Power equipment operation fault monitoring and early warning system

Abstract

The invention relates to the technical field of power equipment fault monitoring, in particular to a power equipment operation fault monitoring and early warning system. Comprising a server, an aging updating module and a monitoring and early warning module, by comprehensively analyzing historical use information and aging information, the aging degree of the power equipment can be evaluated more accurately, the system updates an aging threshold factor once every other month, and equipment fault judgment parameters are dynamically corrected according to the actual use condition of the equipment, so that the early warning system can reflect the latest state of the equipment more timely; the aging threshold factor is used for dynamically correcting the fault judgment parameter of the equipment with the moderate aging tag, and it is ensured that the fault judgment result is still accurate even under the condition that the equipment is aged; for slightly aged equipment, correction does not need to be carried out; and performing operation deviation analysis on the corrected parameters to obtain an operation deviation value, analyzing an operation deviation development trend according to the operation deviation value so as to judge whether the power equipment has a fault risk, and outputting risk equipment according to the fault risk.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment fault monitoring, and in particular to a power equipment operation fault monitoring and early warning system. Background Art

[0002] As the core of the modern power system, power equipment does cover all aspects of power generation, transmission, transformation and distribution. As a basic energy source, electricity widely serves all areas of society, so the reliability and stability of power equipment are directly related to social operation and economic development. Once a power equipment fails, it will not only affect the continuous operation of the power system and cause power outages, but may also trigger a chain reaction, affecting industrial production, daily life, and even threatening public safety; therefore, fault monitoring and early warning of power equipment are very important.

[0003] However, the existing fault monitoring and early warning systems have limitations. For example, the patent with publication number CN115664011A provides a method, system and computer storage medium for early warning of fault information of power equipment, in which the early warning levels of different parts of the power equipment are divided and the early warning information of different corresponding parts is used to monitor whether the working parameters of the power equipment exceed the corresponding early warning thresholds. If exceeded, the corresponding early warning information is sent to the central control platform; in this method of judging the fault status based on a fixed threshold, the equipment will gradually age during long-term use, and the performance and failure mode will change, showing operating characteristics different from new equipment, making this static threshold judgment method difficult to adapt to the changes in operating conditions during the aging process, resulting in old equipment frequently triggering alarms due to monitoring errors. Summary of the invention

[0004] The purpose of the present invention is to provide a power equipment operation fault monitoring and early warning system to solve the problems mentioned in the above background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A power equipment operation fault monitoring and early warning system, comprising: a server, an aging update module and a monitoring and early warning module;

[0007] The server communicates with each device in the power grid and the sensors mounted on the devices to collect and save historical usage information and aging information; the historical usage information includes the number of times the power equipment has worked and the working time of each work, the time it has been put into use (specifically, the time between the start of the power equipment and the current time of the system), and the initial image of the power equipment (referring to the initial appearance image of the power equipment when it is just put into use); the aging information includes vibration amplitude and noise value; the aging information includes equipment temperature (the equipment temperature here refers to the temperature of the winding part of the power equipment, and the average temperature of the winding part every day in a month is calculated to obtain the equipment temperature of each month), insulation resistance, and appearance image (the appearance of the power equipment is imaged on a fixed day every month, for example, the appearance image is obtained by image acquisition on the last day of each month);

[0008] The aging update module conducts a comprehensive analysis based on the historical usage information and aging information of the power equipment to determine the aging degree of the power equipment, and obtains the aging threshold factor accordingly; when the aging threshold factor is greater than the upper limit of the set aging interval, the power equipment is recorded as a severely aged device, and its number and location are extracted and sent to the corresponding engineer; when the aging threshold factor is within the set aging interval, the moderately aged device is used as the label of the power equipment; when the aging threshold factor is less than the lower limit of the set aging interval, the slightly aged device is used as the label of the power equipment;

[0009] The monitoring and early warning module obtains the equipment information of the power equipment in real time, and uses the aging threshold factor to perform aging correction to output the judgment parameters, and performs fault prediction based on the judgment parameters; specifically:

[0010] Step 1: Obtain the equipment information at each collection moment in real time, wherein the equipment information includes actual power, three-dimensional temperature model of power equipment, power factor, vibration amplitude, insulation resistance and noise value; the actual power, power factor, vibration amplitude, insulation resistance and noise value are recorded as Ga, Ya, Fa, Ra and Da respectively; wherein a=1, 2, 3, ..., A; A is a positive integer, A represents the total number of collection moments, and a represents the sequence number of any one of the collection moments;

[0011] Step 2: Set a standard operating parameter for each power device, including rated power, standard power factor, standard insulation resistance, standard vibration amplitude and standard noise value, and record them as PG, PY, PR, PF and PD respectively; analyze the temperature sensitivity of the power equipment according to the three-dimensional temperature model of the power equipment to obtain the sensitivity value;

[0012] Step 3: Extract the label of the power equipment. If the label of the power equipment is a moderately aged device, retrieve the aging threshold factor closest to the current time of the system and use it to perform aging correction on the sensitive value, actual power, power factor, insulation resistance and noise value at each acquisition moment to obtain the corrected judgment parameters. The specific judgment parameters include: judgment sensitive value Determine power Determining the power factor Determining insulation resistance And determine the noise value The specific correction formula is:

[0013]

