Wind power operation and maintenance system based on intelligent robot

Abstract

The invention belongs to the field of wind power operation and maintenance, relates to an intelligent robot technology, is used for solving the problems that an existing wind power operation and maintenance system is difficult to guarantee the safety and efficiency of operation and maintenance work and cannot give an early warning on abnormal power generation conditions, and particularly relates to a wind power operation and maintenance method and system based on an intelligent robot. The wind power operation and maintenance platform is in communication connection with an inspection planning module, an intelligent acquisition module, an equipment analysis module, an environment monitoring module, a power generation analysis module, a power generation early warning module and a database; in the monitoring time period, the intelligent robot performs periodic inspection on the operation and maintenance equipment, evaluates the operation state of the equipment, evaluates the power generation condition in combination with the actual periodic power generation power and the ideal periodic power generation range based on the environment, and performs early warning on abnormal power generation in the next monitoring time period; according to the method, the safety and efficiency of operation and maintenance work are improved, and the accuracy of judging the equipment state by the intelligent robot and the timeliness of early warning of abnormal power generation are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of wind power operation and maintenance, relates to intelligent robot technology, and specifically is a wind power operation and maintenance system based on intelligent robots. Background Art

[0002] Wind is one of the pollution-free energy sources. It is very suitable and has great potential for using wind power generation in accordance with local conditions in coastal islands, grassland pastoral areas, mountainous areas and plateau areas that are short of water, fuel and have inconvenient transportation. The development of wind power operation and maintenance industry is also moving towards a more intelligent and efficient direction with the advancement of technology.

[0003] The existing wind power operation and maintenance system is often maintained manually by the operation and maintenance manufacturer. Due to the wind power production environment, it is difficult to ensure the safety and efficiency of the operation and maintenance work; and the existing technology cannot combine the equipment status and power generation data to warn of abnormal power generation conditions.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a wind power operation and maintenance system based on an intelligent robot, which is used to solve the problem that the existing wind power operation and maintenance system is difficult to ensure the safety and efficiency of the operation and maintenance work, and is unable to provide early warning of abnormal power generation conditions in combination with equipment status and power generation data; The technical problem to be solved by the present invention is: how to provide a wind power operation and maintenance system based on intelligent robots that can ensure the safety and efficiency of operation and maintenance work and provide early warning of abnormal power generation conditions.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A wind power operation and maintenance system based on an intelligent robot, comprising a wind power operation and maintenance platform, wherein the wind power operation and maintenance platform is communicatively connected with an inspection planning module, an intelligent acquisition module, an equipment analysis module, an environmental monitoring module, a power generation analysis module, a power generation early warning module and a database; The inspection planning module is used to generate an inspection planning route for the operation and maintenance equipment si: the wind turbine generator set of the wind farm is marked as the operation and maintenance unit s, the wind turbine generator component is marked as the operation and maintenance component i, and the operation and maintenance component i in the operation and maintenance unit s is marked as the operation and maintenance equipment si; wherein s=1, 2, ..., n, i=1, 2, ..., m; the operation and maintenance personnel set fixed checkpoints for the operation and maintenance equipment si, and the intelligent robot plans the inspection route according to the fixed checkpoints; The intelligent acquisition module is used to periodically collect the parameters of the operation and maintenance equipment si: the daily power generation period of the wind farm is marked as the monitoring period, and an inspection cycle is generated. The intelligent robot obtains the temperature monitoring value WDsi and the vibration monitoring value ZDsi of the operation and maintenance equipment si in turn through the temperature sensor and the vibration sensor; The equipment analysis module is used to analyze the operating status of the operation and maintenance equipment si: numerically calculate the temperature monitoring value WDsi and the vibration monitoring value ZDsi to obtain the state coefficient ZTsi of the operation and maintenance equipment si, and judge the operating status of the operation and maintenance equipment si according to the state coefficient ZTsi; The environmental monitoring module is used to monitor the environment of the wind farm: by acquiring the wind speed FS and the temperature QW of the wind farm in real time, the average environmental coefficient HP of each inspection cycle is obtained; The power generation analysis module is used to monitor and analyze the power generation status of the operation and maintenance equipment s: the ideal cycle power generation LPs of the operation and maintenance equipment s during normal operation is obtained through historical data, and compared with the actual power generation power SPs of the inspection cycle, and a comprehensive power generation analysis is performed in combination with the equipment operation status; The power generation warning module is used to warn of abnormal power generation during the monitoring period: when the operation and maintenance unit s generates power abnormally and the operation and maintenance equipment si operates abnormally, the number of abnormalities of the operation and maintenance equipment si is marked as the equipment abnormality value YCsi, and the core influencing equipment si is judged by comparing with the threshold, and an early warning is issued for the power generation abnormality in the next monitoring period.

