Abnormal temperature remote monitoring method for low-voltage power distribution cabinet

Abstract

The invention relates to the technical field of temperature monitoring, in particular to an abnormal temperature remote monitoring method for a low-voltage power distribution cabinet, and the method comprises the steps: obtaining temperature change data and temperature sensing data, obtaining the similarity between the temperature sensing data and the temperature change data, obtaining the temperature weight of the internal part of the power distribution cabinet according to the similarity, and obtaining the temperature of the internal part of the power distribution cabinet. The overall temperature in the power distribution cabinet is obtained according to the temperature weight of the internal part of the power distribution cabinet; according to the overall temperature in the power distribution cabinet, the temperature of each time node in the temperature sensing data of the abnormal heat accumulation part, the total number of times of abnormal heat accumulation and the duration of abnormal heat accumulation, the heat accumulation degree of the heat accumulation part in the power distribution cabinet is obtained, and the safety coefficient of the power distribution cabinet is obtained according to the heat accumulation degree. And performing anomaly evaluation on the power distribution cabinet according to the safety coefficient. By analyzing the abnormal heat accumulation condition of the power distribution cabinet, the safety coefficient of the power distribution cabinet is calculated, the abnormal degree of the power distribution cabinet can be well evaluated, and potential safety hazards possibly existing can be found in time.

Description

Technical Field

[0001] The present application relates to the technical field of temperature monitoring, and in particular to a method for remotely monitoring abnormal temperature of a low-voltage power distribution cabinet. Background Art

[0002] In various power generation scenarios, the main functions of the power distribution cabinet include power distribution, control and protection. Therefore, it is necessary to ensure the safe operation of the power distribution cabinet. Remote monitoring of the power distribution cabinet is an important means to ensure safe operation. Monitoring the temperature of the power distribution cabinet can detect abnormalities in time, thereby preventing potential failures and risks. In the actual use of the power distribution cabinet, the internal temperature has a direct impact on the safety of use.

[0003] Normally, due to factors such as high ambient temperature, direct sunlight or heat reflection from the ground, the internal temperature of the distribution cabinet is often higher than the ambient temperature. According to the operation and management standards of the distribution cabinet, its working environment temperature is usually required to be between -5℃ and 40℃. However, after the arrival of summer, due to the increase in ambient temperature, the internal temperature of the distribution cabinet generally exceeds 60℃. Such internal ambient temperature is prone to insulation aging problems in the circuit and may cause various circuit safety hazards.

[0004] At present, the research on the operating status of distribution cabinets mainly focuses on the operating status of electrical equipment. Therefore, it is very important to consider the detection of abnormal heat accumulation areas caused by heating areas. Summary of the invention

[0005] The present application provides a method for remotely monitoring abnormal temperature of a low-voltage power distribution cabinet to solve the existing problems.

[0006] The present invention discloses a remote monitoring method for abnormal temperature of a low-voltage power distribution cabinet using the following technical solution: An embodiment of the present application provides a method for remotely monitoring abnormal temperature of a low-voltage power distribution cabinet, the method comprising the following steps: Collect temperature change data on the outer surface of the distribution cabinet and temperature sensing data at multiple locations inside the distribution cabinet; According to the temperature of each time node in the temperature change data and the temperature of each time node in the temperature sensing data of each part, the similarity between the temperature sensing data of each part and the temperature change data is obtained; The weight score of each part inside the distribution cabinet is obtained according to the similarity between the temperature sensor data of each part and the temperature change data; the temperature weight of each part inside the distribution cabinet is obtained according to the weight score of each part inside the distribution cabinet; the overall temperature inside the distribution cabinet at each time node is obtained according to the temperature weight of each part inside the distribution cabinet and the temperature of each time node in the temperature sensor data of each part; A first derivative sequence and a second derivative sequence are obtained according to the temperature change data and the temperature sensing data, a temperature threshold and a time threshold are obtained according to the first derivative sequence and the second derivative sequence, and a total number of abnormal heat accumulation, a duration of abnormal heat accumulation, and a location where abnormal heat accumulation occurs are obtained according to the time threshold and the temperature threshold; The degree of heat accumulation at the location where heat accumulation occurs inside the power distribution cabinet is obtained according to the overall temperature inside the power distribution cabinet at each time node, the temperature at each time node in the temperature sensor data of the location where abnormal heat accumulation occurs, the total number of abnormal heat accumulations and the duration of abnormal heat accumulation; The safety factor of the power distribution cabinet is obtained according to the degree of heat accumulation at the heat accumulation location inside the power distribution cabinet, the temperature at each time node in the temperature change data, and the overall temperature inside the power distribution cabinet at each time node; Evaluate the switchboard based on the safety factor.

[0007] Preferably, the obtaining of the similarity between the temperature sensing data of each part and the temperature change data according to the temperature of each time node in the temperature change data and the temperature of each time node in the temperature sensing data of each part comprises the following specific steps: In the formula, is the temperature at the tth time node in the temperature change data, is the mean temperature of all time nodes in the temperature change data, is the temperature at the tth time node in the temperature sensing data of the pth part, is the mean temperature of all time nodes in the temperature sensing data of the pth part, is the covariance of the temperature change data and the temperature sensing data of the pth location, is the similarity between the temperature sensing data and the temperature change data of the pth part, The total number of time nodes in a day.