[0014] Among them, δ1, δ2, δ3, δ4, and δ5 are respectively the set weight constants, and δ1, δ2, δ3, δ4, and δ5 are all greater than zero; μ is the aging threshold factor of the power equipment;

[0015] If the label of the power equipment is a slightly aged device, there is no need to perform aging correction. The sensitive value, actual power, power factor, insulation resistance and noise value collected at each collection time are directly used as equipment judgment parameters. The specific judgment parameters include: judging the sensitive value, judging the power, judging the power factor, judging the insulation resistance and judging the noise value;

[0016] Thus, the judgment parameters of each power device can be obtained, and the specific judgment parameters are: judgment sensitivity value, judgment power, judgment power factor, judgment insulation resistance and judgment noise value;

[0017] Step 4: Extract the judgment parameters of each power equipment: judge the sensitive value Determine power Determining the power factor Determining insulation resistance And determine the noise value And normalize it with the standard working parameters of the power equipment and take its value, calculate and analyze the value by formula to obtain the operation deviation value corresponding to the power equipment at each collection time; the specific calculation formula is:

[0018]

[0019] Among them, Ω1, Ω2, Ω3, Ω4, and Ω5 are respectively set weight constants; according to the operation deviation value, the development trend of the power equipment in the time domain is analyzed to obtain the fault warning value;

[0020] Step 5: When the fault warning value is greater than the set warning threshold, the power equipment is recorded as risky equipment, and its number and location are extracted and sent to the corresponding engineer.

[0021] Preferably, the specific steps of analyzing the temperature sensitivity of the power equipment according to the three-dimensional temperature model of the power equipment are as follows:

[0022] A sensitivity coefficient is set to correspond to each part in the three-dimensional temperature model of the power equipment, and the operating temperature of each part in the three-dimensional temperature model of the power equipment is multiplied by the corresponding sensitivity coefficient to obtain the temperature sensitivity value; the temperature sensitivity value of each part is averaged to obtain the temperature sensitivity mean; the temperature sensitivity value of each part is compared and analyzed with the set sensitivity range to divide the part corresponding to the temperature sensitivity value into a highly sensitive part, a moderately sensitive part and a slightly sensitive part, and the cumulative number of the highly sensitive part, the moderately sensitive part and the slightly sensitive part in the three-dimensional temperature model of the power equipment is counted respectively;

[0023] The temperature-sensitive mean value W1, the cumulative number of highly sensitive parts W2, the cumulative number of moderately sensitive parts W3, and the cumulative number of slightly sensitive parts W4 are normalized and their values ​​are taken. The values ​​are analyzed and calculated by formula to obtain the sensitivity value of the three-dimensional temperature model of the power equipment. The specific calculation formula is:

[0024]

[0025] Among them, γ1, γ2, and γ3 are set weight constants respectively, and γ1>γ2>γ3>1.

[0026] Preferably, the specific steps of performing time domain development trend analysis on the power equipment according to the operation deviation value are as follows:

[0027] A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the operation deviation value as the vertical coordinate, the operation deviation value is input into the coordinate axis according to its corresponding collection time, and the position of the operation deviation value in the coordinate axis is recorded as the deviation point, and a smooth curve is used to connect each deviation point in sequence to obtain a curve diagram of the operation deviation value of the power equipment changing with time;

[0028] A tangent line is drawn at each deviation point, and the slope of the tangent line is calculated by data fitting and recorded as the deviation slope; the deviation slopes greater than zero are summed to obtain the deviation increase value, and the deviation slopes less than zero are summed and then the absolute value is taken to obtain the deviation decrease value;

[0029] The operation deviation value PZa, deviation slope La, deviation increase value C1 and deviation decrease value C2 at each acquisition time are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the fault warning value LP. The specific calculation formula is:

[0030]

[0031] Among them, φ1, φ2, and φ3 are the set weight constants respectively.

[0032] Preferably, the specific process of performing comprehensive analysis based on the historical usage information and aging information of the power equipment to determine the aging degree of the power equipment is:

[0033] 5-1: Retrieve the historical usage information of the power equipment, and analyze the usage degree of the power equipment to obtain the equipment usage value; the specific historical usage information includes the number of operations, the working hours of each operation, and the time of operation;

[0034] 5-2: Retrieve the aging information of the power equipment every month. The specific aging information includes equipment temperature, insulation resistance, and appearance image; perform appearance aging analysis based on the appearance image of the power equipment to obtain the color difference value;

[0035] 5-3: Convert the appearance image into a gray image, use Canny edge detection to identify the defect types in the appearance image, the number of defects corresponding to each type of defect, and the corresponding defect area, and perform defect degree analysis based on this to obtain the appearance defect value;

[0036] 5-4: Normalize the monthly equipment usage value Bλ, color difference value △E, appearance defect value QM, equipment temperature T, and insulation resistance R of the power equipment and take their values. Perform formula calculation and analysis on the values ​​to obtain the aging threshold factor μ. The specific calculation formula is:

[0037]

[0038] Among them, η1, η2, η3, η4, and η5 are the set weight constants, and PT and PR are the standard winding temperature and standard insulation resistance.

