CN117332215A - High-low voltage power distribution cabinet abnormal fault information remote monitoring system - Google Patents
High-low voltage power distribution cabinet abnormal fault information remote monitoring system Download PDFInfo
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Abstract
The invention relates to the technical field of power distribution monitoring, in particular to a high-low voltage power distribution cabinet abnormal fault information remote monitoring system. The system comprises: the system comprises a high-low power distribution cabinet data acquisition module, a high-low power distribution cabinet data processing module and a high-low power distribution cabinet data monitoring module, wherein voltage data of the high-low power distribution cabinet are acquired; constructing corrected voltage data; acquiring all voltage data sets before correction and voltage data sets after correction; constructing the correlation of the corrected voltage data set elements; constructing a data correlation degree of the voltage data set before correction; acquiring a window voltage data set; analyzing the data periodicity of the window voltage data set to obtain the smooth quantity of the window voltage data set; constructing a smoothing coefficient of the predicted voltage data; and the prediction voltage data is obtained, the abnormal fault monitoring of the high-low voltage power distribution cabinet is completed, the accuracy of data prediction is effectively improved, and the accuracy of abnormal fault monitoring is ensured.
Description
Technical Field
The invention relates to the technical field of power distribution monitoring, in particular to a high-low voltage power distribution cabinet abnormal fault information remote monitoring system.
Background
The high-low voltage power distribution cabinet is power distribution equipment used in a power system, and has the main functions of distributing electric energy from a power transmission network or a generator set in a segmented mode and ensuring that various electric equipment can obtain required electric energy. These power distribution cabinets typically include circuit breakers, contactors, relays, transformers, and the like, to facilitate control and distribution of electrical energy. Plays a vital role in the aspects of industry, business and residential electricity. The fault monitoring of the high-low voltage power distribution cabinet has very important effect, and various possible problems including current overload, short circuit, grounding faults and the like can be timely found and diagnosed through the fault monitoring of the power distribution cabinet. Therefore, the safe and stable operation of the power system can be ensured, and safety accidents and power failure events caused by faults are avoided. Meanwhile, fault monitoring is also beneficial to improving the reliability and the persistence of the equipment, reducing the maintenance cost, prolonging the service life of the equipment and improving the overall efficiency and the reliability of the power system.
The exponential moving average (Exponential Moving Average) algorithm is an averaging method that gives more weight to recent data, has sensitive trend-reflected and smooth predictions, and is widely used for data prediction in time series. The performance and result of the exponential moving average algorithm depends on the choice of the smoothing coefficient in the algorithm, but the smoothing coefficient in the conventional exponential moving average algorithm is usually a constant value selected by an empirical value, and for a more complex scene, the smoothing coefficient set by the empirical value may result in an excessively smooth or excessively sensitive prediction result, so that it is difficult to obtain a more accurate prediction result. Although some adaptive smoothing coefficients exist at present, the smoothing coefficients cannot adapt to the scene of the high-voltage and low-voltage power distribution cabinet.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a high-low voltage power distribution cabinet abnormal fault information remote monitoring system, which adopts the following technical scheme:
the invention provides a high-low voltage power distribution cabinet abnormal fault information remote monitoring system, which comprises:
the high-low power distribution cabinet data acquisition module acquires voltage data of the high-low power distribution cabinet;
the high-low power distribution cabinet data processing module acquires corrected voltage data according to the relation between the voltage data and standard voltage data; dividing the voltage data and the corrected voltage data to obtain a voltage data set before correction and a voltage data set after correction; acquiring the correlation of each element of the corrected voltage data set according to the distribution of each element of the corrected voltage data set; acquiring the data correlation degree of each pre-correction voltage data set according to the correlation of each element of the post-correction voltage data set and the correlation of the pre-correction voltage data set; acquiring a window voltage data set according to the data correlation degree of the voltage data set before correction; acquiring the periodicity of window voltage data set elements according to the data distribution characteristics of the window voltage data set; acquiring the smoothing quantity of the window voltage data set according to the periodicity of the window voltage data set elements; obtaining a smoothing coefficient of the predicted voltage data according to the smoothing quantity of the window voltage data set;
and the high-low power distribution cabinet data monitoring module is used for completing abnormal fault monitoring of the high-low power distribution cabinet according to the smoothing coefficient of the predicted voltage data and the window voltage data set.
