing the weight coefficient obeying Gaussian distribution, the older data points are given lower weight, the weighted filtering can be better adapted to the change of the data, the degree of abnormality of the data points in the current data is accurately quantized according to the difference of the current values before and after the weighted filtering of the current data and the change condition of the temperature data, the detection accuracy of the current abnormality output by the frequency converter is improved, and the operation stability and the reliability of the equipment are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a structural block diagram of a mining flameproof intrinsically safe frequency converter operation monitoring system.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of the specific implementation, structure, characteristics and effects of the mining flameproof intrinsically safe frequency converter operation monitoring system according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a concrete scheme of the mining flameproof intrinsically safe frequency converter operation monitoring system, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a mining flameproof intrinsically safe frequency converter operation monitoring system according to an embodiment of the present invention is shown, where the system includes the following modules:
and the data acquisition module is used for acquiring rated current, current data and temperature data of the frequency converter.
In order to realize the operation monitoring system of the mining flameproof intrinsically safe frequency converter provided by the embodiment, temperature data, current data and rated current of the frequency converter are required to be collected, and the specific process is as follows:
The rated current of the frequency converter is obtained, the sensor is used for collecting the current value and the temperature value of the frequency converter in the working process, the frequency converter starts to work during collection, the sampling interval is set to be 0.1s until the frequency converter stops working, the collected data are respectively arranged according to the time sequence, the current data and the temperature data of the frequency converter are respectively obtained, and one data point in the current data corresponds to one time point and one current value.
So far, the current data of the frequency converter are obtained through the method.
The weighting filtering module is used for dividing the current data into a plurality of data segments through the maximum value point in the current data, and carrying out weighting filtering on the current values in each data segment in the current data according to the distribution condition of the current values in the data segments to obtain the filtered current values of the data points in the data segments.
The current data is divided into a plurality of data segments by the maximum value point in the current data.
Because the corresponding current data changes along with the running state of the frequency converter in the running process of the frequency converter, and the corresponding current data has different stages of change trends in different running states of the frequency converter, the embodiment is convenient for carrying out filtering processing and anomaly analysis on the current data under different change trends by dividing the current data into a plurality of data segments.
Preferably, in one embodiment of the present invention, the method for acquiring a data segment includes:
Obtaining the maximum point in the current data, dividing the current data into the following points by utilizing the extreme point in the current data The segments are denoted as data segments, where/>Representing the number of maximum points in the current data.
It should be noted that, because the maximum point in the current data represents the point where the current change trend in the current data turns, the current data is divided into a plurality of data segments by using the maximum value in this embodiment, and each data segment represents the change trend of the current data in a local range.
After a plurality of data segments are obtained, further, weighting filtering is carried out on the current values in each data segment in the current data according to the distribution condition of the current values in the data segments, and the filtered current values of the data points in the data segments are obtained.
Because the frequency converter may be subjected to electromagnetic interference in the operation process so that current data of the frequency converter can be subjected to severe fluctuation, in order to avoid the overlarge fluctuation degree of the current data and influence the monitoring result of the operation state of the frequency converter, the current data needs to be subjected to filtering smoothing processing, and the filtered current value of the data point is obtained.
Optionally, the specific method for obtaining the filtered current value of the data point in the data segment includes:
And presetting a weight coefficient of an EWMA algorithm, combining the preset weight coefficient, carrying out filtering treatment on the current data by using the EWMA algorithm, and recording the numerical value of the data point in the current data after the filtering treatment as a current value after the filtering treatment.
It should be noted that, the weight coefficient of the EWMA algorithm may be preset to 0.4 according to experience, and the specific value may be adjusted according to the implementation, which is not limited in this embodiment.
In addition, the chinese name of EWMA (Exponential Weighted Moving Average) algorithm is an exponential weighted moving average algorithm, and since the EWMA algorithm is an existing filtering algorithm, the description of this embodiment is omitted.
Optionally, the specific method for acquiring the filtered current value of the data point in the data segment further includes: obtaining standard deviation of current values of all data points in current dataWill/>And using the weight coefficient as the weight coefficient of the EWMA algorithm, and filtering the current data by using the EWMA algorithm to obtain the filtered current value of the data point in the current data.