[0039] Preferably, the specific steps of analyzing the usage degree of the electric equipment are:

[0040] Compare and analyze the working time of each work with the set time interval to divide the number of work times corresponding to the working time into the first echelon load operation, the second echelon load operation and the third echelon load operation; count the cumulative number of the first echelon load operation, the second echelon load operation and the third echelon load operation respectively, and calculate the average of the working time of each work to obtain the total working time;

[0041] The cumulative number of first-tier load operations B1, the cumulative number of second-tier load operations B2, the cumulative number of third-tier load operations B3, the total working time B4 and the time of use B5 are normalized and their values ​​are taken, and the values ​​are analyzed to obtain the equipment usage value Bλ; the specific calculation formula is:

[0042]

[0043] Among them, λ1, λ2, λ3, and λ4 are set weight constants respectively, and λ1>λ2>λ3>1.

[0044] Preferably, the specific steps of appearance aging analysis are as follows:

[0045] The appearance image is converted from RBG color space to HSV color space, which includes three channels. Each channel is a two-dimensional matrix. Each value in the matrix represents the value of a pixel in the channel. The value in each channel is averaged to obtain the channel values ​​of the three channels, specifically: H current , S current and V current ;

[0046] Similarly, the initial image of the power equipment is converted into the same color to obtain the channel values ​​of the three channels, specifically: H baseline , S baseline and V baseline ;

[0047] The three-channel values ​​of the appearance image and the three-channel values ​​of the initial image are normalized and their values ​​are taken. The color difference value △E is obtained by formula calculation and analysis of the values. The specific calculation formula is:

[0048]

[0049] Among them, α1, α2, and α3 are the set weight constants respectively.

[0050] Preferably, the specific steps of defect degree analysis are as follows:

[0051] The appearance image is converted into a gray image, and the Canny edge detection is used to identify the defect types existing in the appearance image, the defect quantity corresponding to each type of defect, and the corresponding defect area, and each type of defect is set to correspond to a defect value, and each defect type is extracted and compared with all the set defect types to match the corresponding defect value;

[0052] The defect areas corresponding to each defect of a certain defect type are compared and analyzed in the set area interval to divide the defect areas into high impact area, medium impact area and low impact area, and the cumulative numbers of high impact area, medium impact area and low impact area are counted respectively, and the defect areas corresponding to each defect are summed up to obtain the total impact area value; the cumulative number of high impact area M1, the cumulative number of medium impact area M2, the cumulative number of low impact area M3 and the total impact area M4 are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the defect impact value Mσ. The specific calculation formula is:

[0053]

[0054] Where σ1, σ2, and σ3 are respectively the set weight constants, and σ1>σ2>σ3>1; thus, the defect impact value corresponding to each type of defect is recorded as Mσj; j=1, 2, 3, ..., J; J is a positive integer, J represents the total number of defect types, and j represents the serial number of any defect type;

[0055] The defect value Qj, defect quantity Xj and defect influence value Mσj corresponding to each type of defect in the appearance image of the power equipment are normalized and their values ​​are taken. The appearance defect value QM is obtained by formula calculation and analysis of the values. The specific calculation formula is:

[0056]

[0057] Among them, σ4 and σ5 are the set weight constants respectively.

[0058] Beneficial effects of the present invention:

[0059] 1. Through comprehensive analysis of historical usage information and aging information, the aging degree of power equipment can be more accurately assessed. The system updates the aging threshold factor every month and dynamically corrects the parameters for equipment failure judgment based on the actual usage of the equipment, so that the early warning system can reflect the latest status of the equipment more timely. When the equipment is marked as severely aged, the relevant engineers are immediately notified to conduct inspection and maintenance, effectively reducing potential failures caused by equipment aging; providing data support for aging correction of power equipment;

[0060] 2. By using the aging threshold factor, the fault judgment parameters of the equipment labeled as moderately aged are dynamically corrected to ensure that the fault judgment results are still accurate even in the case of equipment aging; no correction is required for equipment with mild aging; the operation deviation analysis of the corrected parameters is performed to obtain the operation deviation value, and the operation deviation development trend is analyzed based on this to determine whether the power equipment has a failure risk, and the risk equipment is output based on this, and the corresponding engineer is notified in time; the fault judgment parameters are dynamically adjusted according to the aging degree of the equipment, so that the early warning system can work effectively under equipment with different aging degrees, improving the timeliness and accuracy of the early warning;

[0061] In summary, the implementation of the aging update module and the monitoring and early warning module enables the fault monitoring system to be upgraded from static thresholds to adaptive dynamic correction parameters, improves the system's adaptability to old equipment, and effectively solves the technical problems of monitoring errors and frequent alarms caused by equipment aging. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention will be further described below in conjunction with the accompanying drawings.

[0063] Figure 1It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example 1

[0066] See also Figure 1 As shown, the present invention is a power equipment operation fault monitoring and early warning system, comprising: a server, an aging update module and a monitoring and early warning module;

[0067] The server communicates with each device in the power grid and the sensors installed on the devices to collect and save historical usage information and aging information; the historical usage information includes the number of times the power equipment has worked and the working time of each work, the time it has been put into use (specifically, the time from the start of the power equipment to the current time of the system), and the initial image of the power equipment (referring to the initial appearance image of the power equipment when it was just put into use); the aging information includes vibration amplitude and noise value; the aging information includes equipment temperature (the equipment temperature here refers to the temperature of the winding part of the power equipment, and the average temperature of the winding part of each day in a month is calculated to obtain the equipment temperature for each month), insulation resistance, and appearance image (the image of the appearance of the power equipment is collected on a fixed day each month, for example, the image is collected on the last day of each month to obtain the appearance image).

