Intelligent monitoring method and system for new energy operation and maintenance management

Abstract

The invention discloses an intelligent monitoring method and system for new energy operation and maintenance management, and relates to the technical field of operation and maintenance monitoring, and the method comprises the steps: calculating a hidden fault coefficient through obtaining hidden fault data of new energy equipment, comparing the hidden fault coefficient with a preset threshold value, and judging whether there is a hidden fault which needs to be checked and maintained immediately; when the equipment has a hidden fault, the system obtains the maintenance frequency and the current position of idle operation and maintenance personnel, calculates a dispatching priority coefficient, and dispatches a corresponding number of operation and maintenance personnel from high to low according to a preset operation and maintenance management monitoring number of people and the priority coefficient to carry out monitoring and maintenance; according to the method, potential hidden faults can be detected before periodic inspection, and operation and maintenance personnel are timely notified to perform maintenance, so that equipment fault accumulation is reduced, and stable power supply is ensured; meanwhile, operation and maintenance personnel are reasonably dispatched according to actual conditions, the maintenance cost is effectively reduced, the operation and maintenance efficiency is improved, in-place maintenance of equipment is guaranteed, and the influence of faults on a power system is reduced.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance monitoring technology, and in particular to an intelligent monitoring method and system for new energy operation and maintenance management. Background Art

[0002] In recent years, the proportion of new energy equipment such as wind turbines and solar energy systems in power production has gradually increased; the operating efficiency, stability and long-term reliability of new energy equipment are crucial to power production; in order to ensure that these equipment can operate in the best condition, reduce failure rates and increase energy output, the operation and maintenance management of the corresponding new energy equipment is particularly important; in traditional new energy operation and maintenance management, equipment fault monitoring usually relies on regular inspections and maintenance, which are usually carried out on a fixed cycle (such as weekly or monthly). Professional operation and maintenance personnel are dispatched to inspect new energy equipment according to a fixed inspection cycle to ensure that the failure of new energy equipment can be discovered in time, and the operation and maintenance personnel can perform timely repairs to achieve monitoring of new energy equipment operation and maintenance management, so as to ensure the normal operation of new energy equipment and ensure stable power supply; However, with the operation of new energy equipment, some equipment may have quietly developed hidden faults without showing obvious abnormalities before the next periodic inspection. However, if the operation and maintenance personnel are not notified to conduct inspections and repairs until the next periodic inspection, it may lead to the accumulation of equipment failures and even threaten the stability of power supply. In addition, if new energy equipment has hidden faults, operation and maintenance management and monitoring need to be carried out in advance. If the appropriate operation and maintenance personnel cannot be reasonably dispatched to monitor the operation and maintenance management of new energy equipment according to the actual situation, it may lead to inadequate maintenance of new energy equipment, increased maintenance costs, and unstable power supply. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned problems and provide an intelligent monitoring method and system for new energy operation and maintenance management.

[0004] In a first aspect of the implementation of the present invention, an intelligent monitoring method for new energy operation and maintenance management is first proposed, the method comprising: Obtaining hidden fault data of new energy equipment, and calculating hidden fault coefficients of new energy equipment according to the hidden fault data; the hidden fault data includes current fluctuation coefficient, environmental impact coefficient and historical fault coefficient; According to the hidden fault coefficient of the new energy equipment and the preset hidden fault coefficient threshold, it is judged whether the new energy equipment has hidden faults and needs to be checked and repaired immediately; When there is a hidden fault in the new energy equipment that needs to be checked and repaired immediately, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment repairs and current positions of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set. The data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; According to the preset number of operation, maintenance, management and monitoring personnel for the new energy equipment, a corresponding number of available operation and maintenance personnel will be dispatched in descending order of dispatch priority coefficients to carry out operation, maintenance, management and monitoring of the new energy equipment.

[0005] Optionally, the current fluctuation coefficient calculation step is: Taking the time from the last inspection cycle of the new energy equipment to the current time as the target time interval, obtaining the output current of the new energy equipment within the target time interval, and obtaining a current sequence based on the time sequence; The standard deviation of the calculated current sequence is used as the current fluctuation value of the new energy equipment; The current fluctuation value of the new energy device is divided by the preset current fluctuation value threshold to obtain the current fluctuation coefficient of the new energy device.

[0006] Optionally, the environmental impact coefficient calculation step is: The time from the last inspection cycle of the new energy equipment to the current time is taken as the target time interval, the temperature and humidity of the environment where the new energy equipment is located within the target time interval are obtained, and the environmental impact coefficient is calculated. The calculation formula is: ; in, represents the environmental impact coefficient, , Respectively represent the temperature and humidity of the environment where the new energy equipment is located within the target time interval, [ , ] indicates the time period when the temperature is outside the preset optimal temperature range, [ , ] indicates the time period when the temperature is outside the preset optimal humidity range. and Represent the preset influence weights of temperature and humidity respectively.

[0007] Optionally, the historical failure coefficient calculation step is: Obtain the number of historical failures of new energy equipment, obtain the time required for repair of each failure, and calculate the average time required for repair of the failure as the average repair time; Obtain the time interval of the number of historical failures of the new energy equipment, obtain a failure interval sequence based on the time series, and calculate the average value of the failure interval sequence to obtain the average failure interval; obtain the time interval between the current time of the new energy equipment and the last failure maintenance as the first time interval, and calculate the difference between the first time interval and the average failure interval as the second time interval; The historical failure coefficient is obtained based on the mean repair time and the second time interval.

[0008] Optionally, the hidden fault coefficient of the new energy equipment is calculated according to the hidden fault data as follows: The hidden fault coefficient is calculated based on the current fluctuation coefficient, environmental impact coefficient and historical fault coefficient. The calculation formula is: ; In the formula, is the hidden fault coefficient, , and They are current fluctuation coefficient, environmental impact coefficient and historical failure coefficient, Respectively , and The preset scaling factor of Both are greater than 0.

