CN117849662A - Ammeter case that possesses electric leakage monitoring early warning system - Google Patents
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Abstract
The invention relates to the technical field of current leakage monitoring, in particular to an ammeter box with a leakage monitoring and early warning system. The system obtains differential order by the degree of offset, the degree of data fluctuation and autocorrelation on the current data signal. The differential signal is obtained according to the differential order. A detrending signal of the differential signal is obtained. Obtaining a change trend sequence of the current data signal; and respectively predicting according to the change trend sequence and the trending signal to obtain predicted current data, obtaining a leakage coefficient according to the difference between the actual current data and the predicted current data, and carrying out early warning according to the leakage coefficient. According to the invention, the current data signal is decomposed into the trending signal and the change trend sequence through an effective signal decomposition method, so that prediction is performed respectively, and accurate leakage early warning is realized according to an accurate prediction result.
Description
Technical Field
The invention relates to the technical field of current leakage monitoring, in particular to an ammeter box with a leakage monitoring and early warning system.
Background
The environment in the ammeter box is complex, the related interfaces of the electrical equipment are numerous, and the phenomena of electric leakage can be generated with high probability. Therefore, the electric meter box needs to comprise an electric leakage monitoring and early warning system besides the function of the traditional electric meter box so as to ensure the safe operation of the electric equipment.
In order to realize timely and accurate electric leakage early warning in the prior art, collected current data signals are predicted, and whether electric leakage risks occur is judged through predicted current data at future time. However, in the prediction process, because the electric power equipment connected to the electric meter box is more, the collected current data signals have great instability, and the prediction result in the prior art is poor because the operation period of different equipment shows an irregular trend, so that the accurate early warning of the leakage phenomenon cannot be realized.
Disclosure of Invention
In order to solve the technical problems that the current data signal prediction effect is poor and accurate leakage monitoring cannot be realized in the prior art, the invention aims to provide an ammeter box with a leakage monitoring and early warning system, and the adopted technical scheme is as follows:
the invention provides an ammeter box with an electric leakage monitoring and early warning system, which comprises an ammeter box body, wherein the ammeter box further comprises the electric leakage monitoring and early warning system, and the electric leakage monitoring and early warning system comprises:
the current information acquisition module is used for acquiring current data signals of the ammeter box;
the differential order acquisition module is used for acquiring the offset degree of each signal point on the current data signal relative to the whole signal point; obtaining the data fluctuation degree of the current data signal; obtaining the autocorrelation of data points of each signal point on the current data signal between preset hysteresis periods; obtaining a differential order according to the offset degree, the data fluctuation degree and the autocorrelation on the current data signal;
the differential current data acquisition module is used for acquiring a differential signal of the current data signal according to the differential order, taking an n-order derivative of the differential signal as a trending signal, wherein n is the differential order;
the electric leakage early warning module is used for obtaining a change trend sequence of the current data signal; and respectively predicting according to the change trend sequence and the trending signal to obtain predicted current data, obtaining a leakage coefficient according to the difference between the actual current data and the predicted current data, and carrying out early warning according to the leakage coefficient.
Further, the method for acquiring the offset degree includes:
and obtaining a signal value difference between the signal value of each signal point and the average signal value of the current data signal, and taking the ratio of the signal value difference to the signal value standard deviation of the current data signal as the offset degree.
Further, the method for acquiring the data fluctuation degree comprises the following steps:
and taking the variation coefficient of the current data signal as the data fluctuation degree.
Further, the method for acquiring the autocorrelation includes:
obtaining the autocorrelation according to an autocorrelation calculation formula, the autocorrelation calculation formula comprising:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the autocorrelation +.>Is an exponential function based on natural constants, < ->For the number of signal points on the current data signal, and (2)>For the preset lag phase, < >>Is->Signal value of individual signal points,/->For the average signal value of the current data signal, < >>Is->Signal values of the signal points.
Further, the method for obtaining the differential order comprises the following steps:
mapping and normalizing the average offset degree negative correlation of all the signal points to obtain the stability of the current data signal; taking the ratio of the stationarity to the data fluctuation degree as a stationary distribution characteristic; multiplying the stable distribution characteristics by the autocorrelation and then normalizing to obtain a mapping coefficient; multiplying the mapping coefficient with a preset maximum differential order to obtain the differential order.
