An intelligent inspection and early warning system for faults in new energy wind power generation

Abstract

The present invention relates to the field of fault inspection and early warning technology, and in particular to an intelligent inspection and early warning system suitable for new energy wind power generation faults, comprising an inspection and early warning platform, an inspection information unit, an operation deviation unit, a cooling evaluation unit, a verification and analysis unit, and a fault management unit; the present invention analyzes from two points of the state performance of the generator main shaft and the cooling mechanism when it is monitored that the temperature rise of the generator of a certain wind turbine in the wind power generation system is abnormal, and at the same time, on the premise that the main shaft is normal, further analyzes the generator from an overall perspective to reduce the failure risk of the target generator, and performs cooling performance supervision and evaluation operations on the cooling mechanism to determine whether the abnormal temperature rise of the target generator is caused by the abnormal cooling of the target generator, and analyzes by means of information feedback and information fusion, so as to carry out reasonable and targeted management, so as to improve the management efficiency and operation safety of the target generator.

Description

Technical Field

[0001] The present invention relates to the technical field of fault inspection and early warning, and in particular to an intelligent inspection and early warning system for faults of new energy wind power generation. Background Art

[0002] As a clean and renewable energy, wind energy has the prospect of large-scale development and utilization. Governments of many countries in the world have continuously issued policies to encourage the development and utilization of wind energy. With the rapid development of new energy wind power stations, the continuous construction of wind turbines, and the increasing years of operation and maintenance, the safety risks generated by operation are also increasing, and the pressure on on-site operation and maintenance inspections of wind turbines is also increasing;

[0003] A wind turbine is an electric device that converts wind energy into mechanical work, which drives the rotor to rotate and finally outputs alternating current. However, in the prior art, it is impossible to timely analyze the fault factors when the temperature rise of a certain wind turbine in the wind power generation system is abnormal, which affects the accuracy of the temperature rise management of the generator. At the same time, it is impossible to perform reasonable and targeted early warning management, which reduces the management efficiency and operation safety of the generator.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent inspection and early warning system for faults of new energy wind power generation to solve the technical defects mentioned above. When the temperature rise of a generator of a wind turbine in a wind power generation system is detected to be abnormal, the present invention analyzes the status performance of the generator main shaft and the cooling mechanism, that is, the status data of the generator main shaft is monitored, verified, evaluated and analyzed to determine whether the abnormal temperature rise is caused by the abnormal operation of the target generator main shaft, so as to rationally manage the target generator main shaft. At the same time, under the premise that the main shaft is normal, the generator is further analyzed in depth from an overall perspective to reduce the failure risk of the target generator, and the cooling performance supervision and evaluation operation of the cooling mechanism is performed to determine whether the abnormal temperature rise of the target generator is caused by the abnormal cooling of the target generator, and the analysis is performed through information feedback and information fusion to enable accurate early warning management, and reasonable and targeted management is performed according to the text information feedback to improve the management efficiency and operation safety of the target generator.

[0006] The object of the present invention can be achieved by the following technical solutions: an intelligent inspection and early warning system for faults in renewable energy wind power generation, comprising an inspection and early warning platform, an inspection information unit, an operation deviation unit, a cooling evaluation unit, a verification and analysis unit, and a fault management unit;

[0007] The inspection information unit is used to collect infrared characteristic images of the target generator, and perform image feedback supervision evaluation analysis on the infrared characteristic images to obtain operating temperature change instructions and early warning interference signals;

[0008] The operation deviation unit is used to collect the state data of the main shaft of the target generator, the state data including the deviation rotation value and the deviation obstruction value, and perform point monitoring, verification, evaluation and analysis on the state data, send the obtained normal signal to the verification and analysis unit, and send the obtained deviation signal to the fault management unit;

[0009] The verification and analysis unit is used to collect real-time operating data of the target generator, perform interval change safety supervision assessment analysis on the real-time operating data, and send the obtained abnormal signal to the fault management unit;

[0010] After receiving the operating temperature change instruction, the cooling evaluation unit immediately collects the cooling data of the target generator, the cooling data represents the average water flow rate, and performs cooling performance supervision and evaluation operations on the cooling data, and performs discriminant analysis on the alarm management evaluation coefficient R, and sends the obtained first-level warning signal, second-level warning signal and third-level warning signal to the fault management unit.