[0009] Optionally, judging whether the new energy equipment has a hidden fault and needs immediate inspection and repair according to the hidden fault coefficient of the new energy equipment and a preset hidden fault coefficient threshold includes: Compare the latent fault coefficient of the new energy equipment with the preset latent fault coefficient threshold. If the latent fault coefficient of the new energy equipment is less than the preset latent fault coefficient threshold, it means that there is no latent fault in the new energy equipment and it needs to be checked and repaired immediately. The new energy equipment is still operated and maintained according to the originally set monitoring cycle. If the latent fault coefficient of the new energy equipment is not less than the preset latent fault coefficient threshold, it means that the new energy equipment has a latent fault and needs to be checked and repaired immediately.

[0010] Optionally, the number of new energy equipment maintenance times and current locations of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set, and clustering the data set to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel includes: Obtain the total number of times each dispatchable operation and maintenance personnel participated in the maintenance of new energy equipment and the number of successful fault recovery, and divide the number of successful fault recovery by the total number of maintenance to obtain the fault repair rate of the corresponding dispatchable operation and maintenance personnel; Obtain the current location of each dispatchable operation and maintenance personnel and the current location of the new energy equipment, and obtain the shortest drivable distance between the current location of the dispatchable operation and maintenance personnel and the current location of the new energy equipment as the shortest distance of the corresponding dispatchable operation and maintenance personnel; The fault repair rate and the inverse of the shortest distance are used as data points of dispatchable operation and maintenance personnel to obtain a data set, and the data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel.

[0011] Optionally, clustering the data set to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel includes: The steps to cluster the data set using the K-means clustering method are: Step 1: Used to determine the optimal number of clusters for the data set using the elbow method; Step 2: Randomly select a corresponding number of data points as initial clusters according to the optimal number of clusters; Step 3: For each data point, use the Euclidean distance algorithm to calculate the distance between the data point and each initial cluster center, and assign the data point to the cluster to which the nearest cluster center belongs. After traversing all data points, the initial cluster center is obtained. Step 4: Calculate the average value of all data points for each initial cluster and use the average value as the new cluster center; Step 5: It is used to determine whether the cluster center has changed. If it has changed, repeat steps 3 and 4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; The final clusters and the final cluster centers are mapped to two-dimensional coordinates to obtain the dispatch priority coefficients of each dispatchable operation and maintenance personnel.

[0012] Optionally, the dispatch priority coefficients of each dispatchable operation and maintenance personnel include: Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters; Calculate the distance of each data point in each final cluster to the coordinate origin on the two-dimensional coordinate to obtain the preliminary coordinate distance of the data point, and multiply the preliminary coordinate distance of the data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the data point; The actual coordinate distance of each data point is used as the dispatch priority coefficient of each dispatchable operation and maintenance personnel.

[0013] In a second aspect of the present invention, an intelligent monitoring system for new energy operation and maintenance management is proposed, the system comprising: Hidden fault module: obtains hidden fault data of new energy equipment and calculates hidden fault coefficient of new energy equipment according to the hidden fault data; the hidden fault data includes current fluctuation coefficient, environmental impact coefficient and historical fault coefficient; Judgment module: judge whether the new energy equipment has hidden faults and needs immediate inspection and maintenance according to the hidden fault coefficient of the new energy equipment and the preset hidden fault coefficient threshold; Dispatch priority module: When there is a hidden fault in the new energy equipment that needs to be checked and repaired immediately, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment repairs and current positions of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set. The data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; Monitoring module: According to the preset number of operation and maintenance management and monitoring personnel of the new energy equipment, a corresponding number of available operation and maintenance personnel are dispatched in descending order of dispatch priority coefficients to perform operation and maintenance management and monitoring of the new energy equipment.

[0014] Beneficial effects of the present invention: 1. The present invention proposes an intelligent monitoring method and system for operation and maintenance management of new energy, which calculates the hidden fault coefficient of new energy equipment by acquiring the hidden fault data of new energy equipment; and judges whether the new energy equipment has hidden faults and needs to be inspected and repaired immediately according to the hidden fault coefficient of the new energy equipment and the preset hidden fault coefficient threshold; in this way, it is possible to detect whether the new energy equipment has hidden faults that do not show obvious abnormalities before the next periodic inspection, and promptly notify the operation and maintenance personnel to conduct inspections and repairs, thereby reducing the accumulation of equipment faults and ensuring the stability of power supply; 2. When new energy equipment has hidden faults and needs immediate inspection and maintenance, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment maintenance and current location of each dispatchable operation and maintenance personnel is obtained as the data point of the dispatchable operation and maintenance personnel to obtain a data set, and the data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; according to the preset number of operation and maintenance management and monitoring personnel of the new energy equipment, the corresponding number of available operation and maintenance personnel are dispatched in descending order of the dispatch priority coefficient to perform operation and maintenance management and monitoring of the new energy equipment; in this way, when new energy equipment has hidden faults and needs to be operated and managed in advance, appropriate operation and maintenance personnel can be reasonably dispatched according to the actual situation to monitor the operation and maintenance management of the new energy equipment, so that the new energy equipment can be repaired in place, the maintenance cost can be reduced, and the stable power supply can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 is a flow chart of an intelligent monitoring method for new energy operation and maintenance management; Figure 2 The framework diagram of an intelligent monitoring system for new energy operation and maintenance management. DETAILED DESCRIPTION

[0017] 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.