[0008] Preferably, the step of obtaining the temperature weight of each part inside the power distribution cabinet according to the weight score of each part inside the power distribution cabinet includes the following specific steps: In the formula, is the weight score of the pth part inside the distribution cabinet, P is the total number of parts inside the distribution cabinet, is the temperature weight of the pth part inside the distribution cabinet.

[0009] Preferably, the method of obtaining the overall temperature inside the power distribution cabinet at each time node according to the temperature weight of each part inside the power distribution cabinet and the temperature of each time node in the temperature sensor data of each part includes the following specific steps: In the formula, is the temperature at the tth time node in the temperature sensing data of the pth part, is the temperature weight of the pth part inside the distribution cabinet, P is the total number of parts inside the distribution cabinet, is the overall temperature inside the power distribution cabinet at the tth time node.

[0010] Preferably, the step of obtaining the first derivative sequence and the second derivative sequence according to the temperature change data and the temperature sensing data comprises the following specific steps: A temperature sensor data with the highest similarity to the temperature change data is obtained, recorded as the highest temperature sensor data; a temperature sensor data with the lowest similarity to the temperature change data is obtained, recorded as the lowest temperature sensor data; a first difference sequence is obtained by subtracting the temperature at the time node corresponding to the highest temperature sensor data from that of the temperature change data; a second difference sequence is obtained by subtracting the temperature at the time node corresponding to the lowest temperature sensor data from that of the temperature change data; The first difference sequence and the second difference sequence are respectively subjected to polynomial fitting to obtain a fitting curve of the first difference sequence and a fitting curve of the second difference sequence, the derivative of each time node in the fitting curve of the first difference sequence is obtained to obtain a first derivative sequence, and the derivative of each time node in the fitting curve of the second difference sequence is obtained to obtain a second derivative sequence.

[0011] Preferably, the step of obtaining the temperature threshold and the time threshold according to the first derivative sequence and the second derivative sequence includes the following specific steps: The preset time window, the length of the time window is WT, the moving step of the time window is WT, and WT is a preset value; Using a preset time window, the first derivative sequence is equally divided according to the order of time nodes to obtain a number of first derivative subsequences; Using a preset time window, the second derivative sequence is equally divided according to the order of time nodes to obtain a number of second derivative subsequences; Wherein, each first derivative subsequence uniquely corresponds to a second derivative subsequence, and corresponds to a fixed preset time window; Obtaining a trend difference parameter of each preset time window according to each first derivative subsequence and the corresponding second derivative subsequence; The preset time window with the trend difference parameter greater than th1 and less than th2 is recorded as an abnormal window, th1 is the preset first value, th2 is the preset second value, all abnormal windows in one day are obtained, and the second derivative subsequence corresponding to each abnormal window is obtained. For any second derivative subsequence, the derivatives in the second derivative subsequence are continuously 0 and recorded as State, when the derivatives in the second derivative subsequence are continuously positive, it is recorded as State, when the derivatives in the second derivative subsequence are continuously negative, record them as State, if the state in the second derivative subsequence corresponding to any abnormal window is When the state All time nodes corresponding to the 0 state between the states are abnormal weather time nodes. All abnormal weather time nodes in a day are obtained, and the two 0 states closest to the abnormal weather time node are recorded as the first zero state and the second zero state respectively. The time node closest to the abnormal weather time node in the first zero state is recorded as , the time node closest to the abnormal weather time node in the second zero state is recorded as , ,Will As an abnormal weather cycle, obtain all abnormal weather cycles in a day and convert As a cycle value of the abnormal weather cycle, the maximum cycle value of all abnormal weather cycles is used as the time threshold, all abnormal weather cycles are corresponded to the second difference sequence, and several second cycle ranges are obtained. The maximum value in the second difference sequence within all second cycle ranges is used as the temperature threshold.

[0012] Preferably, the step of obtaining the trend difference parameter of each preset time window according to each first derivative subsequence and the corresponding second derivative subsequence includes the following specific steps: In the formula, is the mean of the nth second derivative subsequence, is the mean of the nth first derivative subsequence, is the standard deviation of the second derivative subsequence, is the standard deviation of the first derivative subsequence, yes , The L2 norm of the constituent vectors, is the trend difference parameter of the nth preset time window.

[0013] Preferably, the method of obtaining the total number of abnormal heat accumulation, the duration of abnormal heat accumulation and the location where abnormal heat accumulation occurs according to the time threshold and the temperature threshold comprises the following specific steps: If the difference between the temperature at the tth time node in the pth temperature sensor data and the overall temperature inside the distribution cabinet at the tth time node is greater than the temperature threshold, then the duration of the temperature being greater than the temperature threshold is counted starting from the tth time node. If the duration is greater than the time threshold, abnormal heat accumulation has occurred in the pth part inside the distribution cabinet. The total number of abnormal heat accumulations in one day and the duration of each abnormal heat accumulation are counted.

[0014] Preferably, the step of obtaining the degree of heat accumulation at the location where heat accumulation occurs inside the power distribution cabinet according to the overall temperature inside the power distribution cabinet at each time node, the temperature at each time node in the temperature sensor data of the location where abnormal heat accumulation occurs, the total number of abnormal heat accumulations and the duration of abnormal heat accumulation comprises the following specific steps: In the formula, is the overall temperature inside the power distribution cabinet at the tth time node, is the temperature at the tth time node in the temperature sensing data of the pth part, is the heat accumulation degree of the pth part inside the power distribution cabinet, is the temperature threshold, is the duration of the mth abnormal heat accumulation, is the time threshold, and N is the total number of abnormal heat accumulation.

