Distribution transformer equipment detection and evaluation method based on big data analysis

Abstract

The invention discloses a distribution transformer equipment detection and evaluation method based on big data analysis, and the method comprises the steps: setting a detection time interval based on the operation age limit of a distribution transformer, and collecting the operation parameters and use parameters of the distribution transformer after the set detection time interval is reached; therefore, the operation evaluation index and the use evaluation index of the distribution transformer in the current detection process are obtained, so that the operation state and the use state of the distribution transformer in the current detection process are reflected, and the equipment condition of the distribution transformer is comprehensively evaluated. The problem that in the prior art, the condition of the distribution transformer is evaluated mostly from a single angle, and the operation and use state of the distribution transformer cannot be comprehensively evaluated from multiple angles is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment detection, and particularly to a detection and evaluation method for distribution transformer equipment based on big data analysis. Background Art

[0002] With the acceleration of the urbanization process, especially in the construction and transformation of urban power grids, box-type substations have been widely used due to their advantages such as small floor area and beautiful appearance.

[0003] In the actual application process, it is necessary to regularly detect the status of distribution transformers in the substation, and the existing detection methods still have the following deficiencies: Relying on manual inspections, there are problems such as low detection efficiency, low accuracy, poor real-time performance, and potential safety hazards; Furthermore, if simple automated instruments are used for detection, such as sensors, most of the existing technologies evaluate the condition of the distribution transformer from a single angle, and fail to comprehensively evaluate the operation and use status of the distribution transformer from multiple angles, such as temperature, power, operating sound, etc., resulting in low accuracy and intelligence in the detection and evaluation of distribution transformers, and potential safety hazards still exist.

[0004] Therefore, a detection and evaluation method for distribution transformer equipment based on big data analysis is introduced. Summary of the Invention

[0005] In view of this, the present invention provides a detection and evaluation method for distribution transformer equipment based on big data analysis to solve the problems raised in the above background art.

[0006] The object of the present invention can be achieved by the following technical solutions: A detection and evaluation method for distribution transformer equipment based on big data analysis, including: Data collection: Extract the operating years of the distribution transformer from the log records. Based on the operating years of the distribution transformer, set the detection time interval, and after reaching the set detection time interval, collect the operating parameters and usage parameters of the distribution transformer; the operating parameters include temperature, power factor, and load loss; the usage parameters include operating sound and insulation resistance; Communication technology: Transmit the collected operating parameters and usage parameters to the central processing platform through wireless communication technology; Central processing: Analyze the operating parameters and usage parameters of the distribution transformer respectively, and thus obtain the operating evaluation index Yx and the usage evaluation index Yf of the distribution transformer in the current detection process; Intelligent diagnosis: Set the warning threshold indexes for the operation evaluation index Yx and the usage evaluation index Yf. If the operation evaluation index Yx is greater than the corresponding set warning threshold index, an operation warning signal is triggered. If the usage evaluation index Yf is greater than the corresponding set warning threshold index, a usage warning signal is triggered; Remote maintenance: When an operation warning signal is triggered, select the technicians who are in the working state at the current time point, and send the operation warning signal to the mobile terminals of these personnel. At the same time, allow these authorized personnel to establish a communication connection with the distribution transformer remotely and conduct remote troubleshooting; On-site operation: When a usage warning signal is triggered, perform corresponding steps to select the processing personnel with the largest on-site effectiveness value Ne at the current time point, and send the location of the distribution transformer to the mobile terminal of this personnel; Diagnosis optimization: If a fault anomaly occurs in the distribution transformer during subsequent actual use, a warning optimization signal is triggered and sent to the mobile terminal of the technician. The technician receives the warning optimization signal and dynamically adjusts the set warning threshold index.