Preferably, the specific method for acquiring the filtered current value of the data point in the data segment further comprises:
Firstly, obtaining filtering weights of data points in a data section of current data, wherein the specific calculation mode is as follows:
Wherein, First/>, representing current dataFirst/>, in data sectionFiltering weights for data points; /(I)First/>, representing current dataFirst/>, in data sectionTime points of the data points; /(I)First/>, representing current dataA maximum point in time in the data segment; /(I)First/>, representing current dataStandard deviation of current values of all data points in the data segments; /(I)An exponential function based on a natural constant is represented.
Then, according to the filtering weight of the data points in the data section of the current data, filtering the data section of the current data by using an EWMA algorithm to obtain the filtered current value of the data points in the data section, wherein the specific calculation method comprises the following steps:
Wherein, First/>, representing current dataFirst/>, in data sectionFiltered current values for the data points; /(I)First/>, representing current dataFirst/>, in data sectionA current value of the data point; /(I)First/>, representing current dataFirst/>, in data sectionFiltering weights for data points; /(I)First/>, representing current dataFirst/>, in data sectionCurrent values for data points.
It should be noted that, in this embodiment, by combining the time point differences of different data points in the same data segment and the standard deviation of the current values of all the data points in the data segment, a gaussian function is constructed to be used as the filtering weight of the data points in the data segment, the filtering weight of the data points in the data segment obeys gaussian distribution, and the weight coefficient obeying the gaussian distribution in the EWMA algorithm can enable the EWMA filter to assign a higher weight to the nearest data point and assign a lower weight to the older data point, so that the EWMA algorithm can better adapt to the change of the data, reduce the influence of abnormal values and noise, and improve the filtering effect.
So far, the filtered current value of the data point in the data section is obtained through the method.
The abnormality quantification module is used for obtaining the abnormality degree of each data point in each data section according to the difference between the current value of the data point in the data section and the filtered current value, the current value difference of different data points, the rated current of the frequency converter and the temperature data.
Since the waveform of the current data of the frequency converter is similar to a sine wave, the current data of the frequency converter has a certain periodic variation rule, and the difference between the data points with the same ordinal number in different data segments is smaller by dividing the current data into a plurality of data segments.
Optionally, the method for acquiring the abnormality degree of each data point in each data segment includes:
Recording the absolute value of the difference between the current value of each data point in the current data and the filtered current value as the first difference of the corresponding data point, and recording the first difference in the current data Data points and/>Absolute value of current value difference of data point is recorded as the/>And (3) a second difference value of the data points, and recording the sum value of the first difference value and the second difference value of the data points as the abnormality degree of the data points.
Preferably, the method for acquiring the abnormality degree of each data point in each data segment includes:
step (1), the first step in the current data Maximum point and No./>The absolute value of the difference value of the current values among the maximum value points is recorded as the extremum difference parameter of the current data; let/>Maximum point and No./>Absolute value of difference between corresponding time points among maximum points is recorded as the/>Maximum point and No./>The time intervals of the maximum points are recorded as a time difference sequence of the current data points, wherein the time intervals of all adjacent maximum points in the current data form a sequence.
Step (2), obtaining a period parameter of the current data according to the extremum difference parameter and the time difference sequence of the current data, wherein the specific calculation method comprises the following steps:
Wherein, A cycle parameter representing current data; /(I)First/>, representing current dataA extremum difference parameter; /(I)The number of extremum difference parameters representing the current data; /(I)A/>, in a time-series of differences representing current dataThe numerical value of the individual elements; /(I)A/>, in a time-series of differences representing current dataThe numerical value of the individual elements; /(I)Representing the number of elements in the time-differential sequence of current data; /(I)Representing an absolute value function.
It should be noted that, the maximum points in the current data reflect the change of the trend of the current data in the local range, and the distance between the maximum points, that is, the difference between the maximum points at corresponding time points in the current data reflects the time length of the change of the trend of the current data, and the cycle parameters obtained according to the difference between the corresponding time points of the maximum points reflect the cycle characteristics of the current data. If the difference between the corresponding time points of the adjacent maximum value points in the current data is smaller, the period parameter of the current data is larger, the period characteristic of the current data is more obvious, and the data segments obtained based on the periodicity of the current data are more similar.
In addition, the difference between the current value of the data point in the data section of the current data and the current value after filtering is recorded as the residual value of the data point.
