A converter station equipment fault intelligent monitoring and linkage inspection system

Abstract

The present invention discloses a converter station equipment fault intelligent monitoring and linkage inspection system, and specifically relates to the technical field of power fault monitoring. The present invention determines the risk coefficient of the converter station equipment by comparing the final evaluation value corresponding to each fault type of the converter station equipment with the preset final evaluation value, and then calculates the inspection state coefficients of all the inspection equipment, and sorts the inspection state coefficients of all the inspection equipment in order from small to large, and selects the inspection equipment corresponding to the minimum inspection state coefficient to inspect the converter station equipment. In this way, the relative importance of different indicators of the converter station equipment is taken into account, so that the monitoring result of the converter station equipment is more accurate; in addition, during the linkage inspection, the influence of other factors is taken into account, and the appropriate inspection equipment is selected from multiple aspects for detailed inspection, so that the inspection equipment finally selected is most suitable for the inspection of the converter station equipment, and the final inspection efficiency is improved and the accuracy is also high.

Description

Technical Field

[0001] The present invention relates to the technical field of power fault monitoring, and in particular to a converter station equipment fault intelligent monitoring and linkage inspection system. Background Art

[0002] The converter station is a key facility in the power system, mainly used in the high-voltage direct current transmission system to convert alternating current into direct current, or vice versa. If the equipment in the converter station fails or is abnormal, it will have a significant impact on the safe and stable operation of the power system. By monitoring the status of the converter station equipment and timely linking the inspection equipment to conduct accurate inspections of the converter station equipment according to the status of the converter station equipment, potential problems can be discovered in time to avoid equipment damage or serious power accidents. By providing real-time converter station data, operation and maintenance personnel can understand the operating status of the equipment in a timely manner, so that preventive measures can be taken before the equipment fails, reducing the occurrence of equipment failures and improving the reliability of the entire transmission system.

[0003] In the traditional way, when monitoring the converter station equipment, various sensors are often installed at key positions of the equipment to collect data from each position in real time and analyze and evaluate the status of the converter station equipment. However, this method only analyzes the data of each position, which may ignore other important factors and does not consider the relative importance of different indicators of the converter station equipment, ultimately resulting in inaccurate monitoring results of the converter station equipment. In addition, the traditional method often selects the nearest equipment for detailed inspection during joint inspection, ignoring the influence of other factors, resulting in the selected inspection equipment may not be the most suitable for the inspection of the converter station equipment, resulting in low inspection efficiency and low accuracy. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of inaccurate monitoring results, low inspection efficiency and low accuracy of converter station equipment, and proposes a converter station equipment fault intelligent monitoring and linkage inspection system.

[0005] The present invention proposes a converter station equipment fault intelligent monitoring and linkage inspection system, the converter station equipment includes multiple power equipment, the target monitoring equipment is any one of the multiple power equipment, and the system includes:

[0006] Weight vector module: obtains the monitoring index corresponding to the target monitoring device, and determines the weight vector of each monitoring index of the target monitoring device;

[0007] The first evaluation value module: extracts the impact factors of different fault types on the target monitoring device according to historical data, and for each fault type, combines the weight vector, the specific value of the monitoring indicator and the corresponding impact factor to obtain the first evaluation value of the target monitoring device for the fault type;

[0008] Self-influence coefficient module: obtains self-operation data of the target monitoring device, and obtains the self-influence coefficient of the target monitoring device according to the self-operation data; the self-operation data includes at least one of the equipment life coefficient, the maintenance interval coefficient and the temperature and humidity deviation coefficient;

[0009] Final evaluation value module: for each fault type, calculates the final evaluation value of the converter station equipment according to the self-influence coefficients of multiple power equipment and the corresponding first evaluation values;

[0010] Risk coefficient module: compares the final evaluation value corresponding to each fault type converter station equipment with the preset final evaluation value to determine the risk coefficient of the converter station equipment;

[0011] Equipment determination module: obtains the inspection status data of each inspection device, obtains the inspection status coefficient of the inspection device according to the inspection status data, and determines the inspection device according to the risk coefficient and the inspection status coefficient of the converter station equipment; the inspection status data includes at least one of the distance coefficient, the entry and exit difficulty coefficient and the equipment sustainable time coefficient.