Further, the method for acquiring the predicted current data includes:
performing reference prediction on the trending signal by using an AR model to obtain reference current data; predicting the change trend sequence by using an ARIMA prediction algorithm to obtain an accidental predicted value; the predicted current data is obtained from the reference current data and the contingency predicted value.
Further, the obtaining the predicted current data from the reference current data and the contingency predicted value includes:
and taking the sum value of the reference current data and the accidental predictive value as the predictive current data.
Further, the method for obtaining the leakage coefficient comprises the following steps:
acquiring a first current difference between actual current data and predicted current data; and acquiring a second current difference between each signal point on the current data signal and the reference current data, and taking the ratio of the first current difference to the maximum second current difference as the leakage coefficient.
Further, the early warning according to the leakage coefficient includes:
if the leakage coefficient is greater than 1, the leakage phenomenon is considered to occur, and the early warning command is executed.
Further, the current data signal is processed by a simple sliding average method to obtain the variation trend sequence.
The invention has the following beneficial effects:
according to the embodiment of the invention, the fact that the DC component occurs due to the complex circuit composition in the ammeter box, and the data is unstable, so that the prediction effect is affected is considered. Therefore, the embodiment of the invention carries out trending treatment on the current data signal, firstly carries out initial trending treatment by utilizing a differential method, the differential order is related to the data distribution characteristics of the current data signal, and the effective initial trending treatment can be realized by acquiring the proper differential order to carry out differential, so as to obtain the differential signal. The main removal in the initial trend is a relatively obvious large trend, and the current data signal also contains accidental data trend caused by random power utilization period of electric equipment, so that the differential signal needs to be further subjected to trending treatment, and the trending signal is obtained through a fractional differentiation method. The trending signal is a signal with stable trend-free distribution, and the change trend sequence is a signal containing larger trend, so that the signals are respectively predicted according to the two types of signals, analyzed and integrated from the two types of dimensions, accurate predicted current data can be obtained, and accurate leakage early warning can be performed by comparing the predicted current with actual current data.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a leakage monitoring and early warning system according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of an electric meter box with an electric leakage monitoring and early warning system according to the invention, and the specific implementation, structure, characteristics and effects thereof are as follows. 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 specific scheme of the ammeter box with the leakage monitoring and early warning system provided by the invention is specifically described below with reference to the accompanying drawings.
The electric meter box in the embodiment of the invention comprises an electric meter box body and an electric leakage monitoring and early warning system, wherein the electric meter box body can realize basic electric meter box functions, and specific components and structures are well known to those skilled in the art and are not described herein.
Referring to fig. 1, a block diagram of a leakage monitoring and early warning system according to an embodiment of the present invention is shown, where the system includes: the system comprises a current information acquisition module 101, a differential order acquisition module 102, a differential current data acquisition module 103 and a leakage early warning module 104.
The current information acquisition module 101 is configured to acquire a current data signal of an electric meter box, and in this embodiment of the present invention, a current sensor installed in the electric meter box may be specifically used to acquire a data signal in real time within a period of time, and it should be noted that, because the embodiment of the present invention is intended to implement early warning of electric leakage by predicting data, for subsequent actual current data, the acquired current data signal should be a historical signal of the actual current data, that is, the current data at a historical time before the real time corresponding to the actual current data. In one embodiment of the invention, the current data is collected once at a frequency of 1 second, and the data thirty minutes before the real-time moment is taken as a current data signal.
The current data signals in the ammeter box can cause data instability when the direct current component appears on the signals due to factors such as environment, signal source bias, sensor error and the like, so that the current data signals show more random change trend, the whole data is unstable, and the accuracy of the predicted data can be greatly reduced if the current data signals are directly utilized for data prediction. Therefore, the current data signal needs to be subjected to direct current component removal and trending removal, so that accurate prediction can be performed in a subsequent process.