[0015] Preferably, the safety factor of the power distribution cabinet is obtained according to the degree of heat accumulation at the heat accumulation location inside the power distribution cabinet, the temperature at each time node in the temperature change data, and the overall temperature inside the power distribution cabinet at each time node, and the specific steps include the following: In the formula, is the overall temperature inside the power distribution cabinet at the tth time node, is the temperature at the tth time node in the temperature change data, M is the total number of time nodes in a day, is the average value of the heat accumulation degree of all abnormal heat accumulation parts inside the power distribution cabinet. is an exponential function with a natural constant as base, is the abnormal degree of internal temperature, is the safety factor of the power distribution cabinet; The specific method for obtaining the abnormal degree of the internal temperature is as follows: The number of times that the overall temperature inside the power distribution cabinet is greater than the preset normal temperature and lasts longer than the time threshold in a day is taken as the abnormal degree of the internal temperature.

[0016] The beneficial effect of the technical solution of the present application is that by calculating the safety factor, the present application can better evaluate the abnormal degree and heat accumulation degree of the internal temperature of the distribution cabinet, timely discover possible safety hazards, and according to the result of the safety factor, corresponding measures can be taken to improve the safety of the distribution cabinet and reduce the risks brought by equipment aging.

[0017] When calculating the overall temperature inside the distribution cabinet, a weighted approach is used to consider whether the temperature data of different parts inside the distribution cabinet changes due to the environment or due to self-heating, so as to better estimate the overall temperature inside the distribution cabinet. When analyzing whether heat accumulation occurs in parts inside the distribution cabinet, time thresholds and temperature thresholds are introduced to more accurately reflect whether heat accumulation occurs in parts inside the distribution cabinet. The heat accumulation phenomenon of the distribution cabinet is quantified by calculating the degree of heat accumulation in different parts inside the distribution cabinet, and then the safety factor of the distribution cabinet is obtained according to the degree of heat accumulation to reflect the abnormal situation of the distribution cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flowchart of a method for remotely monitoring abnormal temperature of a low-voltage power distribution cabinet provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the abnormal temperature remote monitoring method for a low-voltage distribution cabinet proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0021] 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 application belongs.

[0022] The following is a detailed description of a specific scheme of a method for remotely monitoring abnormal temperature of a low-voltage distribution cabinet provided by the present application in conjunction with the accompanying drawings.

[0023] See also Figure 1, which shows a flowchart of a method for remotely monitoring abnormal temperature of a low-voltage power distribution cabinet provided by an embodiment of the present application, the method comprising the following steps: Step S001, obtain temperature data and perform preprocessing.

[0024] It should be noted that this embodiment is about remote monitoring of the temperature of a low-voltage distribution cabinet, and firstly, it is necessary to obtain the corresponding data and perform certain preprocessing.

[0025] Specifically, a high-precision infrared camera is installed outside the low-voltage distribution cabinet to collect infrared images, and surface temperature data of the outer surface of the distribution cabinet is obtained based on the infrared images. At the same time, for locations inside the low-voltage distribution cabinet where heat is easily accumulated, including but not limited to moving and static contacts, disconnector contacts, and cable terminals, temperature sensors are installed at such locations to collect multiple temperature data inside the low-voltage distribution cabinet, and one temperature data corresponds to a location inside the distribution cabinet; the surface temperature data and temperature data are both time series temperature data.

[0026] It should be noted that, for an infrared image at any time node, the grayscale information of the infrared image itself reflects the temperature information. The temperature of each position can be easily calibrated through the grayscale to temperature mapping conversion. CN104346768B discloses a temperature calibration processing method for infrared images. The embodiment of the present application obtains the temperature of each position through this method, and takes the temperature average of all positions as the temperature of the time node. The above analysis is based on the infrared image of any time node, and the temperatures of all time nodes are obtained to obtain the surface temperature data of the outer surface of the distribution cabinet.

[0027] The temperature data collection time is from 6 am to 12 pm every day in the past week, and the sampling frequency is once every 1 minute to obtain the surface temperature data of the outer surface of the distribution cabinet and multiple temperature data inside the distribution cabinet. The collected temperature data is transmitted to the remote monitoring center through communication methods such as the Internet of Things and communication networks for subsequent data preprocessing and data analysis.

[0028] It should be noted that the surface temperature data and the temperature data in this example are analyzed based on data from any one day, and one minute is used as a time node in this embodiment.

[0029] Furthermore, the remote monitoring center uses the moving average method to smooth and denoise the surface temperature data and multiple temperature data respectively. The moving average method is a prior art and will not be described in detail in this embodiment. In this embodiment, the data after smoothing and denoising are recorded as temperature change data and temperature sensing data respectively.

[0030] So far, temperature change data and multiple temperature sensing data have been obtained.

[0031] Step S002, obtain the weight score of each part inside the distribution cabinet according to the temperature of each time node in the temperature change data and the temperature of each time node in the temperature sensing data of each part; obtain the overall temperature inside the distribution cabinet at each time node according to the weight score of each part inside the distribution cabinet.

[0032] It should be noted that the temperature sensing data of several parts inside the distribution cabinet that are prone to heat accumulation, as well as the temperature change data of the external surface of the distribution cabinet, are also included. If the similarity coefficient between the temperature at a certain position in the distribution cabinet and the temperature on the external surface of the distribution cabinet is high, it can be considered that the temperature at this position is highly correlated with the overall temperature inside the distribution cabinet.

