Optimization system and method for intelligent operation and maintenance of new energy generator set

Abstract

The invention discloses an optimization system for intelligent operation and maintenance of a new energy generator set. The optimization system comprises a data acquisition module for acquiring operation state data and external environment data of the generator set through a sensor network; the data processing and storage module is used for carrying out preliminary data processing by utilizing edge computing equipment; the algorithm model module comprises a maintenance optimization model and a dynamic energy efficiency optimization model based on reinforcement learning; the invention discloses an optimization method for intelligent operation and maintenance of a new energy generator set. The optimization method comprises the following steps: S1, data acquisition and preprocessing; s2, constructing a maintenance optimization model based on reinforcement learning; s3, constructing a dynamic energy efficiency optimization model; s4, fault prediction and maintenance strategy optimization; and S5, system integration and feedback execution. According to the method, the operation parameters of the generator set are adjusted in real time, it is guaranteed that the generator set is always in the optimal operation state under different external environment conditions, and the dual purposes of maximum utilization of resources and minimum energy consumption are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of generator set operation and maintenance, and in particular to an optimization system and method for intelligent operation and maintenance of new energy generator sets. Background Art

[0002] With the rapid development of new energy technologies, new energy generators are playing an increasingly important role in the power system. Traditional generator operation and maintenance methods mainly rely on manual monitoring and regular maintenance, and cannot respond to the operating status of the equipment and changes in the external environment in real time. In addition, as the scale of generators continues to expand, traditional operation and maintenance models often lead to waste of resources and inefficiency. Therefore, intelligent, data-driven operation and maintenance management systems have become an important direction for improving the operating efficiency of generators and reducing downtime. In recent years, operation and maintenance optimization systems that combine sensor networks, cloud computing and intelligent algorithms have begun to be widely used.

[0003] Existing operation and maintenance optimization systems mostly rely on static models for data collection and processing, and cannot fully consider the dynamic changes of generator sets in actual operation, resulting in insufficient accuracy in fault prediction and maintenance decisions. Traditional operation and maintenance methods lack the ability to respond to environmental changes in real time and cannot flexibly adjust operation strategies for different generator sets or operating conditions. Therefore, it is necessary to design an optimization system and method for intelligent operation and maintenance of new energy generator sets to solve the above problems. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing optimization system and method for intelligent operation and maintenance of new energy power generation sets, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide an optimization system and method for intelligent operation and maintenance of new energy power generation sets, which is suitable for solving the problems of insufficient accuracy of fault prediction and maintenance decision-making, and inability to flexibly adjust operating strategies for different power generation sets or operating conditions.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: an optimization system for intelligent operation and maintenance of new energy generator sets, comprising: Data acquisition module: collects the operating status data and external environment data of the generator set through the sensor network; Data processing and storage module: Use edge computing devices to perform preliminary data processing and upload key parameters to the cloud database for storage and analysis; Algorithm model module: includes maintenance optimization model and dynamic energy efficiency optimization model based on reinforcement learning; Execution feedback module: adjusts generator set operating parameters in real time and provides maintenance recommendations.

[0008] An optimization method for intelligent operation and maintenance of a new energy generator set, the optimization method is applicable to the above optimization system, and the optimization method includes the following steps: S1: Data acquisition and preprocessing; S2: Construction of maintenance optimization model based on reinforcement learning; S3: Construction of dynamic energy efficiency optimization model; S4: Fault prediction and maintenance strategy optimization; S5: System integration and feedback execution.