[0007] In some embodiments, analyze the operating parameters of the distribution transformer. Specifically: Obtain the active power and apparent power of the distribution transformer at each time point within a set time period during the current detection process, and further convert the active power and apparent power at each time point into power factors, that is, calculate by active power / apparent power to obtain the power factors of the distribution transformer at each time point; Use the standard deviation formula to calculate the power factors of the distribution transformer at each time point, and take the calculated result as the power fluctuation of the distribution transformer within the set time period during the current detection process; Extract the maximum value and the minimum value from the power factors at each time point, and calculate the difference between the maximum value and the minimum value, so as to obtain the power difference of the distribution transformer within the set time period during the current detection process; Calculate the average value of the power factors of the distribution transformer at each time point, and take the calculated average value as the power average value of the distribution transformer within the set time period during the current detection process; Preset the maximum allowable power fluctuation, the maximum allowable power difference, and the minimum allowable power average value corresponding to the power fluctuation, power difference, and power average value of the distribution transformer respectively; Calculate the ratios of the power fluctuation, power difference, and power average value of the distribution transformer within the set time period during the current detection process to the corresponding preset maximum allowable power fluctuation, maximum allowable power difference, and minimum allowable power average value respectively, that is, calculate by power fluctuation / maximum allowable power fluctuation, power difference / maximum allowable power difference, and minimum allowable power average value / power average value, thereby obtaining the fluctuation ratio, difference ratio, and average ratio; Set the weight coefficients corresponding to the fluctuation ratio, difference ratio, and mean ratio. Multiply the fluctuation ratio, difference ratio, and mean ratio of the distribution transformer during the current detection process within the set time period by the corresponding weight coefficients respectively, and then sum them to obtain the power evaluation value Dw of the distribution transformer during the current detection process.

[0008] In some embodiments, analyzing the operating parameters of the distribution transformer further includes: Obtain the temperature change conditions of each divided area of the distribution transformer during the current detection process within the set time period, and preset the maximum allowable value of the temperature of each divided area of the distribution transformer; Extract the temperature values of each divided area of the distribution transformer at each time point within the set time period, and take the mean of the temperature values of each divided area corresponding to each time point as the temperature mean of each divided area during the current detection process within the set time period, and denote it as Kai; where i represents the number of the corresponding divided area, where i = 1, 2... t; At the same time, mark the maximum allowable value of the preset temperature corresponding to each divided area as , and according to the formula Perform weighted calculation on the temperature means of each divided area of the distribution transformer, thereby obtaining the temperature evaluation value Dq of the distribution transformer during the current detection process; where Zi represents the preset influence weight factor corresponding to the temperature mean Kai of each divided area. Based on the active power of each time point of the distribution transformer during the current detection process within the set time period, calculate the load loss of each time point; calculate the mean of the load losses of each time point, thereby obtaining the average load loss De of the distribution transformer.

[0009] In some embodiments, obtain the operation evaluation index Yx of the distribution transformer during the current detection process, specifically: According to the formula , perform weighted calculation on the temperature evaluation value Dq, power evaluation value Dw, and average load loss De of the distribution transformer during the current detection process within the set time period, thereby obtaining the operation evaluation index Yx of the distribution transformer during the current detection process; where , and respectively represent the preset reference thresholds corresponding to the temperature evaluation value Dq, power evaluation value Dw, and average load loss De of the distribution transformer, and c1, c2, and c3 are the preset influence weight factors corresponding to the temperature evaluation value Dq, power evaluation value Dw, and average load loss De respectively.

[0010] In some embodiments, analyzing the usage parameters of the distribution transformer specifically includes: Capture the sound signals generated during the operation of the distribution transformer within a set time period during the current detection process; compare and analyze the captured sound signals with the sound waveforms in the normal state; Preset two sets of allowable difference distances during the waveform comparison and analysis process. If the distance by which a certain section of the waveform in the sound signal is higher than the sound waveform in the normal state is greater than the preset allowable difference distance, then mark this section as an abnormal sound section, and determine the start and end time points of the abnormal sound section to obtain the duration of the abnormal sound section. Accumulate the durations of each group of abnormal sound sections to obtain the abnormal duration during the current detection process of the distribution transformer, denoted as Ma;

[0011] Obtain the number of vibrations of the distribution transformer during the current detection process, identify the vibration amplitudes in each number of vibrations, and set a demarcation threshold for the vibration amplitudes. Compare the vibration amplitudes of each number of vibrations with the set demarcation threshold. If the vibration amplitude of a certain number of vibrations is greater than the set demarcation threshold, then mark this vibration as an abnormal vibration, and count the number of abnormal vibrations of the distribution transformer during the current detection process, denoted as Mb.