[0007] Furthermore, the operation and maintenance component i in the operation and maintenance unit s includes a wind turbine, a rotating shaft, a gear box, a generator, a wind power converter, blades and a tower.

[0008] Furthermore, at the beginning of the monitoring period, the intelligent robot in each operation and maintenance unit s performs periodic inspections on fixed checkpoints, and the periodic inspections end accordingly at the end of the monitoring period. The duration of the inspection cycle is L1 minutes. In each inspection cycle, according to the planned inspection route, the intelligent robot obtains the temperature value and vibration frequency of the operation and maintenance equipment si at the fixed checkpoints through temperature sensors and vibration sensors in turn.

[0009] Furthermore, the temperature monitoring value WDsi and the vibration monitoring value ZDsi are numerically calculated through the formula ZTsi=k1*WDsi+k2*ZDsi to obtain the state coefficient ZTsi of the operation and maintenance equipment si, wherein k1 and k2 are both proportional coefficients, and k2>k1>1; the state coefficient ZTsi of the operation and maintenance equipment si is compared with the preset state threshold ZTmax: if the state coefficient ZTsi is less than the state threshold ZTmax, it is judged that the operating state of the operation and maintenance equipment si is normal and no processing is required; if the state coefficient ZTsi is greater than or equal to the state threshold ZTmax, it is judged that the operating state of the operation and maintenance equipment si is abnormal, and an equipment abnormality signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel.

[0010] Furthermore, the wind speed FS and the air temperature QW are numerically calculated using the formula HJ=α1*FS+α2*QW to obtain the external environment coefficient HJ of the wind farm, wherein α1 and α2 are both proportional coefficients, and α1>α2>1; at the end of the inspection cycle, the average value of the external environment coefficient HJ during the inspection cycle is marked as the environmental average coefficient HP.

[0011] Furthermore, the ideal cycle power generation power LPs* allowable fluctuation coefficient (1±a%) is used to obtain the ideal cycle power generation range, wherein the ideal cycle power generation power LPs* (1+a%) is used to obtain the ideal cycle power generation high value LPsmax of the operation and maintenance unit s, and the ideal cycle power generation power LPs* (1-a%) is used to obtain the ideal cycle power generation low value LPsmin of the operation and maintenance unit s, where a is a natural constant set by the management personnel; at the end of each inspection cycle, the actual cycle power generation power SPs of the operation and maintenance unit s is compared with the ideal cycle power generation range: when the actual cycle power generation power SPs is greater than or equal to the ideal cycle power generation low value LPsmin and less than or equal to the ideal cycle power generation high value LPsmax, it is judged that the power generation of the operation and maintenance unit s is normal within the cycle; When the operation and maintenance unit s generates electricity normally, check the operating status of the operation and maintenance equipment si within the inspection cycle: if the operation and maintenance equipment si operates normally, no processing is performed; if the operation and maintenance equipment si operates abnormally, it is determined that the abnormality of the operation and maintenance equipment si has no effect on power generation or the state threshold ZTmax of the operation and maintenance equipment si is too small, and a manual re-inspection signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel, and the state threshold ZTmax is adjusted according to the result of the manual re-inspection to see if there is any equipment problem; When the actual cycle power generation SPs is less than the ideal cycle power generation low value LPsmin or greater than the ideal cycle power generation high value LPsmax, it is judged that the power generation of the operating and maintenance unit s during the inspection period is abnormal, and a power generation abnormality alarm signal is generated and sent to the mobile terminal of the operation and maintenance personnel; When the operation and maintenance unit s generates electricity abnormally, check the operating status of the operation and maintenance equipment si within the inspection cycle: if the operation and maintenance equipment si operates abnormally, it is judged that the abnormality of the operation and maintenance equipment si may have an impact on power generation, and the number of abnormalities of the operation and maintenance equipment si is marked as the equipment abnormality value YCsi, and the equipment abnormality value YCsi is increased by 1; if the operation and maintenance equipment si operates normally, it is judged that the state threshold ZTmax of the operation and maintenance equipment si is too large or the intelligent robot has an equipment problem that cannot be troubleshooted, and a manual re-inspection signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel, and the state threshold ZTmax is adjusted according to the result of the manual re-inspection to see if there is any equipment problem.