[0068] The aging update module monitors and analyzes the aging status of the power equipment based on the historical usage information and aging information of the power equipment to determine its aging degree, and obtains the aging threshold factor accordingly; specifically:

[0069] Retrieve historical usage information of power equipment, and specific historical usage information includes the number of working times, the working time of each working time, and the time of use; compare and analyze the working time of each working time with the set time interval. When the working time is greater than the maximum value in the set time interval, it means that the longer the working time, the greater the impact of long-term load operation on equipment aging and wear, and then accumulate one first-tier load operation; when the working time is within the set time interval, accumulate one second-tier load operation; when the working time is less than the minimum value in the set time interval, accumulate one third-tier load operation; count the cumulative number of first-tier load operations, second-tier load operations, and third-tier load operations, and record them as B1, B2, and B3, respectively; calculate the average of the working time of each working time to obtain the total working time, record it as B4, and record the time of use as B5;

[0070] The cumulative number of first-tier load operations B1, the cumulative number of second-tier load operations B2, the cumulative number of third-tier load operations B3, the total working time B4 and the time of use B5 are normalized and their values ​​are taken, and the values ​​are analyzed to obtain the equipment usage value Bλ; the specific calculation formula is:

[0071]

[0072] Among them, λ1, λ2, λ3, and λ4 are respectively set weight constants, and their sizes are custom settings, and λ1>λ2>λ3>1; specifically, λ1 is 2.987, λ2 is 2.456, and λ3 is 2.015; It can be seen from the formula that when the working time of each time is longer, the aging and wear of the corresponding power equipment is greater, and the equipment usage value is greater; when the time of use is longer, the corresponding equipment usage value is greater;

[0073] Taking months as the analysis time unit, retrieve the aging information of power equipment in each month. The specific aging information includes equipment temperature, insulation resistance, and appearance image;

[0074] The appearance image is converted from the RBG color space to the HSV (hue H, saturation S, brightness V) color space, which includes three channels. Each channel is a two-dimensional matrix. Each value in the matrix represents the value of a pixel in the channel. The values ​​in each channel are averaged to obtain the channel values ​​of the three channels, specifically: H current , S current and V current ;

[0075] Similarly, the initial image of the power equipment is converted into the same color to obtain the channel values ​​of the three channels, specifically: H baseline , S baseline and V baseline ;

[0076] The three-channel values ​​of the appearance image (H current , S current and V current ) and the three-channel values ​​of the initial image (H baseline , S baseline and V baseline ) is normalized and its value is taken, and the color difference value △E is obtained by formula calculation and analysis of the value. The specific calculation formula is:

[0077]

[0078] Among them, α1, α2, and α3 are respectively set weight constants, and their sizes are custom settings. Specifically, the value of α1 is 0.42, the value of α2 is 0.25, and the value of α3 is 0.33. It can be seen from the formula that when the color difference value is high, it indicates obvious fading, rust or surface damage, which means that the power equipment has obvious appearance aging;

[0079] The appearance image is converted into a gray image, and Canny edge detection is used to identify the defect types existing in the appearance image, the defect quantity and the corresponding defect area corresponding to each type of defect. The specific defects are cracks, scratches and dents, etc. Different defect types have different effects on the degree of aging of power equipment. For example, dents are mostly the result of mechanical damage. The larger the dent area, the more likely it is to affect the normal operation of the equipment. Cracks are usually a sign of material structure damage and may affect the overall stability of the equipment. The larger the number and area of ​​cracks, the more serious the aging degree. Each type of defect is set to correspond to a defect value, and each defect type is extracted and compared with all the set defect types to match the corresponding defect value, which is recorded as Qj; where j = 1, 2, 3, ..., J; J is a positive integer, J represents the total number of defect types, and j represents the serial number of any one of the defect types; thus, each defect can be obtained. The number of defects corresponding to the defect type Xj; the defect areas corresponding to each defect of a certain defect type are compared and analyzed in the set area interval. When the defect area is greater than the maximum value in the set area interval, a high impact area is accumulated; when the defect area is in the set area interval, a medium impact area is accumulated; when the defect area is less than the minimum value in the set area interval, a low impact area is accumulated; the cumulative number of high impact area, medium impact area and low impact area are counted respectively, and they are recorded as M1, M2 and M3 respectively; the defect areas corresponding to each defect are summed up to obtain the total impact area value recorded as M4; the cumulative number of high impact area M1, the cumulative number of medium impact area M2, the cumulative number of low impact area M3 and the total impact area M4 are normalized and their values ​​are taken, and the numerical values ​​are calculated and analyzed in a formula to obtain the defect impact value Mσ. The specific calculation formula is:

[0080]

[0081] Among them, σ1, σ2, and σ3 are respectively the set weight constants, and σ1>σ2>σ3>1, the specific value of σ1 is 1.744, σ2 is 1.421, and σ3 is 1.067; the larger the defect area of ​​the power equipment and the more the number, the more serious the aging of the equipment appearance, and the greater the defect impact value; thus, the defect impact value corresponding to each type of defect is recorded as Mσj; the defect value Qj, the number of defects Xj, and the defect impact value Mσj are normalized and their values ​​are taken, and the numerical values ​​are calculated and analyzed by formula to obtain the appearance defect value QM. The specific calculation formula is:

[0082]

[0083] Among them, σ4 and σ5 are the set weight constants, and the specific values ​​of σ4 and σ5 are 0.36 and 0.64 respectively;

[0084] The monthly equipment usage value Bλ, color difference value △E, appearance defect value QM, equipment temperature T, and insulation resistance R of the power equipment are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the aging threshold factor μ; the specific calculation formula is:

[0085]

[0086] Among them, η1, η2, η3, η4, and η5 are respectively the set weight constants, PT and PR are the standard winding temperature and standard insulation resistance. It should be noted that the standard winding temperature and standard insulation resistance of the power equipment refer to the initial values ​​measured when the power equipment leaves the factory without being used, that is, the winding temperature and insulation resistance under standard conditions when the equipment winding material is not affected by aging, environmental influences, or load operation (the insulation resistance will usually gradually decrease due to humidity, temperature, aging, etc. during use); it can be seen from the formula that the larger the equipment usage value, color difference value, and appearance defect value of the power equipment, the more serious the aging of the power equipment, and the larger the aging threshold factor; the larger the difference between the equipment temperature and insulation resistance of the power equipment and the standard winding temperature and standard insulation resistance, the more serious the aging of the power equipment, and the larger the aging threshold factor.

[0087] From the above-mentioned aging status monitoring and analysis process of power equipment, it can be seen that the threshold factor of power equipment is updated every month;

[0088] When the aging threshold factor is compared and analyzed with the set aging interval, when the aging threshold factor is greater than the upper limit of the set aging interval, it means that the aging of the power equipment is serious and there is a greater risk of failure. The power equipment is recorded as severely aged equipment, and its number and location are extracted and sent to the corresponding engineer, so that the engineer can replace the severely aged equipment in time or replace parts for maintenance; when the aging threshold factor is within the set aging interval, the moderately aged equipment is used as the label of the power equipment, indicating that it has an aging status and will affect the fault judgment of the power equipment. When judging its fault, the aging factor needs to be added to ensure the accuracy of the fault judgment result; when the aging threshold factor is less than the lower limit of the set aging interval, the slightly aged equipment is used as the label of the power equipment, indicating that the aging of the equipment is very slight and can be ignored. When judging its fault, there is no need to introduce the aging factor for correction.

[0089] By comprehensively analyzing historical usage information and aging information, the aging degree of power equipment can be evaluated more accurately. The system updates the aging threshold factor every month and dynamically corrects the parameters for equipment failure judgment based on the actual usage of the equipment, so that the early warning system can reflect the latest status of the equipment more promptly. When the equipment is marked as severely aged, the relevant engineers are immediately notified to conduct inspections and maintenance, effectively reducing potential failures caused by equipment aging, and providing data support for aging correction of power equipment.

[0090] The monitoring and early warning module performs dynamic threshold analysis on the current operating status of power equipment based on equipment information and aging threshold factors to accurately determine whether the equipment has a failure risk and reduce false alarms caused by equipment aging; specifically:

[0091] Step 1: Obtain the equipment information at each acquisition moment in real time, wherein the equipment information includes actual power, three-dimensional temperature model of power equipment, power factor, vibration amplitude, insulation resistance and noise value; it should be noted that each power equipment stores its corresponding three-dimensional model, and the whole power equipment is divided into several parts. The operating temperature of each part is monitored by a temperature sensor and filled into the corresponding part of the three-dimensional model of the power equipment, thereby obtaining the temperature model of the three-dimensional power equipment;

[0092] Step 2: Set a standard operating parameter for each power equipment. The specific standard operating parameters include rated power, standard power factor (usually the standard power factor of power equipment is 0.8-0.9), standard insulation resistance, standard vibration amplitude and standard noise value, and record them as PG, PY, PR, PF and PD respectively; it should be noted that due to the actual needs of the operation of power equipment, especially high-power equipment, it will inevitably generate vibration and noise during operation. The standard amplitude value and standard noise value are the maximum amplitude value and noise value generated by the power equipment when it is running under ideal or normal conditions;

[0093] A sensitivity coefficient is set for each part in the three-dimensional model of the power equipment. It should be noted that different parts have different functions, and the number and type of electronic components in each part are different, which means that the temperature tolerance of each part is different, so the sensitivity coefficient of each part is also different; the specific sensitivity coefficient value of each part is set by the personnel in this field according to the actual situation of the power equipment; the operating temperature of each part in the three-dimensional temperature model of the power equipment is multiplied by the corresponding sensitivity coefficient to obtain the temperature sensitivity value; the temperature sensitivity value of each part is averaged to obtain the temperature sensitivity mean value recorded as W1; the temperature sensitivity value of each part is compared and analyzed with the set sensitivity range. When the temperature sensitivity value is greater than the set sensitivity range, When the temperature sensitivity value is greater than the upper limit of the set sensitivity interval, a highly sensitive part is accumulated; when the temperature sensitivity value is within the set sensitivity interval, a moderately sensitive part is accumulated; when the temperature sensitivity value is greater than the lower limit of the set sensitivity interval, a slightly sensitive part is accumulated; the cumulative number of highly sensitive parts, moderately sensitive parts and slightly sensitive parts in the three-dimensional temperature model of the power equipment are counted respectively, and they are recorded as W2, W3, and W4 respectively; the temperature sensitivity mean W1, the cumulative number of highly sensitive parts W2, the cumulative number of moderately sensitive parts W3 and the cumulative number of slightly sensitive parts W4 are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the sensitivity value of the three-dimensional temperature model of the power equipment. The specific calculation formula is:

[0094]

[0095] Where γ1, γ2, and γ3 are respectively set weight constants, and γ1>γ2>γ3>1; thus, the sensitivity value of the three-dimensional temperature model of the power equipment at each acquisition time is recorded as Wγa, where a=1, 2, 3, ..., A; A is a positive integer, A represents the total number of acquisition times, and a represents the sequence number of any one of the acquisition times;

[0096] Step 3: Extract the label of the power equipment. If the label of the power equipment is a moderately aged device, retrieve the aging threshold factor μ closest to the current time of the system, and use it to perform aging correction on the sensitive value Wγa, actual power Ga, power factor Ya, insulation resistance Ra and noise value Da at each acquisition moment to obtain the corrected judgment parameters. The specific judgment parameters include: judgment sensitive value Determine power Determining the power factor Determining insulation resistance And determine the noise value The specific correction formula group is:

[0097]

[0098] Among them, δ1, δ2, δ3, δ4, and δ5 are respectively set weight constants, and δ1, δ2, δ3, δ4, and δ5 are all greater than zero. The specific value range is designed by the personnel in this field according to the actual production needs. For example, δ1 is larger than other δ2, δ3, δ4, and δ5, so that the difference between the sensitivity value before correction and the judgment sensitivity value after correction will not be particularly large. This is because the power equipment is particularly sensitive to temperature. This makes it possible to effectively retain the important influence of temperature in fault judgment while correcting the sensitivity value;

[0099] If the label of the power equipment is a slightly aged device, there is no need to perform aging correction. The sensitive value Wγa, actual power Ga, power factor Ya, insulation resistance Ra and noise value Da collected at each collection time are directly used as equipment judgment parameters. The specific judgment parameters include: judgment of sensitive value, judgment of power, judgment of power factor, judgment of insulation resistance and judgment of noise value;

[0100] Thus, the judgment parameters of each power equipment can be obtained. Specifically, each judgment parameter is: judgment sensitivity value Determine power Determining the power factor Determining insulation resistance And determine the noise value

[0101] Step 4: Extract the judgment parameters of each power device, normalize them with the standard working parameters of the power device and take their values, calculate and analyze the values ​​by formula to obtain the operation deviation value PZ corresponding to the power device at each collection time. The specific calculation formula is:

[0102]

[0103] Among them, Ω1, Ω2, Ω3, Ω4, and Ω5 are respectively set weight constants, and their sizes are custom settings. Usually, the power factor and insulation resistance will become smaller and smaller with the use of power equipment. Therefore, the insulation resistance is judged to be smaller than the standard insulation resistance, and the power factor is judged to be smaller than the standard power factor; from the formula, it can be seen that when the power factor and the insulation resistance are judged to be smaller, the failure risk of the power equipment is greater, and the operation deviation value is greater; when the judgment sensitivity value is larger, it means that the power equipment is more disturbed by the temperature, the failure risk is greater, and the operation deviation value is greater; when the power is judged to be The larger the value, the greater the load of the power equipment. If the equipment runs under overload for a long time, the greater the risk of failure, and the greater the operation deviation value. The larger it is, the greater the risk of failure of the power equipment, and the greater the operation deviation value;

[0104] Step 5: construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the operation deviation value as the vertical coordinate, input the operation deviation value into the coordinate axis according to its corresponding collection time, and record the position of the operation deviation value in the coordinate axis as the deviation point, use a smooth curve to connect each deviation point in sequence to obtain a curve diagram of the operation deviation value of the power equipment changing with time; draw a tangent line at each deviation point, and use data fitting to calculate the slope of the tangent line and record it as the deviation slope La; it should be noted that when the deviation slope is greater than zero, it means that the deviation value of the power equipment at the deviation point shows an increasing trend, which means that the failure risk of the power equipment continues to increase. If the operation deviation value is larger, the corresponding increase is larger; on the contrary, when the deviation slope is less than zero, it means that the deviation value of the power equipment at the deviation point shows a decreasing trend, which means that the failure risk of the power equipment is reduced. If the absolute value of the operation deviation value is larger, the corresponding decrease is larger; the deviation slopes greater than zero are summed up to obtain the deviation increase value and recorded as C1, and the deviation slopes less than zero are summed up and then taken to calculate the absolute value to obtain the deviation decrease value and recorded as C2;

[0105] The operation deviation value PZa, deviation slope La, deviation increase value C1 and deviation decrease value C2 at each acquisition time are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the fault warning value LP. The specific calculation formula is:

[0106]

[0107] Among them, φ1, φ2, and φ3 are respectively set weight constants, and their sizes are custom settings. The specific values ​​of φ1, φ2, and φ3 are 0.41, 0.33, and 0.26, respectively. It can be seen from the formula that when the deviation increase value is larger and the deviation decrease value is smaller, it means that the risk of the power equipment increases more, and the fault warning value is larger; when the operation deviation value of the power equipment fluctuates more, it means that the operation of the power equipment is more unstable, and the fault warning value is larger;

[0108] Step 6: From the monitoring and analysis steps of the fault warning value of the above-mentioned power equipment, it can be seen that as the use time of the power equipment continues, its corresponding aging threshold factor will be updated, and the parameters for judging the fault of the power equipment will be corrected accordingly, so that the fault warning value of the power equipment changes dynamically under equipment with different aging degrees, rather than a single static threshold; the fault warning value of the power equipment is compared and analyzed with the set warning threshold. When the fault warning value is greater than the set warning threshold, it indicates that there is a greater risk of failure of the power equipment. The power equipment is recorded as a risky equipment, and its number and location are extracted and sent to the corresponding engineer for timely inspection and maintenance to ensure the safety of the power grid and the stable output of electricity.