[0012] Optionally, the weight vector module is specifically used for:

[0013] According to the target monitoring device and the corresponding monitoring indicators, a fuzzy contrast matrix of the target monitoring device is established, the value of each column of the fuzzy contrast matrix is ​​divided by the sum of the column, and the average value of each row is calculated as the weight vector of each monitoring indicator of the target monitoring device.

[0014] Optionally, the first evaluation value module includes:

[0015] Impact factor module: extracts different fault types based on historical data and determines the impact factor of each fault type on the target monitoring device;

[0016] Basic evaluation value module: obtains the specific value of each monitoring indicator of the target monitoring device, multiplies the specific value of each monitoring indicator by the corresponding weight vector, and adds the multiplication results to obtain the basic evaluation value of the target sub-monitoring device;

[0017] A module for calculating a first evaluation value: for each fault type, a product of an impact factor of the target monitoring device and the basic evaluation value is calculated to obtain a first evaluation value of the target monitoring device for the fault type.

[0018] Optionally, the self-influence coefficient module includes:

[0019] Equipment life coefficient module: obtains the actual use time and the preset use time of the target monitoring equipment, and calculates the ratio of the actual use time to the preset use time to obtain the equipment life coefficient;

[0020] Maintenance interval coefficient module: obtains the current time interval between the last maintenance time of the target monitoring equipment and the current time, obtains the average maintenance interval time of the target monitoring equipment, calculates the ratio of the current time interval and the average maintenance interval time, and obtains the maintenance interval coefficient;

[0021] Temperature and humidity deviation coefficient module: obtain the actual temperature and humidity around the target monitoring device in different time periods within a fixed time interval, calculate the absolute difference between the actual temperature and humidity and the preset temperature and humidity, and sum the absolute differences of all time periods to obtain the temperature and humidity deviation coefficient;

[0022] A module for calculating a self-influence coefficient is provided for obtaining a self-influence coefficient of the target monitoring device by performing a weighted summation on the equipment life coefficient, the maintenance interval coefficient and the temperature and humidity deviation coefficient.

[0023] Optionally, the final evaluation value module includes:

[0024] Second evaluation value module: for each fault type, obtain the self-influence coefficients of multiple power devices and the corresponding first evaluation values, and multiply the self-influence coefficient of each power device by the corresponding first evaluation value to obtain the second evaluation value of the power device in the fault type;

[0025] Final evaluation value calculation module: for each fault type, the second evaluation values ​​of all power equipment are added together to obtain the final evaluation value of the converter station equipment of the fault type.

[0026] Optionally, the risk coefficient module includes:

[0027] Low risk module: if the final evaluation value of the converter station equipment in each fault type is less than the corresponding preset final evaluation value threshold, the risk of the converter station equipment in this fault type is recorded as low risk;

[0028] High-risk module: If the final evaluation value of the converter station equipment in each fault type is not less than the corresponding preset final evaluation value threshold, the risk of the converter station equipment in this fault type is recorded as high risk;

[0029] Risk coefficient calculation module: obtain the number of high-risk faults and the number of all risks among all fault types, divide the number of high-risk faults by the number of all risks, and obtain the risk coefficient of the converter station equipment.

[0030] Optionally, the device determination module includes:

[0031] Distance coefficient module: obtains the shortest drivable distance from each inspection device to the converter station device and the fastest moving speed of the inspection device, and calculates the ratio of the shortest drivable distance and the fastest moving speed to obtain the distance coefficient;

[0032] Entry and exit difficulty coefficient module: obtain all obstacles on the shortest drivable route from each inspection device to the converter station device, and obtain the difficulty of crossing each obstacle corresponding to the drivable route of the inspection device through the preset obstacle crossing difficulty corresponding table, and add up all the obstacle crossing difficulties to obtain the entry and exit difficulty coefficient;

[0033] Sustainable duration coefficient module: obtains the power stored in each inspection device and the average power consumption rate, and divides the stored power by the average power consumption rate to obtain the device sustainable duration coefficient;

[0034] A patrol state coefficient calculation module: performing weighted summation of the distance coefficient, the entry and exit difficulty coefficient and the sustainable duration coefficient to obtain the patrol state coefficient of the patrol device;

[0035] Inspection equipment determination module: sort the inspection status coefficients of all inspection equipment in ascending order, and select the inspection equipment corresponding to the minimum inspection status coefficient to inspect the converter station equipment.

