Digital intelligent equipment management method and system for novel power system

Abstract

The invention relates to the technical field of power system management, in particular to a digital intelligent equipment management method and system for a novel power system, and the method comprises the steps: confirming the type of to-be-accessed new equipment, and querying the same type of decommissioned equipment; constructing a life cycle path of the decommissioning equipment, including an installation node, a check node and a decommissioning node which are connected through a maintenance link, and a stored maintenance scheme; randomly selecting nodes to construct a new equipment management path; performing optimization by using a grey wolf optimization algorithm to obtain an optimal equipment management path; and carrying out new equipment maintenance according to the optimal path. According to the method, by analyzing the historical data of the decommissioning equipment and intelligently optimizing the maintenance strategy, the transformation from passive maintenance to active prevention is realized. Compared with a traditional method, the method has the advantages that the equipment failure frequency and downtime can be remarkably reduced, the maintenance cost is reduced, the equipment utilization rate is improved, and a powerful guarantee is provided for stable operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system management, and in particular to a digital intelligent equipment management method and system for a new power system. Background Art

[0002] The power system is an electric energy production and consumption system composed of power generation, transmission, distribution and power consumption. It includes primary equipment and secondary equipment. The primary equipment mainly includes generators, transformers, switchgear and busbars, while the secondary equipment includes measuring meters, relay protection devices, DC power supply equipment, communication equipment, control and signal devices, etc. Regardless of whether it is primary equipment or secondary equipment, it needs corresponding maintenance throughout the life cycle of the power system application to ensure that the equipment can work stably and reduce the occurrence of failures. For the same type of equipment, the inspection schedule throughout its life cycle is basically the same. The staff performs regular maintenance according to the inspection schedule. However, due to the fact that maintenance personnel Due to differences in the equipment used, the maintenance plans for the same type of equipment in the same maintenance period may be different, resulting in different failures in the same type of equipment in the same time period. When introducing new equipment of the same type, the established maintenance plans are often selected based on experience. The maintenance plans are not optimally customized based on the historical experience of the same type of equipment, which often results in the new equipment still being unable to perform at its maximum performance. With the development of digitalization and intelligence of communication data networks, it has become possible to digitally manage equipment through data analysis functions after collecting data from the power system. However, there are currently no related applications. Therefore, how to achieve digital management of equipment has become an urgent problem to be solved. Summary of the invention

[0003] In view of the problems existing in the prior art, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is how to provide an optimal maintenance plan for new equipment, reduce the frequency of new equipment failures, and ensure the normal use of the new power system.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a digital intelligent device management method for a new power system, which includes confirming the type of new equipment to be connected to the power system, and querying retired equipment of the same type in the power system according to the type of the new equipment;

[0007] Constructing a life cycle path for each retired device, the life cycle path including an installation node, a number of inspection nodes and a retirement node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link;

[0008] Randomly select nodes in the life cycle path to construct a new device management path in order, and use the gray wolf optimization algorithm to find the optimal device management path;

[0009] Maintain new equipment according to the maintenance plan in the optimal equipment management path.

[0010] As a preferred solution of a digital intelligent device management method for a new power system of the present invention, wherein: confirming the type of new equipment to be connected to the power system, querying the retired equipment of the same type in the power system according to the type of the new equipment also includes the following steps:

[0011] receiving an access request from a new device, where the access request includes the type of the new device;

[0012] According to the type of new equipment, search for old equipment of the same type from the power system ledger;

[0013] Obtaining maintenance manuals for retired equipment from older equipment;

[0014] Check the service life of old equipment and the average service life of equipment of the same type as the old equipment;

[0015] The old equipment whose difference between the average equipment life and the service life of the old equipment is less than a preset threshold is output as retired equipment.

[0016] As a preferred solution of a digital intelligent equipment management method for a new power system of the present invention, wherein: constructing a life cycle path of each retired equipment, the life cycle path includes an installation node, a plurality of inspection nodes and a retired node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link also includes the following steps:

[0017] Obtain work logs of retired equipment from the power system, including installation time, inspection time, maintenance records, and retirement time;

[0018] According to the installation time, the inspection time and the retirement time, the installation node, the inspection node and the retirement node are obtained respectively;

[0019] Connect the installation node, several inspection nodes, and the decommissioned node through the maintenance link in chronological order, and map the maintenance record on the maintenance link;

[0020] Maintenance records include maintenance plans, number of failures, downtime and type of failure.