Further, the obtaining corrected voltage data according to the relationship between the voltage data and the standard voltage data includes:
calculating the absolute value of the difference value between the voltage data at each moment and the standard voltage data; and taking the sum of the voltage data at each moment and the absolute value of the difference value as corrected voltage data.
Further, the dividing the voltage data and the corrected voltage data to obtain a voltage data set before correction and a voltage data set after correction includes:
regarding the voltage data before correction, taking n continuous voltage data including each voltage data as each element of the voltage data set before correction, wherein n is sequentially increased from 2 until the number of the voltage data is increased to be different from 1;
and obtaining a corrected voltage data set by adopting an acquisition method of the voltage data set before correction.
Further, the obtaining the correlation of each element of the corrected voltage data set according to the distribution of each element of the corrected voltage data set specifically includes:
setting a correlation balance factor; acquiring a hurst index of each element in the corrected voltage data set; and taking the absolute value of the difference between the Hurst index and the correlation balance factor of each element as the correlation of each element of the corrected voltage data set.
Further, the obtaining the data correlation of each pre-correction voltage data set according to the correlation of each element of the post-correction voltage data set and the correlation of the pre-correction voltage data set includes:
acquiring the sum of the correlation of each element of the corrected voltage data set and the latter element; acquiring the DTW distance between each element of the voltage data set before correction and the latter element; and taking the sum value of the ratio of the sum value of all elements and the following element to the DTW distance as the data correlation degree of each corrected voltage data set.
Further, the acquiring the window voltage data set according to the data correlation of the voltage data set before correction includes:
acquiring the data length corresponding to the voltage data set before correction with the maximum data correlation degree; and taking the data length before correction of the voltage data at the current moment as elements of a window voltage data set.
Further, the obtaining the periodicity of the window voltage data set element according to the data distribution characteristic of the window voltage data set includes:
taking the voltage data in the window voltage data set as the input of a KMP algorithm, and taking the non-overlapping longest repeated word strings of the window voltage data set as the output of the KMP algorithm;
obtaining the product of the longest length of the non-overlapped longest repeated word strings and the number of the non-overlapped longest repeated word strings; and taking the ratio of the product to the character length of the element as the periodicity of the window voltage data set element.
Further, the periodically obtaining the smoothing quantity of the window voltage data set according to the window voltage data set element includes:
acquiring a differential sequence of a window voltage data set by adopting a first-order differential method; counting the types and the number of the different elements of the checking and classifying sequence, and obtaining the occurrence probability of each type in the differential sequence;
acquiring the minimum value of the probability; calculating the difference between the probability and the minimum value of each category; taking the difference value as an index of an exponential function based on a natural constant; obtaining the product of the sum of the exponential functions of all kinds and the number of the kinds; calculating the ratio of the product to the number of elements of the differential sequence; taking the product of the inverse of the periodicity of the window voltage data set and the ratio as the smoothed quantity of the window voltage data set.
Further, the obtaining the smoothing coefficient of the predicted voltage data according to the smoothing quantity of the window voltage data set includes:
setting a first smoothing adjustment factor and a second smoothing adjustment factor; calculating a product of the first smoothing adjustment factor and a smoothing amount of a window voltage data set; and taking the sum of the product and the second smoothing adjustment factor as a smoothing coefficient of the predicted voltage data.
Further, the step of completing abnormal fault monitoring of the high-voltage and low-voltage power distribution cabinet according to the smoothing coefficient of the predicted voltage data and the window voltage data set comprises the following steps:
taking the data of the window voltage data set and the smoothing coefficient as the input of an exponential moving average algorithm, and outputting predicted voltage data at the next moment;
calculating root mean square error values of the voltage data in the predicted voltage data and the window voltage data set, and storing the root mean square error values as error values between the predicted data and the monitored data;
calculating the absolute value of the difference value between the monitored voltage data and the predicted voltage data at the next moment; when the absolute value of the difference value is smaller than or equal to the error value, no abnormality occurs in the power distribution cabinet; and when the absolute value of the difference value is larger than the error value, the power distribution cabinet is abnormal.
The invention has the following beneficial effects:
according to the invention, firstly, the acquired voltage data is corrected, the correlation of the voltage data set is obtained through calculation of the corrected data and the uncorrected data, the size of the sliding window is acquired by adopting the set with the largest correlation, and the sliding window with the index moving average is acquired through the correlation among the data, so that the predicted data is not influenced by irrelevant data.