[0033] It should be further explained that the power distribution cabinet is affected by the ambient temperature and other non-self-environmental factors. A high similarity coefficient value means that the temperature change at this location tends to be similar to the external ambient temperature change, that is, when the external ambient temperature rises or falls, the temperature at this location will also rise or fall accordingly. This indicates that this location is greatly affected by the external ambient temperature and is consistent with the overall temperature change trend of the power distribution cabinet. Therefore, it is considered to assign a weight to the temperature data of each internal part by judging the correlation. The greater the correlation, the higher the weight. By weighting each internal part with the weight and accumulating it, the overall internal temperature is obtained.

[0034] Specifically, the similarity between the temperature sensing data of each part and the temperature change data is obtained according to the temperature of each time node in the temperature change data and the temperature of each time node in the temperature sensing data of each part, as follows: In the formula, is the temperature at the tth time node in the temperature change data, is the mean temperature of all time nodes in the temperature change data, is the temperature at the tth time node in the temperature sensing data of the pth part, is the mean temperature of all time nodes in the temperature sensing data of the pth part, is the covariance of the temperature change data and the temperature sensing data of the pth location, is the similarity between the temperature sensing data and the temperature change data of the pth part, is the total number of time nodes in a day. .

[0035] in, It can reflect the similarity between the temperature of the pth internal part and the temperature of the outer surface of the distribution cabinet. The closer the result is to 1, the greater the similarity between the two. It can be considered that the temperature at this position has a high correlation with the overall temperature inside the distribution cabinet.

[0036] It should be noted that since the sampling frequency is , and the length of a day's time period is , thus we get , that is, the total number of time nodes in a day.

[0037] It should be noted that after obtaining the time series data of the temperature of each part, it is necessary to quantify the similarity based on the data characteristics of each part and the temperature analysis of the outer surface, calculate different temperature weights based on the similarity results, and assign a weight to the temperature data of each internal part of the distribution cabinet. By weighting each internal part with the weight and then accumulating it, the overall internal temperature can be obtained.

[0038] Specifically, the weight score of each part inside the power distribution cabinet is obtained according to the similarity between the temperature sensing data and the temperature change data of each part, as follows: In the formula, is the similarity between the temperature sensing data and the temperature change data of the pth part, is the weight score of the pth part inside the distribution cabinet.

[0039] Furthermore, the temperature weight of each part inside the distribution cabinet is obtained according to the weight score of each part inside the distribution cabinet, as follows: In the formula, is the weight score of the pth part inside the distribution cabinet, P is the total number of parts inside the distribution cabinet, is the temperature weight of the pth part inside the distribution cabinet.

[0040] Furthermore, according to the temperature weight of each part inside the power distribution cabinet and the temperature of each time node in the temperature sensor data of each part, the overall temperature inside the power distribution cabinet at each time node is obtained, as follows: In the formula, is the temperature at the tth time node in the temperature sensing data of the pth part, is the temperature weight of the pth part inside the distribution cabinet, P is the total number of parts inside the distribution cabinet, is the overall temperature inside the power distribution cabinet at the tth time node.

[0041] It should be noted that for The temperature sensing data of each part is calculated by calculating the similarity between the overall data change and the temperature data change of the outer surface temperature. , which can reflect the degree of correlation between the internal part and the overall temperature of the power distribution cabinet. The higher the similarity, the greater the correlation between the two. For parts with a greater degree of correlation, a higher temperature weight should be assigned, and the correlation coefficient is mapped to the weight range. The purpose is to determine the weight according to its correlation, so that the parts with higher correlation obtain higher weights. By setting the mapping rules, different weights can be given according to different degrees of correlation.

[0042] Based on the weight of each part, the temperature of each part is weighted and calculated according to the weight, so as to obtain the first The purpose of this is to accurately reflect the temperature conditions inside the entire power distribution cabinet, and to focus on the parts that are more correlated with the external ambient temperature. By weighting, we can consider whether the temperature data at different locations changes due to changes in the overall ambient temperature or due to changes in self-heating, so as to better estimate the overall temperature inside the power distribution cabinet.

[0043] At this point, the overall temperature inside the power distribution cabinet at each time node is obtained.

[0044] Step S003, according to the time threshold and the temperature threshold, the total number of abnormal heat accumulation, the duration of the abnormal heat accumulation and the location where the abnormal heat accumulation occurs are obtained, and the degree of heat accumulation at the location where heat accumulation occurs inside the distribution cabinet is obtained according to the overall temperature inside the distribution cabinet at each time node, the temperature at each time node in the temperature sensor data of the location where abnormal heat accumulation occurs, the total number of abnormal heat accumulation and the duration of abnormal heat accumulation.

[0045] It should be noted that the overall temperature inside the power distribution cabinet at each time node is obtained in step S002. In order to describe the abnormal heat accumulation phenomenon that may occur in a single part due to abnormal discharge or high-load operation, it is necessary to calculate the heat accumulation degree of each internal part over a continuous time. Take each part as an example to analyze and calculate the degree of heat accumulation.