[0018] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0019] The embodiment of the present invention provides an intelligent monitoring method for new energy operation and maintenance management. Figure 1 , Figure 1 A flowchart of an intelligent monitoring method for new energy operation and maintenance management provided by an embodiment of the present invention. The method comprises the following steps: Obtaining the hidden fault data of new energy equipment, and calculating the hidden fault coefficient of the new energy equipment based on the hidden fault data; the hidden fault data includes the current fluctuation coefficient, the environmental impact coefficient and the historical fault coefficient; According to the hidden fault coefficient of the new energy equipment and the preset hidden fault coefficient threshold, it is judged whether the new energy equipment has hidden faults and needs to be checked and repaired immediately; When there is a hidden fault in the new energy equipment that needs to be checked and repaired immediately, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment repairs and current positions of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set. The data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; According to the preset number of operation, maintenance, management and monitoring personnel for the new energy equipment, a corresponding number of available operation and maintenance personnel will be dispatched in descending order of dispatch priority coefficients to carry out operation, maintenance, management and monitoring of the new energy equipment.

[0020] Based on an intelligent monitoring method for new energy operation and maintenance management provided by an embodiment of the present invention, through the above-mentioned method, it is possible to detect whether the new energy equipment has hidden faults that do not show obvious abnormalities before the next periodic inspection, and promptly notify the operation and maintenance personnel to conduct inspections and repairs, thereby reducing the accumulation of equipment failures and ensuring the stability of power supply; in addition, when new energy equipment has hidden faults and needs operation and maintenance management monitoring in advance, appropriate operation and maintenance personnel can be reasonably dispatched to monitor the operation and maintenance management of the new energy equipment according to the actual situation, so that the new energy equipment can be repaired in place, the maintenance cost can be reduced, and the stability of power supply can be ensured.

[0021] In one embodiment, the current fluctuation coefficient calculation steps are: Taking the time from the last inspection cycle of the new energy equipment to the current time as the target time interval, obtaining the output current of the new energy equipment within the target time interval, and obtaining a current sequence based on the time sequence; The standard deviation of the calculated current sequence is used as the current fluctuation value of the new energy equipment; The current fluctuation value of the new energy device is divided by the preset current fluctuation value threshold to obtain the current fluctuation coefficient of the new energy device.

[0022] It should be noted that the output current of new energy equipment can be obtained through the monitoring system corresponding to the equipment, usually using sensors and monitoring systems. For example, new energy equipment such as wind turbines and solar panels are usually equipped with current sensors to monitor the output current of the equipment in real time and transmit it to the monitoring system corresponding to the equipment; taking wind turbines as an example, the current sensor will be installed at the power output end of the generator to collect the current generated by the generator in real time, and then upload these data to the remote management platform through the monitoring system for operation and maintenance personnel to view and analyze; it can also be other acquisition methods, which are not limited or elaborated in detail. In addition, the preset current fluctuation value threshold is set by professionals according to the actual situation, which is not limited or elaborated in detail.

[0023] It should be noted that the current fluctuation coefficient refers to the degree of fluctuation of the output current from the last inspection cycle to the current time; if the degree of fluctuation is greater, it means that although the new energy equipment has not shown obvious abnormalities, it may have quietly appeared hidden faults. If the operation and maintenance personnel are notified to inspect and repair until the next cycle inspection, it may lead to the accumulation of equipment faults and even threaten the stability of power supply; because the larger the current fluctuation coefficient, the more likely it is that the internal components of the equipment have slight wear, aging or poor contact. These problems may not immediately cause equipment failure or abnormal shutdown at the beginning, but as time goes by, minor problems may gradually accumulate and even develop into more serious faults; if you do not pay attention to it at this time and wait until the next cycle inspection to repair it, the equipment may have suffered greater damage, resulting in the failure cannot be repaired in time. Ultimately, this hidden fault may cause more serious equipment failures, affect the normal operation of the equipment, and even threaten the stability of power supply; therefore, by timely monitoring the current fluctuation coefficient, it can help to detect potential hidden faults early, reduce the accumulation of equipment faults, and ensure the safety and stability of power supply.

[0024] In one embodiment, the environmental impact coefficient calculation steps are: The time from the last inspection cycle of the new energy equipment to the current time is taken as the target time interval, the temperature and humidity of the environment where the new energy equipment is located within the target time interval are obtained, and the environmental impact coefficient is calculated. The calculation formula is: ; in, represents the environmental impact coefficient, , Respectively represent the temperature and humidity of the environment where the new energy equipment is located within the target time interval, [ , ] indicates the time period when the temperature is outside the preset optimal temperature range, [ , ] indicates the time period when the temperature is outside the preset optimal humidity range. and Respectively represent the preset impact weights of temperature and humidity; It should be noted that and It is set by professionals according to the actual situation, and generally , The sum of is 1, specifically , The value depends on the actual situation and is not limited or elaborated on.