The embodiment of the invention realizes the removal of the direct current component by using the difference method, and the difference method can eliminate obvious trend among signal points by carrying out difference among signals, thereby obtaining stable data signals. Considering that the differential order has a larger influence on the differential result, if the differential order is too large, more information is lost in the subsequent differential signals, and the method cannot be used for predicting the current data; if the differential order is too small, the subsequent differential signals still have a relatively obvious trend. The differential order acquisition module 102 is therefore configured to obtain the degree of offset of each signal point on the current data signal relative to the overall signal point. The larger the offset degree is, the more unstable the current data signal is, and the more complex the contained information is, so that the smaller differential order is needed for differential, and the current information is prevented from being lost due to the overlarge differential order; obtaining the data fluctuation degree of the current data signal, and similarly to the offset degree, the larger the fluctuation degree is, the more complex the current data signal is, and the smaller the difference order is needed to be differentiated; the autocorrelation of each data point on the current data signal between preset hysteresis periods is obtained, and the stronger the autocorrelation is, the more obvious trend on the current data signal is indicated, and the larger differential order is needed to conduct the difference so as to eliminate obvious trend characteristics caused by the direct current component. The differential order is thus obtained based on the degree of offset, the degree of data fluctuation and the autocorrelation on the current data signal.
Preferably, in one embodiment of the present invention, the method for acquiring the offset degree includes:
and obtaining a signal value difference between the signal value of each signal point and the average signal value of the current data signal, and taking the ratio of the signal value difference to the standard deviation of the signal value of the current data signal as the offset degree. I.e. the degree of offset is formulated as:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein->Is->Degree of offset of individual signal points, +.>Is->In the signal of the individual signal points, < >>Is the average signal value of the current data signal, +.>Is the standard deviation of the current data signal.
The deviation degree formula can be used for obtaining that the deviation degree is the absolute value of the standard fraction of the signal point, and the larger the absolute value of the standard fraction is, the more the signal point deviates from the integral data characteristic, and the larger the deviation degree is.
Preferably, in one embodiment of the present invention, the method for acquiring the degree of fluctuation of data includes: the coefficient of variation of the current data signal is taken as the degree of fluctuation of the data. The larger the coefficient of variation, the greater the degree of fluctuation of the data value on the current data signal. It should be noted that the coefficient of variation is a technical means well known to those skilled in the art, and will not be described herein.
Preferably, in one embodiment of the present invention, the method for acquiring autocorrelation includes:
the autocorrelation is obtained according to an autocorrelation calculation formula, which includes:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is autocorrelation, ++>Is an exponential function based on natural constants, < ->For the number of signal points on the current data signal, < >>For the preset lag phase, < >>Is->Signal value of individual signal points,/->Is the average signal value of the current data signal, +.>Is->Signal values of the signal points.
In the autocorrelation calculation formula, one can applyIs regarded as skinThe transformation of the pearson correlation coefficient is achieved by considering the whole current data signal as two sequences, namely a normal time sequence and a hysteresis sequence influenced by the hysteresis, and the data of the two sequences are identical in nature, so that the self-correlation formula can be obtained by transforming and simplifying the pearson correlation coefficient formula, wherein the meaning of an exponential function based on a natural constant is that a numerical range is mapped, and the influence of a negative value is avoided. It should be noted that, the pearson correlation coefficient formula is a technical means well known to those skilled in the art, and the specific content and simplification method are not described in detail.
Preferably, the method for acquiring the differential order in one embodiment of the present invention includes:
because the offset degree and the data fluctuation degree are in negative correlation with the differential order, the average offset degree of all signal points is mapped and normalized in a negative correlation manner, the stability of the current data signal is obtained, and the ratio of the stability to the data fluctuation degree is used as a stable distribution characteristic, namely, the larger the stable distribution characteristic is, the larger the differential order is. And multiplying the stable distribution characteristics by the autocorrelation and then normalizing to obtain the mapping coefficient. That is, the larger the stationary distribution feature is, the larger the autocorrelation is, the larger the differential order is required, and the larger the mapping coefficient is, so that the mapping coefficient is multiplied by the preset maximum differential order to obtain the differential order. In one embodiment of the present invention, the maximum differential order is set to 2 th order, considering that the order of the fractional differential is typically between 0 and 2. The mapping coefficients are formulated as:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein->For mapping coefficients +.>For the average degree of offset +.>Degree of fluctuation of data>Is natural constant (18)>Is autocorrelation, ++>Is a normalization function. The data average offset degree is subjected to negative correlation mapping and normalization by using an exponential function with a natural constant as a base. The normalization function can be set by adopting the prior art such as hyperbolic tangent function mapping, polar difference normalization and the like, and is not limited and described in detail herein.