[0011] Preferably, the image feedback supervision evaluation analysis process of the inspection information unit is as follows:

[0012] The working period corresponding to the generator is obtained and set as the time threshold. The generator is set as the target generator at the same time. The infrared characteristic image of the target generator within the time threshold is obtained. The infrared characteristic image is divided into i sub-region blocks, where i is a natural number greater than zero. The maximum temperature of each sub-region block within the time threshold is obtained, and the maximum temperature is compared and analyzed with the preset maximum temperature threshold value recorded and stored internally:

[0013] If there is no temperature maximum value greater than the preset temperature maximum value threshold, no signal is generated;

[0014] If there is a maximum temperature greater than a preset maximum temperature threshold, a temperature change instruction is generated.

[0015] Preferably, when the inspection information unit generates an operating temperature change instruction:

[0016] The operating status of the early warning mechanism within the time threshold is obtained, and the operating status of the early warning mechanism is discriminated and analyzed. If the early warning mechanism is in working state, the real-time early warning response value of the early warning mechanism within the time threshold is obtained. The real-time early warning response value represents the difference between the time when the operating temperature change instruction is generated and the time when the early warning mechanism executes the early warning operation. The real-time early warning response value is compared and analyzed with the preset real-time early warning response value range stored in the internal input:

[0017] If the real-time warning response value is within the preset real-time warning response value range, no signal is generated;

[0018] If the real-time warning response value deviates from the preset real-time warning response value range, a warning interference signal is generated.

[0019] Preferably, the point monitoring, verification, evaluation and analysis process of the operation deviation unit is as follows:

[0020] S1: mark n monitoring points on the main shaft of the target generator in a circle around the main shaft surface, where n is a natural number greater than zero, obtain the rotational friction value of each monitoring point of the main shaft of the target generator within a time threshold, obtain the maximum value and the minimum value of the rotational friction value, and set the difference between the maximum value and the minimum value as the deviation rotation value;

[0021] S2: obtaining a lubricating oil temperature characteristic change curve of the target generator main shaft within the time threshold, and obtaining a preset lubricating oil temperature characteristic change curve of the target generator main shaft at the same time, obtaining a difference value between the lubricating oil temperature characteristic change curve and the preset lubricating oil temperature characteristic change curve, and setting it as a deviation risk value, and setting the portion of the deviation risk value greater than the preset deviation risk value threshold as a deviation barrier value;

[0022] S3: Compare and analyze the deviation rotation value and the deviation obstruction value with the preset deviation rotation value threshold and the preset deviation obstruction value threshold that are recorded and stored internally:

[0023] If the deviation rotation value is less than the preset deviation rotation value threshold, and the deviation obstruction value is less than the preset deviation obstruction value threshold, a normal signal is generated;

[0024] If the deviation rotation value is greater than or equal to the preset deviation rotation value threshold, or the deviation obstruction value is greater than or equal to the preset deviation obstruction value threshold, then a deviation signal is generated.

[0025] Preferably, the interval change safety supervision assessment analysis process of the verification and analysis unit is as follows:

[0026] T1: The real-time operation data of the overall target generator within the time threshold is obtained, and the real-time operation data includes the real-time power generation, the real-time operation power and the real-time vibration amplitude. The curve corresponding to the real-time operation data within the time threshold is obtained, and it is set as the operation change curve, and then the maximum peak value and the minimum trough value corresponding to each operation change curve are obtained, and then the range interval composed of the maximum peak value and the minimum trough value in each operation change curve is set as the floating change interval;

[0027] T2: Obtain the time period corresponding to the normal temperature of the overall target generator based on the inspection and early warning platform, and set it as the normal time period, obtain the real-time operation data of the target generator in the historical m normal time periods, where m is a natural number greater than zero, and then obtain the maximum value of the maximum peak value and the minimum value of the minimum trough value in each curve corresponding to the real-time operation data of the target generator in the m normal time periods, and set the range interval composed of them as the critical change interval, and compare and analyze the floating change interval with the critical change interval:

[0028] If all floating change intervals are within the critical change interval, no signal is generated;

[0029] If each floating change interval does not fall within the critical change interval, an abnormal signal is generated.