[0036] Beneficial effects of the present invention:

[0037] The present invention proposes an intelligent monitoring and linkage inspection system for converter station equipment faults, which takes into account the relative importance of different indicators of converter station equipment, so that the monitoring results of converter station equipment are more accurate; in addition, during the linkage inspection, the influence of other factors is taken into account, and suitable inspection equipment is selected from multiple aspects for detailed inspection, so that the inspection equipment finally selected is most suitable for the inspection of the converter station equipment, and the final inspection efficiency is increased and the accuracy is also higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 A framework diagram of a converter station equipment fault intelligent monitoring and linkage inspection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] 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.

[0041] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0042] The embodiment of the present invention provides a converter station equipment fault intelligent monitoring and linkage inspection system. Figure 1 , Figure 1 A framework diagram of a converter station equipment fault intelligent monitoring and linkage inspection system provided by an embodiment of the present invention; the converter station equipment includes multiple power equipment, the target monitoring equipment is any one of the multiple power equipment, and the system includes:

[0043] Weight vector module: obtains the monitoring indicators corresponding to the target monitoring device and determines the weight vector of each monitoring indicator of the target monitoring device;

[0044] The first evaluation value module: extracts the impact factors of different fault types on the target monitoring equipment according to historical data, and for each fault type, combines the weight vector, the specific value of the monitoring indicator and the corresponding impact factor to obtain the first evaluation value of the target monitoring equipment for the fault type;

[0045] Self-influence coefficient module: obtains the self-operation data of the target monitoring device, and obtains the self-influence coefficient of the target monitoring device according to the self-operation data;

[0046] Final evaluation value module: for each fault type, calculates the final evaluation value of the converter station equipment according to the self-influence coefficients of multiple power equipment and the corresponding first evaluation values;

[0047] Risk coefficient module: compares the final evaluation value corresponding to each fault type converter station equipment with the preset final evaluation value to determine the risk coefficient of the converter station equipment;

[0048] Equipment determination module: obtains the inspection status data of each inspection device, obtains the inspection status coefficient of the inspection device according to the inspection status data, and determines the inspection device according to the risk coefficient and the inspection status coefficient of the converter station equipment.

[0049] Based on the intelligent monitoring and linkage inspection system for converter station equipment faults provided by the embodiment of the present invention, the relative importance of different indicators of the converter station equipment is taken into consideration, so that the monitoring results of the converter station equipment are more accurate; in addition, during the linkage inspection, the influence of other factors is taken into consideration, and suitable inspection equipment is selected from multiple aspects for detailed inspection, so that the inspection equipment finally selected is most suitable for the inspection of the converter station equipment, and the final inspection efficiency is increased and the accuracy is also higher.

[0050] In one embodiment, the weight vector module is specifically used to:

[0051] According to the target monitoring device and the corresponding monitoring indicators, a fuzzy contrast matrix of the target monitoring device is established, the value of each column of the fuzzy contrast matrix is ​​divided by the sum of the column, and the average value of each row is calculated as the weight vector of each monitoring indicator of the target monitoring device.

[0052] In one implementation method, by establishing a fuzzy contrast matrix and calculating a weight vector, the importance of different factors can be quantified, and then these factors can be comprehensively considered to evaluate the health status of the battery swap station equipment, making the evaluation results more accurate.

[0053] It should be noted that the target monitoring equipment and the corresponding monitoring indicators are determined by staff and professionals based on the actual operation of the battery swap station equipment, and no specific restrictions are made; the fuzzy comparison matrix of each target sub-monitoring device is established based on the relative importance of the monitoring indicators obtained from the historical battery swap station operation data, which can be obtained based on expert evaluation or other methods, and no restrictions are made;

[0054] The above steps are explained with an example: for example, for a transformer in a certain battery swap station equipment, according to the actual operation of the battery swap station equipment, the monitoring indicators are determined by the staff as temperature (T), humidity (H) and equipment aging (A), and the established fuzzy matrix is ​​as follows:

[0055] T H A T 1 3 5 H 1 / 3 1 3 A 1 / 5 1 / 3 1

[0056] The explanation is as follows:

[0057] The first row (T row): represents the importance of temperature relative to other factors:

[0058] Relative importance of temperature: 1;

[0059] Relative importance of temperature to humidity: 3 (temperature is 3 times more important than humidity);

[0060] Relative importance of temperature to aging: 5 (temperature is 5 times more important than aging);

[0061] The second row (H row): indicates the importance of humidity relative to other factors:

[0062] Relative importance of humidity to temperature: 1 / 3 (humidity is less important than temperature, and its importance is 1 / 3 of that of temperature);

[0063] Relative importance of humidity to humidity: 1;

[0064] Relative importance of humidity to aging: 3 (humidity is 3 times more important than aging):

[0065] The third row (row A): indicates the importance of equipment aging relative to other factors:

[0066] Relative importance of aging to temperature: 1 / 5 (aging is less important than temperature, and its importance is 1 / 5 of that of temperature);

[0067] Relative importance of aging to humidity: 1 / 3 (aging is less important than humidity, and its importance is 1 / 3 of humidity);

[0068] Relative Importance of Aging to Aging:1.

[0069] Using the above matrix, the weight vector is calculated by the following steps:

[0070] Calculate the sum of each column:

[0071] First column (T): 1+1 / 3+1 / 5≈1.533;

[0072] Second column (H): 3+1+1 / 3≈4.333;

[0073] The third column (A): 5+3+1=9;

[0074] Divide the value of each column by the sum of the column, and the processed matrix is ​​as follows:

[0075] T H A T 1 / 1.533 3 / 4.333 5 / 9 H 1 / 3 / 1.533 1 / 4.333 3 / 9 A 1 / 5 / 1.533 1 / 3 / 4.333 1 / 9

[0076] The actual calculation is:

[0077] T H A T 0.652 0.692 0.556 H 0.218 0.231 0.333 A 0.130 0.077 0.111

[0078] Calculate the mean of each row (i.e. the weight vector):

[0079] Temperature (T): (0.652+0.692+0.556) / 3≈0.633;

[0080] Humidity (H): (0.218+0.231+0.333) / 3≈0.261;

[0081] Equipment aging (A): (0.130+0.077+0.111) / 3≈0.106;

[0082] The weight vectors of each monitoring indicator of the transformer are:

[0083] [0.633, 0.261, 0.106]. To facilitate subsequent calculations, each monitoring indicator weight vector of the transformer can be expressed as [0.6, 0.3, 0.1].

[0084] According to the above method, the weight vector of each target sub-monitoring device can be calculated, and the details will not be repeated here.

[0085] In one embodiment, the first evaluation value module includes:

[0086] Impact factor module: extracts different fault types based on historical data and determines the impact factor of each fault type on the target monitoring equipment;

[0087] Basic evaluation value module: obtains the specific value of each monitoring indicator of the target monitoring device, multiplies the specific value of each monitoring indicator by the corresponding weight vector, and adds the multiplication results to obtain the basic evaluation value of the target sub-monitoring device;

[0088] Module for calculating the first evaluation value: for each fault type, the product of the impact factor of the target monitoring device and the basic evaluation value is calculated to obtain the first evaluation value of the target monitoring device for the fault type.

[0089] In one implementation method, various influencing factors can be comprehensively considered to provide a more comprehensive and accurate status assessment of the battery swap station equipment, thereby guiding preventive maintenance and decision-making.

[0090] It should be noted that different fault types are the types of equipment failures in the battery swap station summarized based on historical data, which can be obtained through historical logs and will not be described in detail. In addition, the impact of different fault types extracted from historical data on each target monitoring device can be obtained based on expert evaluation or other methods, without limitation;

[0091] In one embodiment, the self-influence coefficient module includes:

[0092] Equipment life coefficient module: obtains the actual use time and preset use time of the target monitoring equipment, and calculates the ratio of the actual use time to the preset use time to obtain the equipment life coefficient;

[0093] Maintenance interval coefficient module: obtains the current time interval between the last maintenance time and the current time of the target monitoring equipment, obtains the average maintenance interval time of the target monitoring equipment, calculates the ratio of the current time interval and the average maintenance interval time, and obtains the maintenance interval coefficient;

[0094] Temperature and humidity deviation coefficient module: obtains the actual temperature and humidity around the target monitoring device in different time periods within a fixed time interval, calculates the absolute difference between the actual temperature and humidity and the preset temperature and humidity, and sums the absolute differences of all time periods to obtain the temperature and humidity deviation coefficient;

[0095] Module for calculating self-influence coefficient: The self-influence coefficient of the target monitoring equipment is obtained by weighted summing of the equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient.

