New energy power generation equipment safety monitoring method and system based on remote operation and maintenance

Abstract

The invention discloses a new energy power generation equipment safety monitoring method and system based on remote operation and maintenance, and relates to the technical field of energy power generation equipment safety monitoring. Therefore, the equipment parameters and the power generation data of the cooperative power generation equipment are monitored, the safety condition of the cooperative power generation equipment is evaluated, the optimal cooperative power generation proportion of the cooperative power generation equipment in each time period is obtained through analysis, power generation is carried out on the cooperative power generation equipment according to the optimal cooperative power generation proportion, the service life of the equipment can be prolonged, and the power generation efficiency is improved. According to the method, the optimal cooperative power generation proportion of each time period is analyzed, the stability of equipment work can be guaranteed, the integration degree between new energy equipment is greatly improved, finally, the relation between each optimal cooperative power generation proportion and each equipment parameter in each time period is analyzed, the safety condition of each cooperative equipment is evaluated, and new energy data and safety monitoring become more efficient and accurate.

Description

Technical Field

[0001] The present application relates to the technical field of safety monitoring of new energy power generation equipment, and specifically to a safety monitoring method and system for new energy power generation equipment based on remote operation and maintenance. Background Art

[0002] Safety monitoring of renewable energy power generation equipment is a core means to ensure the reliable operation of renewable energy power generation equipment, avoid accident risks, and reduce economic losses. Therefore, this application proposes a safety monitoring method and system for renewable energy power generation equipment based on remote operation and maintenance.

[0003] Existing technologies such as the invention application patent with announcement number: CN118232527A discloses a distributed new energy system safety monitoring method and system based on digital twins, including: constructing a joint monitoring device for monitoring the operating status parameters of new energy power generation equipment, energy storage equipment and the power grid; constructing a digital twin system, and establishing a relationship model between the monitoring parameters and the operating status of the energy storage unit based on the monitored historical data based on digital twin technology, and performing real-time evaluation of the operating status of distributed new energy power generation and energy storage units according to the real-time monitored data, and using a pre-built prediction model to predict the operating status of the new energy power generation equipment according to the real-time monitored data; determining the control strategy for the energy storage equipment and the new energy power generation equipment according to the prediction results, and determining whether to trigger an early warning.

[0004] Prior art, such as the invention application patent with announcement number: CN116865447B, discloses a distributed wind and photovoltaic complementary power generation control and management method and system, including a wind turbine, a photovoltaic side, a wind-solar complementary controller connected to the wind turbine and the photovoltaic side, a power communication cabinet connected to the wind-solar complementary controller, and a power generation monitoring, operation and maintenance management system platform connected to the power communication cabinet; the present invention utilizes a wind-solar complementary controller to connect wind and photovoltaic power generation equipment, so that the two power generation systems are independent of each other and complement each other, and can generate electricity safely, reliably and efficiently, which greatly improves the integration of new energy power generation, and integrates the two power generation equipment, as well as the intelligent speed control of the wind turbine. The equipment is compatible with each other, making the power generation and data monitoring of new energy more efficient and accurate; at the same time, combined with the wind-solar complementary controller and wind and photovoltaic power generation equipment, the monitoring, remote control, operation and maintenance and management effects are improved.

[0005] Regarding the above scheme, there are the following technical problems: 1. The current technology mainly evaluates the operating status of new energy power generation equipment through the relationship model between monitoring parameters and operating status. However, the current technology does not take into account that when the environmental data changes, the equipment parameters of the new energy power generation equipment will also change. Frequent fluctuations in equipment parameters will lead to loss of new energy power generation equipment components. When the loss accumulates to a certain extent, it will endanger the safety of the new energy power generation equipment.

[0006] 2. Current technology combines two types of power generation equipment to make power generation more efficient and reliable, but current technology does not take into account the monitoring and analysis of equipment parameters, environmental data, and power generation data when the two types of power generation equipment work together. The current technology's neglect of this level has led to the lack of comprehensiveness and perfection in the monitoring and management of the equipment's collaborative power generation. Summary of the Invention

[0007] The purpose of this application is to provide a new energy power generation equipment safety monitoring method and system based on remote operation and maintenance, which solves the problems existing in the background technology.