[0025] It should be noted that the temperature and humidity data of the environment in which the new energy equipment is located within the target time interval can usually be obtained through temperature sensors and humidity sensors installed around the equipment. They monitor the temperature and humidity changes in the equipment's operating environment in real time, and upload these data to the remote management platform through the monitoring system for operation and maintenance personnel to view and analyze. Other acquisition methods may also be used, which are not limited or elaborated on. In addition, the preset optimal humidity range and the preset optimal temperature range refer to the most suitable ambient temperature and humidity range defined for specific new energy equipment (such as wind turbines, solar panels, etc.) during the operation of the equipment, based on the equipment's design requirements, manufacturer's recommended values, and industry standards. Within this range, the equipment can operate in the most efficient and stable state. The optimal temperature range of some wind turbines may be 10°C to 30°C, and the optimal humidity range may be 40% to 60%. If the ambient temperature exceeds this range, or the humidity is too high or too low, the performance of the equipment may be affected, increasing the risk of failure. The specific preset optimal humidity range and preset optimal temperature range are set by professionals according to actual conditions, and are not limited or elaborated. In addition, the time period when the temperature is outside the preset optimal humidity range and the time period when the temperature is outside the preset optimal temperature range are obtained through the monitoring system corresponding to the equipment. When the temperature or humidity exceeds the set range, the system will automatically record these time periods and save them for viewing and analysis by the operation and maintenance personnel. It should be noted that the environmental impact coefficient refers to the degree to which the temperature and humidity of the environment exceed the set range from the last inspection cycle to the current time; if the degree of exceeding the set range is greater, that is, the environmental impact coefficient is greater, it means that although the new energy equipment has not shown obvious abnormalities, it may have quietly appeared hidden faults. If the operation and maintenance personnel are notified to inspect and repair until the next cycle inspection, it may lead to the accumulation of equipment faults and even threaten the stability of power supply; because when the ambient temperature and humidity of the new energy equipment exceed the set range, the working conditions of the equipment may become unsatisfactory. Long-term exposure to extreme temperature and humidity may accelerate the aging and damage of equipment, especially the impact on electrical components and mechanical parts. For example, high temperature may cause overheating of batteries or inverters, thereby affecting their efficiency and even causing internal damage; while high humidity may cause corrosion, short circuit and other problems in electrical components. Although these effects may not directly lead to obvious faults or downtime in the short term, these hidden faults may gradually accumulate over time and eventually cause serious damage to the equipment. If the problem is not discovered until the next inspection cycle, the faults have accumulated to a certain extent, which may cause the equipment to fail suddenly, thereby affecting the stability of the overall power supply. Therefore, by real-time monitoring of environmental conditions and calculating environmental impact coefficients, problems can be discovered in time before hidden faults become serious, avoiding the accumulation of equipment failures and thus ensuring the reliability of power supply.

[0026] In one embodiment, the historical failure coefficient calculation steps are: Obtain the number of historical failures of new energy equipment, obtain the time required for repair of each failure, and calculate the average time required for repair of the failure as the average repair time; Obtain the time interval of the number of historical failures of the new energy equipment, obtain a failure interval sequence based on the time series, and calculate the average value of the failure interval sequence to obtain the average failure interval; obtain the time interval between the current time of the new energy equipment and the last failure maintenance as the first time interval, and calculate the difference between the first time interval and the average failure interval as the second time interval; The historical failure coefficient is obtained based on the mean repair time and the second time interval.

[0027] It should be noted that the specific calculation steps for obtaining the historical failure coefficient based on the average maintenance time and the second time interval are as follows: , where is the historical failure coefficient, and are the preset proportional factors of the mean maintenance time and the second time interval, respectively, and and are all greater than 0; it should be noted that and It is set by professionals according to the actual situation. Generally, and The sum of is 1, for example and They can be 0.5, 0.5, or other numbers respectively, and are not specifically limited. In addition, before calculating the historical failure coefficient, it is necessary to remove the unit from the average maintenance time and the second time interval.

[0028] It should be noted that the data acquisition method involved in the calculation of the historical failure coefficient of new energy equipment mainly relies on the equipment's maintenance records and monitoring system; the equipment's historical failure times and the repair time required for each failure can be obtained from the maintenance log in the operation and maintenance management system; the time interval between failures can be calculated by analyzing the failure timestamp data. These timestamps are usually obtained by the equipment operation and maintenance management system and uploaded to the monitoring system for storage. The time interval between the current time and the last failure repair can also be obtained directly from the monitoring system.

[0029] It should be noted that the larger the historical failure coefficient, the more it means that although the new energy equipment has not shown obvious abnormalities, hidden failures may have quietly occurred. If the operation and maintenance personnel are notified to conduct inspections and repairs until the next periodic inspection, it may lead to the accumulation of equipment failures and even threaten the stability of the power supply; the larger the historical failure coefficient, the more failures and repairs the new energy equipment has experienced in the past period of time, and the time required to repair each failure is longer, and the time since the last repair is longer, which means that the overall health of the equipment may be poor; such frequent failures and long maintenance cycles will increase the fatigue accumulation of certain components inside the equipment, increasing the possibility of hidden failures; although these hidden failures may not immediately cause equipment shutdown or obvious abnormal performance, they may subtly affect the stability and performance of the equipment. If maintenance is not performed until the next periodic inspection, early identification and intervention of these potential hidden dangers may be missed, allowing the failures to gradually accumulate, and eventually leading to sudden and serious problems with the equipment, such as short circuits, overheating or other system failures; this may not only cause equipment downtime, but may also affect the stability of the power supply, increase the difficulty and cost of fault repair, and may even pose a threat to the safety of the entire power grid; therefore, timely detection and intervention of potential hidden failures of equipment, especially risk points predicted based on historical failure data, can effectively avoid the accumulation of equipment failures and ensure stable and reliable power supply.

[0030] In one embodiment, the implicit fault coefficient of the new energy equipment is calculated according to the implicit fault data as follows: The hidden fault coefficient is calculated based on the current fluctuation coefficient, environmental impact coefficient and historical fault coefficient. The calculation formula is: ; In the formula, is the hidden fault coefficient, , and They are current fluctuation coefficient, environmental impact coefficient and historical failure coefficient, Respectively , and The preset scaling factor of Both are greater than 0.

[0031] It should be noted that Set according to actual situation. The sum is 1, for example, The value of can be 0.3, 0.3, 0.4, or other values, depending on the actual situation, without limitation or elaboration; in addition, before calculating the hidden fault coefficient, it is necessary to remove the units of the current fluctuation coefficient, the environmental impact coefficient, and the historical fault coefficient, and perform normalization processing before calculation. Commonly used normalization methods include Min-Max normalization, Z-Score standardization, etc., depending on the actual situation, without limitation or elaboration.