[0096] In one implementation, through the above method, the equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient obtained are more accurate and can better reflect the operating status of the target monitoring equipment itself.

[0097] In one implementation, the calculated self-influence coefficient takes into account three factors: the actual use time of the equipment, the maintenance interval time, and the temperature and humidity environment. It can more accurately reflect the actual operating status of the equipment. Through this comprehensive evaluation, we can have a more comprehensive understanding of the health status of the equipment and avoid misjudgment caused by a single factor.

[0098] It should be noted that the larger the equipment life coefficient is, the worse the operating status of the target monitoring equipment itself may be, because this indicates that the actual use time of the equipment has far exceeded its preset service life. Long-term overload operation of the equipment will lead to component aging, performance degradation and increased failure risk. At this time, the larger the self-influence coefficient is, and vice versa.

[0099] The larger the maintenance interval coefficient is, the worse the operating status of the target monitoring equipment may be, because it indicates that the actual maintenance interval of the equipment is far longer than its average maintenance interval. If the equipment is not maintained for a long time, it may cause aging and wear accumulation of equipment parts, thereby increasing the risk of failure. Therefore, the larger the self-influence coefficient is, the smaller it is;

[0100] The larger the temperature and humidity deviation coefficient is, the worse the operating status of the target monitoring equipment itself may be, because too high or too low temperature will accelerate the aging and fatigue of materials, and too high humidity will cause the insulating materials in electrical equipment to absorb moisture, thereby reducing the insulation performance and increasing the risk of short circuits; temperature changes cause frequent changes in thermal expansion and contraction of internal and external components of the equipment, resulting in increased mechanical stress, which can easily cause mechanical failures, loose bolts or cracks, etc.; under high humidity conditions, condensation water is easy to form on the surface of the equipment, especially when the temperature drops sharply; this moisture can cause corrosion of metal parts, short circuits and failures of electrical components; when temperature and humidity changes exceed the design range, the operating efficiency of the equipment will decrease; for example, excessively high temperatures will increase the load on the cooling system, affect the heat dissipation effect of the equipment, and cause frequent overheating protection actions; therefore, the larger the temperature and humidity deviation coefficient is, the greater the self-influence coefficient is, and vice versa.

[0101] It should be noted that the actual usage time of each target sub-monitoring device can be obtained based on the equipment work log, and the preset usage time can be evaluated by the staff based on the actual situation and the equipment's factory delivery, without specific restrictions; the actual temperature and humidity around each target sub-monitoring device can be obtained through the temperature and humidity sensors around the device.

[0102] It should be noted that the weighted sum of the equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient can be obtained by the following formula to obtain the self-influence coefficient:

[0103]

[0104] Where, Lgt is the self-influence coefficient, Wh, W Z and DF are equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient respectively, α, β, γ are preset proportional coefficients of equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient respectively, and α, β, γ are all greater than 0.

[0105] It should be noted that the reason why the self-influence coefficients obtained by selecting the equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient instead of other parameters is that these three parameters are easy to obtain and are closer to the actual application scenarios, and can better reflect the impact of the actual battery swap station equipment's own status during operation.

[0106] In one embodiment, the final evaluation value module includes:

[0107] Second evaluation value module: for each fault type, obtain the self-influence coefficients of multiple power devices and the corresponding first evaluation values, and multiply the self-influence coefficient of each power device by the corresponding first evaluation value to obtain the second evaluation value of the power device in the fault type;

[0108] Final evaluation value calculation module: for each fault type, the second evaluation values ​​of all power equipment are added together to obtain the final evaluation value of the converter station equipment of the fault type.

[0109] In one implementation method, through the above method, the self-influence coefficient is introduced to adjust the second evaluation value of the target monitoring device for each fault type initially obtained, taking into account the influence of the device itself, and the final evaluation value of the target monitoring device for the fault type is more accurate. The final evaluation value obtained can more accurately reflect the operating status of the battery swap station, and the monitoring results are more accurate.