[0030] Preferably, the cooling performance monitoring and evaluation operation process of the cooling evaluation unit is as follows:

[0031] The product value obtained by multiplying the number of times the average water flow velocity in the cooling pipe of the target generator within the time threshold is lower than the preset water flow velocity average value and the value corresponding to the duration is obtained, and it is set as the cooling resistance value. At the same time, the maximum value of the duration corresponding to the fault parameter of the cooling water pump of the target generator within the time threshold exceeds the preset threshold is obtained, and it is set as the risk duration. The fault parameters include the operating voltage average and the operating temperature average. The product value obtained by multiplying the cooling resistance value and the value corresponding to the risk duration is set as the cooling abnormality rate;

[0032] Compare and analyze the cooling abnormality rate with the preset cooling abnormality rate threshold value stored in the internal data entry:

[0033] If the ratio between the cooling abnormality rate and the preset cooling abnormality rate threshold is less than 1, no signal is generated;

[0034] If the ratio between the cooling abnormality rate and the preset cooling abnormality rate threshold is greater than or equal to 1, a feedback instruction is generated.

[0035] Preferably, when the cooling evaluation unit generates a feedback instruction:

[0036] TT1: obtain the real-time water flow velocity value and the average water temperature rise rate in the cooling pipe of the target generator within the time threshold, and label the real-time water flow velocity value and the average water temperature rise rate as SL and SW respectively;

[0037] TT2: The cooling performance evaluation coefficient L is obtained according to the formula, the cooling performance evaluation coefficient curve of the target generator within the time threshold is obtained, and then the change trend value of the cooling performance evaluation coefficient curve is obtained, and the change trend value of the cooling performance evaluation coefficient curve is set as the dynamic risk value DT;

[0038] TT3: Obtain the sub-region block corresponding to the maximum temperature within the time threshold, and mark it as the analysis region block, and at the same time obtain the heating rate characteristic curve of the analysis region block within the time threshold, and then obtain the area enclosed by the heating rate characteristic curve and the X-axis, and set the part of the area enclosed by the heating rate characteristic curve and the X-axis that exceeds the preset area threshold as the temperature rise out of control value SK, and at the same time obtain the real-time warning response value SX of the warning agency within the time threshold;

[0039] TT4: The alarm management evaluation coefficient R is obtained according to the formula, and the alarm management evaluation coefficient R is compared and analyzed with the preset alarm management evaluation coefficient range stored internally:

[0040] If the alarm management evaluation coefficient R is greater than the maximum value in the preset alarm management evaluation coefficient interval, a first-level warning signal is generated; if the alarm management evaluation coefficient R belongs to the preset alarm management evaluation coefficient interval, a second-level warning signal is generated; if the alarm management evaluation coefficient R is less than the minimum value in the preset alarm management evaluation coefficient interval, a third-level warning signal is generated.

[0041] The beneficial effects of the present invention are as follows:

[0042] The present invention understands the temperature rise of the generator from the perspective of image analysis in order to carry out reasonable management. When the temperature rise of a certain wind turbine in the wind power generation system is detected to be abnormal, the generator main shaft state performance and cooling mechanism are analyzed, that is, the status data of the generator main shaft is monitored, verified, evaluated and analyzed to determine whether the abnormal temperature rise is caused by the abnormal operation of the target generator main shaft, so as to rationally manage the target generator main shaft. At the same time, under the premise that the main shaft is normal, the real-time operation data of the generator is further analyzed from an overall perspective to reduce the failure risk of the target generator.