In addition, the periodicity among elements is calculated by utilizing elements in the sliding window, so that the smoothing quantity of the window voltage data set is constructed, and the weight added to the predicted data can be reflected; and calculating a smoothing coefficient of the predicted voltage data through the smoothing adjustment factor and the smoothing amount, so that the obtained predicted result is closer to the real data, and the accuracy of monitoring the abnormal faults of the high-voltage and low-voltage power distribution cabinet is further improved.
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 remote monitoring system for abnormal fault information of a high-low voltage power distribution cabinet according to an embodiment of the present invention;
fig. 2 is a flowchart of the acquisition of the smoothing coefficients.
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 system for remotely monitoring the abnormal fault information of the high-low voltage power distribution cabinet according to the invention, which is provided by the invention, with reference to 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 specific scheme of the high-low voltage power distribution cabinet abnormal fault information remote monitoring system, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a remote monitoring system for abnormal fault information of a high-low voltage power distribution cabinet according to an embodiment of the present invention is shown, where the system includes: the high-low power distribution cabinet data acquisition module 101, the high-low power distribution cabinet data processing module 102 and the high-low power distribution cabinet data monitoring module 103.
And the high-low power distribution cabinet data acquisition module 101 acquires historical data of the high-low power distribution cabinet and performs preprocessing on the data.
And transmitting the voltage data of the high-low voltage power distribution cabinet to a remote monitoring system by utilizing remote connection, acquiring the historical voltage data of the high-low voltage power distribution cabinet through a database of the remote monitoring system, and arranging the data according to a time sequence. The LOF algorithm is adopted to detect the abnormality, so that the abnormal value in the voltage data can be identified, and the abnormal value is removed. Meanwhile, in order to ensure the continuity of data, the positions of the removed data are required to be filled with the data in a manner that the average value of the voltage data at the left end and the right end of the removed data is used as filling data and is filled in the positions of the removed data.
And obtaining the processed voltage data of the high-low voltage power distribution cabinet.
The high-low power distribution cabinet data processing module 102 corrects the acquired voltage data, calculates the correlation of the voltage data set through the corrected data and the data before correction, acquires the size of an index moving average sliding window by adopting the set with the largest correlation, and calculates the window through the correlation between the data; calculating periodicity between elements using elements within the sliding window; calculating a smoothing amount of the window voltage data set using the periodicity between the elements and the first order difference data; and calculating a smoothing coefficient of the predicted voltage data by the smoothing adjustment factor and the smoothing amount.
In data prediction, it is important to select data having a certain degree of association or correlation. The related data can provide more information and clues, better understanding the relationships and trends between the data. By selecting data with high correlation for prediction, modes and changes among the data can be captured more accurately, so that a prediction result which is closer to a true value is obtained.
Specifically, the processed voltage data is stored as，/>Representing the ith voltage data. In order to acquire the fluctuation condition of the voltage data of the power distribution cabinet when the voltage data is carried out, the voltage data needs to be corrected, and all the data under the standard voltage are moved to be above the standard voltage.
And correcting the voltage data through the following formula to obtain corrected voltage data, wherein the expression is:
in the method, in the process of the invention,representing the ith modified voltage data; />Representing the voltage data before the ith correction; />Representing standard voltage data; />Representing taking an absolute function.
When the fluctuation of the voltage data before correction is larger, the difference between the voltage data before correction and the standard voltage data is larger, namely the calculated value of the voltage data after correction is larger, and if the voltage data is larger than the standard voltage data, the value of the voltage data after correction is equal to the value of the voltage data before correction.
Dividing the voltage data before correction into a group according to time sequence, dividing the two continuous voltage data into a set, and recording as a setThe data amount of the elements in the set is recorded as +.>The method comprises the steps of carrying out a first treatment on the surface of the For example, the voltage data are +.>、/>、/>、/>The data division result is composed of a set of +.>. Similarly, the set of three consecutive voltage data is +.>The data amount of the elements in the set is recorded as +.>The method comprises the steps of carrying out a first treatment on the surface of the …; the set of N-1 consecutive voltage data is +.>The data amount of the elements in the set is recorded as +.>. The corrected electricity is obtained by the same methodCompression data set, named +.>. And obtaining the correlation of each element of the voltage data set through the similarity and the correlation among the data, wherein the expression is as follows: />
In the method, in the process of the invention,representing the correlation of the j-th element in the k-th modified voltage data set; />The jth element in the kth corrected voltage data set; />Taking a tested value of 0.5 as a correlation balance factor; />Represents a hurst index; />Representing taking an absolute function. It should be noted that, the hurst index is a known technology, and the specific process is not described herein.