[0021] As a preferred solution of a digital intelligent equipment management method for a new power system of the present invention, wherein: randomly selecting nodes in the life cycle path to sequentially construct a new equipment management path, and optimizing the optimal equipment management path by the gray wolf optimization algorithm also includes the following steps:

[0022] Randomly select an installation node of a retired device;

[0023] Randomly select check nodes in chronological order until the last selected node is the retired node;

[0024] Obtain the maintenance links behind the selected node in chronological order, and connect the selected nodes through the maintenance links to form a new device management path;

[0025] The Grey Wolf Optimization Algorithm is used to optimize the new equipment management path based on the best maintenance effect, and the optimal equipment management path is obtained.

[0026] As a preferred solution of the digital equipment management method for a new power system of the present invention, the Grey Wolf Optimization Algorithm is used to optimize the new equipment management path based on the best maintenance effect, wherein the best maintenance effect is evaluated by the difficulty of the fault type and quantified, and the quantified fault type difficulty, number of faults and downtime are assigned values ​​respectively, and the assigned data are summed, and the larger the sum result, the worse the maintenance effect.

[0027] As a preferred solution of the digital intelligent equipment management method for a new power system of the present invention, the gray wolf optimization algorithm is used to optimize the new equipment management path based on the best maintenance effect, and the optimal equipment management path is obtained, which includes the following steps:

[0028] Initialize the gray wolf population and the number of iterations;

[0029] A new equipment management path is randomly selected, and the fitness value of the gray wolf is calculated based on the best maintenance effect;

[0030] Select another new device management path, calculate the fitness value and compare it with the fitness value calculated last time, and retain the new device management path with the larger fitness value;

[0031] Perform iterative calculations and output the new device management path with the largest fitness value as the optimal device management path;

[0032] The number of iterations is the number of new device management paths.

[0033] As a preferred solution of a digital intelligent equipment management method for a new power system of the present invention, the maintenance of new equipment according to the maintenance plan in the optimal equipment management path includes the following steps:

[0034] Extracting maintenance links in the optimal equipment management path;

[0035] The maintenance plan stored in the maintenance link is used as the maintenance plan for the new device between nodes.

[0036] In a second aspect, an embodiment of the present invention provides a digital intelligent equipment management system for a new power system, which includes a confirmation module, confirming the type of new equipment to be connected to the power system, and querying retired equipment of the same type in the power system according to the type of the new equipment;

[0037] A construction module constructs a life cycle path of each retired device, where the life cycle path includes an installation node, a plurality of inspection nodes and a retirement node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link;

[0038] The management module randomly selects nodes in the life cycle path to construct a new device management path in order, and obtains the optimal device management path through the gray wolf optimization algorithm;

[0039] The maintenance module performs maintenance on new equipment according to the maintenance plan in the optimal equipment management path.

[0040] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a digital equipment management method for a new power system as in the first aspect of the present invention are implemented.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a digital equipment management method for a new power system as in the first aspect of the present invention are implemented.

[0042] The beneficial effects of the present invention are as follows: the present invention provides a digital equipment management method for a new power system, which obtains multiple retired equipment of the same type according to the type of new equipment to be connected to the power system, and then constructs the entire life cycle path of the retired equipment, wherein the life cycle path includes the entire time line of the retired equipment from installation to multiple maintenance and retirement, and at the same time, the maintenance record is stored in the maintenance link set after each node, and then multiple new equipment management paths are reconstructed, the new equipment management path includes the nodes in the life cycle path of the retired equipment, and multiple new equipment management paths are obtained by random combination, and finally the optimal equipment management path is obtained by optimization, and the new equipment is maintained according to the maintenance plan in the optimal equipment management path, thereby reducing the number of failures of the new equipment during operation and ensuring the normal operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 It is a flow chart of a digital equipment management method for a new power system;

[0045] Figure 2 A computer equipment diagram of a digital equipment management method for a new power system. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] Example 1

[0050] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a digital intelligent equipment management method for a new power system, including:

[0051] A digital equipment management method for a new power system of the present invention is mainly used in the access management of new equipment in the power system, and confirms the maintenance plan after the new equipment is connected to the new power system. The selection of the maintenance plan is not confirmed by manual experience, but by previous retired equipment of the same type. Therefore, it is necessary to first confirm the type of new equipment to be connected to the power system, and then find the retired equipment of the same type in the power system according to the type. The reason for selecting retired equipment is that retired equipment has a complete life cycle, and the maintenance plan adopted in its life cycle can be confirmed. The maintenance plan for the new equipment is confirmed by taking advantage of each other's strengths and weaknesses. Therefore, after obtaining the retired equipment, a life cycle path of each retired equipment is constructed. Each life cycle path includes the entire timeline of the retired equipment from installation to multiple inspections to retirement, and each node is connected by a maintenance link. The maintenance plan is stored on the maintenance link, that is, from installation to the first The maintenance plan adopted between maintenance, between multiple maintenances, and from the last maintenance to retirement. If the maintenance plan is reasonable, the retired equipment will be in a better state when it is regularly inspected at the inspection node. If the maintenance plan is unreasonable, the retired equipment will be in a worse state at the inspection node. Each node is only used to divide the life cycle path. After obtaining the life cycle paths of all retired equipment, it is necessary to construct a new equipment management path. When constructing, all nodes of the life cycle paths of retired equipment are used as nodes to be selected. After selecting the installation node in chronological order, the next inspection node is selected, and the retirement node is selected in turn. Therefore, several new equipment management paths will eventually be formed. Then the gray wolf optimization algorithm is used for selection and the optimal equipment management path is obtained. Then the maintenance plan in the optimal equipment management path is used as the maintenance plan for new equipment, which can minimize the occurrence of new equipment failures and ensure the stable operation of the power system.

[0052] S100: confirming the type of new equipment to be connected to the power system, and querying the retired equipment of the same type in the power system according to the type of the new equipment;

[0053] S101: confirming the type of new equipment to be connected to the power system, and querying the retired equipment of the same type in the power system according to the type of the new equipment also includes the following steps:

[0054] receiving an access request from a new device, where the access request includes the type of the new device;

[0055] According to the type of new equipment, search for old equipment of the same type from the power system ledger;

[0056] Obtaining maintenance manuals for retired equipment from older equipment;

[0057] Check the service life of old equipment and the average service life of equipment of the same type as the old equipment;

[0058] The old equipment whose difference between the average equipment life and the service life of the old equipment is less than a preset threshold is output as retired equipment.

[0059] When managing new equipment, the new equipment sends an access request, and then the power system can confirm the type of the new equipment from the access request, find the same type of old equipment from the ledger according to the type of the new equipment, and then determine the retired equipment from the old equipment. In addition, some old equipment that is about to be retired can also be equivalent to retired equipment. Therefore, it is necessary to calculate the service life of the old equipment that has not been retired, and calculate the average life of the equipment of this type, and calculate the difference between the average life of the equipment and the service life of the old equipment. If the difference is less than the preset threshold, it means that the life of the old equipment is about to expire, so the equipment can be output as a retired equipment, so as to more reasonably determine the maintenance plan for the new equipment. In addition, the preset threshold can be adaptively adjusted according to different types of equipment.

[0060] S200: constructing a life cycle path for each retired device, the life cycle path including an installation node, a plurality of inspection nodes and a retired node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link;

[0061] S201: constructing a life cycle path for each retired device, the life cycle path including an installation node, a plurality of inspection nodes and a retired node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link, further comprising the following steps:

[0062] Obtain work logs of retired equipment from the power system, including installation time, inspection time, maintenance records, and retirement time;

[0063] According to the installation time, the inspection time and the retirement time, the installation node, the inspection node and the retirement node are obtained respectively;

[0064] Connect the installation node, several inspection nodes, and the decommissioned node through the maintenance link in chronological order, and map the maintenance record on the maintenance link;

[0065] Maintenance records include maintenance plans, number of failures, downtime and type of failure.

[0066] After confirming the retired equipment, obtain the work log of the retired equipment from the power system, and extract the installation time, inspection time, maintenance record and retirement time from the work log. The installation time can confirm the installation node, the inspection time can confirm the inspection node, and the retirement time can confirm the retirement node. Then, the installation node, inspection node and retirement node are connected into a timeline in chronological order. The connection is carried out using the maintenance link, and the maintenance record is mapped to the maintenance link at the same time. The entire life cycle path includes the entire process of retired equipment from installation to inspection to retirement, and the maintenance plan between each node is also recorded on the maintenance link.