[0012] Furthermore, at the end of the monitoring period, the equipment abnormality value YCsi of the operation and maintenance equipment si in the operation and maintenance unit s is compared with the preset equipment abnormality threshold value YCmax: if the equipment abnormality value YCsi is greater than or equal to the equipment abnormality threshold value YCmax, it is judged that the operation abnormality of the operation and maintenance equipment si affects the normal power generation of the operation and maintenance unit s, and the operation and maintenance equipment si is marked as the core influencing equipment si; in the next monitoring period, if the operating status of the core influencing equipment si of the operation and maintenance unit s is abnormal, it is judged that the power generation of the operation and maintenance unit s is abnormal, and a power generation abnormality warning signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel.

[0013] A wind power operation and maintenance method based on an intelligent robot comprises the following steps: Step 1: The inspection planning module obtains the operation and maintenance units and components in the wind farm. The operation and maintenance personnel set fixed checkpoints for the operation and maintenance equipment, and the intelligent robot plans the inspection route based on the fixed checkpoints. Step 2: The intelligent collection module generates an inspection cycle during the monitoring period to periodically collect the temperature value and vibration frequency of the operation and maintenance equipment; Step 3: The equipment analysis module comprehensively calculates the temperature standard value and the vibration standard value of the operation and maintenance equipment to obtain the state coefficient of the operation and maintenance equipment, and compares it with the preset state threshold to determine the operating state of the operation and maintenance equipment; Step 4: The environmental monitoring module monitors the wind speed and temperature of the wind farm in real time through the meteorological platform, and performs comprehensive calculations to obtain the average environmental coefficient of the inspection cycle; Step 5: The power generation analysis module obtains the ideal cycle power generation range corresponding to the environmental average coefficient of the inspection cycle when the operation and maintenance equipment is operating normally through historical data, and compares it with the actual power generation to determine whether the power generation is normal; by combining the operating status of the operation and maintenance equipment, a comprehensive analysis of the operation and maintenance units is performed; Step 6: The power generation warning module compares the equipment abnormality value of the operation and maintenance equipment with the preset equipment abnormality threshold. If the equipment abnormality value is greater than the equipment abnormality threshold, it will be marked as a core influencing device. If the core influencing device is found to be operating abnormally in the next monitoring period, a power generation abnormality warning signal will be generated.