[0012] In some embodiments, analyzing the usage parameters of the distribution transformer further includes: Measure the insulation resistance of the distribution transformer during the current detection process; read the temperature value of the surrounding environment where the current distribution transformer is located, and compare it with the set base temperature value. If it is higher than the set base temperature value, then calculate the difference between the two and denote it as the high difference value. If it is lower than the set base temperature value, then calculate the difference between the two and take the absolute value and denote it as the low difference value. Preset the respective difference value ranges for the high difference value and the low difference value in each group, and set a compensation value corresponding to each group of difference value ranges for the high difference value and the low difference value. Match the calculated high difference value or low difference value with the corresponding preset difference value ranges in each group to obtain the compensation value of the distribution transformer during the current detection process; Add the measured insulation resistance to the obtained compensation value to obtain the compensated resistance value of the distribution transformer; set the reference standard value of the insulation resistance of the distribution transformer; calculate the difference between the compensated resistance value and the set reference standard value, and take the absolute value of the calculation result to obtain the abnormal resistance value Mc of the distribution transformer during the current detection process.

[0013] In some embodiments, obtain the usage evaluation index Yf of the distribution transformer during the current detection process, specifically: According to the formula , perform weighted calculation on the abnormal duration Ma, the number of abnormal vibrations Mb, and the abnormal resistance value Mc of the distribution transformer during the current detection process, thereby obtaining the usage evaluation index Yf of the distribution transformer during the current detection process; where , and respectively represent the preset reference thresholds corresponding to the abnormal duration Ma, the abnormal vibration times Mb, and the abnormal resistance value Mc; n1, n2, and n3 are respectively the preset influence weight factors corresponding to the abnormal duration Ma, the abnormal vibration times Mb, and the abnormal resistance value Mc.

[0014] In some embodiments, the specific steps for obtaining the on-site effectiveness value Ne of the processing personnel are as follows: Taking the location of the current distribution transformer as the center, draw a circle with a set distance as the radius; screen all the warning signal processing personnel within the circle and mark them as candidate personnel; send a location feedback signal to the mobile terminals of each candidate personnel, and each candidate personnel obtains their current location after confirming the location feedback signal; Based on the location of the distribution transformer and the locations of each candidate personnel, obtain the required travel distance for each candidate personnel to reach the distribution transformer, denoted as g; extract the working years of each candidate personnel from their work logs, denoted as u; count the number of times each candidate personnel is selected in the current month, denoted as e; According to the formula , perform weighted calculations on the required travel distance g, the working years u, and the number of times e of each candidate personnel, so as to obtain the on-site effectiveness value Ne of each candidate personnel; where r1, r2, and r3 are respectively the preset influence weight factors corresponding to the required travel distance g, the working years u, and the number of times e; is a preset correction factor.

[0015] In some embodiments, based on the operating years of the distribution transformer, set the detection time interval, specifically: preset each group of years value ranges of the operating years, and set a detection time interval corresponding to each group of years value ranges; match the operating years of the distribution transformer with the preset groups of years value ranges, so as to obtain the detection time interval of the distribution transformer.

[0016] Compared with the prior art, the beneficial effects of the present invention are: The present invention sets the detection time interval based on the operating years of the distribution transformer, and after reaching the set detection time interval, collects the operating parameters and usage parameters of the distribution transformer, thereby obtaining the operating evaluation index and usage evaluation index of the distribution transformer in the current detection process, so as to reflect the operating state and usage state of the distribution transformer in the current detection process, comprehensively evaluate the equipment condition of the distribution transformer, and solve the problem that in the prior art, the condition of the distribution transformer is mostly evaluated from a single angle, and the operating and usage states of the distribution transformer cannot be comprehensively evaluated from multiple angles; The present invention improves the processing efficiency of detection results by separately setting warning threshold indices for the operation evaluation index and the usage evaluation index. If the operation evaluation index is greater than the corresponding set warning threshold index, an operation warning signal is triggered. If the usage evaluation index is greater than the corresponding set warning threshold index, a usage warning signal is triggered, and targeted operations are performed according to the specific type of the triggered warning signal. When the present invention triggers a usage warning signal, it executes corresponding steps to select the processing personnel with the largest on-site effect value at the current time point, realizing the intelligence of personnel selection and improving the processing speed and efficiency when the warning is triggered. Description of the Drawings