[0032] In one embodiment, judging whether the new energy equipment has a hidden fault and needs immediate inspection and repair according to the hidden fault coefficient of the new energy equipment and a preset hidden fault coefficient threshold includes: Compare the latent fault coefficient of the new energy equipment with the preset latent fault coefficient threshold. If the latent fault coefficient of the new energy equipment is less than the preset latent fault coefficient threshold, it means that there is no latent fault in the new energy equipment and it needs to be checked and repaired immediately. The new energy equipment is still operated and maintained according to the originally set monitoring cycle. If the latent fault coefficient of the new energy equipment is not less than the preset latent fault coefficient threshold, it means that the new energy equipment has a latent fault and needs to be checked and repaired immediately.

[0033] It should be noted that the preset hidden fault coefficient threshold is set by professionals according to actual conditions and will not be limited or elaborated on in detail.

[0034] It should be noted that when the hidden fault coefficient of new energy equipment is less than the preset threshold, it means that the equipment does not show obvious abnormality or fault signs in the current operating state, and the risk of hidden fault is relatively low. Therefore, the equipment can still be routinely operated and monitored according to the original plan and the established inspection cycle, without taking additional emergency maintenance measures. In this way, the normal rhythm of equipment operation can be maintained and unnecessary waste of resources can be avoided. However, if the hidden fault coefficient of new energy equipment is not less than the preset threshold, it means that the potential risk of the equipment has increased, and the possibility of hidden faults is high. The equipment may have quietly suffered some faults. Although there are no obvious fault symptoms at present, its operating state may have been affected. In this case, waiting until the next periodic inspection to notify the operation and maintenance personnel to conduct inspection and maintenance may lead to the accumulation of equipment failures and even threaten the stability of power supply. Therefore, additional operation and maintenance monitoring and maintenance measures need to be taken immediately to avoid further accumulation or aggravation of failures. At this time, the operation and maintenance personnel should adjust the operation and maintenance strategy according to the hidden fault coefficient of the equipment, and implement more frequent or targeted inspections and maintenance to ensure the reliability of the equipment and the stability of power supply.

[0035] In one embodiment, when a new energy device has a hidden fault and needs to be checked and repaired immediately, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment maintenance times and current positions of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set, and the data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; When new energy equipment has hidden faults and needs to be checked and repaired immediately, in order to ensure that the maintenance work is carried out efficiently and accurately, it is necessary to evaluate and dispatch the dispatchable operation and maintenance personnel. First, all currently idle operation and maintenance personnel need to be recorded as "dispatched operation and maintenance personnel". These personnel are resources that can be immediately put into maintenance work. Next, obtain the relevant data of each dispatchable operation and maintenance personnel, including the number of times they have repaired new energy equipment in the past and their current location. The number of repairs can reflect the experience and proficiency of the operation and maintenance personnel, while the location data helps to judge the response speed of the operation and maintenance personnel and avoid maintenance delays due to long distances.

[0036] This information will form a data set, and each data point represents the basic situation of an operation and maintenance personnel. Cluster analysis is performed on these data points to calculate the "dispatch priority coefficient" of each operation and maintenance personnel. For example, experienced operation and maintenance personnel who are closer to the equipment will be assigned a higher priority, while those with less experience or farther away may be assigned to other tasks with relatively lower urgency. In this way, it can be ensured to the greatest extent that when hidden failures occur in new energy equipment, the most suitable operation and maintenance personnel can conduct inspections and repairs in a timely manner, thereby effectively reducing the impact of equipment failures on the stability of power supply.

[0037] The specific steps are: Obtain the total number of times each dispatchable operation and maintenance personnel participated in the maintenance of new energy equipment and the number of successful fault recovery, and divide the number of successful fault recovery by the total number of maintenance to obtain the fault repair rate of the corresponding dispatchable operation and maintenance personnel; Obtain the current location of each dispatchable operation and maintenance personnel and the current location of the new energy equipment, and obtain the shortest drivable distance between the current location of the dispatchable operation and maintenance personnel and the current location of the new energy equipment as the shortest distance of the corresponding dispatchable operation and maintenance personnel; The fault repair rate and the inverse of the shortest distance are used as data points of dispatchable operation and maintenance personnel to obtain a data set, and the data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel.

[0038] It should be noted that the total number of repairs and the number of successful fault recovery of each dispatchable operation and maintenance personnel can be extracted from the corresponding maintenance management system or equipment maintenance records. These records will record in detail the maintenance tasks participated in by each operation and maintenance personnel, the number of successful repairs, etc.; secondly, the current location of the operation and maintenance personnel and the current location of the new energy equipment can be obtained through the GPS positioning system or real-time location tracking technology. Especially in the operation and maintenance management of large-scale new energy equipment, the location data is tracked in real time through an integrated monitoring platform; through these two data sources, the shortest drivable distance can be calculated, usually using a geographic information system (GIS) or other location analysis tools to determine the shortest path from the current location of the operation and maintenance personnel to the equipment location.

[0039] In one embodiment, clustering the data set to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel includes: The steps to cluster the data set using the K-means clustering method are: Step 1: Used to determine the optimal number of clusters for the data set using the elbow method; Step 2: Randomly select a corresponding number of data points as initial clusters according to the optimal number of clusters; Step 3: For each data point, use the Euclidean distance algorithm to calculate the distance between the data point and each initial cluster center, and assign the data point to the cluster to which the nearest cluster center belongs. After traversing all data points, the initial cluster center is obtained. Step 4: Calculate the average value of all data points for each initial cluster and use the average value as the new cluster center; Step 5: It is used to determine whether the cluster center has changed. If it has changed, repeat steps 3 and 4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; The final clusters and the final cluster centers are mapped to two-dimensional coordinates to obtain the dispatch priority coefficients of each dispatchable operation and maintenance personnel.