After the differential order is obtained, the differential current data obtaining module 103 can be used to obtain a differential signal of the current data signal according to the differential order. Note that the fractional difference is a technical means well known to those skilled in the art, and will not be described herein. Further, the current in the ammeter box can be changed unstably along with actually used electric equipment, and the direct current component removed by the differential signal is a more obvious trend, so that the differential signal is further processed, and the n-order derivative of the differential signal is used as a trending signal, and n is the differential order. According to the physical meaning of the fractional differentiation, irregular random fluctuation of the signal can be eliminated through the fractional differentiation, so that the signal is smoother. It should be noted that the fractional differentiation is a technical means well known to those skilled in the art, and will not be described herein.
Although the trending signal eliminates complex trend fluctuation on the current data signal, if only the trending signal is used for data prediction, the prediction result is too ideal and does not conform to the accurate electric field scene for the actual ammeter box. Therefore, the leakage pre-warning module 104 further obtains a variation trend sequence of the current data signal, and predicts according to the variation trend sequence and the detrending signal, respectively, to obtain predicted current data. Because the change trend sequence has obvious data trend, the trend signals are outstanding in idealizing stable current data, so that the two types of signals are respectively predicted, stable ideal data can be obtained, meanwhile, accidental information in an actual scene can be considered, and the obtained predicted current data is more accurate. And obtaining the leakage coefficient according to the difference between the actual current data and the predicted current data, and carrying out early warning according to the leakage coefficient.
In one embodiment of the invention, the current data signal is processed using a simple moving average method to obtain a trend sequence. It should be noted that, in the embodiment of the present invention, the sliding window size of the simple sliding average method is set to 5, and the specific processing method is a technical means well known to those skilled in the art, and will not be described herein.
Preferably, in one embodiment of the present invention, the method for acquiring predicted current data includes:
the AR model is a statistical and time series model, and can be regarded as a wireless impulse response filter, and the AR model can be used to accurately predict a smooth trending signal, so as to obtain reference current data. The ARIMA prediction algorithm is a combination of an AR prediction method and an MA prediction method, wherein MA can be regarded as a finite impulse response, and the input is white noise, so that the ARIMA prediction algorithm can be utilized to predict a change trend sequence containing trend information, and then an accidental prediction value is obtained. And combining the reference current data and the accidental predictive value, and obtaining accurate predictive current data according to the reference current data and the accidental predictive value. In one embodiment of the present invention, the sum of the reference current data and the occasional predicted value is taken as the predicted current data.
Preferably, in one embodiment of the present invention, the method for acquiring the leakage current coefficient includes:
a first current difference between the actual current data and the predicted current data is obtained. The larger the first current difference, the larger the actual value is changed relative to the predicted value, and the more electric leakage is likely to occur. However, the normal current data in the ammeter box can have fluctuation in a certain range due to the difference of the electrical appliances, and the fluctuation range needs to be considered, and because the reference current data is a basic current value when the trend change of the current data does not exist, the second current difference between each signal point on the current data signal and the reference current data is obtained, and the ratio of the first current difference to the maximum second current difference is taken as the leakage coefficient. That is, the maximum second current difference is the normal maximum fluctuation, if the ratio is too large, the difference between the actual value and the predicted value exceeds the maximum fluctuation, and the more possible occurrence of the leakage phenomenon at real time is indicated, and the larger the leakage coefficient is. Therefore, in one embodiment of the present invention, if the leakage coefficient is greater than 1, the leakage phenomenon is considered to occur, and the early warning command is executed.
The leakage coefficient is expressed as:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is the leakage coefficient->For actual current data, +.>For predicting current data, +.>Is->Signal value of individual signal points,/->Is the reference current data. />The function is selected for the maximum value.
In one embodiment of the present invention, if the early warning command is executed to generate an alarm, the power supply can be automatically turned off and the maintenance personnel can be notified to check the leakage position and isolate the leakage area. Repairing the leakage line or device. Test verification is needed before re-electrifying, a leakage protector is installed, a leakage event is recorded, a leakage maintenance report is generated, and the leakage maintenance report is uploaded to a terminal.