According to the correlation of each element of the voltage data set, constructing the data correlation of each voltage data set, wherein the expression is as follows:
in the method, in the process of the invention,a data correlation representing a kth voltage data set; />Representing the number of kth voltage data set elements; />、/>Respectively representing the correlation of the jth and the (j+1) th elements in the kth corrected voltage data set; />Represents the j-th element, the +_in the k-th pre-correction voltage data set>Representing the j+1th element in the kth pre-correction voltage data set; />Representation->Distance. It should be noted that->The distance is the existing known technology, and the detailed process is not repeated in this embodiment.
When the correlation of an element in a voltage data set is greater, whether positive or negative, the element's hurst indexThe closer to 1 or 0, the greater the value of the correlation of the element, the greater the value of the data correlation of the collection; the more similar between elements in the voltage data set, +.>The smaller the value of the distance, the greater the value of the data correlation of the voltage data set.
Selecting the voltage data set before correction with highest data correlation degree, and recording the element number asI.e. the data length corresponding to each element. Before acquiring the current voltage data->The data, according to the arrangement of time, is named as window voltage data set C.
For a certain periodicity possibly existing between the collected voltage data in the window, when data prediction is performed, the data with periodicity is provided, the voltage data closer to the predicted position has smaller influence on the data, and the periodicity among elements in the window voltage data collection is obtained, wherein the expression is as follows:
in the method, in the process of the invention,representing the periodicity of elements in the window voltage data set; />For non-overlapping longest repeating substrings in the set; />The longest length of the character strings in the set; />Representing the number of non-overlapping longest repeated substrings under the KMP algorithm; />Representing the number of elements in the window voltage data set. It should be noted that, the non-overlapping longest repeated substring may be obtained by a KMP algorithm, which is a known technique, and this embodiment will not be described in detail.
When the length of periodicity in the window voltage data set is longer, the length of the non-overlapping longest repeated substring in the set is larger, so that the value of periodicity of elements in the window voltage data set is larger; the greater the number of non-overlapping longest repeating substrings in the set, the higher the periodicity is explained, such that the greater the value of the periodicity of the elements in the window voltage data set.
Due to the influence of load changes, power supply fluctuation or other factors, the voltage of the power distribution cabinet may fluctuate to a certain extent, and the fluctuation may be irregular or regular. For the exponential moving average algorithm (EMA), smaller data rates can be selected when the data rate is more regular and the data rate is slowerAnd a value such that the weight of the old data is higher. In this way, long-term trends can be better captured, and the response to short-term fluctuations is smoother. Conversely, if the data fluctuation is large or the change is fast, a large +.>And a value such that the weight of the new data is higher. This allows for faster tracking of changes in data and more sensitive response to short term fluctuations.
And constructing a differential sequence of the window voltage data set by adopting first-order differential. Counting the types of different elements in a differential sequence, marking the number of the types as m, and marking the probability of the different types as，/>Representing the probability of occurrence of the I-th class), the probabilities of the different classes are formed into a set P. The smoothing quantity of the window voltage data set is constructed through the number of types and the probability of each type, and the expression is: />
In the method, in the process of the invention,representing a smoothed quantity of the window voltage data set; />Representing the periodicity of elements in the window voltage data set; />Representing the number of different categories in the differential sequence of the window voltage data set; />Representing the probability of occurrence of the I-th different element; />Representing a minimum function; />An exponential function based on a natural constant; />Representing the number of elements in the differential sequence.
When the voltage fluctuation of the power distribution cabinet is more irregular, the value of the number of different elements in the first-order differential sequence is larger, and the difference value sum of the occurrence probabilities of the different elementsIs increased such that the smoothing amount of the window voltage data set +.>The value of (2) increases so that the predicted value is more dependent on voltage data close to the predicted value in time, and the obtained predicted value is more close to a true value.