[0110] In one embodiment, the risk factor module includes:

[0111] Low risk module: if the final evaluation value of the converter station equipment in each fault type is less than the corresponding preset final evaluation value threshold, the risk level of the converter station equipment in this fault type is recorded as low risk;

[0112] High-risk module: If the final evaluation value of the converter station equipment in each fault type is not less than the corresponding preset final evaluation value threshold, the risk of the converter station equipment in this fault type is recorded as high risk;

[0113] Risk coefficient calculation module: obtain the number of high-risk faults and the number of all risks among all fault types, divide the number of high-risk faults by the number of all risks, and obtain the risk coefficient of the converter station equipment.

[0114] In one implementation, the risk factor obtained through the above method is more accurate and can better reflect the actual operating status of the battery swap station equipment, while providing an intuitive indicator for staff to help them determine how frequent and strict inspections and maintenance of the equipment are required.

[0115] It should be noted that the preset final evaluation value threshold is set by professionals based on actual circumstances and will not be elaborated on in detail.

[0116] In one embodiment, the device determination module includes:

[0117] Distance coefficient module: obtains the shortest drivable distance from each inspection device to the converter station device and the fastest moving speed of the inspection device, and calculates the ratio of the shortest drivable distance and the fastest moving speed to obtain the distance coefficient;

[0118] Entry and exit difficulty coefficient module: obtain all obstacles on the shortest drivable route from each inspection device to the converter station device, and obtain the difficulty of crossing each obstacle corresponding to the drivable route of the inspection device through the preset obstacle crossing difficulty corresponding table, and add up all the obstacle crossing difficulties to obtain the entry and exit difficulty coefficient;

[0119] Sustainable duration coefficient module: obtains the power stored in each inspection device and the average power consumption rate, and divides the stored power by the average power consumption rate to obtain the device sustainable duration coefficient;

[0120] Module for calculating inspection status coefficient: weighted sum of distance coefficient, entry and exit difficulty coefficient and sustainable duration coefficient to obtain the inspection status coefficient of the inspection equipment;

[0121] Inspection equipment determination module: sort the inspection status coefficients of all inspection equipment in ascending order, and select the inspection equipment corresponding to the minimum inspection status coefficient to inspect the converter station equipment

[0122] In one implementation method, through the above method, during the joint inspection, the influence of other factors is taken into consideration, and suitable inspection equipment is selected from multiple aspects for detailed inspection, so that the inspection equipment finally selected is most suitable for the inspection of the battery swap station equipment, and the final inspection efficiency is increased and the accuracy is also higher.

[0123] It should be noted that the larger the distance coefficient of the inspection equipment, the worse the inspection status of the inspection equipment may be. The larger the distance coefficient means that the inspection equipment needs longer to reach the target converter station equipment. This increases the inspection time, delays the response to the fault, and may cause the problem to deteriorate further. In addition, a larger distance coefficient also means that the inspection equipment needs to spend more time and resources when performing tasks, which will reduce the inspection efficiency. Especially in situations where a quick response is required, a longer inspection time may affect the execution of the overall inspection plan. Therefore, when the distance coefficient is larger, the inspection status coefficient of the inspection equipment is larger;

[0124] The greater the entry and exit difficulty coefficient of the inspection equipment, the worse the inspection status of the inspection equipment may be. A larger entry and exit difficulty coefficient means that the inspection equipment will encounter more obstacles or obstacles that are more difficult to cross during driving. It takes more time to cross these obstacles, which extends the total duration of the inspection task and causes the response time to faults to be longer. The more obstacles the inspection equipment encounters on the way and the more difficult it is to cross them, the lower its inspection efficiency will be. This may affect the progress of the inspection task, especially when rapid inspection and maintenance are required, and the fault problem cannot be handled in time. When the inspection equipment crosses complex obstacles, the wear and failure risk of mechanical parts will increase. This will not only increase the maintenance cost of the equipment, but may also cause the equipment to fail when performing tasks, further affecting the smooth completion of the inspection task. Therefore, when the entry and exit difficulty coefficient is greater, the inspection status coefficient of the inspection equipment will be greater.