[0009] As a preferred solution of the optimization method for intelligent operation and maintenance of a new energy generator set described in the present invention, the construction of the maintenance optimization model is achieved by the following steps: S21: Use the sensor network to collect the operating status data of the generator set and construct a health status evaluation formula. The health status evaluation formula is as follows: ,in, The health status score reflects the overall health of the generator set. is the maintenance benefit function, which represents the improvement effect of maintenance operation on the status of the generator set, is the probability density function of failure, which indicates the possibility of equipment failure at time t'. To maintain the collection weights, used for adjustment The importance of is the failure risk weight, which is used to adjust the impact of failure probability on health status; S22: reinforcement learning model initialization; S23: During reinforcement learning training, a maintenance cost and benefit optimization formula is constructed based on the output of the health status assessment formula; S24: Generate dynamic maintenance recommendations based on the health status of the generator set and output maintenance strategies.

[0010] As a preferred solution of the optimization method of intelligent operation and maintenance of a new energy generator set described in the present invention, the health status standard threshold is set in the output result of the health status evaluation formula. ; like ≤ , indicating that the health status of the generator set is poor and it is necessary to enter the maintenance decision-making stage; like , indicating that the generator set is in normal health and does not require maintenance.

[0011] As a preferred solution of the optimization method for intelligent operation and maintenance of a new energy generator set described in the present invention, the maintenance cost and benefit optimization formula is as follows: ,in, For the optimized maintenance cost-benefit ratio, is the direct cost of the ith maintenance operation, The output result of the health status assessment formula is equivalent to the health status score, which represents the health status of the generator set after the i-th maintenance. Delay loss for maintenance operations, is the maintenance cost weight, which is used to adjust the importance of maintenance cost. is the health status influence coefficient, which is used to control the effect of health status on the cost-benefit ratio. is the delay loss weight, which is used to adjust the impact of delay loss.

[0012] As a preferred solution of the optimization method of intelligent operation and maintenance of a new energy generator set described in the present invention, in which: in the output result of the maintenance cost and benefit optimization formula, a threshold is set ; like ≤ When , it means that the current maintenance strategy has low cost and high benefit, so the strategy is executed; like , it means that the current maintenance strategy has high cost or low benefit and needs to be adjusted.

[0013] As a preferred solution of the optimization method for intelligent operation and maintenance of a new energy generator set described in the present invention, the construction of a dynamic energy efficiency optimization model is achieved by the following steps: S31: Real-time monitoring of external environment data through sensors; S32: Construct a dynamic energy efficiency optimization model formula, and substitute the collected external environment data into the dynamic energy efficiency optimization model formula; S33: Dynamic adjustment of operating parameters; S34: Optimize feedback and continuous improvement.

[0014] As a preferred solution of the optimization method for intelligent operation and maintenance of a new energy generator set described in the present invention, the dynamic energy efficiency optimization model formula is as follows: ,in, represents the optimized energy efficiency value, Indicates the power generation efficiency, Indicates the decay rate of load change, Indicates the load variation range, It represents the total output power per unit time, represents the power loss, is the loss function.

[0015] As a preferred solution of the optimization method of intelligent operation and maintenance of a new energy generator set described in the present invention, a threshold value is set in the output result of the dynamic energy efficiency optimization model formula. ; like ≤ and > , indicating that the current maintenance strategy has controllable cost and high energy efficiency, so this maintenance strategy is implemented; like or , maintenance and energy efficiency optimization strategies need to be re-evaluated.

[0016] As a preferred solution of the optimization method for intelligent operation and maintenance of a new energy generator set described in the present invention, wherein: in said S4, the maintenance strategy selection is optimized according to the failure probability and maintenance cost; If the risk of failure is high and the maintenance cost is low, perform maintenance immediately; If the risk of failure is low and the energy efficiency gain is low, defer maintenance and optimize operating parameters.