Further, a smoothing coefficient for predicting voltage data is obtained, and the expression is:
in the method, in the process of the invention,representing smoothing coefficients for predicting voltage data; />Representing a smoothed quantity of the window voltage data set; />、/>Respectively represent a first and a second smooth adjustment factors, and respectively take experience values of +.>，/>=0.1。
The larger the value of (2), the more irregular the data, the higher the weight attached to the data closer to the predicted time, i.eThe higher the value of (2); />，/>Effect of two values is regulated->Is prevented from being too large or too small to ignore the influence of other factors or self factors. The flow of obtaining the smoothing coefficient is shown in fig. 2.
The high-low power distribution cabinet data monitoring module 103 obtains a smoothing coefficient at the current moment based on a smoothing coefficient calculation process, predicts current voltage data by using the smoothing coefficient, and monitors the power distribution cabinet through prediction data and real monitoring data.
Using the voltage data before the predicted time as input, the sliding window size ws and the smoothing coefficient of the data are obtained by the above moduleBy combining historical prediction data (monitoring data) within a sliding window with smoothing coefficientsAnd for input, predicting the voltage data by using an exponential moving average algorithm to obtain a predicted voltage value at the next moment. The root mean square error is used as the error between the predicted data and the monitored data.
When the monitoring data is inThe power distribution cabinet can be proved to have no abnormal faults in the range, and the monitoring system is used for monitoring>On the contrary, do not exist->In the range of the abnormal fault of the power distribution cabinet,. The remote monitoring system will->Information is transmitted to a power distribution cabinet technician, and the technician transmits the information according to a remote monitoring system>And (3) checking the power distribution cabinet.
In summary, according to the embodiment of the present invention, the system first corrects the acquired voltage data, calculates the correlation between the corrected data and the uncorrected data to obtain the voltage data set, acquires the size of the sliding window by using the set with the largest correlation, and acquires the sliding window with the exponential moving average by using the correlation between the data, so that the predicted data is not affected by the unrelated data.
In addition, the periodicity among elements is calculated by utilizing elements in the sliding window, so that the smoothing quantity of the window voltage data set is constructed, and the weight added to the predicted data can be reflected; and calculating a smoothing coefficient of the predicted voltage data through the smoothing adjustment factor and the smoothing amount, so that the obtained predicted result is closer to the real data, and the accuracy of monitoring the abnormal faults of the high-voltage and low-voltage power distribution cabinet is further improved.
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.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The utility model provides a high low voltage power distribution cabinet abnormal fault information remote monitoring system which characterized in that, the system includes:
the high-low power distribution cabinet data acquisition module acquires voltage data of the high-low power distribution cabinet;
the high-low power distribution cabinet data processing module acquires corrected voltage data according to the relation between the voltage data and standard voltage data; dividing the voltage data and the corrected voltage data to obtain a voltage data set before correction and a voltage data set after correction; acquiring the correlation of each element of the corrected voltage data set according to the distribution of each element of the corrected voltage data set; acquiring the data correlation degree of each pre-correction voltage data set according to the correlation of each element of the post-correction voltage data set and the correlation of the pre-correction voltage data set; acquiring a window voltage data set according to the data correlation degree of the voltage data set before correction; acquiring the periodicity of window voltage data set elements according to the data distribution characteristics of the window voltage data set; acquiring the smoothing quantity of the window voltage data set according to the periodicity of the window voltage data set elements; obtaining a smoothing coefficient of the predicted voltage data according to the smoothing quantity of the window voltage data set;
and the high-low power distribution cabinet data monitoring module is used for completing abnormal fault monitoring of the high-low power distribution cabinet according to the smoothing coefficient of the predicted voltage data and the window voltage data set.
2. The remote monitoring system for abnormal fault information of a high-low voltage power distribution cabinet according to claim 1, wherein the obtaining corrected voltage data according to the relationship between the voltage data and standard voltage data comprises:
calculating the absolute value of the difference value between the voltage data at each moment and the standard voltage data; and taking the sum of the voltage data at each moment and the absolute value of the difference value as corrected voltage data.
3. The system for remotely monitoring abnormal fault information of a high-low voltage power distribution cabinet according to claim 1, wherein the dividing the voltage data and the corrected voltage data to obtain a voltage data set before correction and a voltage data set after correction comprises:
regarding the voltage data before correction, taking n continuous voltage data including each voltage data as each element of the voltage data set before correction, wherein n is sequentially increased from 2 until the number of the voltage data is increased to be different from 1;
and obtaining a corrected voltage data set by adopting an acquisition method of the voltage data set before correction.