[0017] In the following description of exemplary embodiments in conjunction with the drawings, more details, features, and advantages of the present application are disclosed. In the drawings: Figure 1 is a flowchart of the present invention. Detailed Embodiments

[0018] The following will describe several embodiments of the present application in more detail with reference to the drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0019] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0020] Please refer to Figure 1 as shown, a detection and evaluation method for distribution transformer equipment based on big data analysis includes: Data collection: Extract the operating years of the distribution transformer from the log records. Based on the operating years of the distribution transformer, set the detection time interval, and after reaching the set detection time interval, collect the operating parameters and usage parameters of the distribution transformer; the operating parameters include temperature, power factor, and load loss; the usage parameters include operating sound and insulation resistance. Specifically: Set the value ranges of each group of years for the preset operating years, and set a detection time interval corresponding to each group of years' value ranges; match the operating years of the distribution transformer with the preset value ranges of each group of years to obtain the detection time interval of the distribution transformer; Among them, the higher the operating years, the shorter the corresponding detected time interval; it is set by technicians and can be dynamically adjusted according to the actual situation later; Communication technology: Transmit the collected operating parameters and usage parameters to the central processing platform through wireless communication technology; Wireless communication technologies such as 4G / 5G, Wi-Fi, RF, etc., and use encryption algorithms to encrypt the collected data to ensure the security and reliability during data transmission; Central processing: Analyze the operating parameters and usage parameters of the distribution transformer respectively to obtain the operating evaluation index Yx and usage evaluation index Yf of the distribution transformer during the current detection process; Specifically: Use the temperature sensors pre - installed on the transformer to obtain the temperature change conditions of each divided area within the set time period during the current detection process of the distribution transformer. The divided areas are set by technicians, such as winding temperature, oil surface temperature, core temperature, and clamp temperature; Technicians preset the maximum allowable values of the temperatures of each divided area of the distribution transformer; Extract the temperature values of each time point of each divided area of the distribution transformer within the set time period, and take the average value of the temperature values of each divided area corresponding to each time point as the temperature average value of each divided area within the set time period during the current detection process, and denote it as Kai; where i represents the number of the corresponding divided area, and i = 1, 2... t; At the same time, mark the maximum allowable value of the preset temperature corresponding to each divided area as , and according to the formula Perform weighted calculation on the temperature average values of each divided area of the distribution transformer to obtain the temperature evaluation value Dq of the distribution transformer during the current detection process; where Zi represents the preset influence weight factor corresponding to the temperature average value Kai of each divided area; It should be noted that by arranging temperature sensors in the key areas of the transformer, the comprehensive monitoring of the temperatures of areas such as windings, oil surfaces, cores, and clamps is realized, which helps to comprehensively understand the thermal state of the transformer.

[0021] Use a power analyzer to obtain the active power and apparent power of each time point of the distribution transformer within the set time period during the current detection process, and further convert the active power and apparent power of each time point into power factors, that is, calculate by active power / apparent power to obtain the power factors of each time point of the distribution transformer; The power factor at each time point of the distribution transformer is calculated using the standard deviation formula, and the calculated result is used as the power fluctuation of the distribution transformer within the set time period during the current detection process; The maximum and minimum values are extracted from the power factors at each time point, and the difference between the maximum and minimum values is calculated, so as to obtain the power difference of the distribution transformer within the set time period during the current detection process; The average value of the power factors at each time point of the distribution transformer is calculated, and the calculated average value is used as the power average value of the distribution transformer within the set time period during the current detection process; The maximum allowable power fluctuation, the maximum allowable power difference, and the minimum allowable power average value corresponding to the power fluctuation, power difference, and power average value of the distribution transformer are preset respectively; The ratios of the power fluctuation, power difference, and power average value of the distribution transformer within the set time period during the current detection process to the corresponding preset maximum allowable power fluctuation, maximum allowable power difference, and minimum allowable power average value are calculated respectively, that is, calculated through power fluctuation / maximum allowable power fluctuation, power difference / maximum allowable power difference, and minimum allowable power average value / power average value, thereby obtaining the fluctuation ratio, difference ratio, and average ratio; The weight coefficients corresponding to the fluctuation ratio, difference ratio, and average ratio are set, and the fluctuation ratio, difference ratio, and average ratio of the distribution transformer within the set time period during the current detection process are multiplied by the corresponding weight coefficients respectively, and then summed to obtain the power evaluation value Dw of the distribution transformer during the current detection process; It should be noted that as described above, not only the instantaneous value of the power factor is considered, but also the fluctuation, difference, and average value of the power factor are analyzed, providing a comprehensive understanding of the operating state of the transformer.