[0040] In one implementation, according to the above method, the optimal clustering results of the data set can be obtained. These clustering results can be used to identify different clusters in the data set, as well as the center and characteristics of each cluster. Such clustering results are helpful for further data analysis and interpretation.

[0041] It should be noted that clustering the data set by K-means clustering is simple and efficient, and can quickly process large-scale data sets; K-means clustering is a commonly used unsupervised learning algorithm, which is used to divide the data set into pre-defined K clusters; the goal of this algorithm is to divide the data points into K clusters, so that each data point belongs to the cluster represented by the cluster center closest to it, and the data points within the cluster are as similar as possible, while the data points between different clusters may not be similar; Cluster analysis can help evaluate the dispatch priority of each dispatchable operation and maintenance personnel; if the fault repair rate of each dispatchable operation and maintenance personnel is greater and the inverse of the shortest distance is greater, it means that the dispatch priority of the dispatchable operation and maintenance personnel is greater, otherwise, it means that the dispatch priority of the dispatchable operation and maintenance personnel is smaller; through cluster analysis, the dispatch priority of the dispatchable operation and maintenance personnel can be understood, so as to determine which operation and maintenance personnel should immediately go to the location of the new energy equipment for operation and maintenance management and monitoring.

[0042] Therefore, by taking the fault repair rate and the inverse of the shortest distance as the data points of each dispatchable operation and maintenance personnel, a data set is obtained, and the data set is clustered to determine the dispatch priority of each dispatchable operation and maintenance personnel; the fault repair rate and the inverse of the shortest distance present a common linear relationship. The greater the fault repair rate of each dispatchable operation and maintenance personnel and the greater the inverse of the shortest distance, the greater the dispatch priority of the corresponding dispatchable operation and maintenance personnel.

[0043] It should be noted that step 1: using the elbow method to determine the optimal number of clusters for the data set; the elbow method is one of the common methods used to determine the optimal number of clusters K in K-means clustering. The basic idea is to find an "elbow point" by observing the relationship between the number of clusters K and the total internal deviation square sum (WCSS) of the clustering results. The K value corresponding to the "elbow point" is the position where the rate of decrease of WCSS slows down significantly after the number of clusters increases to a certain extent. WCSS refers to the sum of the squares of the distances from each data point to the center of the cluster to which it belongs, which indicates the compactness of the clustering results.

[0044] Step 2: According to the optimal number of clusters, randomly select the corresponding number of data points from the data points as the initial cluster centers of clustering. The initial cluster centers refer to the K cluster centers randomly selected before starting the clustering process. These initial cluster centers are the starting points of the algorithm. Step 3: For each data point in the data set, calculate the distance between it and each cluster center using the Euclidean distance. For example, calculate the data point and the initial cluster centers The distance between , and its calculation formula is , and assign the data point to the cluster to which the nearest cluster center belongs. Euclidean distance refers to the distance between two points in Euclidean space, and is also one of the most commonly used distance metrics. In K-means clustering, Euclidean distance is usually used to calculate the distance between a data point and a cluster center. Step 4: For each cluster, calculate the mean of all its data points and use the mean as the new cluster center. The calculation formula is: ,in Expressed as The set of data points in a cluster, Expressed as The new cluster centers of the clusters; Step 5: Repeat steps 3 and 4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

[0045] It should be specifically explained that the step of assigning a data point to the cluster to which the nearest cluster center belongs is to sort the distances between the data point and each cluster center from large to small, and select the smallest distance to assign the data point to the cluster to which it belongs.

[0046] In one embodiment, the final clusters and the final cluster centers are mapped to two-dimensional coordinates to obtain dispatch priority coefficients of each dispatchable operation and maintenance personnel, including: Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters; Calculate the distance of each data point in each final cluster to the coordinate origin on the two-dimensional coordinate to obtain the preliminary coordinate distance of the data point, and multiply the preliminary coordinate distance of the data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the data point; The actual coordinate distance of each data point is used as the dispatch priority coefficient of each dispatchable operation and maintenance personnel.

[0047] In one implementation, according to the above method, the dispatch priority coefficient of each dispatchable operation and maintenance personnel is more accurate and can better reflect the dispatch priority of each dispatchable operation and maintenance personnel.

[0048] It should be noted that, because the fault repair rate and the inverse of the shortest distance are selected as data points for clustering, these two data points can now be used as coordinates and mapped to the two-dimensional coordinate axis, and each parking space will be mapped as a point on the two-dimensional coordinate axis; after clustering is completed, all data points will be distributed in different final clustering clusters on the two-dimensional coordinate axis. The closer the final clustering cluster is to the original center and the smaller the dispatch priority coefficient of each dispatchable operation and maintenance personnel corresponding to the data points included in it, the lower the dispatch priority of the dispatchable operation and maintenance personnel, and vice versa; at the same time, the weight coefficient of each final clustering cluster is calculated to correct the distance from the data point to the original center. In this way, the different importance of each final clustering cluster can be ensured, and the influence of noise and outliers on the clustering results can be reduced to a certain extent, so that the dispatch priority coefficient of each dispatchable operation and maintenance personnel calculated in the end is more accurate.

[0049] In one implementation method, the dispatch priority coefficient of each dispatchable operation and maintenance personnel calculated by the above method has the advantages of multi-dimensional analysis, identification of group characteristics, improved accuracy, visual display and dynamic adjustment; this method can comprehensively evaluate the dispatch priority of each dispatchable operation and maintenance personnel, reduce the impact of noise and outliers, so that the dispatch priority coefficient finally calculated can more accurately reflect the dispatch priority of each dispatchable operation and maintenance personnel.