[0014] The present invention has the following beneficial effects: By setting fixed checkpoints for operation and maintenance equipment, the intelligent robot can plan inspection routes according to the fixed checkpoints and conduct periodic inspections on the operation and maintenance equipment, ensuring that the equipment operating status can be periodically evaluated during the working hours of the wind farm. If an abnormal status occurs, the operation and maintenance personnel can take corresponding measures in a timely manner based on the generated abnormal signals, thereby improving the safety and efficiency of the operation and maintenance work; By acquiring real-time external environmental data of the wind farm, we can obtain the ideal cycle power generation range related to the environment, and combine the actual power generation and the operating status of the equipment to conduct a comprehensive analysis of the four situations of power generation and whether the equipment is normal. Then, through manual inspection or adjustment of the status threshold, we can ensure that equipment problems can be solved in time, and improve the accuracy of the intelligent robot's judgment on the equipment status. 3. By counting the number of equipment anomalies of the operation and maintenance equipment when power generation is abnormal and the equipment operating status is abnormal during the monitoring period, it is determined whether the operation and maintenance equipment is the core influencing equipment that affects the power generation of the operation and maintenance unit, thereby ensuring that an early warning of power generation anomalies can be issued for the next monitoring period, thereby improving the timeliness of the intelligent robot's judgment on power generation anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0016] Figure 1 A system block diagram of the present invention as a whole; Figure 2 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, a wind power operation and maintenance system based on intelligent robots includes a wind power operation and maintenance platform, which is communicatively connected to an inspection planning module, an intelligent collection module, an equipment analysis module, an environmental monitoring module, a power generation analysis module, a power generation early warning module and a database.

[0019] The inspection planning module is used to generate the inspection planning route for the operation and maintenance equipment si: the wind turbine generator set in the wind farm is marked as the operation and maintenance unit s, and the wind power generation component in the wind farm is marked as the operation and maintenance component i, then the operation and maintenance component i in the operation and maintenance unit s is marked as the operation and maintenance equipment si; where s=1, 2,…, n, i=1, 2,…, m; the operation and maintenance component i in the operation and maintenance unit s includes a wind turbine, a rotating shaft, a gearbox, a generator, a wind turbine converter, blades, a tower, etc.; the operation and maintenance personnel set fixed checkpoints for the operation and maintenance equipment si according to the inspection plan, and the intelligent robot plans the inspection route according to the fixed checkpoints.

[0020] The intelligent collection module is used to periodically collect the parameters of the operation and maintenance equipment si: the daily power generation period of the wind farm is marked as the monitoring period, and an inspection cycle is generated. At the beginning of the monitoring period, the intelligent robot in each operation and maintenance unit s performs a periodic inspection on the fixed inspection point, and the periodic inspection ends at the end of the monitoring period. The duration of the inspection cycle is L1 minutes; In each inspection cycle, according to the planned inspection route, the intelligent robot obtains the temperature value and vibration frequency of the operation and maintenance equipment si at fixed checkpoints in turn through temperature sensors and vibration sensors, and marks them as the temperature monitoring value WDsi and vibration monitoring value ZDsi of the operation and maintenance equipment si respectively.

[0021] The equipment analysis module is used to analyze the operating status of the operation and maintenance equipment si: the temperature monitoring value WDsi and the vibration monitoring value ZDsi are numerically calculated through the formula ZTsi=k1*WDsi+k2*ZDsi to obtain the state coefficient ZTsi of the operation and maintenance equipment si, where k1 and k2 are both proportional coefficients, and k2>k1>1; the state coefficient ZTsi of the operation and maintenance equipment si is compared with the preset state threshold ZTmax: if the state coefficient ZTsi is less than the state threshold ZTmax, the operating status of the operation and maintenance equipment si is judged to be normal and no processing is required; if the state coefficient ZTsi is greater than or equal to the state threshold ZTmax, the operating status of the operation and maintenance equipment si is judged to be abnormal, and an equipment abnormality signal is generated and sent to the mobile terminal of the operation and maintenance personnel; by setting fixed checkpoints for the operation and maintenance equipment, the intelligent robot can plan the inspection route according to the fixed checkpoints and perform periodic inspections on the operation and maintenance equipment, ensuring that the equipment operating status can be periodically evaluated during the working hours of the wind farm, thereby improving the safety and efficiency of the operation and maintenance work.