[0046] Specifically, the degree of heat accumulation at the heat accumulation location inside the power distribution cabinet is obtained according to the overall temperature inside the power distribution cabinet at each time node, the temperature at each time node in the temperature sensor data of the location where abnormal heat accumulation occurs, the total number of abnormal heat accumulations, and the duration of abnormal heat accumulation, as follows: In the formula, is the overall temperature inside the power distribution cabinet at the tth time node, is the temperature at the tth time node in the temperature sensing data of the pth part, and the pth part has abnormal heat accumulation, is the heat accumulation degree of the pth part inside the power distribution cabinet, The temperature threshold is used to reduce the large amount of errors in the detection of heat accumulation caused by weather changes. Under normal operation of the distribution cabinet, the overall temperature inside the distribution cabinet will be affected by the external environment, and then fluctuate to a certain extent, thereby affecting the overall temperature inside the distribution cabinet, causing the temperature of some parts to exceed the overall temperature inside, which will cause the temperature changes of the distribution cabinet caused by a large amount of weather changes to be judged as abnormal heat accumulation. Therefore, the temperature threshold is introduced here for correction; is the duration of the mth abnormal heat accumulation, is the time threshold. By introducing the time threshold, the abnormal heat accumulation phenomenon can be judged more accurately. N is the total number of abnormal heat accumulation.

[0047] The specific method for obtaining the temperature threshold and time threshold is as follows: Specifically, a temperature sensor data with the highest similarity to the temperature change data is obtained, recorded as the highest temperature sensor data, a temperature sensor data with the lowest similarity to the temperature change data is obtained, recorded as the lowest temperature sensor data, the highest temperature sensor data is subtracted from the temperature at the corresponding time node of the temperature change data to obtain a first difference sequence, and the lowest temperature sensor data is subtracted from the temperature at the corresponding time node of the temperature change data to obtain a second difference sequence; Further, the first difference sequence and the second difference sequence are respectively subjected to polynomial fitting to obtain a fitting curve of the first difference sequence and a fitting curve of the second difference sequence, the derivative of each time node in the fitting curve of the first difference sequence is obtained to obtain a first derivative sequence, and the derivative of each time node in the fitting curve of the second difference sequence is obtained to obtain a second derivative sequence; It should be noted that the first difference sequence and the second difference sequence are respectively subjected to polynomial fitting to obtain fitting curves, wherein the polynomials in the fitting process are all fifth-order polynomials.

[0048] It should be noted that when the value fluctuations of the first difference sequence are stable over a period of time, and the value of the second difference sequence suddenly increases over a period of time and then changes back in a short time, it means that a short-term change in the outer surface temperature has occurred due to weather changes. Therefore, the abnormal weather time nodes can be obtained by analyzing the first derivative sequence and the second derivative sequence.

[0049] Specifically, a preset time window, a length of the preset time window is WT, a moving step of the preset time window is WT, WT is a preset value, in this embodiment, WT=20, that is, 20 time nodes are moved each time, and in this embodiment, the preset time window length is described as 20, that is, the window can contain 20 time nodes, and the preset time window is used to equally divide the first derivative sequence according to the order of time nodes to obtain a number of first derivative subsequences, and similarly, the second derivative sequence is divided in the same way to obtain a number of second derivative subsequences. It should be noted that since the first derivative sequence and the second derivative sequence are equally divided by the preset time window according to the order of time nodes and the lengths of the derivative subsequences obtained by the division are the same, each first derivative subsequence obtained uniquely corresponds to a second derivative subsequence, and each corresponds to a fixed preset time window.

[0050] It should be noted that if the first derivative sequence and the second derivative sequence cannot be equally divided according to the order of time nodes according to the preset time window, that is, when the elements in the first derivative sequence and the second derivative sequence are equally divided, the boundaries of the first derivative sequence and the second derivative sequence may be exceeded. In this embodiment, the part of the first derivative sequence and the second derivative sequence that exceeds the boundary is interpolated and filled with data using a quadratic linear interpolation method.

[0051] Furthermore, the standard deviation and mean of all first derivative subsequences and the standard deviation and mean of all second derivative subsequences are obtained, and the trend difference parameter of each preset time window is obtained according to each first derivative subsequence and the corresponding second derivative subsequence, as follows: In the formula, is the mean of the nth second derivative subsequence, is the mean of the nth first derivative subsequence, is the standard deviation of the second derivative subsequence, is the standard deviation of the first derivative subsequence, yes , The L2 norm of the constituent vectors, is the trend difference parameter of the nth preset time window, .

[0052] It should be noted that if the trend difference parameter of the nth preset time window is greater than th1 and less than th2, th1 is the preset first value, th2 is the preset second value, and in this embodiment, th1=1, th2=-1 is used for description, then it is considered that the trends of the nth first derivative subsequence and the nth second derivative subsequence are significantly different, indicating that changes affected by weather have occurred; if the trend difference parameter of the nth preset time window is greater than or equal to th2 and less than or equal to th1, then it is considered that the trends of the nth first derivative subsequence and the nth second derivative subsequence are similar, and there is no change affected by weather.

[0053] Specifically, the preset time window with the trend difference parameter greater than th1 and less than th2 is recorded as an abnormal window, all abnormal windows in any day are obtained, and the second derivative subsequence corresponding to each abnormal window is obtained. For any second derivative subsequence, the time when the derivative in the second derivative subsequence is continuously 0 is recorded as State, when the derivatives in the second derivative subsequence are continuously positive, it is recorded as State, when the derivatives in the second derivative subsequence are continuously negative, record them as State. If the state in the second derivative subsequence corresponding to any abnormal window is When the state All time nodes corresponding to the 0 state between the states are abnormal weather time nodes. It should be noted that the time node corresponding to the 0 state is an abnormal weather time node, which may correspond to multiple time nodes, that is, multiple abnormal weather time nodes. All abnormal weather time nodes in a day are obtained. For any abnormal weather time node, the two 0 states closest to the abnormal weather time node are recorded as the first zero state and the second zero state respectively, and the time node closest to the abnormal weather time node in the first zero state is recorded as , the time node closest to the abnormal weather time node in the second zero state is recorded as In this embodiment Less than ,Will As an abnormal weather cycle, obtain all abnormal weather cycles in a day and convert As a period value of the abnormal weather cycle, the maximum period value of all abnormal weather cycles is used as the time threshold.