[0008] In order to solve the above technical problems, the present application adopts the following technical solutions: In the first aspect, the present application provides a new energy power generation equipment safety monitoring method based on remote operation and maintenance, including: step one, obtaining the equipment parameters and power generation data of each historical collaborative power generation equipment in the target area, and then analyzing the relationship between the output fluctuation and equipment loss of each collaborative power generation equipment, so as to monitor the equipment parameters and power generation data of each collaborative power generation equipment and evaluate the safety status of each collaborative power generation equipment.

[0009] Step 2: Determine the time periods of the target area based on historical meteorological data, and then analyze and obtain the optimal cooperative power generation ratio of each cooperative power generation device in each time period, so that each cooperative power generation device can generate electricity in each time period according to the optimal cooperative power generation ratio.

[0010] Step 3: Analyze the relationship between the optimal co-generation ratio and the parameters of each device in each time period, and then monitor the power generation data of each co-generation device in each time period to evaluate the working efficiency of each co-generation device and determine the safety status of each co-generation device.

[0011] In the second aspect, the present application provides a new energy power generation equipment safety monitoring system based on remote operation and maintenance, including: an equipment loss assessment module: used to obtain the equipment parameters and power generation data of each historical collaborative power generation equipment in the target area, and then analyze the relationship between the output fluctuation and equipment loss of each collaborative power generation equipment, so as to monitor the equipment parameters and power generation data of each collaborative power generation equipment and evaluate the safety status of each collaborative power generation equipment.

[0012] Collaborative power generation analysis module: used to determine the time periods of the target area based on historical meteorological data, and then analyze and obtain the optimal collaborative power generation ratio of each collaborative power generation equipment in each time period, so that each collaborative power generation equipment can generate electricity in each time period according to the optimal collaborative power generation ratio.

[0013] Collaborative power generation evaluation module: used to analyze the relationship between the optimal collaborative power generation ratio and the parameters of each device in each time period, and then monitor the power generation data of each collaborative power generation device in each time period, so as to evaluate the working efficiency of each collaborative power generation device and judge the safety status of each collaborative power generation device.

[0014] The beneficial effects of the present application are: 1. The present application provides a method and system for safety monitoring of new energy power generation equipment based on remote operation and maintenance. By analyzing the relationship between the output fluctuations and equipment losses of each collaborative power generation equipment, the equipment parameters and power generation data of each collaborative power generation equipment are monitored to evaluate the safety of each collaborative power generation equipment. The optimal collaborative power generation ratio of each collaborative power generation equipment in each time period is analyzed and the collaborative power generation equipment is powered by the optimal collaborative power generation ratio. This not only extends the life of the equipment, but also ensures the stability of the equipment operation, thereby greatly improving the integration between new energy equipment. Finally, the relationship between the optimal collaborative power generation ratio and the equipment parameters in each time period is analyzed to further evaluate the safety of each collaborative equipment, making the data and safety monitoring of new energy more efficient and accurate.

[0015] 2. This application analyzes the relationship between the output fluctuations and equipment losses of each collaborative power generation equipment, and then obtains the loss accumulation situation of each power generation equipment. Since each new energy power generation equipment generates electricity based on environmental changes, when the environmental data changes, the equipment parameters of the new energy power generation equipment will also change accordingly. Therefore, when the equipment parameters change frequently or the amount of change is large, it will cause certain losses to the power generation equipment. The continuous accumulation of losses will threaten the safety of the power generation equipment. Therefore, by analyzing the relationship between the output fluctuations and equipment losses of each collaborative power generation equipment, the power generation ratio of each collaborative power generation equipment can be further allocated and optimized, thereby customizing a differentiated fluctuation allocation strategy, which can not only extend the equipment life but also ensure the stability of the equipment operation.