[0043] The present invention performs cooling performance supervision and evaluation operations on the cooling mechanism to determine whether the abnormal temperature rise of the target generator is caused by abnormal cooling of the target generator, and analyzes it through information feedback and information fusion to facilitate accurate early warning management, as well as reasonable and targeted management based on text information feedback to improve the management efficiency and operational safety of the target generator. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below in conjunction with the accompanying drawings;

[0045] Figure 1 It is a flowchart of the system of the present invention;

[0046] Figure 2It is a local analysis reference diagram of the second embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Embodiment 1

[0049] See also Figure 1 to Figure 2 As shown, the present invention is an intelligent inspection and early warning system for faults of new energy wind power generation, including an inspection and early warning platform, an inspection information unit, an operation deviation unit, a cooling assessment unit, a verification and analysis unit and a fault management unit. The inspection and early warning platform is connected to the inspection information unit in a one-way communication manner, the inspection information unit is connected to the operation deviation unit, the cooling assessment unit and the fault management unit in a one-way communication manner, the operation deviation unit is connected to the verification and analysis unit and the fault management unit in a one-way communication manner, the verification and analysis unit is connected to the fault management unit in a one-way communication manner, and the cooling assessment unit is connected to the fault management unit in a one-way communication manner;

[0050] When the inspection and early warning platform detects that any generator in the wind turbine generator set is working, an operation and management instruction is generated and sent to the inspection information unit. After receiving the operation and management instruction, the inspection information unit immediately collects the infrared characteristic image of the target generator and performs image feedback supervision evaluation analysis on the infrared characteristic image to determine whether the working temperature of the target generator is abnormal during the working period, so as to manage it reasonably. The specific image feedback supervision evaluation analysis process is as follows:

[0051] The working period corresponding to the generator is obtained and set as the time threshold. The generator is set as the target generator at the same time. The infrared characteristic image of the target generator within the time threshold is obtained. The infrared characteristic image is divided into i sub-region blocks, where i is a natural number greater than zero. The maximum temperature of each sub-region block within the time threshold is obtained, and the maximum temperature is compared and analyzed with the preset maximum temperature threshold value recorded and stored internally:

[0052] If there is no temperature maximum value greater than the preset temperature maximum value threshold, no signal is generated;

[0053] If there is a maximum temperature greater than the preset maximum temperature threshold, an operating temperature change instruction is generated, and the operating temperature change instruction is sent to the operation deviation unit and the cooling evaluation unit. At the same time, when the operating temperature change instruction is generated, the operating status of the early warning mechanism within the time threshold is obtained, and the operating status of the early warning mechanism is discriminated and analyzed. If the early warning mechanism is in a working state, the real-time early warning response value of the early warning mechanism within the time threshold is obtained. The real-time early warning response value represents the difference between the time when the operating temperature change instruction is generated and the time when the early warning mechanism executes the early warning operation. The real-time early warning response value is compared and analyzed with the preset real-time early warning response value range stored in the internal input:

[0054] If the real-time warning response value is within the preset real-time warning response value range, no signal is generated;

[0055] If the real-time warning response value deviates from the preset real-time warning response value range, a warning interference signal is generated and sent to the fault management unit. After receiving the warning interference signal, the fault management unit immediately displays the preset warning text corresponding to the warning interference signal, so as to optimize and maintain the warning mechanism to improve the timeliness of the warning mechanism;

[0056] Among them, when it is monitored that the temperature rise of the generator of a wind turbine in the wind power generation system is abnormal, indicating that there may be a problem with the temperature rise of the generator, in order to ensure the accuracy of the detection, a verification mechanism is set up that combines real-time operating data, main shaft status performance, and cooling performance.