[0125] The smaller the sustainable duration coefficient of the inspection equipment, the worse the inspection status of the inspection equipment may be, because the smaller the sustainable duration coefficient means that the inspection equipment stores relatively less power, or its average power consumption rate is faster. This will result in limited endurance of the inspection equipment during the inspection process, and it will be unable to complete long-term inspection tasks; a smaller sustainable duration coefficient means that the inspection equipment may stop working midway due to insufficient power when performing tasks. This will not only delay the inspection progress, but may also result in certain key areas not being inspected in time, increasing the risk of equipment failure; therefore, the smaller the sustainable duration coefficient of the inspection equipment, the larger the inspection status coefficient of the inspection equipment;

[0126] It should be noted that the inspection status coefficient can be obtained by weighted summing the distance coefficient, the entry and exit difficulty coefficient, and the sustainable duration coefficient through the following formula:

[0127]

[0128] Wherein, K is the inspection status coefficient, Yz, GH and Qd are the distance coefficient, the entry and exit difficulty coefficient and the sustainable duration coefficient respectively, a1, a2 and a3 are the preset proportional coefficients of the distance coefficient, the entry and exit difficulty coefficient and the sustainable duration coefficient respectively, and a1, a2 and a3 are all greater than 0.

[0129] It should be noted that the reason why the distance coefficient, entry and exit difficulty coefficient and equipment sustainable time coefficient are selected to obtain the inspection status coefficient of each inspection device instead of other parameters is that these three parameters can be monitored and calculated in real time, can dynamically reflect the current status and performance of the inspection equipment, have good real-time performance and are easy to obtain.

[0130] It should be noted that the shortest drivable distance between each inspection device and the target sub-monitoring device, and all obstacles along the shortest drivable distance can be obtained by combining the paths stored in the device's own system or other methods, and the details will not be repeated in detail; the fastest moving speed of different inspection devices can be obtained according to the inspection device's instruction manual or other methods, and the details will not be repeated in detail; the power stored in each inspection device and the average power consumption rate can be obtained according to the inspection device's own battery monitoring system or other methods, and the details will not be repeated in detail.

[0131] It should be noted that the preset obstacle crossing difficulty correspondence table is set by professionals based on actual conditions and will not be described in detail.