[0022] The environmental monitoring module is used to monitor the environment of the wind farm: at the beginning of each inspection cycle, the external environmental data of the wind farm is obtained in real time through the meteorological platform, and the external environmental data include wind speed FS and temperature QW; the wind speed FS and temperature QW are numerically calculated through the formula HJ=α1*FS+α2*QW to obtain the external environmental coefficient HJ of the wind farm, where α1 and α2 are both proportional coefficients, and α1>α2>1; at the end of the inspection cycle, the average value of the external environmental coefficient HJ during the inspection cycle is marked as the environmental average coefficient HP.

[0023] The power generation analysis module is used to monitor and analyze the power generation status of the operation and maintenance equipment s: at the end of each inspection cycle, the actual cycle power generation SPs of the operation and maintenance unit s in the inspection cycle is obtained, and the historical power generation data of the operation and maintenance unit s during normal operation is retrieved through the database to obtain the ideal cycle power generation LPs corresponding to the environmental average coefficient HP of the inspection cycle; the ideal cycle power generation LPs* allowable fluctuation coefficient (1±a%) is used to obtain the ideal cycle power generation range, where the ideal cycle power generation LPs* (1+a%) is used to obtain the ideal cycle power generation high value LPsmax of the operation and maintenance unit s, and the ideal cycle power generation LPs* (1-a%) is used to obtain the ideal cycle power generation low value LPsmin of the operation and maintenance unit s; a is a natural constant set by the management personnel; At the end of each inspection cycle, the actual cycle power generation SPs of the operating and maintenance unit s is compared with the ideal cycle power generation range: when the actual cycle power generation SPs is greater than or equal to the ideal cycle power generation low value LPsmin and less than or equal to the ideal cycle power generation high value LPsmax, it is judged that the operating and maintenance unit s generates power normally during this cycle; When the operation and maintenance unit s generates electricity normally, check the operating status of the operation and maintenance equipment si within the inspection cycle: if the operation and maintenance equipment si operates normally, no processing is performed; if the operation and maintenance equipment si operates abnormally, it is determined that the abnormality of the operation and maintenance equipment si has no effect on power generation or the state threshold ZTmax of the operation and maintenance equipment si is too small, and a manual re-inspection signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel, and the state threshold ZTmax is adjusted according to the result of the manual re-inspection to see if there is any equipment problem; When the actual cycle power generation SPs is less than the ideal cycle power generation low value LPsmin or greater than the ideal cycle power generation high value LPsmax, it is judged that the power generation of the operating and maintenance unit s during the inspection period is abnormal, and a power generation abnormality alarm signal is generated and sent to the mobile terminal of the operation and maintenance personnel; When the operation and maintenance unit s generates power abnormally, check the operating status of the operation and maintenance equipment si within the inspection cycle: if the operation and maintenance equipment si operates abnormally, it is judged that the abnormality of the operation and maintenance equipment si may have an impact on power generation, and the number of abnormalities of the operation and maintenance equipment si is marked as the equipment abnormality value YCsi, and the equipment abnormality value YCsi is increased by 1; if the operation and maintenance equipment si operates normally, it is judged that the state threshold ZTmax of the operation and maintenance equipment si is too large or the intelligent robot has an equipment problem that cannot be troubleshooted, and a manual re-inspection signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel, and the state threshold ZTmax is adjusted according to the result of the manual re-inspection to see if there is any equipment problem; through manual inspection and troubleshooting or adjustment of the state threshold, it is ensured that equipment problems can be solved in a timely manner, and the accuracy of the intelligent robot's judgment on the equipment status is also improved.

[0024] The power generation warning module is used to warn of abnormal power generation during the monitoring period: at the end of the monitoring period, the equipment abnormality value YCsi of the operation and maintenance equipment si in the operation and maintenance unit s is compared with the preset equipment abnormality threshold YCmax: if the equipment abnormality value YCsi is greater than or equal to the equipment abnormality threshold YCmax, it is judged that the operation abnormality of the operation and maintenance equipment si affects the normal power generation of the operation and maintenance unit s, and the operation and maintenance equipment si is marked as the core influencing equipment si; in the next monitoring period, if the operating state of the core influencing equipment si of the operation and maintenance unit s is abnormal, it is judged that the power generation of the operation and maintenance unit s is abnormal, and a power generation abnormality warning signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel.