[0017] Beneficial effects of the present invention: The present invention adopts a maintenance optimization model based on reinforcement learning and a dynamic energy efficiency optimization model, achieving coordinated optimization of the two. Combined with real-time data analysis, it realizes intelligent, data-driven maintenance decision-making. Through accurate fault prediction and dynamic adjustment, it can effectively avoid excessive or insufficient maintenance of generator sets. The maintenance optimization model can not only predict faults and make maintenance decisions based on the real-time operating status of the generator set, but also provide the necessary operating parameters for the energy efficiency optimization model through a feedback mechanism. The dynamic energy efficiency optimization model ensures that the generator set is always in the optimal operating state under different external environmental conditions by adjusting the operating parameters of the unit in real time. This interaction enables a dynamic balance to be maintained between maintenance decisions and energy efficiency improvement, avoiding the disconnection between maintenance and energy efficiency optimization in the traditional model, and achieving the dual goals of maximizing resource utilization and minimizing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them: Figure 1This is a schematic diagram of the overall framework structure of an optimization system for intelligent operation and maintenance of new energy generator sets proposed by the present invention; Figure 2 This is a schematic diagram of the implementation steps of an optimization method for intelligent operation and maintenance of new energy power generation sets proposed by the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. 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.

[0021] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0022] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0023] Example 1

[0024] Reference Figure 1-Figure 2 , which is an embodiment of the present invention, provides an optimization system for intelligent operation and maintenance of new energy generator sets, including: Data acquisition module: collects generator set operating status data (such as vibration, temperature, current and voltage parameters) and external environment data (such as wind speed and light intensity) through a sensor network; Data processing and storage module: Use edge computing devices to perform preliminary data processing and upload key parameters to the cloud database for storage and analysis; Algorithm model module: includes maintenance optimization model and dynamic energy efficiency optimization model based on reinforcement learning; Execution feedback module: adjusts generator set operating parameters in real time and provides maintenance recommendations.

[0025] An optimization method for intelligent operation and maintenance of a new energy generator set is provided. The optimization method is applicable to the above optimization system and includes the following steps: S1: Data acquisition and preprocessing; Sensors collect the operating status of generator sets and external environmental parameters in real time. Historical maintenance records are stored through blockchain technology to ensure data integrity and traceability, and normalize different data dimensions. S2: Construction of maintenance optimization model based on reinforcement learning; Building a maintenance optimization model is achieved through the following steps: S21: Use the sensor network to collect the operating status data of the generator set and construct a health status evaluation formula. The health status evaluation formula is as follows: ,in, The health status score reflects the overall health of the generator set. is the maintenance benefit function, which represents the improvement effect of maintenance operation on the status of the generator set, is the probability density function of failure, which indicates the possibility of equipment failure at time t'. To maintain the collection weights, used for adjustment The importance of is the failure risk weight, which is used to adjust the impact of failure probability on health status; Set the health status standard threshold in the output of the health status assessment formula ; like ≤ , indicating that the health status of the generator set is poor and it is necessary to enter the maintenance decision-making stage; like , indicating that the generator set is in normal health and does not require maintenance.

[0026] S22: Initialize the reinforcement learning model. Define the state space of reinforcement learning, including H(t) and failure probability, the action space (such as different maintenance decision plans), and the reward function (comprehensive consideration of maintenance costs and benefits). Use the scoring result of the health state H(t) as the initial state to guide the model's policy learning.

[0027] S23: During reinforcement learning training, a maintenance cost and benefit optimization formula is constructed based on the output of the health status assessment formula; The maintenance cost and benefit optimization formula is as follows: ,in, For the optimized maintenance cost-benefit ratio, is the direct cost of the ith maintenance operation, The output result of the health status assessment formula is equivalent to the health status score, which represents the health status of the generator set after the i-th maintenance. Delay loss for maintenance operations, is the maintenance cost weight, which is used to adjust the importance of maintenance cost. is the health status influence coefficient, which is used to control the effect of health status on the cost-benefit ratio. is the delay loss weight, which is used to adjust the impact of delay loss.

[0028] In the output of the maintenance cost and benefit optimization formula, set the threshold ; like ≤ When , it means that the current maintenance strategy has low cost and high benefit, so the strategy is executed; like , it means that the current maintenance strategy has high cost or low benefit and needs to be adjusted.