[0057] Embodiment 2

[0058] After receiving the operation temperature change instruction, the operation deviation unit immediately collects the status data of the target generator main shaft, which includes the deviation rotation value and the deviation obstruction value, and performs point monitoring, verification, evaluation and analysis on the status data to determine whether the abnormal temperature rise of the target generator is caused by the abnormal operation of the target generator main shaft. The specific point monitoring, verification, evaluation and analysis process is as follows:

[0059] Mark n monitoring points on the main shaft of the target generator in a circle around the main shaft surface, where n is a natural number greater than zero, obtain the rotational friction value of each monitoring point of the main shaft of the target generator within the time threshold, obtain the maximum and minimum values ​​of the rotational friction value, and set the difference between the maximum and minimum values ​​as the deviation from the rotation value. It should be noted that the greater the deviation from the rotation value, the greater the risk of deflection of the main shaft of the target generator, and the greater the risk of frictional heat anomaly;

[0060] The lubricating oil temperature characteristic change curve of the target generator main shaft within the time threshold is obtained, and the preset lubricating oil temperature characteristic change curve of the target generator main shaft is obtained at the same time, and the difference value between the lubricating oil temperature characteristic change curve and the preset lubricating oil temperature characteristic change curve is obtained, and it is set as the deviation risk value, and the part of the deviation risk value greater than the preset deviation risk value threshold is set as the deviation obstacle value. It should be noted that the larger the value of the deviation obstacle value is, the greater the risk of deflection of the target generator main shaft is, and the greater the risk of friction heat anomaly is;

[0061] The deviation rotation value and the deviation obstruction value are compared and analyzed with the preset deviation rotation value threshold and the preset deviation obstruction value threshold that are stored internally:

[0062] If the deviation rotation value is less than the preset deviation rotation value threshold, and the deviation obstruction value is less than the preset deviation obstruction value threshold, a normal signal is generated and sent to the verification and analysis unit;

[0063] If the deviation rotation value is greater than or equal to the preset deviation rotation value threshold, or the deviation obstruction value is greater than or equal to the preset deviation obstruction value threshold, a deviation signal is generated and sent to the fault management unit. After receiving the deviation signal, the fault management unit immediately displays the preset warning text corresponding to the deviation signal, so as to rationally manage the main shaft of the target generator and reduce the risk of operation failure of the target generator;

[0064] After receiving the normal signal, the verification and analysis unit immediately collects the real-time operation data of the target generator, and performs interval change safety supervision assessment and analysis on the real-time operation data to determine whether the abnormal temperature rise of the target generator is caused by the abnormal operation of the target generator, so as to reasonably manage and reduce the failure risk of the target generator. The specific interval change safety supervision assessment and analysis process is as follows:

[0065] The real-time operation data of the overall target generator within the time threshold is obtained, and the real-time operation data includes real-time power generation, real-time operation power, and real-time vibration amplitude, etc. The curve corresponding to the real-time operation data within the time threshold is obtained, and it is set as the operation change curve, and then the maximum peak value and minimum trough value corresponding to each operation change curve are obtained, and then the range interval composed of the maximum peak value and the minimum trough value in each operation change curve is set as the floating change interval;

[0066] The time period corresponding to the normal temperature of the overall target generator based on the inspection and early warning platform is obtained, and it is set as the normal time period. The real-time operation data of the target generator in the historical m normal time periods are obtained, where m is a natural number greater than zero. Then, the maximum value of the maximum peak value and the minimum value of the minimum trough value in each curve corresponding to the real-time operation data of the target generator in the m normal time periods are obtained, and the range interval composed of them is set as the critical change interval, and the floating change interval is compared and analyzed with the critical change interval:

[0067] If all floating change intervals are within the critical change interval, no signal is generated;

[0068] If all floating change intervals are not within the critical change interval, an abnormal signal is generated and sent to the fault management unit. After receiving the abnormal signal, the fault management unit immediately performs a preset warning operation corresponding to the abnormal signal, so as to perform early warning management on the target generator corresponding to the abnormal signal, so as to reduce the failure risk of the target generator;

[0069] In an embodiment of the present invention, the actual operating parameters of the generator may include but are not limited to: real-time power generation, real-time operating power, and real-time vibration amplitude. It can be understood that this example is only used to illustrate the actual operating parameters of the generator. In actual application, the actual operating parameters of the generator obtained can be preset according to actual needs, and the server can be preset to obtain different actual operating parameters for different generators.