[0016] 3. This application analyzes the relationship between the optimal collaborative power generation ratio and the parameters of each device in each time period, and then determines the safety status of each collaborative power generation device, allowing the equipment to operate at the most suitable power generation in each time period, ensuring the power generation while greatly improving the stability of the power generation equipment. Due to the dynamic coupling relationship between environmental conditions, power generation equipment operating status and equipment parameters, the optimal collaborative power generation ratio of each power generation equipment and the equipment parameters and power generation are monitored to efficiently reflect the safety status of each collaborative power generation device. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 This is a flowchart of the steps for implementing the application method.

[0019] Figure 2 This is a schematic diagram of the system structure connection for this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] Reference Figure 1 As shown, the present application provides a new energy power generation equipment safety monitoring method based on remote operation and maintenance in the first aspect, including the following steps: Step 1, obtain the equipment parameters and power generation data of each historical collaborative power generation equipment in the target area, and then analyze the relationship between the output fluctuation and equipment loss of each collaborative power generation equipment, so as to monitor the equipment parameters and power generation data of each collaborative power generation equipment and evaluate the safety status of each collaborative power generation equipment.

[0022] In a specific example, the device parameters and power generation data of each historical collaborative power generation device in the target area are obtained, wherein each collaborative power generation device includes solar power generation equipment and wind power generation equipment; each device parameter includes common power generation parameters and characteristic power generation parameters; each power generation data includes the power generation of the solar power generation equipment and the power generation of the wind power generation equipment.

[0023] It should be noted that the common power generation parameters represent the parameters shared by all collaborative power generation devices, including power, frequency and voltage collected using multi-parameter integrated intelligent sensors; the characteristic power generation parameters represent the parameters unique to each collaborative power generation device, including the photovoltaic panel illumination area of ​​solar power generation equipment, the blade speed of wind power generation equipment, etc.

[0024] In a specific example, the relationship between the output fluctuation and equipment loss of each collaborative power generation device is further analyzed. The specific analysis process is as follows: each device that is exactly the same as the collaborative power generation device in the target area is recorded as each test device. First, the light intensity and wind speed in the test chamber are set to 0. Then, based on the environmental data of the target area, the changes in light intensity and wind speed in the test chamber are simulated, and the change data of each device parameter of each collaborative power generation device when the light intensity and wind speed change are recorded. At the same time, the loss data of each component of each collaborative power generation device is detected by a detection device.

[0025] The recorded data on changes in light intensity and wind speed, the data on changes in parameters of each collaborative power generation device when light intensity and wind speed change, and the loss data of each component of each collaborative power generation device are preprocessed and inserted into the digital twin model. The model is trained and verified to obtain a loss accumulation prediction model for each collaborative power generation device in the target area.

[0026] It should be noted that fluctuations in light intensity and wind speed will cause local current imbalance in the components of the power generation equipment, thereby generating hot spots. The temperature of the hot spot is positively correlated with the amplitude of light fluctuations. Changes in the hot spot temperature will accelerate the aging of the photovoltaic components of the power generation equipment.

[0027] It should be noted that the loss data of each component of each collaborative power generation equipment is detected by the detection device, for example, the number and length of cracks in the blades of wind power generation equipment are detected by the acoustic sensor, and the temperature and capacitance of the photovoltaic components of the solar power generation equipment are detected by the capacitive sensor.

[0028] It should be noted that preprocessing includes data alignment and spatiotemporal matching, noise filtering and outlier processing, etc., among which data alignment and spatiotemporal matching, noise filtering and outlier processing are all existing technologies and will not be described in detail.

[0029] It should be noted that the digital twin model is an existing model technology and will not be described in detail here.