It should be noted that, since the residual value of the data point in the current data cannot fully reflect the change of the data point itself for the whole data segment or the data sample, the degree of abnormality of the data reflected by the data residual needs to be corrected, the degree of abnormality of the data point in which the change is actually normal is reduced, and this is achieved by analyzing the difference between the data segments and between the data points in the data segments.
Since the current data has a periodic characteristic, the current data is divided into a plurality of data segments according to a maximum value reflecting the periodicity, and the data segments should have a similar relationship with each other, and the data segments closer to each other should have a higher degree of similarity with each other. Analyzing the filtered data segments, calculating the difference of the data segments at the corresponding positions between the two adjacent data segments, if the difference between the data segments is smaller, conforming to the periodic characteristics of the data samples, namely, the degree of abnormality between the two current data segments is smaller, weighting the difference between the data segments according to the interval between the data segments to obtain parameters reflecting the degree of difference between the data segments and other data segments, and if the degree of difference is larger, the degree of abnormality of the current data segment is larger.
Step (3), a DTW algorithm is utilized to obtain the DTW distance between any two data segments in the current data; the specific calculation method for obtaining the difference parameters of the data segments in the current data comprises the following steps:
Wherein the method comprises the steps of ，/>First/>, representing current dataA difference parameter for each data segment; /(I)Representing the number of data segments in the current data; /(I)First/>, representing current dataData segment and/>DTW distance of individual data segments.
Note that, the chinese name of DTW (Dynamic Time Warping) algorithm is a dynamic time warping algorithm, and the DTW algorithm is an existing algorithm, so this embodiment is not specifically limited.
Because the current data output by the frequency converter has a certain change rule in the normal working process, when the current value of a data point appears in the current data and the current value of other data points in the data section where the data point is located is suddenly changed, the larger the current value of the data point is, the larger the probability of abnormality of the current value of the data point is.
Step (4), the first data segment of the current data is selectedData points and/>The absolute value of the difference in current values of the data points is recorded as the/>The current change degree of the data points is used for obtaining the abnormal mutation degree of the data points in the data section of the current data according to the current change degree of the data points in the data section, and the specific calculation method comprises the following steps:
Wherein, First/>, representing current dataFirst/>, in data sectionAbnormal mutation degree of data points; /(I)First/>, representing current dataFirst/>, in data sectionThe degree of current change of the data points; /(I)First/>, representing current dataNumber of data points in data segment,/>Representing an absolute value function; /(I)Representing a linear normalization function.
Step (5), obtaining the sudden change confidence level of the data points in the data section of the current data, wherein the specific calculation method comprises the following steps:
Wherein, First/>, representing current dataFirst/>, in data sectionMutation confidence of data points; /(I)A cycle parameter representing current data; /(I)First/>, representing current dataA difference parameter for each data segment; /(I)Representing the rated current of the frequency converter; /(I)First/>, representing current dataFirst/>, in data sectionA current value of the data point; /(I)First/>, representing current dataFirst/>, in data sectionAbnormal mutation degree of data points; /(I)Representing a preset first super parameter; /(I)Representing an absolute value function.
It should be noted that the super parameters are preset according to experience2, Which can be adjusted according to practical situations, the present embodiment is not particularly limited.
It should be noted that the number of the substrates,The difference between the current value of the down converter and the rated current of the frequency converter at different moments is reflected, the larger the difference is, the more abnormal the current value of the output of the frequency converter is likely to exist, the larger the abrupt change degree of confidence of the data point at the corresponding moment is, the abrupt change degree of confidence of the data point is used for describing the probability of data point abnormality, the larger the abrupt change degree of confidence of the data point is, the larger the probability of data point abnormality is, and otherwise, the smaller is.
It should be noted that, the worse the period characteristics between different data segments of the current data, the larger the difference parameters between the data segments, the more can reflect the difference of the data segment relative to other data segments in the current data; when the degree of abnormal mutation of a data point in the current data is larger, the current value of the data point is more likely to have abnormality, the embodiment combines the difference parameters of the data segments andAnd adjusting the abnormal mutation degree of the data points, and determining the mutation confidence level of the data points so as to reflect the probability of the data point abnormality in the current data.