[0109] By using the aging threshold factor to dynamically correct the fault judgment parameters of equipment labeled as moderately aged, it is ensured that the fault judgment results are still accurate even in the case of equipment aging; no correction is required for slightly aged equipment; the corrected parameters are analyzed for operating deviation to obtain operating deviation values, and the operating deviation development trend is analyzed based on this to determine whether the power equipment has a failure risk, and the risk equipment is output accordingly, and the corresponding engineers are notified in a timely manner; the fault judgment parameters are dynamically adjusted according to the aging degree of the equipment, so that the early warning system can work effectively on equipment with different aging degrees, improving the timeliness and accuracy of the early warning.

[0110] It can significantly reduce false alarms caused by aging, improve the accuracy and robustness of the monitoring system, and thus ensure the safe operation of power equipment and the reliability of fault warning.

[0111] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A power equipment operation fault monitoring and early warning system, characterized in that: include: Aging update module and monitoring and early warning module; The aging update module conducts a comprehensive analysis based on the historical usage information and aging information of the power equipment to determine the aging degree of the power equipment, and obtains the aging threshold factor accordingly; when the aging threshold factor is greater than the upper limit of the set aging interval, the power equipment is recorded as a severely aged device, and its number and location are extracted and sent to the corresponding engineer; When the aging threshold factor is within the set aging interval, the moderately aged device is used as the label of the power equipment; when the aging threshold factor is less than the lower limit of the set aging interval, the slightly aged device is used as the label of the power equipment; The monitoring and early warning module obtains the equipment information of the power equipment in real time, and uses the aging threshold factor to perform aging correction to output judgment parameters, and issues fault warnings based on the judgment parameters.

2. The power equipment operation fault monitoring and early warning system according to claim 1 is characterized in that: The specific steps of fault warning are: Step 1: Obtain device information at each collection moment in real time; Step 2: Set a standard working parameter for each power device, and analyze the temperature sensitivity of the power device according to the three-dimensional temperature model of the power device to obtain a sensitivity value; Step 3: Extract the label of the power equipment. If the label of the power equipment is a moderately aged device, retrieve the aging threshold factor closest to the current time of the system, and use it to perform aging correction on the sensitive value, actual power, power factor, insulation resistance and noise value at each acquisition moment to obtain the corrected judgment parameters. If the label of the power equipment is a slightly aged device, there is no need to perform aging correction. The sensitive value, actual power, power factor, insulation resistance and noise value collected at each collection time are directly used as equipment judgment parameters. The specific judgment parameters include: judging the sensitive value, judging the power, judging the power factor, judging the insulation resistance and judging the noise value; Thus, the judgment parameters of each power device can be obtained, and each judgment parameter is specifically: judgment sensitivity value, judgment power, judgment power factor, judgment insulation resistance and judgment noise value; Step 4: Extract the judgment parameters of each power equipment, normalize them with the standard working parameters of the power equipment and take their numerical values, perform formula calculation and analysis on the numerical values ​​to obtain the corresponding operation deviation value of the power equipment at each collection time; perform time domain development trend analysis on the power equipment based on the operation deviation value to obtain the fault warning value; when the fault warning value is greater than the set warning threshold, the power equipment is marked as a risky equipment, and its number and location are extracted and sent to the corresponding engineer.

3. The power equipment operation fault monitoring and early warning system according to claim 2 is characterized in that: The specific steps for analyzing the temperature sensitivity of power equipment based on the three-dimensional temperature model of power equipment are as follows: A sensitivity coefficient is set to correspond to each part in the three-dimensional temperature model of the power equipment, and the operating temperature of each part in the three-dimensional temperature model of the power equipment is multiplied by the corresponding sensitivity coefficient to obtain the temperature sensitivity value; the temperature sensitivity value of each part is averaged to obtain the temperature sensitivity mean; the temperature sensitivity value of each part is compared and analyzed with the set sensitivity range to divide the part corresponding to the temperature sensitivity value into a highly sensitive part, a moderately sensitive part and a slightly sensitive part, and the cumulative number of the highly sensitive part, the moderately sensitive part and the slightly sensitive part in the three-dimensional temperature model of the power equipment is counted respectively; The temperature-sensitive mean, the cumulative number of highly sensitive parts, the cumulative number of moderately sensitive parts and the cumulative number of slightly sensitive parts are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formulas to obtain the sensitivity value of the three-dimensional temperature model of the power equipment.