[0025] like Figure 2 As shown, a wind power operation and maintenance method based on an intelligent robot includes the following steps: Step 1: The inspection planning module obtains the operation and maintenance units in the wind farm and the operation and maintenance components in the operation and maintenance units. The operation and maintenance personnel set fixed inspection points for the operation and maintenance equipment according to the inspection plan, and the intelligent robot plans the inspection route according to the fixed inspection points; Step 2: The intelligent collection module generates an inspection cycle during the monitoring period to periodically collect the temperature value and vibration frequency of the operation and maintenance equipment; Step 3: The equipment analysis module comprehensively calculates the temperature standard value and the vibration standard value of the operation and maintenance equipment to obtain the state coefficient of the operation and maintenance equipment, and compares the state coefficient of the equipment with the preset state threshold to determine the operating state of the operation and maintenance equipment; Step 4: The environmental monitoring module monitors the wind speed and temperature of the wind farm in real time through the meteorological platform, and performs comprehensive calculations to obtain the average environmental coefficient of the inspection cycle; Step 5: The power generation analysis module obtains the ideal cycle power generation range corresponding to the environmental average coefficient of the inspection cycle when the operation and maintenance equipment is operating normally through historical data, and compares it with the actual power generation power of the inspection cycle. If the actual power generation power is within the range, it is judged that the power generation is normal. If the power generation is normal but the operation and maintenance equipment is in an abnormal state, it is judged that the abnormality of the operation and maintenance equipment has no effect on the power generation or the state threshold of the operation and maintenance equipment is too small, and a manual re-inspection signal is generated and sent to the mobile terminal of the operation and maintenance personnel, and the state threshold is adjusted according to the result of the manual re-inspection of whether there is any equipment problem; if the actual power generation power is not within the range, it is judged that the power generation is abnormal. If the power generation is abnormal and the state of the operation and maintenance equipment is abnormal, it is judged that the abnormality of the operation and maintenance equipment may have an impact on the power generation, and the component abnormality value is increased by 1. If the power generation is abnormal and the state of the operation and maintenance equipment is normal, it is judged that the state threshold of the operation and maintenance equipment is too large or the intelligent robot has an equipment problem that cannot be checked; Step 6: The power generation warning module compares the equipment abnormality value of the operation and maintenance equipment with the preset equipment abnormality threshold. If the equipment abnormality value is greater than the equipment abnormality threshold, it is determined that the operation abnormality of the operation and maintenance equipment affects normal power generation, and it is marked as a core influencing equipment. If the core influencing equipment is found to be operating abnormally in the next monitoring period, a power generation abnormality warning signal is generated; A wind power operation and maintenance system based on intelligent robots. When working, the operation and maintenance personnel set fixed checkpoints for the operation and maintenance equipment according to the inspection plan. The intelligent robot plans the inspection route according to the fixed checkpoints, and periodically collects the temperature value and vibration frequency of the operation and maintenance equipment to obtain the state coefficient of the operation and maintenance equipment, and compares it with the state threshold to judge the operation state of the operation and maintenance equipment; the wind speed and temperature of the wind farm are monitored in real time through the meteorological platform to obtain the environmental average coefficient of the inspection period, and then the ideal cycle power generation range is obtained from the historical data, and compared with the actual cycle power generation power to judge the power generation situation of the unit, generate a signal for manual review or adjust the state threshold, and generate core influencing equipment. If the core influencing equipment is found to be operating abnormally in the next monitoring period, an early warning of the power generation abnormality is issued.

[0026] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0027] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; such as: formula ZTsi=k1*WDsi+k2*ZDsi; technicians in this field collect multiple groups of sample data and set corresponding state coefficients for each group of sample data; substitute the set state coefficients and the collected sample data into the formula, any two formulas constitute a set of two-variable linear equations, screen the calculated coefficients and take the average, and obtain the values ​​of k1 and k2 as 3.24 and 2.18 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding state coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the state coefficient is proportional to the value of the temperature monitoring value.