[0030] In a specific example, the safety of each collaborative power generation device is evaluated by monitoring the equipment parameters and power generation data of each collaborative power generation device. The specific analysis process is as follows: obtaining meteorological change data within a preset period of the target area from the meteorological center, substituting the meteorological change data into the loss accumulation prediction model of each collaborative power generation device, predicting the loss accumulation of each collaborative power generation device within the preset period through the loss accumulation prediction model of each collaborative power generation device, comparing the loss accumulation of each collaborative power generation device within the preset period with the set loss accumulation threshold of each collaborative power generation device, and evaluating whether each collaborative power generation device is safe based on this.

[0031] It should be noted that the preset period is set by the relevant staff and can be 6 hours or 24 hours, and there is no specific restriction here.

[0032] It should be noted that the loss accumulation of each collaborative power generation device within the preset period is compared with the set loss accumulation threshold of each collaborative power generation device, and the safety of each collaborative power generation device is evaluated based on this. For example, when it is predicted that the loss accumulation of a certain collaborative device is greater than or equal to the set loss accumulation threshold of the collaborative device, it indicates that the collaborative device is unsafe, otherwise it indicates that the collaborative device is safe. The loss accumulation threshold is set by the staff. The smaller the loss accumulation threshold, the higher the safety requirements for each collaborative power generation device. No specific restrictions are made here.

[0033] Step 2: Determine the time periods of the target area based on historical meteorological data, and then analyze and obtain the optimal cooperative power generation ratio of each cooperative power generation device in each time period, so that each cooperative power generation device can generate electricity in each time period according to the optimal cooperative power generation ratio.

[0034] In a specific example, the time periods of the target area are determined based on historical meteorological data. The specific analysis process is as follows: the historical meteorological data of the target area is obtained from the meteorological center, and then the average meteorological change data of the target area in the history of one day is calculated, and the average meteorological change data of the target area in the history of one day is compared with the set meteorological data change threshold. The continuous time period in which the meteorological average change data is less than or equal to the set meteorological data change threshold is recorded as a time period, and the time periods within a day are obtained accordingly.

[0035] It should be noted that the average meteorological change data of the target area in a historical day is compared with the set meteorological data change threshold, and the continuous time period in which the average meteorological change data is less than or equal to the set meteorological data change threshold is recorded as a time period, and the time periods within a day are obtained accordingly. For example, the maximum light change from 6 o'clock to 10 o'clock is 1000lx, and the maximum wind speed change is 3m / s, where the set light change threshold is 1000lx and the wind speed change threshold is 2m / s. Since the maximum light change is equal to the set light change threshold, 6 o'clock to 10 o'clock is recorded as a time period, etc. The above is only an example and not the only limitation. The meteorological data change threshold is set by the relevant staff and is not specifically restricted here.

[0036] In a specific example, the optimal cooperative power generation ratio of each cooperative power generation device in each time period is further analyzed. The specific process is as follows: according to the average meteorological change data in each time period, the parameter change data and loss accumulation data of each cooperative power generation device in each time period are obtained.

[0037] The average meteorological change data of each time period and the equipment parameter change data of each collaborative power generation equipment are substituted into the neural network model, and the neural network model is trained and verified to obtain the optimal collaborative power generation ratio prediction model for each collaborative power generation equipment in the target area. The meteorological change data of the current time period obtained from the meteorological center is input into the optimal collaborative power generation ratio prediction model, and the optimal collaborative power generation ratio prediction model is solved with the maximum power generation behavior characteristic value as the constraint condition to obtain the optimal equipment parameters of each collaborative power generation equipment in the current time period, thereby obtaining the optimal collaborative power generation ratio of each collaborative power generation equipment in the current time period.

[0038] It should be noted that, through the calculation formula: Calculate the maximum power generation behavior characteristic value , where A and S represent the total power generation and total accumulated losses of each collaborative power generation equipment after standardization.

[0039] It should be noted that generating electricity in each time period at the optimal co-generation ratio can greatly suppress the output fluctuations of each co-generation equipment and avoid equipment losses caused by frequent fluctuations.

[0040] Step 3: Analyze the relationship between the optimal co-generation ratio and the parameters of each device in each time period, and then monitor the power generation data of each co-generation device in each time period to evaluate the working efficiency of each co-generation device and determine the safety status of each co-generation device.