[0054] Furthermore, all abnormal weather periods are mapped to the second difference sequence to obtain a number of second period ranges, and the maximum value in the second difference sequence within all the second period ranges is used as the temperature threshold.

[0055] The specific method for obtaining the total number of abnormal heat accumulation and the duration of abnormal heat accumulation is as follows: The total number of abnormal heat accumulation and the duration of abnormal heat accumulation are obtained according to the time threshold and the temperature threshold; Specifically, if the difference between the temperature at the tth time node in the pth temperature sensor data and the overall temperature inside the power distribution cabinet at the tth time node is greater than the temperature threshold, it is considered that abnormal heat accumulation may occur in the pth part inside the power distribution cabinet at the tth time node. At this time, the duration greater than the temperature threshold is counted starting from the tth time node. If the duration is greater than the time threshold, it is considered that abnormal heat accumulation has occurred in the pth part inside the power distribution cabinet. The total number of abnormal heat accumulations in one day is counted and recorded as M. At the same time, the duration of the mth abnormal heat accumulation is recorded as .

[0056] It should be noted that the internal parts of the distribution cabinet where no abnormal heat accumulation occurs are not included in the calculation of the heat accumulation degree.

[0057] It should be noted that when certain environmental changes occur, the temperature of the outer surface will suddenly drop, thus affecting the overall temperature inside the distribution cabinet. At this time, some parts will react slowly to the outer surface temperature, such as the parts corresponding to the lowest temperature sensor data, and some parts will react more sensitively to the outer surface temperature, such as the parts corresponding to the highest temperature sensor data. When analyzing the heat accumulation phenomenon, it is necessary to avoid the situation where the temperature of some parts exceeds the overall temperature by a large value. The way to avoid this is to compare the time threshold The degree of heat accumulation is calculated based on the temperature threshold cc. If it does not exceed the value, it will not be calculated.

[0058] At this point, the degree of heat accumulation in the heat accumulation area inside the distribution cabinet is obtained.

[0059] Step S004: Obtain the safety factor of the power distribution cabinet according to the degree of heat accumulation at the heat accumulation location inside the power distribution cabinet, the temperature at each time node in the temperature change data, and the overall temperature inside the power distribution cabinet at each time node.

[0060] It should be noted that if the overall temperature of a certain period of time in a day exceeds , indicating that the temperature inside the distribution cabinet exceeds the safety threshold. It is necessary to analyze the degree of abnormality based on the situation inside the distribution cabinet. The calculation of the degree of abnormality consists of three parts. The first is the ratio of the external temperature to the calculated internal overall temperature. The closer this value is to The more dangerous it is, the more likely it is to The second is that the internal temperature exceeds The higher the temperature exceeds the limit, the more dangerous it is at this time point. The third is the degree to which a certain part of the distribution cabinet exceeds the overall internal temperature, that is, the degree of heat accumulation, which can be used as the parameter of the third part of the formula. Note that the temperature here is The temperature is the upper limit of the standard operating temperature of the distribution cabinet. Generally, if this value is exceeded, it is considered to be dangerous to operate. It needs to be set according to the specific model of the distribution cabinet.

[0061] Specifically, according to the degree of heat accumulation at the heat accumulation location inside the power distribution cabinet, the temperature at each time node in the temperature change data, and the overall temperature inside the power distribution cabinet at each time node, the safety factor of the power distribution cabinet is obtained, as follows: In the formula, is the overall temperature inside the power distribution cabinet at the tth time node, is the temperature at the tth time node in the temperature change data, is the total number of time nodes in a day, is the average value of the heat accumulation degree of all abnormal heat accumulation parts inside the power distribution cabinet. is an exponential function with a natural constant as base, is the degree of abnormality of the internal temperature; The specific method for obtaining the abnormal degree of the internal temperature is as follows: The number of times that the overall temperature inside the power distribution cabinet is greater than the preset normal temperature and the duration is greater than the time threshold in a day is taken as the abnormal degree of the internal temperature. In this embodiment, the preset normal temperature is 60°C. is the safety factor of the distribution cabinet.

[0062] At this point, the safety factor of the distribution cabinet is obtained.

[0063] Step S005: perform abnormality assessment on the power distribution cabinet according to the safety factor.

[0064] It should be noted that the safety factor of the distribution cabinet can be obtained through the calculation of the above formula. There is a safety factor threshold for the daily temperature data. If this threshold is exceeded, it means that multiple situations have occurred that may affect the safety of the distribution cabinet and further inspection is required. Engineering personnel can arrive at the site for inspection to determine the safety of the distribution cabinet. Specifically: observe whether the wiring terminals, switches, fuses and other components of the distribution cabinet are loose, detached or have signs of burning; check whether the insulation layer of the cables inside the distribution cabinet is intact to ensure that there is no damage or aging; check whether the grounding system of the distribution cabinet is working properly; ensure that the grounding wire is firmly connected and the grounding resistance meets the requirements; open the distribution cabinet. Since the abnormally hot parts may have current overload, poor contact or other faults, it is necessary to use appropriate voltmeters and ammeters to measure whether the voltage and current of the abnormally hot parts (such as wiring terminals) in the distribution cabinet are within the normal range.