[0067] S300: randomly selecting nodes in the life cycle path to sequentially construct a new device management path, and using the grey wolf optimization algorithm to find the optimal device management path;

[0068] After obtaining the life cycle path of retired equipment, a new equipment management path is constructed by randomly selecting nodes in chronological order. After a node is selected, the maintenance link behind the node is also selected. Therefore, the new equipment management path finally formed will include several maintenance links of retired equipment, that is, it includes several different maintenance plans for retired equipment. Then, the gray wolf optimization algorithm is used for optimization. The optimization criterion is based on the best maintenance effect, and finally the optimal equipment management path can be obtained.

[0069] Preferably, the best evaluation method for maintenance effect is: evaluate and quantify the difficulty of the fault type, assign values ​​to the quantified fault type difficulty, number of faults and downtime, and sum the assigned data. The larger the sum, the worse the maintenance effect.

[0070] The evaluation of maintenance effect is confirmed in a quantitative form. Maintenance records are stored on the maintenance link. The maintenance records include the maintenance plans taken by the staff between the two nodes, as well as the equipment failures that occurred between the two nodes. When optimizing, the maintenance effect is evaluated to be the best with the least failures, the shortest downtime, and the simplest failure type on the maintenance link. Therefore, after quantifying the difficulty of the failure type, the quantified difficulty, number of failures, and downtime are assigned values, and the assigned data are summed. The maintenance effect is determined based on the summation result. The larger the summation result, the more complex the failure type, the more frequent the failures, and the longer the downtime, which means the maintenance effect is poor.

[0071] S301: randomly selecting nodes in the life cycle path to construct a new device management path in order, and finding the optimal device management path through the gray wolf optimization algorithm also includes the following steps:

[0072] Randomly select an installation node of a retired device;

[0073] Randomly select check nodes in chronological order until the last selected node is the retired node;

[0074] Obtain the maintenance links behind the selected node in chronological order, and connect the selected nodes through the maintenance links to form a new device management path;

[0075] The Grey Wolf Optimization Algorithm is used to optimize the new equipment management path based on the best maintenance effect, and the optimal equipment management path is obtained.

[0076] S302: The Grey Wolf Optimization Algorithm is used to optimize the new equipment management path based on the best maintenance effect. The best maintenance effect is evaluated by the difficulty of the fault type and quantified. The quantified fault type difficulty, number of faults and downtime are assigned values ​​respectively, and the assigned data are summed. The larger the sum, the worse the maintenance effect.

[0077] S303: Using the Grey Wolf Optimization Algorithm to optimize the new equipment management path based on the best maintenance effect, and obtaining the optimal equipment management path includes the following steps:

[0078] Initialize the gray wolf population and the number of iterations;

[0079] A new equipment management path is randomly selected, and the fitness value of the gray wolf is calculated based on the best maintenance effect;

[0080] Select another new device management path, calculate the fitness value and compare it with the fitness value calculated last time, and retain the new device management path with the larger fitness value;

[0081] Perform iterative calculations and output the new device management path with the largest fitness value as the optimal device management path;

[0082] The number of iterations is the number of new device management paths.

[0083] The present invention adopts the Grey Wolf Optimization Algorithm to digitize and intelligently process the data of the power system. By calculating the fitness value of each new equipment management path and comparing them, the new equipment management path with the largest fitness value can be obtained, and this path is the optimal equipment management path.

[0084] S400: Perform maintenance on the new device according to the maintenance plan in the optimal device management path.

[0085] S401: Maintaining new equipment according to the maintenance plan in the optimal equipment management path includes the following steps:

[0086] Extracting maintenance links in the optimal equipment management path;

[0087] The maintenance plan stored in the maintenance link is used as the maintenance plan for the new device between nodes.

[0088] Furthermore, this embodiment also provides a digital intelligent equipment management system for a new power system, including:

[0089] A confirmation module confirms the type of new equipment to be connected to the power system and queries the retired equipment of the same type in the power system according to the type of the new equipment;

[0090] A construction module constructs a life cycle path of each retired device, where the life cycle path includes an installation node, a plurality of inspection nodes and a retirement node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link;

[0091] The management module randomly selects nodes in the life cycle path to construct a new device management path in order, and obtains the optimal device management path through the gray wolf optimization algorithm;

[0092] The maintenance module performs maintenance on new equipment according to the maintenance plan in the optimal equipment management path.

[0093] This embodiment also provides a computer device, which is suitable for a digital and intelligent equipment management method for a new power system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a digital and intelligent equipment management method for a new power system as proposed in the above embodiment.