[0028] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0029] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A wind power operation and maintenance system based on intelligent robots, characterized in that: It includes a wind power operation and maintenance platform, which is communicatively connected with an inspection planning module, an intelligent acquisition module, an equipment analysis module, an environmental monitoring module, a power generation analysis module, a power generation early warning module and a database; The inspection planning module is used to generate an inspection planning route for the operation and maintenance equipment si: the wind turbine generator set of the wind farm is marked as the operation and maintenance unit s, the wind turbine generator component is marked as the operation and maintenance component i, and the operation and maintenance component i in the operation and maintenance unit s is marked as the operation and maintenance equipment si; wherein s=1, 2, ..., n, i=1, 2, ..., m; the operation and maintenance personnel set fixed checkpoints for the operation and maintenance equipment si, and the intelligent robot plans the inspection route according to the fixed checkpoints; The intelligent acquisition module is used to periodically collect the parameters of the operation and maintenance equipment si: the daily power generation period of the wind farm is marked as the monitoring period, and an inspection cycle is generated. The intelligent robot obtains the temperature monitoring value WDsi and the vibration monitoring value ZDsi of the operation and maintenance equipment si in turn through the temperature sensor and the vibration sensor; The equipment analysis module is used to analyze the operating status of the operation and maintenance equipment si: numerically calculate the temperature monitoring value WDsi and the vibration monitoring value ZDsi to obtain the state coefficient ZTsi of the operation and maintenance equipment si, and judge the operating status of the operation and maintenance equipment si according to the state coefficient ZTsi; The environmental monitoring module is used to monitor the environment of the wind farm: by acquiring the wind speed FS and the temperature QW of the wind farm in real time, the average environmental coefficient HP of each inspection cycle is obtained; The power generation analysis module is used to monitor and analyze the power generation status of the operation and maintenance equipment s: the ideal cycle power generation LPs of the operation and maintenance equipment s during normal operation is obtained through historical data, and compared with the actual power generation power SPs of the inspection cycle, and a comprehensive power generation analysis is performed in combination with the equipment operation status; The power generation warning module is used to warn of abnormal power generation during the monitoring period: when the operation and maintenance unit s generates power abnormally and the operation and maintenance equipment si operates abnormally, the number of abnormalities of the operation and maintenance equipment si is marked as the equipment abnormality value YCsi, and the core influencing equipment si is judged by comparing with the threshold, and an early warning is issued for the power generation abnormality in the next monitoring period.

2. The wind power operation and maintenance system based on intelligent robots according to claim 1 is characterized in that: The operation and maintenance components i in the operation and maintenance unit s include a wind turbine, a rotating shaft, a gearbox, a generator, a wind turbine converter, blades and a tower.

3. The wind power operation and maintenance system based on intelligent robots according to claim 2 is characterized in that: At the beginning of the monitoring period, the intelligent robot in each operation and maintenance unit s performs periodic inspections on fixed checkpoints, and the periodic inspection ends accordingly at the end of the monitoring period. The duration of the inspection cycle is L1 minutes. In each inspection cycle, according to the planned inspection route, the intelligent robot obtains the temperature value and vibration frequency of the operation and maintenance equipment si at the fixed checkpoints in turn through the temperature sensor and vibration sensor.

4. The wind power operation and maintenance system based on intelligent robots according to claim 3 is characterized in that: The temperature monitoring value WDsi and the vibration monitoring value ZDsi are numerically calculated using the formula ZTsi=k1*WDsi+k2*ZDsi to obtain the state coefficient ZTsi of the operation and maintenance equipment si, wherein k1 and k2 are both proportional coefficients, and k2>k1>1; the state coefficient ZTsi of the operation and maintenance equipment si is compared with the preset state threshold ZTmax: if the state coefficient ZTsi is less than the state threshold ZTmax, it is judged that the operating state of the operation and maintenance equipment si is normal and no processing is required; if the state coefficient ZTsi is greater than or equal to the state threshold ZTmax, it is judged that the operating state of the operation and maintenance equipment si is abnormal, and an equipment abnormality signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel.