[0022] Based on the active power at each time point of the distribution transformer within the set time period during the current detection process, the load loss at each time point is calculated; that is, through for calculation, where is the load loss, is the input active power, is the output active power; the average value of the load losses at each time point is calculated, thereby obtaining the average load loss De of the distribution transformer; According to the formula , the weighted calculation of the temperature evaluation value Dq, power evaluation value Dw, and average load loss De of the distribution transformer within the set time period during the current detection process is carried out, thereby obtaining the operation evaluation index Yx of the distribution transformer during the current detection process; where , and respectively represent the preset reference thresholds corresponding to the temperature evaluation value Dq, power evaluation value Dw, and average load loss De of the distribution transformer, and c1, c2, and c3 are respectively the preset influence weight factors corresponding to the temperature evaluation value Dq, power evaluation value Dw, and average load loss De; It should be noted that, in summary, the comprehensive analysis of the evaluation results of the temperature, power, and load loss of the distribution transformer during the detection process comprehensively reflects the operating state of the distribution transformer.

[0023] Use a sound acquisition device to capture the sound signal generated during the operation of the distribution transformer within a set time period during the current detection process; Preprocess the sound signal. The preprocessing includes but is not limited to steps such as filtering and denoising to improve the accuracy of subsequent analysis; Compare and analyze the captured sound signal with the sound waveform under normal conditions; the sound waveform under normal conditions is stored in the constructed sound waveform data model library; Preset two sets of allowable difference distances during the waveform comparison and analysis process. If the distance by which a certain section of the waveform in the sound signal is higher than the sound waveform under normal conditions is greater than the preset allowable difference distance, then mark this section as an abnormal sound section, and determine the start and end time points of the abnormal sound section to obtain the duration of the abnormal sound section; Accumulate the durations of each group of abnormal sound sections to obtain the abnormal duration of the distribution transformer during the current detection process, denoted as Ma; Use pre - deployed vibration sensors to obtain the vibration times of the distribution transformer during the current detection process, identify the vibration amplitudes in each vibration time, and set a demarcation threshold for the vibration amplitude; the demarcation threshold is set according to industry standards; Compare the vibration amplitudes of each vibration time with the set demarcation threshold. If the vibration amplitude of a certain vibration time is greater than the set demarcation threshold, then mark this vibration as an abnormal vibration, and count the number of abnormal vibrations of the distribution transformer during the current detection process, denoted as Mb; Use a megohmmeter that meets the voltage level requirements to measure the insulation resistance of the distribution transformer during the current detection process; Read the temperature value of the surrounding environment where the current distribution transformer is located, and compare it with the set basic temperature value. If it is higher than the set basic temperature value, then calculate the difference between the two and denote it as the high difference value. If it is lower than the set basic temperature value, then calculate the difference between the two and take the absolute value and denote it as the low difference value. Respectively preset the difference value ranges for each group of high difference values and low difference values, and set a compensation value corresponding to each group of high difference values and low difference values; match the calculated high difference value or low difference value with the corresponding preset difference value ranges to obtain the compensation value of the distribution transformer during the current detection process; The compensation values corresponding to various difference value ranges are set by technicians, where the compensation value obtained by matching the high difference value is a positive number, and the compensation value obtained by matching the low difference value is a negative number; For example, for every 10°C increase in temperature, the insulation resistance value may decrease to 2 / 3 of the original value; while for every 10°C decrease in temperature, the insulation resistance value may increase to 1.5 times the original value; the base temperature value can be set to 20 degrees, and the difference between the current ambient temperature and the base temperature value is calculated, and the measured insulation resistance is compensated based on a preset matching rule, thereby improving the accuracy of data analysis; The measured insulation resistance is added to the obtained compensation value to obtain the compensated resistance value of the distribution transformer; Set the reference standard value of the insulation resistance of the distribution transformer; it is set by technicians in combination with the historical data and operating conditions of the transformer; The difference between the compensated resistance value and the set reference standard value is calculated, and the absolute value of the calculation result is taken to obtain the abnormal resistance value Mc of the distribution transformer during the current detection process; According to the formula , the weighted calculation of the abnormal duration Ma, abnormal vibration times Mb, and abnormal resistance value Mc of the distribution transformer during the current detection process is carried out, and thus the usage evaluation index Yf of the distribution transformer during the current detection process is obtained; where , and respectively represent the preset reference thresholds corresponding to the abnormal duration Ma, abnormal vibration times Mb, and abnormal resistance value Mc; n1, n2, and n3 are the preset influence weight factors corresponding to the abnormal duration Ma, abnormal vibration times Mb, and abnormal resistance value Mc respectively; It should be noted that in summary, through sound signal analysis, vibration monitoring, and insulation resistance measurement, the usage status of the transformer has been comprehensively inspected from different angles, which helps to more comprehensively understand the health status of the transformer.