[0094] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0095] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a digital and intelligent equipment management method for a new power system as proposed in the above embodiment is implemented.

[0096] In summary, by confirming the type of new equipment to be connected and querying the steps of similar retired equipment, full use of historical data is achieved. This method can not only quickly locate relevant historical information, but also provide a reliable data basis for subsequent analysis, ultimately achieving the effect of improving the efficiency of new equipment management.

[0097] By constructing the life cycle path of retired equipment, a systematic combing of the equipment's full life cycle data is achieved. This method converts discrete maintenance records into a continuous timeline, which not only intuitively displays the equipment's usage history, but also provides structured data support for subsequent optimization, ultimately achieving a comprehensive understanding of the equipment's operating status.

[0098] By randomly selecting nodes in the life cycle path to build a new equipment management path and using the Gray Wolf Optimization Algorithm to find the best solution, we can achieve intelligent optimization of maintenance plans. This method not only breaks the limitations of traditional reliance on manual experience, but also makes full use of historical data mining best practices, ultimately achieving the effect of formulating a more scientific and reasonable maintenance plan.

[0099] By maintaining new equipment according to the maintenance plan in the optimal equipment management path, accurate matching of maintenance plans is achieved. This method can not only reduce unnecessary maintenance operations, but also prevent potential failures in a targeted manner, ultimately achieving the effect of extending equipment life and reducing maintenance costs.

[0100] During the implementation of the entire method, objective measurement of maintenance effect is achieved through quantitative evaluation of factors such as fault type difficulty, fault frequency, downtime, etc. This method not only avoids the deviation caused by subjective judgment, but also provides clear direction guidance for continuous improvement, and ultimately achieves the effect of continuously optimizing maintenance strategy.

[0101] By intelligently applying the experience of retired equipment to the management of new equipment, knowledge inheritance and accumulation are achieved. This method can not only avoid repeating mistakes, but also promote the continuous improvement of maintenance technology, and ultimately achieve the effect of improving the operation and maintenance level of the entire power system.

[0102] By introducing the Grey Wolf Optimization Algorithm, a large number of possible maintenance solutions can be quickly screened and optimized. This method can not only obtain a near-optimal solution in a short time, but also adapt to the complex and changing power system environment, ultimately achieving the effect of improving decision-making efficiency and quality.

[0103] In general, the present invention combines digital and intelligent technologies with traditional power equipment management to achieve a shift from passive maintenance to active prevention, which not only significantly improves the reliability and efficiency of new equipment, but also provides a strong guarantee for the stable operation of the entire power system. This innovative management method is expected to significantly reduce the operation and maintenance costs of the power system and significantly improve the service quality, which is of great significance to promoting the intelligent transformation of the power industry.

[0104] Example 2

[0105] Reference Figure 1-2 , which is the second embodiment of the present invention, provides a digital equipment management method for a new power system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0106] Experimental preparation and implementation process:

[0107] This experiment selected 10 220kV smart substations under a provincial power company as test objects, and conducted a one-year management optimization test on the main transformers. Before the start of the experiment, the type of new main transformer to be connected to the system was first confirmed, which was a 220kV / 110kV three-phase double-winding on-load voltage-changing self-cooling transformer. Based on this type, the complete operating data of 20 retired main transformers of the same type were found from the power system ledger.

[0108] Next, the life cycle path of each retired main transformer was constructed. By analyzing the work log, key information such as installation time, inspection time, maintenance record and retirement time was extracted. Taking one of the retired main transformers as an example, its life cycle path includes 1 installation node, 48 inspection nodes and 1 retirement node, which are connected in sequence through 47 maintenance links. Detailed maintenance plans are stored on each maintenance link, including specific contents such as oil chromatography analysis, insulation resistance test, and partial discharge detection.

[0109] After constructing the life cycle paths of all retired main transformers, 100 new equipment management paths were constructed by random selection. Each path contains a combination of maintenance experience of different retired equipment. In order to evaluate the maintenance effect, the difficulty of the fault type was quantified: general faults were assigned a value of 1, more serious faults were assigned a value of 3, and severe faults were assigned a value of 5. At the same time, the number of faults and the cumulative downtime (hours) on each maintenance link were recorded.

[0110] Subsequently, the improved gray wolf optimization algorithm was applied to optimize these 100 management paths. The fitness function of the algorithm was set as: f = 1 / (0.4*fault type difficulty + 0.3*fault number + 0.3*downtime). The gray wolf population size was initialized to 30, and the maximum number of iterations was set to 1000. Through iterative calculation, the optimal device management path with the highest fitness value was finally obtained.