[0070] Embodiment 3

[0071] After receiving the operating temperature change instruction, the cooling evaluation unit immediately collects the cooling data of the target generator, which represents the average water flow rate, and performs cooling performance supervision and evaluation operations on the cooling data to determine whether the abnormal temperature rise of the target generator is caused by abnormal cooling of the target generator, so as to reasonably perform early warning management. The specific cooling performance supervision and evaluation operation process is as follows:

[0072] The product value obtained by multiplying the number of times the average water flow velocity in the cooling pipe of the target generator within the time threshold is lower than the preset water flow velocity average value and the value corresponding to the duration is obtained, and it is set as the cooling resistance value. At the same time, the maximum value of the duration corresponding to the fault parameter of the cooling water pump of the target generator within the time threshold that exceeds the preset threshold is obtained, and it is set as the risk duration. The fault parameters include the average operating voltage, the average operating temperature, etc. The product value obtained by multiplying the cooling resistance value and the value corresponding to the risk duration is set as the cooling abnormality rate. It should be noted that the larger the value of the cooling abnormality rate, the greater the cooling abnormality risk of the target generator;

[0073] Compare and analyze the cooling abnormality rate with the preset cooling abnormality rate threshold value stored in the internal data entry:

[0074] If the ratio between the cooling abnormality rate and the preset cooling abnormality rate threshold is less than 1, no signal is generated;

[0075] If the ratio between the cooling abnormality rate and the preset cooling abnormality rate threshold is greater than or equal to 1, a feedback instruction is generated. When the feedback instruction is generated, the real-time water flow velocity value and the average water temperature rise rate in the cooling pipe of the target generator within the time threshold are obtained, and the real-time water flow velocity value and the average water temperature rise rate are labeled SL and SW respectively;

[0076] According to the formula A cooling performance evaluation coefficient is obtained, wherein a1 represents a preset error factor coefficient, a1 is greater than zero, L is a cooling performance evaluation coefficient, a cooling performance evaluation coefficient curve of a target generator within a time threshold is obtained, and then a change trend value of the cooling performance evaluation coefficient curve is obtained, and the change trend value of the cooling performance evaluation coefficient curve is set as a dynamic risk value DT. It should be noted that the larger the value of the dynamic risk value DT is, the greater the impact of cooling abnormality of the cooling mechanism is;

[0077] The sub-region block corresponding to the maximum temperature within the time threshold is obtained, and it is marked as the analysis region block. At the same time, the heating rate characteristic curve of the analysis region block within the time threshold is obtained, and then the area enclosed by the heating rate characteristic curve and the X-axis is obtained, and the part of the area enclosed by the heating rate characteristic curve and the X-axis that exceeds the preset area threshold is set as the temperature runaway value SK. It should be noted that the larger the value of the temperature runaway value SK, the greater the temperature control abnormality risk of the target generator, and the higher the early warning management demand. At the same time, the real-time early warning response value SX of the early warning agency within the time threshold is obtained;

[0078] According to the formula The alarm management evaluation coefficient is obtained, where f1, f2 and f3 are the preset proportional factor coefficients of the dynamic risk value, the temperature rise out of control value and the real-time warning response value respectively. The proportional factor coefficient is used to correct the deviation of various parameters in the formula calculation process, so as to make the calculation result more accurate. f4 is the preset correction factor coefficient. f1, f2, f3 and f4 are all greater than zero. R is the alarm management evaluation coefficient. The alarm management evaluation coefficient R is compared and analyzed with the preset alarm management evaluation coefficient interval stored in the internal input:

[0079] If the alarm management evaluation coefficient R is greater than the maximum value in the preset alarm management evaluation coefficient interval, a first-level warning signal is generated;

[0080] If the alarm management evaluation coefficient R belongs to the preset alarm management evaluation coefficient interval, a secondary warning signal is generated;