In summary, the embodiment of the invention obtains the differential order through the offset degree, the data fluctuation degree and the autocorrelation on the current data signal. The differential signal is obtained according to the differential order. A detrending signal of the differential signal is obtained. Obtaining a change trend sequence of the current data signal; and respectively predicting according to the change trend sequence and the trending signal to obtain predicted current data, obtaining a leakage coefficient according to the difference between the actual current data and the predicted current data, and carrying out early warning according to the leakage coefficient. According to the invention, the current data signal is decomposed into the trending signal and the change trend sequence through an effective signal decomposition method, so that prediction is performed respectively, and accurate leakage early warning is realized according to an accurate prediction result.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. The utility model provides a possess electric leakage monitoring early warning system's ammeter case, includes ammeter case body, its characterized in that, ammeter case still includes electric leakage monitoring early warning system, electric leakage monitoring early warning system includes:
the current information acquisition module is used for acquiring current data signals of the ammeter box;
the differential order acquisition module is used for acquiring the offset degree of each signal point on the current data signal relative to the whole signal point; obtaining the data fluctuation degree of the current data signal; obtaining the autocorrelation of data points of each signal point on the current data signal between preset hysteresis periods; obtaining a differential order according to the offset degree, the data fluctuation degree and the autocorrelation on the current data signal;
the differential current data acquisition module is used for acquiring a differential signal of the current data signal according to the differential order, taking an n-order derivative of the differential signal as a trending signal, wherein n is the differential order;
the electric leakage early warning module is used for obtaining a change trend sequence of the current data signal; and respectively predicting according to the change trend sequence and the trending signal to obtain predicted current data, obtaining a leakage coefficient according to the difference between the actual current data and the predicted current data, and carrying out early warning according to the leakage coefficient.
2. The electric meter box with the leakage monitoring and early warning system according to claim 1, wherein the method for acquiring the offset degree comprises the following steps:
and obtaining a signal value difference between the signal value of each signal point and the average signal value of the current data signal, and taking the ratio of the signal value difference to the signal value standard deviation of the current data signal as the offset degree.
3. The electric meter box with the leakage monitoring and early warning system according to claim 1, wherein the method for acquiring the fluctuation degree of the data comprises the following steps:
and taking the variation coefficient of the current data signal as the data fluctuation degree.
4. The electricity meter box with the leakage monitoring and early warning system according to claim 1, wherein the method for acquiring the autocorrelation comprises the following steps:
obtaining the autocorrelation according to an autocorrelation calculation formula, the autocorrelation calculation formula comprising:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the autocorrelation +.>Is an exponential function based on natural constants, < ->For the number of signal points on the current data signal, and (2)>For the preset lag phase, < >>Is->Signal value of individual signal points,/->For the average signal value of the current data signal, < >>Is->Signal values of the signal points.
5. The electric meter box with the leakage monitoring and early warning system according to claim 1, wherein the method for obtaining the differential order comprises the following steps:
mapping and normalizing the average offset degree negative correlation of all the signal points to obtain the stability of the current data signal; taking the ratio of the stationarity to the data fluctuation degree as a stationary distribution characteristic; multiplying the stable distribution characteristics by the autocorrelation and then normalizing to obtain a mapping coefficient; multiplying the mapping coefficient with a preset maximum differential order to obtain the differential order.
6. The electricity meter box with the leakage monitoring and early warning system according to claim 1, wherein the method for acquiring the predicted current data comprises the following steps:
performing reference prediction on the trending signal by using an AR model to obtain reference current data; predicting the change trend sequence by using an ARIMA prediction algorithm to obtain an accidental predicted value; the predicted current data is obtained from the reference current data and the contingency predicted value.
7. The electrical meter box with leakage monitoring and warning system according to claim 6, wherein the obtaining the predicted current data from the reference current data and the contingency predicted value comprises:
and taking the sum value of the reference current data and the accidental predictive value as the predictive current data.
8. The electricity meter box with the leakage monitoring and early warning system according to claim 7, wherein the method for obtaining the leakage coefficient comprises the following steps:
acquiring a first current difference between actual current data and predicted current data; and acquiring a second current difference between each signal point on the current data signal and the reference current data, and taking the ratio of the first current difference to the maximum second current difference as the leakage coefficient.
9. The electricity meter box with the leakage monitoring and early warning system according to claim 8, wherein the early warning according to the leakage coefficient comprises:
if the leakage coefficient is greater than 1, the leakage phenomenon is considered to occur, and the early warning command is executed.
10. The electricity meter box with the leakage monitoring and early warning system according to claim 1, wherein the current data signal is processed by a simple moving average method to obtain the change trend sequence.
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