[0111] Based on the optimal equipment management path, a maintenance plan for the new main transformer was developed. The plan includes: oil chromatography analysis once a month, insulation resistance test once a quarter, partial discharge test once every six months, and other test items dynamically adjusted according to the operating conditions. At the same time, a fault early warning system based on big data analysis was established. When the test data approaches the preset threshold, the system will automatically issue an early warning message.

[0112] In order to verify the effect of the new management method, 10 substations were randomly divided into two groups, 5 in each group. Group A adopted the traditional fixed-cycle maintenance plan, and Group B adopted the intelligent management method of the present invention. The experiment lasted for one year, and the operating conditions, fault conditions and maintenance costs of the main transformers of the two groups of substations were recorded.

[0113] The test results are shown in the following table:

[0114] index Group A (traditional method) Group B (Intelligent Methods) Improvement Average number of failures (times / year) 3.8 1.6 57.9％ Average downtime (hours / year) 18.5 6.2 66.5％ Average maintenance cost (10,000 yuan / year) 42.3 28.7 32.2％ Equipment utilization 91.2％ 97.8％ 7.2％ Preventive maintenance ratio 65％ 89％ 36.9％

[0115] By comparing the test results of Group A and Group B, it can be clearly seen that the intelligent equipment management method proposed in the present invention is significantly superior to the traditional fixed-cycle maintenance method in many aspects.

[0116] First, in terms of the average number of failures, Group B reduced the number by 57.9% compared to Group A, from 3.8 to 1.6 per year. This significant improvement is mainly due to two innovations in the method of the present invention: first, by constructing the life cycle path of retired equipment, the maintenance experience contained in historical data is fully utilized; second, the maintenance plan is intelligently optimized using the Gray Wolf Optimization Algorithm, making maintenance activities more accurate and effective. This data-driven approach can better predict potential failures, so that timely preventive measures can be taken.

[0117] Secondly, the average downtime was reduced even more significantly, with Group B reducing it by 66.5% compared to Group A, from 18.5 hours per year to 6.2 hours. This improvement not only reflects the reduction in the number of failures, but more importantly, it reflects the improvement in the efficiency of fault handling. Through in-depth analysis and optimization of historical maintenance records, the new management method can diagnose the cause of the failure more quickly and accurately, and take targeted solutions, which greatly shortens the handling time of each failure.

[0118] In terms of maintenance costs, Group B reduced costs by 32.2% compared to Group A, saving RMB 136,000 per main transformer per year. This cost saving comes from the reduction in the number of failures and downtime, as well as a more accurate maintenance strategy. The method of the present invention analyzes historical data through intelligent algorithms, avoids unnecessary over-maintenance, and can detect potential problems in a timely manner, thus achieving "maintenance on demand" and effectively balancing maintenance costs and equipment reliability.

[0119] The improvement of equipment utilization is also an important indicator. The equipment utilization of Group B reached 97.8%, 7.2 percentage points higher than that of Group A. This means that after adopting the new method, the main transformer can operate more continuously and stably, reducing planned and unplanned downtime, and providing more reliable guarantee for the safe and stable operation of the power grid.

[0120] Finally, the substantial increase in the proportion of preventive maintenance (from 65% to 89%, an increase of 36.9%) reflects the fundamental change in the maintenance strategy of the method of the present invention. Through the intelligent analysis of a large amount of historical data, the system can more accurately predict potential failures, thereby taking proactive preventive measures. This shift from "passive repair" to "active prevention" not only improves the reliability of equipment, but also greatly reduces the impact of sudden failures on the operation of the power grid. In summary, the intelligent equipment management method proposed in the present invention has shown significant advantages in practical applications. It has not only achieved significant improvements in various key indicators, but more importantly, it has achieved an innovative transformation in the management mode of power equipment. By combining advanced data analysis technology with rich historical experience, this method has opened up a new path for the intelligent and efficient operation and maintenance of power systems, and is expected to be promoted and applied on a larger scale, making important contributions to the intelligent transformation of the power industry.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A digital intelligent equipment management method for a new power system, characterized by: include, Confirm the type of new equipment to be connected to the power system, and query the retired equipment of the same type in the power system according to the type of new equipment; Constructing a life cycle path for each retired device, the life cycle path including an installation node, a number of inspection nodes and a retirement node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link; Randomly select nodes in the life cycle path to construct a new device management path in order, and use the gray wolf optimization algorithm to find the optimal device management path; Maintain new equipment according to the maintenance plan in the optimal equipment management path.