[0081] If the alarm management evaluation coefficient R is less than the minimum value in the preset alarm management evaluation coefficient interval, a three-level warning signal is generated, wherein the warning degrees corresponding to the first-level warning signal, the second-level warning signal and the third-level warning signal are successively reduced, and the first-level warning signal, the second-level warning signal and the third-level warning signal are sent to the fault management unit. After receiving the first-level warning signal, the second-level warning signal and the third-level warning signal, the fault management unit immediately displays the preset warning texts corresponding to the first-level warning signal, the second-level warning signal and the third-level warning signal, that is, the preset warning text corresponding to the first-level warning signal is "first-level temperature control management", the preset warning text corresponding to the second-level warning signal is "second-level temperature control management", and the preset warning text corresponding to the third-level warning signal is "third-level temperature control management", so as to carry out reasonable and targeted management according to the text information feedback, so as to improve the management efficiency and operation safety of the target generator;

[0082] In summary, the present invention understands the temperature rise of the generator operation from the perspective of image analysis in order to perform reasonable management. When the temperature rise of a certain wind turbine in the wind power generation system is detected to be abnormal, the state performance of the generator main shaft and the cooling mechanism are analyzed, that is, the state data of the generator main shaft is monitored, verified, evaluated and analyzed to determine whether the abnormal temperature rise is caused by the abnormal operation of the target generator main shaft, so as to rationally manage the target generator main shaft to reduce the risk of target generator operation failure. At the same time, under the premise that the main shaft is normal, the real-time operation data of the generator is further in-depth from the overall perspective. The interval change safety supervision evaluation analysis is performed to determine whether the target generator temperature rise is abnormal due to the abnormal operation of the target generator, so as to reasonably manage it to reduce the risk of failure of the target generator, and the cooling performance supervision evaluation operation is performed on the cooling mechanism to determine whether the target generator temperature rise is abnormal due to abnormal cooling of the target generator, and the analysis is performed by information feedback and information fusion to enable accurate early warning management, and reasonable and targeted management is performed according to the text information feedback to improve the management efficiency and operation safety of the target generator.