[0132] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A converter station equipment fault intelligent monitoring and linkage inspection system, characterized in that: The converter station equipment includes a plurality of power equipment, the target monitoring device is any one of the plurality of power equipment, and the system includes: Weight vector module: obtains the monitoring index corresponding to the target monitoring device, and determines the weight vector of each monitoring index of the target monitoring device; The first evaluation value module: extracts the impact factors of different fault types on the target monitoring device according to historical data, and for each fault type, combines the weight vector, the specific value of the monitoring indicator and the corresponding impact factor to obtain the first evaluation value of the target monitoring device for the fault type; Self-influence coefficient module: obtains self-operation data of the target monitoring device, and obtains the self-influence coefficient of the target monitoring device according to the self-operation data; the self-operation data includes at least one of the equipment life coefficient, the maintenance interval coefficient and the temperature and humidity deviation coefficient; Final evaluation value module: for each fault type, calculates the final evaluation value of the converter station equipment according to the self-influence coefficients of multiple power equipment and the corresponding first evaluation values; Risk coefficient module: compares the final evaluation value corresponding to each fault type converter station equipment with the preset final evaluation value to determine the risk coefficient of the converter station equipment; Equipment determination module: obtains the inspection status data of each inspection equipment, obtains the inspection status coefficient of the inspection equipment according to the inspection status data, and determines the inspection equipment according to the risk coefficient and the inspection status coefficient of the converter station equipment; the inspection status data includes at least one of the distance coefficient, the entry and exit difficulty coefficient and the equipment sustainable time coefficient; The first evaluation value module includes: Impact factor module: extracts different fault types based on historical data and determines the impact factor of each fault type on the target monitoring device; Basic evaluation value module: obtains the specific value of each monitoring indicator of the target monitoring device, multiplies the specific value of each monitoring indicator by the corresponding weight vector, and adds the multiplication results to obtain the basic evaluation value of the target sub-monitoring device; A first evaluation value calculation module: for each fault type, calculates the product of the impact factor of the target monitoring device and the basic evaluation value to obtain a first evaluation value of the target monitoring device for the fault type; The self-influence coefficient module includes: Equipment life coefficient module: obtains the actual use time and the preset use time of the target monitoring equipment, and calculates the ratio of the actual use time to the preset use time to obtain the equipment life coefficient; Maintenance interval coefficient module: obtains the current time interval between the last maintenance time of the target monitoring equipment and the current time, obtains the average maintenance interval time of the target monitoring equipment, calculates the ratio of the current time interval and the average maintenance interval time, and obtains the maintenance interval coefficient; Temperature and humidity deviation coefficient module: obtain the actual temperature and humidity around the target monitoring device in different time periods within a fixed time interval, calculate the absolute difference between the actual temperature and humidity and the preset temperature and humidity, and sum the absolute differences of all time periods to obtain the temperature and humidity deviation coefficient; Calculate the self-influence coefficient module: perform weighted summation of the equipment life coefficient, the maintenance interval coefficient and the temperature and humidity deviation coefficient to obtain the self-influence coefficient of the target monitoring equipment, specifically through the following formula: In the formula, is the self-influence coefficient, and They are equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient. are preset proportional coefficients for equipment life coefficient, maintenance interval coefficient and temperature and humidity deviation coefficient, respectively, and All are greater than 0; The device determination module includes: Distance coefficient module: obtains the shortest drivable distance from each inspection device to the converter station device and the fastest moving speed of the inspection device, and calculates the ratio of the shortest drivable distance and the fastest moving speed to obtain the distance coefficient; Entry and exit difficulty coefficient module: obtain all obstacles on the shortest drivable route from each inspection device to the converter station device, and obtain the difficulty of crossing each obstacle corresponding to the drivable route of the inspection device through the preset obstacle crossing difficulty corresponding table, and add up all the obstacle crossing difficulties to obtain the entry and exit difficulty coefficient; Sustainable duration coefficient module: obtains the power stored in each inspection device and the average power consumption rate, and divides the stored power by the average power consumption rate to obtain the device sustainable duration coefficient; Calculate the inspection status coefficient module: perform weighted summation of the distance coefficient, the entry and exit difficulty coefficient, and the sustainable duration coefficient to obtain the inspection status coefficient of the inspection device; specifically, the following formula is used: In the formula, is the inspection status coefficient, and They are distance coefficient, entry and exit difficulty coefficient and sustainable duration coefficient. are preset proportional coefficients of distance coefficient, entry and exit difficulty coefficient and sustainable duration coefficient, respectively, and All are greater than 0; Inspection equipment determination module: sort the inspection status coefficients of all inspection equipment in ascending order, and select the inspection equipment corresponding to the minimum inspection status coefficient to inspect the converter station equipment.

2. The intelligent monitoring and linkage inspection system for converter station equipment faults according to claim 1 is characterized in that: The weight vector module is specifically used for: According to the target monitoring device and the corresponding monitoring indicators, a fuzzy contrast matrix of the target monitoring device is established, the value of each column of the fuzzy contrast matrix is ​​divided by the sum of the column, and the average value of each row is calculated as the weight vector of each monitoring indicator of the target monitoring device.

3. The intelligent monitoring and linkage inspection system for converter station equipment faults according to claim 1 is characterized in that: The final evaluation value module includes: Second evaluation value module: for each fault type, obtain the self-influence coefficients of multiple power devices and the corresponding first evaluation values, and multiply the self-influence coefficient of each power device by the corresponding first evaluation value to obtain the second evaluation value of the power device in the fault type; Final evaluation value calculation module: for each fault type, the second evaluation values ​​of all power equipment are added together to obtain the final evaluation value of the converter station equipment of the fault type.

4. The intelligent monitoring and linkage inspection system for converter station equipment faults according to claim 1 is characterized in that: The risk factor module includes: Low risk module: if the final evaluation value of the converter station equipment in each fault type is less than the corresponding preset final evaluation value threshold, the risk of the converter station equipment in this fault type is recorded as low risk; High-risk module: If the final evaluation value of the converter station equipment in each fault type is not less than the corresponding preset final evaluation value threshold, the risk of the converter station equipment in this fault type is recorded as high risk; Risk coefficient calculation module: obtain the number of high-risk faults and the number of all risks among all fault types, divide the number of high-risk faults by the number of all risks, and obtain the risk coefficient of the converter station equipment.

Images