[0050] In one embodiment, according to the preset number of operation and maintenance management and monitoring personnel of the new energy equipment, a corresponding number of available operation and maintenance personnel are dispatched in descending order of dispatch priority coefficients to perform operation and maintenance management and monitoring of the new energy equipment; According to the preset number of people for operation and maintenance management monitoring of new energy equipment, when it is determined that the equipment has a hidden fault and needs to be inspected and repaired immediately, the currently available operation and maintenance personnel will first be sorted by the dispatch priority coefficient of the operation and maintenance personnel. The dispatch priority coefficient is calculated based on the historical maintenance performance of the operation and maintenance personnel (such as the fault repair rate) and the shortest drivable distance from the equipment. Specifically, the higher the repair success rate of the operation and maintenance personnel and the closer they are to the equipment, the higher their dispatch priority coefficient. Therefore, the dispatch priority of the operation and maintenance personnel will be sorted according to these data, and tasks will be arranged in order from high to low.

[0051] Then, according to the number of people for each cycle of operation and maintenance management and monitoring of the new energy equipment originally set, the corresponding number of personnel will be selected from the sorted operation and maintenance personnel list according to this number, ensuring that the number of dispatched personnel meets the predetermined standards, and dispatching these operation and maintenance personnel to operate and manage and monitor the new energy equipment. In this way, not only can the equipment be repaired and inspected in a timely manner, but also the operation and maintenance efficiency can be improved, unnecessary time waste can be reduced, and appropriate operation and maintenance personnel can be reasonably dispatched to monitor the operation and maintenance management of the new energy equipment according to the actual situation, and ensure that the new energy equipment is repaired in place, reduce maintenance costs, and ensure stable power supply.

[0052] Based on the same inventive concept, the present invention also provides an intelligent monitoring system for new energy operation and maintenance management. Figure 2 , Figure 2 A framework diagram of an intelligent monitoring system for new energy operation and maintenance management provided by an embodiment of the present invention, the system includes: Hidden fault module: Obtain the hidden fault data of new energy equipment and calculate the hidden fault coefficient of new energy equipment based on the hidden fault data; the hidden fault data includes current fluctuation coefficient, environmental impact coefficient and historical fault coefficient; Judgment module: judge whether the new energy equipment has hidden faults and needs immediate inspection and maintenance according to the hidden fault coefficient of the new energy equipment and the preset hidden fault coefficient threshold; Dispatch priority module: When there is a hidden fault in the new energy equipment that needs to be checked and repaired immediately, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment repairs and current positions of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set. The data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; Monitoring module: According to the preset number of operation and maintenance management and monitoring personnel of the new energy equipment, a corresponding number of available operation and maintenance personnel are dispatched in descending order of dispatch priority coefficients to perform operation and maintenance management and monitoring of the new energy equipment.

[0053] An intelligent monitoring system for operation and maintenance management of new energy provided by an embodiment of the present invention can, through the above method, detect whether there are hidden faults in new energy equipment that do not show obvious abnormalities before the next periodic inspection, and promptly notify the operation and maintenance personnel to conduct inspections and repairs, thereby reducing the accumulation of equipment failures and ensuring the stability of power supply; in addition, when hidden faults occur in new energy equipment and operation and maintenance management monitoring is required in advance, appropriate operation and maintenance personnel can be reasonably dispatched to monitor the operation and maintenance management of the new energy equipment according to the actual situation, so that the new energy equipment can be repaired in place, the maintenance cost can be reduced, and the stability of power supply can be ensured.

[0054] 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 used to artificially 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. An intelligent monitoring method for new energy operation and maintenance management, characterized in that: The following steps are involved: Obtaining hidden fault data of new energy equipment, and calculating hidden fault coefficients of new energy equipment according to the hidden fault data; the hidden fault data includes current fluctuation coefficient, environmental impact coefficient and historical fault coefficient; According to the hidden fault coefficient of the new energy equipment and the preset hidden fault coefficient threshold, it is judged whether the new energy equipment has hidden faults and needs to be checked and repaired immediately; When there is a hidden fault in the new energy equipment that needs to be checked and repaired immediately, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment repairs and current positions of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set. The data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; According to the preset number of operation, maintenance, management and monitoring personnel for the new energy equipment, a corresponding number of available operation and maintenance personnel will be dispatched in descending order of dispatch priority coefficients to carry out operation, maintenance, management and monitoring of the new energy equipment.

2. The intelligent monitoring method for new energy operation and maintenance management according to claim 1, characterized in that: The current fluctuation coefficient calculation steps are as follows: Taking the time from the last inspection cycle of the new energy equipment to the current time as the target time interval, obtaining the output current of the new energy equipment within the target time interval, and obtaining a current sequence based on the time sequence; The standard deviation of the calculated current sequence is used as the current fluctuation value of the new energy equipment; The current fluctuation value of the new energy device is divided by the preset current fluctuation value threshold to obtain the current fluctuation coefficient of the new energy device.

3. The intelligent monitoring method for new energy operation and maintenance management according to claim 1 is characterized in that: The environmental impact coefficient calculation steps are: The time from the last inspection cycle of the new energy equipment to the current time is taken as the target time interval, the temperature and humidity of the environment where the new energy equipment is located within the target time interval are obtained, and the environmental impact coefficient is calculated. The calculation formula is: ； in, represents the environmental impact coefficient, , Respectively represent the temperature and humidity of the environment where the new energy equipment is located within the target time interval, [ , ] indicates the time period when the temperature is outside the preset optimal temperature range, [ , ] indicates the time period when the temperature is outside the preset optimal humidity range. and Represent the preset influence weights of temperature and humidity respectively.