Because of the thermal effect of the current or other reasons, the output current is severely changed due to the damage of the circuit of the frequency converter, and the temperature of the frequency converter is increased, which indicates that the operation of the frequency converter is abnormal, in the embodiment, the abnormal degree of the data points in the current data is obtained according to the abrupt change degree of the data points in the current data and the residual error value by combining the temperature change condition of the frequency converter.
Step (6), dividing the temperature data into the following points according to the time points corresponding to the maximum value points in the current dataA data segment for the temperature dataData points and/>Absolute difference of temperature values of data points, recorded as the/>, of the data segment of the temperature dataThe degree of temperature change of the data points; the abnormal degree of the data points in the data section of the current data is obtained, and the specific calculation method comprises the following steps:
Wherein, First/>, representing current dataFirst/>, in data sectionDegree of anomaly of data points; /(I)First/>, representing current dataFirst/>, in data sectionMutation confidence of data points; /(I)First/>, representing current dataFirst/>, in data sectionResidual values of data points; /(I)First/>, representing temperature dataFirst/>, in data sectionThe degree of temperature change of the data points; /(I)Representing an absolute value function; /(I)Representing a linear normalization function.
So far, the abnormality degree of the data points in the data section of the current data is obtained through the method.
And the operation monitoring module is used for alarming abnormal operation of the frequency converter according to the abnormal degree of the data points.
Specifically, when the abnormality degree of the data points in the data section of the current data is greater than a preset abnormality degree threshold, the data points are marked as target points, and when the number of the target points in the data section is greater than Y times of the number of all the data points in the data section, the frequency converter triggers the alarm device, wherein Y is a preset second super parameter.
It should be noted that, the threshold value of the abnormality degree and the super parameter Y are preset empirically to be 0.6 and 1/5, respectively, and may be adjusted according to actual situations, and the embodiment is not particularly limited.
It should be noted that, in this embodiment, the filtered current value of the data point in the current data is obtained by performing filtering smoothing processing on the current data, and when there is a data point with an abnormal current value in the current data, a larger residual error exists in the current value of the data point before and after filtering, so that the abnormality degree of the data point is obtained by combining the residual error value of the data point in the current data, thereby determining the data point with an abnormal current value in the current data, that is, the target point, and improving the detection accuracy of the abnormal data point in the current data.
This embodiment is completed.
The following examples were usedThe model is only used to represent the negative correlation and the result output by the constraint model is at/>In the section, other models with the same purpose can be replaced in the specific implementation, and the embodiment is only to/>The model is described as an example, and is not particularly limited, wherein/>Refers to the input of the model.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (4)
1. The mining flameproof intrinsically safe frequency converter operation monitoring system is characterized by comprising the following modules:
the data acquisition module is used for acquiring rated current, current data and temperature data of the frequency converter;
The weighting filtering module is used for dividing the current data into a plurality of data segments through the maximum value point in the current data, and carrying out weighting filtering on the current values in each data segment in the current data according to the distribution condition of the current values in the data segments to obtain the filtered current values of the data points in the data segments;
the abnormal quantization module is used for obtaining the abnormal degree of each data point in each data section according to the difference between the current value of the data point in the data section and the filtered current value, the current value difference of different data points, the rated current of the frequency converter and the temperature data;
the operation monitoring module is used for alarming abnormal operation of the frequency converter according to the abnormal degree of the data points;
The method for weighting and filtering the current values in each data segment in the current data according to the distribution condition of the current values in the data segment to obtain the filtered current values of the data points in the data segment comprises the following specific steps:
Acquiring a time point of each data point in the current data;
the specific calculation method of the filtering weight of the data points in the data section of the current data comprises the following steps:
Wherein, First/>, representing current dataFirst/>, in data sectionFiltering weights for data points; /(I)First/>, representing current dataFirst/>, in data sectionTime points of the data points; /(I)First/>, representing current dataA time point maximum for all data points in the data segment; /(I)First/>, representing current dataStandard deviation of current values of all data points in the data segments; an exponential function based on a natural constant;
According to the filtering weight of the data points in the data section of the current data, filtering the data section of the current data through an EWMA algorithm to obtain the filtered current values of the data points in the data section;
The method for obtaining the abnormality degree of each data point in each data section according to the difference between the current value of the data point in the data section and the filtered current value, the current value difference of different data points, the rated current of the frequency converter and the temperature data comprises the following specific steps:
Will be the first in the current data Maximum point and No./>The absolute value of the difference value of the current values of the maximum points is recorded as an extremum difference parameter of the current data; let/>Maximum point and No./>Absolute value of difference between time points corresponding to the maximum points is recorded as the/>Maximum point and No./>The time intervals of the maximum points are recorded as time difference sequences of the current data points, wherein the time intervals of all adjacent maximum points in the current data form sequences;
Obtaining a period parameter of the current data according to the extremum difference parameter and the time difference sequence of the current data;
The DTW algorithm is utilized to acquire the DTW distance between any two data segments in the current data, and the difference parameters of the data segments in the current data are acquired according to the DTW distances of different data segments;
Any data segment of current data Data points and/>The absolute value of the difference in current values of the data points is recorded as the/>The current change degree of the data points is used for obtaining the abnormal mutation degree of the data points in the data section of the current data according to the current change degree of the data points in the data section;
obtaining the mutation confidence level of the data points in the data section of the current data according to the rated current of the frequency converter, the difference parameter of the data section in the current data, the current value of the data points and the abnormal mutation degree of the data points;
dividing the temperature data into the following points according to the time points corresponding to the maximum value points in the current data A data segment for the temperature dataData points and/>Absolute difference of temperature values of data points, recorded as the/>, of the data segment of the temperature dataThe degree of temperature change of the data points;
Recording the difference value between the current value of the data point in the data section of the current data and the current value after filtering as the residual value of the data point; the first to the current data First/>, in data sectionAbrupt failure of data points, the first/>, of current dataFirst/>, in data sectionAbsolute value of residual value of data point and the/>, of temperature dataFirst/>, in data sectionThe cumulative result of the temperature change degree of the data points is recorded as the first/>, of the current dataFirst/>, in data sectionPerforming linear normalization processing on the first values of all data points in the current data, and recording the first values of the data points after the linear normalization processing as the abnormality degree of the data points;
the method for obtaining the cycle parameters of the current data according to the extremum difference parameters and the time difference sequences of the current data comprises the following specific calculation methods:
Wherein, A cycle parameter representing current data; /(I)First/>, representing current dataA extremum difference parameter; /(I)The number of extremum difference parameters representing the current data; /(I)A/>, in a time-series of differences representing current dataThe numerical value of the individual elements; a/>, in a time-series of differences representing current data The numerical value of the individual elements; /(I)Representing the number of elements in the time-differential sequence of current data; /(I)Representing an absolute value function;
the method for acquiring the difference parameters of the data segments in the current data according to the DTW distances of the different data segments comprises the following specific steps:
Wherein the method comprises the steps of ，/>First/>, representing current dataA difference parameter for each data segment; /(I)Representing the number of data segments in the current data; /(I)First/>, representing current dataData segment and/>DTW distance of the individual data segments;
The method for acquiring the abnormal mutation degree of the data points in the data section of the current data according to the current change degree of the data points in the data section comprises the following specific steps:
recording the average value of the current change degree of all data points in any data segment in the current data as a second numerical value of the data segment;
Will be the first in any data segment The absolute value of the difference between the current change degree of the data point and the second value of the corresponding data segment is recorded as the first/>A third value for a data point;
Performing linear normalization processing on third numerical values of all data points in any data segment, and recording the linear normalization processing result of the third numerical values of the data points as abnormal mutation degrees of the data points;
The method for obtaining the sudden change confidence level of the data points in the data section of the current data according to the rated current of the frequency converter, the difference parameter of the data section in the current data, the current value of the data points and the abnormal sudden change degree of the data points comprises the following specific calculation methods:
Wherein, First/>, representing current dataFirst/>, in data sectionMutation confidence of data points; /(I)A cycle parameter representing current data; /(I)First/>, representing current dataA difference parameter for each data segment; /(I)Representing the rated current of the frequency converter; first/>, representing current data First/>, in data sectionA current value of the data point; /(I)First/>, representing current dataFirst/>, in data sectionAbnormal mutation degree of data points; /(I)Representing a preset first super parameter; /(I)Representing an absolute value function.
2. The mining explosion-proof intrinsically-safe frequency converter operation monitoring system of claim 1, wherein the current data is divided into a plurality of data segments by a maximum value point in the current data, and the specific method comprises the following steps:
Obtaining a maximum point in the current data, and dividing the current data into the current data by utilizing the maximum point in the current data The segments are denoted as data segments, where/>Representing the number of maximum points in the current data.