4. The power equipment operation fault monitoring and early warning system according to claim 3 is characterized in that: The specific steps for analyzing the development trend of power equipment in the time domain based on the operation deviation value are as follows: A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the operation deviation value as the vertical coordinate, the operation deviation value is input into the coordinate axis according to its corresponding collection time, and the position of the operation deviation value in the coordinate axis is recorded as the deviation point, and a smooth curve is used to connect each deviation point in sequence to obtain a curve diagram of the operation deviation value of the power equipment changing with time; A tangent line is drawn at each deviation point, and the slope of the tangent line is calculated by data fitting and recorded as the deviation slope; the deviation slopes greater than zero are summed to obtain the deviation increase value, and the deviation slopes less than zero are summed and then the absolute value is taken to obtain the deviation decrease value; The operation deviation value, deviation slope, deviation increase value and deviation decrease value at each collection moment are normalized and their numerical values ​​are taken, and the numerical values ​​are calculated and analyzed by formula to obtain the fault warning value.

5. The power equipment operation fault monitoring and early warning system according to claim 1 is characterized in that: The specific process of conducting a comprehensive analysis based on the historical usage information and aging information of power equipment to determine the aging degree of power equipment is as follows: 5-1: Retrieve the historical usage information of the power equipment, and analyze the usage degree of the power equipment to obtain the equipment usage value; the historical usage information includes the number of operations, the working hours of each operation, and the time of use; 5-2: Retrieve the aging information of the power equipment every month, which includes the equipment temperature, insulation resistance and appearance image; perform appearance aging analysis based on the appearance image of the power equipment to obtain the color difference value; 5-3: Convert the appearance image into a gray image, use Canny edge detection to identify the defect types in the appearance image, the number of defects corresponding to each type of defect, and the corresponding defect area, and perform defect degree analysis based on this to obtain the appearance defect value; 5-4: Normalize the monthly equipment usage value, color difference value, appearance defect value, equipment temperature and insulation resistance of the power equipment respectively and take their numerical values, and perform formula calculation and analysis on the numerical values ​​to obtain the aging threshold factor.

6. The power equipment operation fault monitoring and early warning system according to claim 5 is characterized in that: The specific steps for analyzing the degree of use of power equipment are: Compare and analyze the working time of each work with the set time interval to divide the number of work times corresponding to the working time into the first echelon load operation, the second echelon load operation and the third echelon load operation; count the cumulative number of the first echelon load operation, the second echelon load operation and the third echelon load operation respectively, and calculate the average of the working time of each work to obtain the total working time; The cumulative number of first-tier load operations, the cumulative number of second-tier load operations, the cumulative number of third-tier load operations, the total working time and the time in use are normalized and their values ​​are taken, and the values ​​are analyzed to obtain the equipment usage value.

7. The power equipment operation fault monitoring and early warning system according to claim 6 is characterized in that: The specific steps of appearance aging analysis are as follows: The appearance image is converted from RBG color space to HSV color space, which includes three channels. Each channel is a two-dimensional matrix. Each value in the matrix represents the value of a pixel in the channel. The value in each channel is averaged to obtain the channel values ​​of the three channels, specifically: H current , S current and V current ; Similarly, the initial image of the power equipment is converted into the same color to obtain the channel values ​​of the three channels, specifically: H baseline , S baseline and V baseline ; The three-channel values ​​of the appearance image and the three-channel values ​​of the initial image are normalized and their values ​​are taken. The color difference value △E is obtained by formula calculation and analysis of the values. The specific calculation formula is: Among them, α1, α2, and α3 are the set weight constants respectively.

8. The power equipment operation fault monitoring and early warning system according to claim 7 is characterized in that: The specific steps of defect degree analysis are as follows: The appearance image is converted into a gray image, and the Canny edge detection is used to identify the defect types existing in the appearance image, the defect quantity corresponding to each type of defect, and the corresponding defect area, and each type of defect is set to correspond to a defect value, and each defect type is extracted and compared with all the set defect types to match the corresponding defect value; Compare and analyze the defect areas corresponding to each defect of a certain defect type in the set area range to divide the defect areas into high impact area, medium impact area and low impact area, count the cumulative number of high impact area, medium impact area and low impact area respectively, and sum up the defect areas corresponding to each defect to obtain the total impact area value; The cumulative number of high impact areas, the cumulative number of medium impact areas, the cumulative number of low impact areas and the total value of the impact area are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the defect impact value; thus, the defect impact value corresponding to each type of defect can be obtained; The defect values, defect quantities and defect impact values ​​corresponding to various types of defects in the appearance image of the power equipment are normalized and their numerical values ​​are taken, and the numerical values ​​are calculated and analyzed by formulas to obtain the appearance defect values.

9. The power equipment operation fault monitoring and early warning system according to claim 2 is characterized in that: Equipment information includes actual power, three-dimensional temperature model of power equipment, power factor, vibration amplitude, insulation resistance and noise value; specific standard working parameters include rated power, standard power factor, standard insulation resistance, standard vibration amplitude and standard noise value.

10. The power equipment operation fault monitoring and early warning system according to claim 1, characterized in that: Also includes servers; The server communicates with each device in the power grid and the sensors installed on the devices to collect and save historical usage information and aging information; the historical usage information includes the number of times the power equipment has worked and the working hours of each work, the length of time it has been put into use, and the initial image of the power equipment; the aging information includes vibration amplitude and noise value; the aging information includes equipment temperature, insulation resistance and appearance image.

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