[0029] S24: Generate dynamic maintenance recommendations based on the health status of the generator sets, prioritize maintenance for equipment in poor health, reduce the additional losses caused by maintenance delays, output maintenance strategies, and feed back optimized model parameters for continuous iteration.

[0030] S3: Construction of dynamic energy efficiency optimization model; The construction of dynamic energy efficiency optimization model is achieved through the following steps: S31: Real-time monitoring of external environment data through sensors; S32: Construct a dynamic energy efficiency optimization model formula, and substitute the collected external environment data into the dynamic energy efficiency optimization model formula; The dynamic energy efficiency optimization model formula is as follows: ,in, represents the optimized energy efficiency value, Indicates the power generation efficiency, Indicates the decay rate of load change, Indicates the load variation range, It represents the total output power per unit time, represents the power loss, is the loss function.

[0031] Set thresholds in the output of the dynamic energy efficiency optimization model formula ; like ≤ and > , indicating that the current maintenance strategy has controllable cost and high energy efficiency, so this maintenance strategy is implemented; like or , maintenance and energy efficiency optimization strategies need to be re-evaluated; S33: Dynamic adjustment of operating parameters; according to > Based on the judgment results, the operating parameters of the generator set (such as speed and power generation) are adjusted. When the energy efficiency is lower than the threshold, the energy efficiency is improved by optimizing the load distribution and operating parameters; S34: Optimizing feedback and continuous improvement; According to the historical records and real-time change trends of power generation efficiency, the weight parameters in the model are adjusted, and the dynamic energy efficiency optimization model formula is continuously optimized to make the energy efficiency optimization model more in line with actual operation needs.

[0032] By introducing a comprehensive assessment of health status, maintenance costs, and energy efficiency, the system can maximize power generation efficiency and reduce maintenance costs while ensuring the safe operation of equipment, thereby achieving intelligent operation and maintenance optimization.

[0033] S4: Fault prediction and maintenance strategy optimization; Optimize maintenance strategy selection based on failure probability and maintenance cost; If the risk of failure is high and the maintenance cost is low, perform maintenance immediately; If the risk of failure is low and the energy efficiency gain is low, defer maintenance and optimize operating parameters.

[0034] S5: System integration and feedback execution; The system interface displays the current status of the generator set, fault prediction results and maintenance recommendations, provides real-time feedback on dynamic energy efficiency optimization, uses actuators to adjust operating parameters in real time, generates maintenance plans and notifies maintenance personnel.

[0035] During use, the present invention adopts a maintenance optimization model based on reinforcement learning and a dynamic energy efficiency optimization model to achieve collaborative optimization of the two. Combined with real-time data analysis, it realizes intelligent, data-driven maintenance decision-making. Through accurate fault prediction and dynamic adjustment, it can effectively avoid excessive or insufficient maintenance of the generator set. The maintenance optimization model can not only predict faults and make maintenance decisions based on the real-time operating status of the generator set, but also provide the necessary operating parameters for the energy efficiency optimization model through a feedback mechanism. The dynamic energy efficiency optimization model ensures that the generator set is always in the optimal operating state under different external environmental conditions by adjusting the operating parameters of the unit in real time. This interaction enables a dynamic balance to be maintained between maintenance decisions and energy efficiency improvement, avoiding the disconnection between maintenance and energy efficiency optimization in the traditional model, and achieving the dual goals of maximizing resource utilization and minimizing energy consumption.

[0036] Example 2

[0037] Referring to Tables 1 to 3, which are the second embodiment of the present invention, this embodiment is different from the first embodiment in that, in order to verify its beneficial effects, experimental comparison data between the present invention and the prior art are provided.

[0038] In order to verify the effectiveness of the "intelligent operation and maintenance optimization system and method for new energy generator sets" of the present invention in practical applications, five wind turbines in a coastal area were selected as experimental objects. The experiment included four steps: data collection, model optimization, actual operation test and comparative analysis.