3. The mining flameproof intrinsically-safe frequency converter operation monitoring system according to claim 1, wherein the specific calculation method comprises the following steps of:
Wherein, First/>, representing current dataFirst/>, in data sectionFiltered current values for the data points; /(I)First/>, representing current dataFirst/>, in data sectionA current value of the data point; /(I)First/>, representing current dataThe first data segmentCurrent values for data points.
4. The mining flameproof intrinsically-safe frequency converter operation monitoring system of claim 1, wherein the frequency converter operation abnormality alarm is carried out according to the abnormality degree of the data points, and comprises the following specific steps:
When the abnormality degree of the data points in the data section of the current data is larger than a preset abnormality degree threshold, the data points are marked as target points, and when the number of the target points in the data section is larger than Y times of the number of all the data points in the data section, the frequency converter triggers an alarm device, wherein Y is a preset second super parameter.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104090189A (en) * | 2014-07-21 | 2014-10-08 | Tcl集团股份有限公司 | Device working state detecting method and device |
| RU2013121243A (en) * | 2013-05-07 | 2014-11-20 | Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Уфимский государственный нефтяной технический университет" | HIGH VOLTAGE FREQUENCY CONVERTER SELF-DIAGNOSIS METHOD |
| CN107238756A (en) * | 2017-06-14 | 2017-10-10 | 扬州万泰电子科技有限公司 | A kind of intelligent distribution transformer terminals impact load Identifying Methods of Harmonic Source |
| WO2021043465A1 (en) * | 2019-09-02 | 2021-03-11 | Siemens Aktiengesellschaft | Method for monitoring the state of an electric device and arrangement for carrying out such a method |
| KR102570528B1 (en) * | 2022-04-11 | 2023-08-25 | 김영재 | Smart switchboard fusing heterogeneous sensor data |
| CN117056813A (en) * | 2023-10-11 | 2023-11-14 | 国网山东省电力公司营销服务中心(计量中心) | Detection method for intelligent circuit breaker data acquisition |
| CN117060742A (en) * | 2023-07-20 | 2023-11-14 | 日照职业技术学院 | Mining flameproof and intrinsically safe alternating current frequency converter and frequency converter electrical system |
| CN117349781A (en) * | 2023-12-06 | 2024-01-05 | 东莞市郡嘉电子科技有限公司 | Intelligent diagnosis method and system for faults of transformer |
-
2024
- 2024-03-01 CN CN202410231672.7A patent/CN117805542B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2013121243A (en) * | 2013-05-07 | 2014-11-20 | Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Уфимский государственный нефтяной технический университет" | HIGH VOLTAGE FREQUENCY CONVERTER SELF-DIAGNOSIS METHOD |
| CN104090189A (en) * | 2014-07-21 | 2014-10-08 | Tcl集团股份有限公司 | Device working state detecting method and device |
| CN107238756A (en) * | 2017-06-14 | 2017-10-10 | 扬州万泰电子科技有限公司 | A kind of intelligent distribution transformer terminals impact load Identifying Methods of Harmonic Source |
| WO2021043465A1 (en) * | 2019-09-02 | 2021-03-11 | Siemens Aktiengesellschaft | Method for monitoring the state of an electric device and arrangement for carrying out such a method |
| KR102570528B1 (en) * | 2022-04-11 | 2023-08-25 | 김영재 | Smart switchboard fusing heterogeneous sensor data |
| CN117060742A (en) * | 2023-07-20 | 2023-11-14 | 日照职业技术学院 | Mining flameproof and intrinsically safe alternating current frequency converter and frequency converter electrical system |
| CN117056813A (en) * | 2023-10-11 | 2023-11-14 | 国网山东省电力公司营销服务中心(计量中心) | Detection method for intelligent circuit breaker data acquisition |
| CN117349781A (en) * | 2023-12-06 | 2024-01-05 | 东莞市郡嘉电子科技有限公司 | Intelligent diagnosis method and system for faults of transformer |
Non-Patent Citations (1)
| Title |
|---|
| 空压机的智能PID控制与SPC在线故障检测;李海波;付琛;;计算机测量与控制;20130725(第07期);全文 * |
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