[0041] In a specific example, the relationship between the optimal synergistic power generation ratios and the parameters of each device in each time period is analyzed. The specific process is as follows: the meteorological change data of the target area in each time period is simulated in the experimental chamber, and each test device is made to generate electricity in each time period according to the optimal synergistic power generation ratio, and the power generation, optimal power generation point ratios and device parameters of each test device in each time period are recorded.

[0042] For the equipment parameters of the same nature of different test equipment in each time period, the power generation of each test equipment, the optimal power generation ratio and the equipment parameters of the same nature are input into the deep learning model. The deep learning model is used to fit the nonlinear mapping between the power generation of each test equipment, the optimal power generation ratio and the equipment parameters of the same nature, and then the relationship model and relationship model expression between the power generation, the optimal power generation ratio and the equipment parameters of the same nature are obtained.

[0043] It should be noted that the relational model expression is: ,in is the total power generation, P1 is the power generation of the solar power generation equipment, ,in The optimal co-generation ratio for solar power generation equipment, is the photovoltaic rated power of the solar power generation equipment, k is the temperature coefficient, , G is the actual light intensity, T is the actual temperature, P2 is the power generation of the wind power generation equipment, ,in is the wind power rating, v, 、 and Respectively represent actual wind speed, cut-in wind speed, cut-out wind speed and rated wind speed, is the pitch angle, The optimal co-generation ratio for wind power generation equipment.

[0044] It should be noted that equipment parameters of the same nature include the direct sunlight area of ​​the photovoltaic panel of the solar generator and the blade speed of the wind power generation equipment.

[0045] In a specific example, the working efficiency of each collaborative power generation device is evaluated, and the specific process is as follows: historical time periods with the same optimal power generation ratio and meteorological change data as the current time period are queried from the data center, the historical power generation of each historical time period is averaged to obtain the standard value of power generation for the current time period, and the total power generation for the current time period obtained by monitoring is compared with the standard value of power generation. If the total power generation for the current time period is less than the standard value of power generation, it indicates that the working efficiency of each collaborative power generation device is low. If the total power generation for the current time period is greater than or equal to the standard value of power generation, it indicates that the working efficiency of each collaborative power generation device is high.

[0046] In a specific example, the safety status of each collaborative power generation device is determined, and the specific process is as follows: monitor the actual power generation ratio, each device parameter and total power generation of each collaborative power generation device in the current time period, and judge whether the actual power generation ratio, each device parameter and total power generation of each collaborative power generation device in the current time period conform to the relational model expression based on a pre-trained relational model. If so, each collaborative power generation device is judged to be safe; if not, each collaborative power generation device is judged to be unsafe.

[0047] Reference Figure 2 As shown, the present application provides a new energy power generation equipment safety monitoring system based on remote operation and maintenance in the second aspect, including the following modules: an equipment loss assessment module: used to obtain the equipment parameters and power generation data of each historical collaborative power generation equipment in the target area, and then analyze the relationship between the output fluctuation and equipment loss of each collaborative power generation equipment, so as to monitor the equipment parameters and power generation data of each collaborative power generation equipment and evaluate the safety status of each collaborative power generation equipment.

[0048] Collaborative power generation analysis module: used to determine the time periods of the target area based on historical meteorological data, and then analyze and obtain the optimal collaborative power generation ratio of each collaborative power generation equipment in each time period, so that each collaborative power generation equipment can generate electricity in each time period according to the optimal collaborative power generation ratio.

[0049] Collaborative power generation evaluation module: used to analyze the relationship between the optimal collaborative power generation ratio and the parameters of each device in each time period, and then monitor the power generation data of each collaborative power generation device in each time period, so as to evaluate the working efficiency of each collaborative power generation device and judge the safety status of each collaborative power generation device.