4. The intelligent monitoring method for new energy operation and maintenance management according to claim 1, characterized in that: The historical failure coefficient calculation steps are: Obtain the number of historical failures of new energy equipment, obtain the time required for repair of each failure, and calculate the average time required for repair of the failure as the average repair time; Obtain the time interval of the number of historical failures of the new energy equipment, obtain a failure interval sequence based on the time series, and calculate the average value of the failure interval sequence to obtain the average failure interval; obtain the time interval between the current time of the new energy equipment and the last failure maintenance as the first time interval, and calculate the difference between the first time interval and the average failure interval as the second time interval; The historical failure coefficient is obtained based on the mean repair time and the second time interval.

5. The intelligent monitoring method for new energy operation and maintenance management according to claim 1, characterized in that: The hidden failure coefficient of new energy equipment is calculated based on the hidden failure data as follows: The hidden fault coefficient is calculated based on the current fluctuation coefficient, environmental impact coefficient and historical fault coefficient. The calculation formula is: ； In the formula, is the hidden fault coefficient, , and They are current fluctuation coefficient, environmental impact coefficient and historical failure coefficient, Respectively , and The preset scaling factor of Both are greater than 0.

6. The intelligent monitoring method for new energy operation and maintenance management according to claim 1, characterized in that: According to the hidden fault coefficient of new energy equipment and the preset hidden fault coefficient threshold, it is judged whether there is a hidden fault in the new energy equipment and it needs to be checked and repaired immediately, including: Compare the latent fault coefficient of the new energy equipment with the preset latent fault coefficient threshold. If the latent fault coefficient of the new energy equipment is less than the preset latent fault coefficient threshold, it means that there is no latent fault in the new energy equipment and it needs to be checked and repaired immediately. The new energy equipment is still operated and maintained according to the originally set monitoring cycle. If the latent fault coefficient of the new energy equipment is not less than the preset latent fault coefficient threshold, it means that the new energy equipment has a latent fault and needs to be checked and repaired immediately.

7. The intelligent monitoring method for new energy operation and maintenance management according to claim 1, characterized in that: The number of new energy equipment maintenance and current location of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set. The data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel, including: Obtain the total number of times each dispatchable operation and maintenance personnel participated in the maintenance of new energy equipment and the number of successful fault recovery, and divide the number of successful fault recovery by the total number of maintenance to obtain the fault repair rate of the corresponding dispatchable operation and maintenance personnel; Obtain the current location of each dispatchable operation and maintenance personnel and the current location of the new energy equipment, and obtain the shortest drivable distance between the current location of the dispatchable operation and maintenance personnel and the current location of the new energy equipment as the shortest distance of the corresponding dispatchable operation and maintenance personnel; The fault repair rate and the inverse of the shortest distance are used as data points of dispatchable operation and maintenance personnel to obtain a data set, and the data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel.

8. The intelligent monitoring method for new energy operation and maintenance management according to claim 7 is characterized in that: Clustering the data set to determine the dispatch priority coefficients of each dispatchable operation and maintenance personnel includes: The steps to cluster the data set using the K-means clustering method are: Step 1: Used to determine the optimal number of clusters for the data set using the elbow method; Step 2: Randomly select a corresponding number of data points as initial clusters according to the optimal number of clusters; Step 3: For each data point, use the Euclidean distance algorithm to calculate the distance between the data point and each initial cluster center, and assign the data point to the cluster to which the nearest cluster center belongs. After traversing all data points, the initial cluster center is obtained. Step 4: Calculate the average value of all data points for each initial cluster and use the average value as the new cluster center; Step 5: It is used to determine whether the cluster center has changed. If it has changed, repeat steps 3 and 4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; The final clusters and the final cluster centers are mapped to two-dimensional coordinates to obtain the dispatch priority coefficients of each dispatchable operation and maintenance personnel.

9. The intelligent monitoring method for new energy operation and maintenance management according to claim 8, characterized in that: The dispatch priority coefficients of each dispatchable operation and maintenance personnel include: Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters; Calculate the distance of each data point in each final cluster to the coordinate origin on the two-dimensional coordinate to obtain the preliminary coordinate distance of the data point, and multiply the preliminary coordinate distance of the data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the data point; The actual coordinate distance of each data point is used as the dispatch priority coefficient of each dispatchable operation and maintenance personnel.

10. An intelligent monitoring system for operation and maintenance management of new energy, used to implement an intelligent monitoring method for operation and maintenance management of new energy as described in any one of claims 1 to 9, characterized in that: The system comprises: Hidden fault module: obtains hidden fault data of new energy equipment and calculates hidden fault coefficient of new energy equipment according to the hidden fault data; the hidden fault data includes current fluctuation coefficient, environmental impact coefficient and historical fault coefficient; Judgment module: judge whether the new energy equipment has hidden faults and needs immediate inspection and maintenance according to the hidden fault coefficient of the new energy equipment and the preset hidden fault coefficient threshold; Dispatch priority module: When there is a hidden fault in the new energy equipment that needs to be checked and repaired immediately, each currently idle operation and maintenance personnel is recorded as a dispatchable operation and maintenance personnel, and the number of new energy equipment repairs and current positions of each dispatchable operation and maintenance personnel are obtained as data points of the dispatchable operation and maintenance personnel to obtain a data set. The data set is clustered to determine the dispatch priority coefficient of each dispatchable operation and maintenance personnel; Monitoring module: According to the preset number of operation and maintenance management and monitoring personnel of the new energy equipment, a corresponding number of available operation and maintenance personnel are dispatched in descending order of dispatch priority coefficients to perform operation and maintenance management and monitoring of the new energy equipment.

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