4. The system for remotely monitoring abnormal fault information of high-low voltage power distribution cabinet according to claim 1, wherein the obtaining the correlation of each element of the corrected voltage data set according to the distribution of each element of the corrected voltage data set specifically comprises:
setting a correlation balance factor; acquiring a hurst index of each element in the corrected voltage data set; and taking the absolute value of the difference between the Hurst index and the correlation balance factor of each element as the correlation of each element of the corrected voltage data set.
5. The system for remotely monitoring abnormal fault information of high-low voltage power distribution cabinet according to claim 1, wherein the step of obtaining the data correlation of each pre-correction voltage data set according to the correlation of each element of the post-correction voltage data set and the correlation of the pre-correction voltage data set comprises the following steps:
acquiring the sum of the correlation of each element of the corrected voltage data set and the latter element; acquiring the DTW distance between each element of the voltage data set before correction and the latter element; and taking the sum value of the ratio of the sum value of all elements and the following element to the DTW distance as the data correlation degree of each corrected voltage data set.
6. The remote monitoring system for abnormal fault information of a high-low voltage power distribution cabinet according to claim 5, wherein the acquiring the window voltage data set according to the data correlation of the voltage data set before correction comprises:
acquiring the data length corresponding to the voltage data set before correction with the maximum data correlation degree; and taking the data length before correction of the voltage data at the current moment as elements of a window voltage data set.
7. The remote monitoring system for abnormal fault information of a high-low voltage power distribution cabinet according to claim 6, wherein the acquiring the periodicity of the window voltage data set element according to the data distribution characteristics of the window voltage data set comprises:
taking the voltage data in the window voltage data set as the input of a KMP algorithm, and taking the non-overlapping longest repeated word strings of the window voltage data set as the output of the KMP algorithm;
obtaining the product of the longest length of the non-overlapped longest repeated word strings and the number of the non-overlapped longest repeated word strings; and taking the ratio of the product to the character length of the element as the periodicity of the window voltage data set element.
8. The remote monitoring system for abnormal fault information of a high-low voltage power distribution cabinet according to claim 1, wherein the periodically obtaining the smoothing amount of the window voltage data set according to the window voltage data set element comprises:
acquiring a differential sequence of a window voltage data set by adopting a first-order differential method; counting the types and the number of the different elements of the checking and classifying sequence, and obtaining the occurrence probability of each type in the differential sequence;
acquiring the minimum value of the probability; calculating the difference between the probability and the minimum value of each category; taking the difference value as an index of an exponential function based on a natural constant; obtaining the product of the sum of the exponential functions of all kinds and the number of the kinds; calculating the ratio of the product to the number of elements of the differential sequence; taking the product of the inverse of the periodicity of the window voltage data set and the ratio as the smoothed quantity of the window voltage data set.
9. The remote monitoring system for abnormal fault information of a high-low voltage power distribution cabinet according to claim 1, wherein the obtaining the smoothing coefficient of the predicted voltage data according to the smoothing amount of the window voltage data set comprises:
setting a first smoothing adjustment factor and a second smoothing adjustment factor; calculating a product of the first smoothing adjustment factor and a smoothing amount of a window voltage data set; and taking the sum of the product and the second smoothing adjustment factor as a smoothing coefficient of the predicted voltage data.
10. The system for remotely monitoring abnormal fault information of high-low voltage power distribution cabinets according to claim 1, wherein the system for monitoring abnormal faults of high-low voltage power distribution cabinets according to the smoothing coefficient of the predicted voltage data and the window voltage data set comprises:
taking the data of the window voltage data set and the smoothing coefficient as the input of an exponential moving average algorithm, and outputting predicted voltage data at the next moment;
calculating root mean square error values of the voltage data in the predicted voltage data and the window voltage data set, and storing the root mean square error values as error values between the predicted data and the monitored data;
calculating the absolute value of the difference value between the monitored voltage data and the predicted voltage data at the next moment; when the absolute value of the difference value is smaller than or equal to the error value, no abnormality occurs in the power distribution cabinet; and when the absolute value of the difference value is larger than the error value, the power distribution cabinet is abnormal.
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