[0039] The following is the specific implementation process:

[0040] Experimental preparation: Install a sensor network at the test site, including temperature sensors, vibration sensors, voltage and current sensors, and environmental data acquisition equipment (such as wind speed and direction sensors). Use edge computing devices to collect key operating data of the generator set in real time and upload it to the cloud database for storage using a wireless communication module. Configure the calculation logic of the health status assessment formula, maintenance cost and benefit optimization formula, and dynamic energy efficiency optimization model formula, deploy them in the algorithm module, and prepare existing technologies (traditional fixed-cycle maintenance strategy and manual energy efficiency adjustment model) for comparison.

[0041] Experimental implementation process

[0042] Data collection: Run five generator sets and collect operating status data for each device, including vibration values, temperature, load changes, etc. The collection cycle for each device is 5 minutes. Run continuously for 7 days, generating a total of 1,000 sets of data samples;

[0043] Model optimization and execution: The collected data is analyzed in real time using a health status assessment formula to generate a health status score H(t). When H(t) ≤ 80, the maintenance optimization process is triggered. The optimal maintenance timing and strategy are calculated based on the maintenance cost and benefit optimization formula. The dynamic energy efficiency optimization model adjusts the operating parameters of the generator set, such as load distribution and speed, based on environmental data and operating status.

[0044] Comparative operation test: Under the same operating environment, the differences in energy efficiency, maintenance cost, and failure rate between the optimization system of the present invention and the traditional maintenance strategy were compared. Key indicators before and after system operation, such as changes in health score, maintenance cost, and power generation efficiency, were recorded. The obtained data were recorded in separate tables, as shown below.

[0045] Table 1 Comparison of health status scores and maintenance effects

[0046] Table 2 Comparison of dynamic energy efficiency optimization effects

[0047] Table 3 Comparison of maintenance costs and benefits

[0048] With reference to Tables 1, 2, and 3 above, we analyze the following three aspects: Health score and maintenance effect: The optimization system of the present invention can significantly improve the health score of equipment. After optimization, the health score increased by an average of about 8.6 points, and the number of failures decreased by 60%-100%. In particular, generator sets 1 and 5 did not experience any failures during operation, demonstrating high operational reliability. In contrast, with traditional methods, the frequency of equipment failures is high, which can easily lead to unplanned equipment downtime. Dynamic energy efficiency optimization: The dynamic energy efficiency optimization model of the present invention significantly improves the energy efficiency value. The energy efficiency value after optimization is improved by 5.88%-8.33%. Compared with the traditional method, the present invention dynamically adjusts the parameters of the generator set according to the load changes and wind speed conditions, thereby improving the operating efficiency. In particular, the energy efficiency value of generator set 5 is improved from 84 to 91, and the power generation efficiency is improved by 8.33%. Traditional methods cannot achieve similar results due to the lack of real-time adjustment capabilities.

[0049] Maintenance costs and benefits: The optimized system significantly reduced maintenance frequency, with total maintenance costs dropping by an average of 22.00%. This was especially true for Generator Set 5, which achieved a 22.2% maintenance cost savings rate, demonstrating high economic benefits. Traditional maintenance methods involve high maintenance frequency, significantly increased operating and maintenance costs, and made it difficult to avoid failures.

[0050] In summary, this embodiment verifies the innovation and practicality of the present invention through the combination of health status assessment, dynamic energy efficiency optimization and maintenance cost optimization. Compared with traditional technologies, the present invention significantly improves the reliability, energy efficiency and economic benefits of equipment operation, makes up for the defects of the lag of traditional maintenance strategies and insufficient energy efficiency optimization capabilities, and has extremely high practical application value and innovative advantages.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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. An optimization system for intelligent operation and maintenance of new energy generator sets, characterized in that: include: Data acquisition module: collects the operating status data and external environment data of the generator set through the sensor network; Data processing and storage module: Use edge computing devices to perform preliminary data processing and upload key parameters to the cloud database for storage and analysis; Algorithm model module: including maintenance optimization model based on reinforcement learning and dynamic energy efficiency optimization model; Execution feedback module: adjusts generator set operating parameters in real time and provides maintenance suggestions.