[0050] The present application provides a method and system for safety monitoring of new energy power generation equipment based on remote operation and maintenance. By analyzing the relationship between the output fluctuations and equipment losses of each collaborative power generation equipment, the equipment parameters and power generation data of each collaborative power generation equipment are monitored to evaluate the safety of each collaborative power generation equipment. The optimal collaborative power generation ratio of each collaborative power generation equipment in each time period is obtained by analysis. Each collaborative power generation equipment generates power according to the optimal collaborative power generation ratio, which not only extends the life of the equipment, but also ensures the stability of the equipment operation, greatly improving the integration between new energy equipment. Finally, the relationship between the optimal collaborative power generation ratio and the equipment parameters in each time period is analyzed to further evaluate the safety of each collaborative equipment, making the data and safety monitoring of new energy more efficient and accurate.

[0051] The above content is merely an example and explanation of the concept of the present application. Technicians in this technical field may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they should all fall within the scope of protection of this application.

Claims

1. A safety monitoring method for new energy power generation equipment based on remote operation and maintenance, characterized in that: include: Step 1: Obtain the historical equipment parameters and power generation data of each co-generation device in the target area, and then analyze the relationship between the output fluctuation and equipment loss of each co-generation device. By monitoring the equipment parameters and power generation data of each co-generation device, the safety status of each co-generation device can be evaluated; Step 2: Determine the time periods of the target area based on historical meteorological data, and then analyze and obtain the optimal cooperative power generation ratio of each cooperative power generation device in each time period, so that each cooperative power generation device generates electricity in each time period according to the optimal cooperative power generation ratio; Step 3: Analyze the relationship between the optimal co-generation ratio and the parameters of each device in each time period, and then monitor the power generation data of each co-generation device in each time period to evaluate the working efficiency of each co-generation device and determine the safety status of each co-generation device.

2. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 1, characterized in that: The device parameters and power generation data of each historical collaborative power generation device in the target area are obtained, wherein each collaborative power generation device includes solar power generation equipment and wind power generation equipment; each device parameter includes common power generation parameters and characteristic power generation parameters; each power generation data includes the power generation of the solar power generation device and the power generation of the wind power generation device.

3. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 2, characterized in that: The above analysis further reveals the relationship between the output fluctuation and equipment loss of each cooperative power generation device. The specific analysis process is as follows: Each device that is identical to each co-generation device in the target area is recorded as a test device. First, the light intensity and wind speed in the test chamber are set to 0. Then, based on the environmental data of the target area, the light intensity and wind speed changes in the test chamber are simulated. The change data of each device parameter of each co-generation device when the light intensity and wind speed change are recorded. At the same time, the loss data of each component of each co-generation device is tested using a detection device; The recorded data on changes in light intensity and wind speed, the data on changes in parameters of each collaborative power generation device when light intensity and wind speed change, and the loss data of each component of each collaborative power generation device are preprocessed and inserted into the digital twin model. The model is trained and verified to obtain a loss accumulation prediction model for each collaborative power generation device in the target area.

4. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 3, characterized in that: The safety of each collaborative power generation device is evaluated by monitoring the device parameters and power generation data of each collaborative power generation device. The specific analysis process is as follows: Obtain meteorological change data within a preset period of the target area from the meteorological center, substitute the meteorological change data into the loss accumulation prediction model of each collaborative power generation equipment, predict the loss accumulation of each collaborative power generation equipment within the preset period through the loss accumulation prediction model of each collaborative power generation equipment, compare the loss accumulation of each collaborative power generation equipment within the preset period with the set loss accumulation threshold of each collaborative power generation equipment, and evaluate whether each collaborative power generation equipment is safe based on this.

5. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 4, characterized in that: The specific analysis process for determining the time periods of the target area based on historical meteorological data is as follows: The historical meteorological data of the target area is obtained from the meteorological center, and then the average meteorological change data of the target area in a historical day is calculated. The average meteorological change data of the target area in a historical day is compared with the set meteorological data change threshold. The continuous time period in which the meteorological average change data is less than or equal to the set meteorological data change threshold is recorded as a time period, and each time period within a day is obtained accordingly.

6. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 5, characterized in that: The optimal cooperative power generation ratio of each cooperative power generation device in each time period is obtained by analysis. The specific process is as follows: Obtain parameter change data and loss accumulation data of each co-generation device in each time period based on the average meteorological change data in each time period; The average meteorological change data of each time period and the equipment parameter change data of each collaborative power generation equipment are substituted into the neural network model, and the neural network model is trained and verified to obtain the optimal collaborative power generation ratio prediction model for each collaborative power generation equipment in the target area. The meteorological change data of the current time period obtained from the meteorological center is input into the optimal collaborative power generation ratio prediction model, and the optimal collaborative power generation ratio prediction model is solved with the maximum power generation behavior characteristic value as the constraint condition to obtain the optimal equipment parameters of each collaborative power generation equipment in the current time period, thereby obtaining the optimal collaborative power generation ratio of each collaborative power generation equipment in the current time period.

7. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 6, characterized in that: The specific process of analyzing the relationship between the optimal co-generation ratio and the parameters of each device in each time period is as follows: Simulate the meteorological change data of the target area in each time period in the experimental chamber, make each test device generate electricity in each time period according to the optimal coordinated power generation ratio, and record the power generation of each test device in each time period, the optimal power generation point ratio and each device parameter; For the equipment parameters of the same nature of different test equipment in each time period, the power generation of each test equipment, the optimal power generation ratio and the equipment parameters of the same nature are input into the deep learning model. The deep learning model is used to fit the nonlinear mapping between the power generation of each test equipment, the optimal power generation ratio and the equipment parameters of the same nature, and then the relationship model and relationship model expression between the power generation, the optimal power generation ratio and the equipment parameters of the same nature are obtained.

8. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 7, characterized in that: The working efficiency of each collaborative power generation device is evaluated in the following specific process: The historical time periods with the same optimal power generation ratio and meteorological change data as the current time period are queried from the data center, and the historical power generation of each historical time period is averaged to obtain the standard value of power generation for the current time period. The total power generation of the current time period obtained by monitoring is compared with the standard value of power generation. If the total power generation of the current time period is less than the standard value of power generation, it indicates that the working efficiency of each collaborative power generation equipment is low. If the total power generation of the current time period is greater than or equal to the standard value of power generation, it indicates that the working efficiency of each collaborative power generation equipment is high.

9. A method for safety monitoring of new energy power generation equipment based on remote operation and maintenance according to claim 8, characterized in that: The specific process of determining the safety status of each collaborative power generation device is as follows: Monitor the actual power generation ratio, parameters of each device and total power generation of each collaborative power generation device in the current time period, and judge whether the actual power generation ratio, parameters of each device and total power generation of each collaborative power generation device in the current time period conform to the relational model expression based on the pre-trained relational model. If so, judge that each collaborative power generation device is safe; if not, judge that each collaborative power generation device is unsafe.

10. A remote operation and maintenance-based new energy power generation equipment safety monitoring system that executes the remote operation and maintenance-based new energy power generation equipment safety monitoring method according to any one of claims 1 to 9, characterized in that: include: Equipment loss assessment module: This module is used to obtain the historical equipment parameters and power generation data of each co-generation device in the target area, and then analyze the relationship between the output fluctuation and equipment loss of each co-generation device. By monitoring the equipment parameters and power generation data of each co-generation device, the safety status of each co-generation device can be assessed. Cooperative power generation analysis module: used to determine the time periods of the target area based on historical meteorological data, and then analyze and obtain the optimal cooperative power generation ratio of each cooperative power generation device in each time period, so that each cooperative power generation device can generate electricity in each time period according to the optimal cooperative power generation ratio; Collaborative power generation evaluation module: used to analyze the relationship between the optimal collaborative power generation ratio and the parameters of each device in each time period, and then monitor the power generation data of each collaborative power generation device in each time period, so as to evaluate the working efficiency of each collaborative power generation device and judge the safety status of each collaborative power generation device.

Images