[0024] Intelligent diagnosis: Based on the operation evaluation index Yx and usage evaluation index Yf of the distribution transformer during the current detection process, the warning threshold indexes of the operation evaluation index Yx and usage evaluation index Yf are respectively set. If the operation evaluation index Yx is greater than the corresponding set warning threshold index, an operation warning signal is triggered. If the usage evaluation index Yf is greater than the corresponding set warning threshold index, a usage warning signal is triggered; Remote maintenance: When the operation warning signal is triggered, select the technician who is in the working state at the current time point, and send the operation warning signal to the mobile terminal of this person. At the same time, allow this person to remotely establish a communication connection with the distribution transformer and conduct remote troubleshooting; On-site operation: When the warning signal is triggered for use, perform corresponding steps to select the operator with the maximum on-site effectiveness value Ne at the current time point, and send the location of the distribution transformer to the mobile terminal of this operator; Specifically: Draw a circle with the location of the current distribution transformer as the center and a set distance as the radius; the initial set distance is 3 km, and if no operator can be screened out, it will be expanded in a range of 1 km, and so on; Screen all operators who use the warning signal within the circle and mark them as candidate operators; send a location feedback signal to the mobile terminals of each candidate operator, and each candidate operator obtains their current location after confirming the location feedback signal; the default automatic confirmation is 10 seconds; Based on the location of the distribution transformer and the locations of each candidate operator, obtain the required travel distance for each candidate operator to reach the distribution transformer, denoted as g; Extract the working years of each candidate operator from their work logs, denoted as u; count the number of times each candidate operator has been selected in the current month, denoted as e; The shorter the required travel distance, the faster the candidate operator can reach the site; the higher the working years, the richer the processing experience of the candidate operator; and the more times selected in the current month, the higher the workload of the candidate operator in the current month; According to the formula , perform weighted calculations on the required travel distance g, working years u, and the number of times selected e of each candidate operator, so as to obtain the on-site effectiveness value Ne of each candidate operator; where r1, r2, and r3 are the preset influence weight factors corresponding to the required travel distance g, working years u, and the number of times selected e respectively; is a preset correction factor, with a value of 0.982, which can be dynamically adjusted according to the actual situation in the future; Diagnosis and optimization: If a fault anomaly occurs during the subsequent actual use of the distribution transformer, trigger a warning optimization signal and send it to the mobile terminal of the technician. The technician receives the warning optimization signal and dynamically adjusts the set warning threshold index; The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A distribution transformer equipment detection and evaluation method based on big data analysis, characterized in that: include: Data collection: Extract the operating years of the distribution transformer from the record log, set the detection time interval based on the operating years of the distribution transformer, and collect the operating parameters and usage parameters of the distribution transformer after the set detection time interval is reached; the operating parameters include temperature, power factor and load loss; the usage parameters include operating sound and insulation resistance; Communication technology: The collected operating parameters and usage parameters are transmitted to the central processing platform through wireless communication technology; Central processing: Analyze the operating parameters and usage parameters of the distribution transformer respectively, thereby obtaining the operating evaluation index Yx and usage evaluation index Yf of the distribution transformer in the current detection process; Intelligent diagnosis: Set the warning threshold index of the operation evaluation index Yx and the use evaluation index Yf. If the operation evaluation index Yx is greater than the corresponding set warning threshold index, the operation warning signal is triggered; if the use evaluation index Yf is greater than the corresponding set warning threshold index, the use warning signal is triggered; Remote maintenance: When the operation warning signal is triggered, the technician who is in working state at the current time point is selected, and the operation warning signal is sent to the mobile terminal of the technician. At the same time, the technician is authorized to establish a communication connection with the distribution transformer remotely and conduct remote inspection; On-site operation: When the early warning signal is triggered, the corresponding steps are executed to select the processing personnel with the largest on-site effectiveness value Ne at the current time point, and the location of the distribution transformer is sent to the mobile terminal of the personnel; Diagnostic optimization: If the distribution transformer has a fault abnormality during subsequent actual use, it will trigger an early warning optimization signaling and send it to the technician's mobile terminal. The technician will receive the early warning optimization signaling and dynamically adjust the set early warning threshold index.

2. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: The operating parameters of the distribution transformer are analyzed, specifically: Obtain the active power and apparent power of the distribution transformer at each time point within the set time period during the current detection process, and further convert the active power and apparent power at each time point into a power factor, that is, calculate the active power / apparent power to obtain the power factor of the distribution transformer at each time point; The power factor of the distribution transformer at each time point is calculated using the standard deviation formula, and the calculation result is used as the power fluctuation of the distribution transformer within the set time period during the current detection process; Extract the highest and lowest values ​​from the power factor at each time point, and calculate the difference between the highest and lowest values, so as to obtain the power difference of the distribution transformer within a set time period during the current detection process; Calculate the average value of the power factor of the distribution transformer at each time point, and use the calculated average value as the power average value of the distribution transformer within a set time period during the current detection process; Preset the maximum allowable power fluctuation, maximum allowable power difference and minimum allowable power mean corresponding to the power fluctuation, power difference and power mean of the distribution transformer respectively; Calculate the ratios of the power fluctuation, power difference and power mean of the distribution transformer within a set time period during the current detection process to the corresponding preset maximum allowable power fluctuation, maximum allowable power difference and minimum allowable power mean, that is, calculate through power fluctuation / maximum allowable power fluctuation, power difference / maximum allowable power difference and minimum allowable power mean / power mean, thereby obtaining the fluctuation ratio, difference ratio and mean ratio; Set the weight coefficients corresponding to the fluctuation ratio, difference ratio and mean ratio, multiply the fluctuation ratio, difference ratio and mean ratio of the distribution transformer within the set time period in the current detection process by the corresponding weight coefficients, and then sum them up to obtain the power evaluation value Dw of the distribution transformer in the current detection process.

3. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: Analysis of the operating parameters of distribution transformers also includes: Obtain the temperature change of each divided area of ​​the distribution transformer within a set time period during the current detection process, and preset the maximum allowable value of the temperature of each divided area of ​​the distribution transformer; Extract the temperature values ​​of each divided area of ​​the distribution transformer at each time point within the set time period, take the average value of the temperature values ​​of each divided area corresponding to each time point as the average temperature of each divided area within the set time period during the current detection process, and record it as Kai; where i represents the number of the corresponding divided area, where i=1, 2..., t; At the same time, the maximum allowable value of the preset temperature corresponding to each divided area is marked as , according to the formula The temperature averages of each divided area of ​​the distribution transformer are weightedly calculated, thereby obtaining the temperature evaluation value Dq of the distribution transformer in the current detection process; wherein Zi represents the preset influence weight factor corresponding to the temperature average Kai of each divided area; Based on the active power of the distribution transformer at each time point within the set time period during the current detection process, the load loss at each time point is calculated; the load loss at each time point is averaged to obtain the average load loss De of the distribution transformer.

4. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: The operation evaluation index Yx of the distribution transformer in the current detection process is obtained, which is specifically: According to the formula , weighted calculation is performed on the temperature evaluation value Dq, power evaluation value Dw and average load loss De of the distribution transformer in the set time period during the current detection process, thereby obtaining the operation evaluation index Yx of the distribution transformer in the current detection process; wherein , as well as They respectively represent the preset reference thresholds corresponding to the temperature evaluation value Dq, the power evaluation value Dw and the average load loss De of the distribution transformer, and c1, c2 and c3 are the preset influence weight factors corresponding to the temperature evaluation value Dq, the power evaluation value Dw and the average load loss De.

5. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: The use parameters of the distribution transformer are analyzed, specifically: Capture the sound signal generated by the distribution transformer during operation within the set time period during the current detection process; compare and analyze the captured sound signal with the sound waveform under normal conditions; The allowed difference distance in the comparison and analysis process of the two sets of waveforms is preset. If a certain waveform in the sound signal is higher than the sound waveform in the normal state by a distance greater than the preset allowed difference distance, the segment is marked as an abnormal sound segment, and the start and end time points of the abnormal sound segment are determined to obtain the duration of the abnormal sound segment; The duration of each group of abnormal sound clips is accumulated to obtain the abnormal duration of the distribution transformer in the current detection process, which is recorded as Ma; Obtain the number of vibrations of the distribution transformer in the current detection process, identify the vibration amplitude in each vibration number, and set the demarcation threshold of the vibration amplitude; compare the vibration amplitude of each vibration number with the set demarcation threshold. If the vibration amplitude of a certain vibration number is greater than the set demarcation threshold, mark the vibration as abnormal vibration, and count the number of abnormal vibrations of the distribution transformer in the current detection process, recorded as Mb.

6. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: Analysis of the operating parameters of the distribution transformer also includes: Measure the insulation resistance of the distribution transformer during the current detection process; read the temperature value of the current surrounding environment of the distribution transformer and compare it with the set basic temperature value. If it is higher than the set basic temperature value, calculate the difference between the two and record it as a high difference; if it is lower than the set basic temperature value, calculate the difference between the two and take the absolute value as a low difference. Preset the difference value ranges of each group of high difference and low difference respectively, and set the difference value range corresponding to each group of high difference and low difference to correspond to a compensation value; match the calculated high difference or low difference with the corresponding preset difference value ranges of each group, so as to obtain the compensation value of the distribution transformer during the current detection process; The measured insulation resistance is added to the obtained compensation value to obtain the compensation resistance value of the distribution transformer; a reference standard value of the insulation resistance of the distribution transformer is set; the difference between the compensation resistance value and the set reference standard value is calculated, and the absolute value of the calculation result is taken to obtain the abnormal resistance value Mc of the distribution transformer in the current detection process.

7. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: The use evaluation index Yf of the distribution transformer in the current detection process is obtained, which is: According to the formula , weighted calculation is performed on the abnormal duration Ma, abnormal vibration times Mb and abnormal resistance value Mc of the distribution transformer in the current detection process, thereby obtaining the use evaluation index Yf of the distribution transformer in the current detection process; where , as well as They respectively represent the preset reference thresholds corresponding to the abnormal duration Ma, the abnormal vibration number Mb and the abnormal resistance value Mc; n1, n2 and n3 are respectively the preset influence weight factors corresponding to the abnormal duration Ma, the abnormal vibration number Mb and the abnormal resistance value Mc.

8. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: The specific steps to obtain the on-site effectiveness value Ne of the treatment personnel are: Draw a circle with the current location of the distribution transformer as the center and the set distance as the radius; select all personnel using early warning signal processing within the circle and mark them as candidates; send a position feedback signal to the mobile terminal of each candidate, and each candidate obtains the current location of each candidate after confirming the position feedback signal; Based on the location of the distribution transformer and the location of each candidate, the required distance for each candidate to reach the distribution transformer is obtained, which is recorded as g; the working years of each candidate are extracted from the work log of each candidate, which is recorded as u; the number of times each candidate is selected in the month is counted, which is recorded as e; According to the formula , weighted calculation is performed on the required distance g, working years u and number of times selected e of each candidate, so as to obtain the on-site effectiveness value Ne of each candidate; wherein r1, r2 and r3 are the preset influence weight factors corresponding to the required distance g, working years u and number of times selected e respectively; is the preset correction factor.

9. The distribution transformer equipment detection and evaluation method based on big data analysis according to claim 1 is characterized in that: Based on the operating years of the distribution transformer, the detection time interval is set, specifically: each group of age value ranges of the operating years are preset, and each group of age value ranges is set to correspond to a detection time interval; the operating years of the distribution transformer are matched with the preset groups of age value ranges, so as to obtain the time interval for the detection of the distribution transformer.

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