[0065] Specifically, if there is no abnormal heat accumulation inside the distribution cabinet, and the overall temperature inside the distribution cabinet is lower than the preset normal temperature within a day, it means that the distribution cabinet is operating normally and no other processing is required on the distribution cabinet; if abnormal heat accumulation occurs inside the distribution cabinet, or the overall temperature inside the distribution cabinet is greater than or equal to the preset normal temperature within a day, but the safety factor of the distribution cabinet is lower than the preset safety factor threshold, it means that high-load operation may exist, and there is a risk of equipment aging. If the distribution cabinet is not currently equipped with a cooling fan, it is necessary to immediately take corresponding mitigation measures such as installing a cooling fan. If the distribution cabinet is currently equipped with a cooling fan, the current cooling fan can be replaced with a higher-power cooling fan, or an additional cooling fan can be installed if the space in the distribution cabinet permits. In this embodiment, the preset safety factor threshold is 0.8; if the safety factor of the distribution cabinet is greater than or equal to the preset safety factor threshold, it means that the distribution cabinet may have a short circuit and poor contact, which may pose a safety hazard and needs to be inspected and handled immediately.

[0066] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0067] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A remote monitoring method for abnormal temperature of a low-voltage distribution cabinet, characterized in that: The method comprises the following steps: Collect temperature change data on the outer surface of the distribution cabinet and temperature sensing data at multiple locations inside the distribution cabinet; According to the temperature of each time node in the temperature change data and the temperature of each time node in the temperature sensing data of each part, the similarity between the temperature sensing data of each part and the temperature change data is obtained; The weight score of each part inside the distribution cabinet is obtained according to the similarity between the temperature sensor data of each part and the temperature change data; the temperature weight of each part inside the distribution cabinet is obtained according to the weight score of each part inside the distribution cabinet; the overall temperature inside the distribution cabinet at each time node is obtained according to the temperature weight of each part inside the distribution cabinet and the temperature of each time node in the temperature sensor data of each part; A first derivative sequence and a second derivative sequence are obtained according to the temperature change data and the temperature sensing data, a temperature threshold and a time threshold are obtained according to the first derivative sequence and the second derivative sequence, and a total number of abnormal heat accumulation, a duration of abnormal heat accumulation, and a location where abnormal heat accumulation occurs are obtained according to the time threshold and the temperature threshold; The degree of heat accumulation at the location where heat accumulation occurs inside the power distribution cabinet is obtained according to the overall temperature inside the power distribution cabinet at each time node, the temperature at each time node in the temperature sensor data of the location where abnormal heat accumulation occurs, the total number of abnormal heat accumulations, and the duration of abnormal heat accumulation; The safety factor of the power distribution cabinet is obtained according to the degree of heat accumulation at the heat accumulation location inside the power distribution cabinet, the temperature at each time node in the temperature change data, and the overall temperature inside the power distribution cabinet at each time node; Evaluate the switchboard based on the safety factor.

2. According to claim 1, a method for remotely monitoring abnormal temperature of a low-voltage distribution cabinet is characterized in that: The specific steps of obtaining the similarity between the temperature sensing data of each part and the temperature change data according to the temperature of each time node in the temperature change data and the temperature of each time node in the temperature sensing data of each part are as follows: In the formula, is the temperature at the tth time node in the temperature change data, is the mean temperature of all time nodes in the temperature change data, is the temperature at the tth time node in the temperature sensing data of the pth part, is the mean temperature of all time nodes in the temperature sensing data of the pth part, is the covariance of the temperature change data and the temperature sensing data of the pth location, is the similarity between the temperature sensing data and the temperature change data of the pth part, The total number of time nodes in a day.

3. According to claim 1, a remote monitoring method for abnormal temperature of a low-voltage distribution cabinet is characterized in that: The specific steps of obtaining the temperature weight of each part inside the power distribution cabinet according to the weight score of each part inside the power distribution cabinet are as follows: In the formula, is the weight score of the pth part inside the distribution cabinet, P is the total number of parts inside the distribution cabinet, is the temperature weight of the pth part inside the distribution cabinet.

4. According to claim 1, a method for remotely monitoring abnormal temperature of a low-voltage distribution cabinet is characterized in that: The method of obtaining the overall temperature inside the power distribution cabinet at each time node according to the temperature weight of each part inside the power distribution cabinet and the temperature of each time node in the temperature sensor data of each part includes the following specific steps: In the formula, is the temperature at the tth time node in the temperature sensing data of the pth part, is the temperature weight of the pth part inside the distribution cabinet, P is the total number of parts inside the distribution cabinet, is the overall temperature inside the power distribution cabinet at the tth time node.

5. The abnormal temperature remote monitoring method for a low voltage distribution cabinet according to claim 1 is characterized in that: The step of obtaining the first derivative sequence and the second derivative sequence according to the temperature change data and the temperature sensing data includes the following specific steps: A temperature sensor data with the highest similarity to the temperature change data is obtained, recorded as the highest temperature sensor data; a temperature sensor data with the lowest similarity to the temperature change data is obtained, recorded as the lowest temperature sensor data; a first difference sequence is obtained by subtracting the temperature at the time node corresponding to the highest temperature sensor data from that of the temperature change data; a second difference sequence is obtained by subtracting the temperature at the time node corresponding to the lowest temperature sensor data from that of the temperature change data; The first difference sequence and the second difference sequence are respectively subjected to polynomial fitting to obtain a fitting curve of the first difference sequence and a fitting curve of the second difference sequence, the derivative of each time node in the fitting curve of the first difference sequence is obtained to obtain a first derivative sequence, and the derivative of each time node in the fitting curve of the second difference sequence is obtained to obtain a second derivative sequence.