2. An optimization method for intelligent operation and maintenance of a new energy generator set, the optimization method being applicable to the optimization system in claim 1 above, characterized in that: And the optimization method includes the following steps: S1: Data collection and preprocessing; S2: Construction of maintenance optimization model based on reinforcement learning; S3: Construction of dynamic energy efficiency optimization model; S4: Fault prediction and maintenance strategy optimization; S5: System integration and feedback execution.

3. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 2 is characterized in that: Building a maintenance optimization model is achieved through the following steps: S21: Use the sensor network to collect the operating status data of the generator set and construct a health status evaluation formula, and the health status evaluation formula is as follows: ,in, The health status score reflects the overall health of the generator set. is the maintenance benefit function, which indicates the improvement effect of maintenance operation on the status of the generator set. is the probability density function of failure, which indicates the possibility of equipment failure at time t'. To maintain the collection weights, used to adjust The importance of is the failure risk weight, which is used to adjust the impact of failure probability on health status; S22: reinforcement learning model initialization; S23: In the reinforcement learning training, the maintenance cost and benefit optimization formula is constructed according to the output results of the health status assessment formula; S24: Generate dynamic maintenance recommendations based on the health status of the generator set and output maintenance strategies.

4. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 3 is characterized in that: Set the health status standard threshold in the output of the health status assessment formula ; like ≤ , indicating that the health status of the generator set is poor and it is necessary to enter the maintenance decision-making stage; like , indicating that the generator set is in normal health and does not require maintenance.

5. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 3 is characterized in that: The maintenance cost and benefit optimization formula is as follows: ,in, For the optimized maintenance cost-benefit ratio, is the direct cost of the ith maintenance operation, It is equivalent to the output result of the health status assessment formula, that is, the health status score, which represents the health status of the generator set after the i-th maintenance. Delayed loss for maintenance operations, is the maintenance cost weight, which is used to adjust the importance of maintenance cost. is the health status influence coefficient, which is used to control the effect of health status on the cost-benefit ratio. is the delay loss weight, which is used to adjust the impact of delay loss.

6. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 5 is characterized in that: In the output of the maintenance cost and benefit optimization formula, set the threshold ; like ≤ When , it means that the current maintenance strategy has low cost and high benefit, so the strategy is implemented; like , it means that the current maintenance strategy has high cost or low benefit, and the maintenance strategy needs to be adjusted.

7. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 2 is characterized in that: The construction of dynamic energy efficiency optimization model is achieved through the following steps: S31: Real-time monitoring of external environment data through sensors; S32: construct a dynamic energy efficiency optimization model formula, and substitute the external environment data collected above into the dynamic energy efficiency optimization model formula; S33: Dynamic adjustment of operating parameters; S34: Optimize feedback and continuous improvement.

8. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 7 is characterized in that: The dynamic energy efficiency optimization model formula is as follows: ,in, represents the optimized energy efficiency value, represents the power generation efficiency, Indicates the decay rate of load change, Indicates the load variation range, It represents the total output power per unit time. represents the power loss, is the loss function.

9. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 8, characterized in that: Set thresholds in the output of the dynamic energy efficiency optimization model formula ; like ≤ and > , indicating that the current maintenance strategy has controllable cost and high energy efficiency, so the maintenance strategy is implemented; like or , maintenance and energy efficiency optimization strategies need to be re-evaluated.

10. The optimization method for intelligent operation and maintenance of a new energy generator set according to claim 2, characterized in that: In said S4, optimizing the maintenance strategy selection according to the failure probability and the maintenance cost; If the risk of failure is high and the maintenance cost is low, perform maintenance immediately; If the risk of failure is low and the energy efficiency gain is low, defer maintenance and optimize operating parameters.

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