2. A digital intelligent equipment management method for a new power system as claimed in claim 1, characterized in that: Confirming the type of new equipment to be connected to the power system and querying the retired equipment of the same type in the power system according to the type of the new equipment also includes the following steps: receiving an access request from a new device, where the access request includes the type of the new device; According to the type of new equipment, search for old equipment of the same type from the power system ledger; Obtain retired equipment from the maintenance manuals of older equipment; Check the service life of old equipment and the average service life of equipment of the same type as the old equipment; The old equipment whose difference between the average equipment life and the service life of the old equipment is less than a preset threshold is output as retired equipment.

3. A digital intelligent equipment management method for a new power system as claimed in claim 2, characterized in that: Constructing a life cycle path for each retired device, the life cycle path including an installation node, a plurality of inspection nodes and a retired node connected in sequence through a maintenance link and a maintenance plan stored on the maintenance link also includes the following steps: Obtain work logs of retired equipment from the power system, including installation time, inspection time, maintenance records, and retirement time; According to the installation time, the inspection time and the retirement time, the installation node, the inspection node and the retirement node are obtained respectively; Connect the installation node, several inspection nodes, and the decommissioned node through the maintenance link in chronological order, and map the maintenance record on the maintenance link; Maintenance records include maintenance plans, number of failures, downtime and type of failure.

4. A digital intelligent equipment management method for a new power system as claimed in claim 3, characterized in that: Randomly selecting nodes in the life cycle path to construct a new device management path in order, and finding the optimal device management path through the gray wolf optimization algorithm also includes the following steps: Randomly select an installation node of a retired device; Randomly select check nodes in chronological order until the last selected node is the retired node; Obtain the maintenance links behind the selected node in chronological order, and connect the selected nodes through the maintenance links to form a new device management path; The Grey Wolf Optimization Algorithm is used to optimize the new equipment management path based on the best maintenance effect, and the optimal equipment management path is obtained.

5. A digital intelligent equipment management method for a new power system as claimed in claim 4, characterized in that: The Grey Wolf optimization algorithm is used to optimize the new equipment management path based on the best maintenance effect. The best maintenance effect is evaluated by the difficulty of the fault type and quantified. The quantified fault type difficulty, number of faults and downtime are assigned values ​​respectively, and the assigned data are summed. The larger the sum, the worse the maintenance effect.

6. A digital intelligent equipment management method for a new power system as claimed in claim 5, characterized in that: The Grey Wolf Optimization Algorithm is used to optimize the new equipment management path based on the best maintenance effect, and the optimal equipment management path is obtained, which includes the following steps: Initialize the gray wolf population and the number of iterations; A new equipment management path is randomly selected, and the fitness value of the gray wolf is calculated based on the best maintenance effect; Select another new device management path, calculate the fitness value and compare it with the fitness value calculated last time, and retain the new device management path with the larger fitness value; Perform iterative calculations and output the new device management path with the largest fitness value as the optimal device management path; The number of iterations is the number of new device management paths.

7. A digital intelligent equipment management method for a new power system as claimed in claim 6, characterized in that: The maintenance of new equipment according to the maintenance plan in the optimal equipment management path includes the following steps: Extracting maintenance links in the optimal equipment management path; The maintenance plan stored in the maintenance link is used as the maintenance plan for the new device between nodes.

8. A digital intelligent equipment management system for a new power system, based on a digital intelligent equipment management method for a new power system according to any one of claims 1 to 7, characterized in that: Also includes, A confirmation module confirms the type of new equipment to be connected to the power system and queries the retired equipment of the same type in the power system according to the type of the new equipment; A construction module constructs a life cycle path of each retired device, where the life cycle path includes an installation node, a plurality of inspection nodes and a retirement node connected in sequence through a maintenance link, and a maintenance plan stored on the maintenance link; The management module randomly selects nodes in the life cycle path to construct a new device management path in order, and obtains the optimal device management path through the gray wolf optimization algorithm; The maintenance module performs maintenance on new equipment according to the maintenance plan in the optimal equipment management path.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of any one of claims 1 to 7 of a method for digital equipment management for a new power system.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of any one of claims 1 to 7 of a method for digital equipment management for a new power system are implemented.

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