6. The abnormal temperature remote monitoring method for a low voltage distribution cabinet according to claim 1 is characterized in that: The specific steps of obtaining the temperature threshold and the time threshold according to the first derivative sequence and the second derivative sequence are as follows: The preset time window, the length of the time window is WT, the moving step of the time window is WT, and WT is a preset value; Using a preset time window, the first derivative sequence is equally divided according to the order of time nodes to obtain a number of first derivative subsequences; Using a preset time window, the second derivative sequence is equally divided according to the order of time nodes to obtain a number of second derivative subsequences; Wherein, each first derivative subsequence uniquely corresponds to a second derivative subsequence, and corresponds to a fixed preset time window; Obtaining a trend difference parameter of each preset time window according to each first derivative subsequence and the corresponding second derivative subsequence; The preset time window with the trend difference parameter greater than th1 and less than th2 is recorded as an abnormal window, th1 is the preset first value, th2 is the preset second value, all abnormal windows in one day are obtained, and the second derivative subsequence corresponding to each abnormal window is obtained. For any second derivative subsequence, the derivatives in the second derivative subsequence are continuously 0 and recorded as State, when the derivatives in the second derivative subsequence are continuously positive, it is recorded as State, when the derivatives in the second derivative subsequence are continuously negative, record them as State, if the state in the second derivative subsequence corresponding to any abnormal window is When the state All time nodes corresponding to the 0 state between the states are abnormal weather time nodes. All abnormal weather time nodes in a day are obtained, and the two 0 states closest to the abnormal weather time node are recorded as the first zero state and the second zero state respectively. The time node closest to the abnormal weather time node in the first zero state is recorded as , the time node closest to the abnormal weather time node in the second zero state is recorded as , ,Will As an abnormal weather cycle, obtain all abnormal weather cycles in a day and convert As a cycle value of the abnormal weather cycle, the maximum cycle value of all abnormal weather cycles is used as the time threshold, all abnormal weather cycles are corresponded to the second difference sequence, and several second cycle ranges are obtained. The maximum value in the second difference sequence within all second cycle ranges is used as the temperature threshold.

7. A method for remotely monitoring abnormal temperature of a low-voltage distribution cabinet according to claim 6, characterized in that: The step of obtaining the trend difference parameter of each preset time window according to each first derivative subsequence and the corresponding second derivative subsequence includes the following specific steps: In the formula, is the mean of the nth second derivative subsequence, is the mean of the nth first derivative subsequence, is the standard deviation of the second derivative subsequence, is the standard deviation of the first derivative subsequence, yes , The L2 norm of the constituent vectors, The trend difference parameter for the nth preset time window.

8. The abnormal temperature remote monitoring method for a low voltage distribution cabinet according to claim 1, characterized in that: The method of obtaining the total number of abnormal heat accumulation, the duration of abnormal heat accumulation, and the location where abnormal heat accumulation occurs according to the time threshold and the temperature threshold includes the following specific steps: If the difference between the temperature at the tth time node in the pth temperature sensor data and the overall temperature inside the distribution cabinet at the tth time node is greater than the temperature threshold, then the duration of the temperature being greater than the temperature threshold is counted starting from the tth time node. If the duration is greater than the time threshold, abnormal heat accumulation has occurred in the pth part inside the distribution cabinet. The total number of abnormal heat accumulations in one day and the duration of each abnormal heat accumulation are counted.

9. The abnormal temperature remote monitoring method for a low voltage distribution cabinet according to claim 1, characterized in that: The step of obtaining the degree of heat accumulation at the location where heat accumulation occurs inside the power distribution cabinet according to the overall temperature inside the power distribution cabinet at each time node, the temperature at each time node in the temperature sensor data of the location where abnormal heat accumulation occurs, the total number of abnormal heat accumulations and the duration of abnormal heat accumulation includes the following specific steps: In the formula, is the overall temperature inside the power distribution cabinet at the tth time node, is the temperature at the tth time node in the temperature sensing data of the pth part, is the heat accumulation degree of the pth part inside the power distribution cabinet, is the temperature threshold, is the duration of the mth abnormal heat accumulation, is the time threshold, and N is the total number of abnormal heat accumulation.

10. The abnormal temperature remote monitoring method for a low voltage distribution cabinet according to claim 1, characterized in that: The specific steps of obtaining the safety factor of the power distribution cabinet according to the degree of heat accumulation at the heat accumulation location inside the power distribution cabinet, the temperature at each time node in the temperature change data, and the overall temperature inside the power distribution cabinet at each time node are as follows: In the formula, is the overall temperature inside the power distribution cabinet at the tth time node, is the temperature at the tth time node in the temperature change data, M is the total number of time nodes in a day, is the average value of the heat accumulation degree of all abnormal heat accumulation parts inside the power distribution cabinet. is an exponential function with a natural constant as base, is the abnormal degree of internal temperature, is the safety factor of the power distribution cabinet; The specific method for obtaining the abnormal degree of the internal temperature is as follows: The number of times that the overall temperature inside the power distribution cabinet is greater than the preset normal temperature and lasts longer than the time threshold in a day is taken as the abnormal degree of the internal temperature.

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