5. The wind power operation and maintenance system based on intelligent robots according to claim 4 is characterized in that: The external environment coefficient HJ of the wind farm is obtained by numerically calculating the wind speed FS and the air temperature QW through the formula HJ=α1*FS+α2*QW, where α1 and α2 are both proportional coefficients, and α1>α2>1; at the end of the inspection cycle, the average value of the external environment coefficient HJ during the inspection cycle is marked as the environmental average coefficient HP.

6. The wind power operation and maintenance system based on intelligent robots according to claim 5, characterized in that: The ideal cycle power generation power LPs* allowable fluctuation coefficient (1±a%) is used to obtain the ideal cycle power generation range, where the ideal cycle power generation power LPs* (1+a%) is used to obtain the ideal cycle power generation high value LPsmax of the operation and maintenance unit s, and the ideal cycle power generation power LPs* (1-a%) is used to obtain the ideal cycle power generation low value LPsmin of the operation and maintenance unit s, where a is a natural constant set by the management personnel; at the end of each inspection cycle, the actual cycle power generation power SPs of the operation and maintenance unit s is compared with the ideal cycle power generation range: when the actual cycle power generation power SPs is greater than or equal to the ideal cycle power generation low value LPsmin and less than or equal to the ideal cycle power generation high value LPsmax, it is judged that the power generation of the operation and maintenance unit s is normal during the cycle; When the operation and maintenance unit s generates electricity normally, check the operating status of the operation and maintenance equipment si within the inspection cycle: if the operation and maintenance equipment si operates normally, no processing is performed; if the operation and maintenance equipment si operates abnormally, it is determined that the abnormality of the operation and maintenance equipment si has no effect on power generation or the state threshold ZTmax of the operation and maintenance equipment si is too small, and a manual re-inspection signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel, and the state threshold ZTmax is adjusted according to the result of the manual re-inspection to see if there is any equipment problem; When the actual cycle power generation SPs is less than the ideal cycle power generation low value LPsmin or greater than the ideal cycle power generation high value LPsmax, it is judged that the power generation of the operating and maintenance unit s during the inspection period is abnormal, and a power generation abnormality alarm signal is generated and sent to the mobile terminal of the operation and maintenance personnel; When the operation and maintenance unit s generates electricity abnormally, check the operating status of the operation and maintenance equipment si within the inspection cycle: if the operation and maintenance equipment si operates abnormally, it is judged that the abnormality of the operation and maintenance equipment si may have an impact on power generation, and the number of abnormalities of the operation and maintenance equipment si is marked as the equipment abnormality value YCsi, and the equipment abnormality value YCsi is increased by 1; if the operation and maintenance equipment si operates normally, it is judged that the state threshold ZTmax of the operation and maintenance equipment si is too large or the intelligent robot has an equipment problem that cannot be troubleshooted, and a manual re-inspection signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel, and the state threshold ZTmax is adjusted according to the result of the manual re-inspection to see if there is any equipment problem.

7. The wind power operation and maintenance system based on intelligent robots according to claim 6, characterized in that: At the end of the monitoring period, the equipment abnormality value YCsi of the operation and maintenance equipment si in the operation and maintenance unit s is compared with the preset equipment abnormality threshold YCmax: if the equipment abnormality value YCsi is greater than or equal to the equipment abnormality threshold YCmax, it is judged that the operation abnormality of the operation and maintenance equipment si affects the normal power generation of the operation and maintenance unit s, and the operation and maintenance equipment si is marked as the core influencing equipment si; in the next monitoring period, if the operating status of the core influencing equipment si of the operation and maintenance unit s is abnormal, it is judged that the power generation of the operation and maintenance unit s is abnormal, and a power generation abnormality warning signal is generated and sent to the mobile phone terminal of the operation and maintenance personnel.

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