[0083] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0084] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. An intelligent inspection and early warning system for faults in new energy wind power generation, characterized in that: It includes inspection warning platform, inspection information unit, operation deviation unit, cooling assessment unit, verification and analysis unit and fault management unit; The inspection information unit is used to collect infrared characteristic images of the target generator, and perform image feedback supervision evaluation analysis on the infrared characteristic images to obtain operating temperature change instructions and early warning interference signals; The operation deviation unit is used to collect the state data of the main shaft of the target generator, the state data including the deviation rotation value and the deviation obstruction value, and perform point monitoring, verification, evaluation and analysis on the state data, send the obtained normal signal to the verification and analysis unit, and send the obtained deviation signal to the fault management unit; The verification and analysis unit is used to collect real-time operating data of the target generator, perform interval change safety supervision assessment analysis on the real-time operating data, and send the obtained abnormal signal to the fault management unit; After receiving the operation temperature change instruction, the cooling evaluation unit immediately collects the cooling data of the target generator, the cooling data represents the mean value of the water flow rate, performs cooling performance supervision and evaluation operations on the cooling data, and performs discriminant analysis on the alarm management evaluation coefficient R, and sends the obtained first-level warning signal, second-level warning signal and third-level warning signal to the fault management unit; The image feedback supervision evaluation and analysis process of the inspection information unit is as follows: The working period corresponding to the generator is obtained and set as the time threshold. The generator is set as the target generator at the same time. The infrared characteristic image of the target generator within the time threshold is obtained. The infrared characteristic image is divided into i sub-region blocks, where i is a natural number greater than zero. The maximum temperature of each sub-region block within the time threshold is obtained, and the maximum temperature is compared and analyzed with the preset maximum temperature threshold value recorded and stored internally: If there is no temperature maximum value greater than the preset temperature maximum value threshold, no signal is generated; If there is a maximum temperature greater than the preset maximum temperature threshold, a temperature change instruction is generated; When the inspection information unit generates a temperature change operation instruction: The operating status of the early warning mechanism within the time threshold is obtained, and the operating status of the early warning mechanism is discriminated and analyzed. If the early warning mechanism is in working state, the real-time early warning response value of the early warning mechanism within the time threshold is obtained. The real-time early warning response value represents the difference between the time when the operating temperature change instruction is generated and the time when the early warning mechanism executes the early warning operation. The real-time early warning response value is compared and analyzed with the preset real-time early warning response value range stored in the internal input: If the real-time warning response value is within the preset real-time warning response value range, no signal is generated; If the real-time warning response value deviates from the preset real-time warning response value range, a warning interference signal is generated, and the warning interference signal is sent to the fault management unit. After receiving the warning interference signal, the fault management unit immediately displays the preset warning text corresponding to the warning interference signal; The point monitoring, verification, evaluation and analysis process of the operation deviation unit is as follows: S1: mark n monitoring points on the main shaft of the target generator in a circle around the main shaft surface, where n is a natural number greater than zero, obtain the rotational friction value of each monitoring point of the main shaft of the target generator within a time threshold, obtain the maximum value and the minimum value of the rotational friction value, and set the difference between the maximum value and the minimum value as the deviation rotation value; S2: obtaining a lubricating oil temperature characteristic change curve of the target generator main shaft within the time threshold, and obtaining a preset lubricating oil temperature characteristic change curve of the target generator main shaft at the same time, obtaining a difference value between the lubricating oil temperature characteristic change curve and the preset lubricating oil temperature characteristic change curve, and setting it as a deviation risk value, and setting the portion of the deviation risk value greater than the preset deviation risk value threshold as a deviation barrier value; S3: Compare and analyze the deviation rotation value and the deviation obstruction value with the preset deviation rotation value threshold and the preset deviation obstruction value threshold that are recorded and stored internally: If the deviation rotation value is less than the preset deviation rotation value threshold, and the deviation obstruction value is less than the preset deviation obstruction value threshold, a normal signal is generated; If the deviation rotation value is greater than or equal to the preset deviation rotation value threshold, or the deviation obstruction value is greater than or equal to the preset deviation obstruction value threshold, a deviation signal is generated, and the deviation signal is sent to the fault management unit. After receiving the deviation signal, the fault management unit immediately displays the preset warning text corresponding to the deviation signal; The interval change safety supervision assessment and analysis process of the verification and analysis unit is as follows: T1: The real-time operation data of the overall target generator within the time threshold is obtained, and the real-time operation data includes the real-time power generation, the real-time operation power and the real-time vibration amplitude. The curve corresponding to the real-time operation data within the time threshold is obtained, and it is set as the operation change curve, and then the maximum peak value and the minimum trough value corresponding to each operation change curve are obtained, and then the range interval composed of the maximum peak value and the minimum trough value in each operation change curve is set as the floating change interval; T2: Obtain the time period corresponding to the normal temperature of the overall target generator based on the inspection and early warning platform, and set it as the normal time period, obtain the real-time operation data of the target generator in the historical m normal time periods, where m is a natural number greater than zero, and then obtain the maximum value of the maximum peak value and the minimum value of the minimum trough value in each curve corresponding to the real-time operation data of the target generator in the m normal time periods, and set the range interval composed of them as the critical change interval, and compare and analyze the floating change interval with the critical change interval: If all floating change intervals are within the critical change interval, no signal is generated; If each floating change interval does not fall within the critical change interval, an abnormal signal is generated; The cooling performance monitoring and evaluation operation process of the cooling evaluation unit is as follows: The product value obtained by multiplying the number of times the average water flow velocity in the cooling pipe of the target generator within the time threshold is lower than the preset water flow velocity average value and the value corresponding to the duration is obtained, and it is set as the cooling resistance value. At the same time, the maximum value of the duration corresponding to the fault parameter of the cooling water pump of the target generator within the time threshold exceeds the preset threshold is obtained, and it is set as the risk duration. The fault parameters include the operating voltage average and the operating temperature average. The product value obtained by multiplying the cooling resistance value and the value corresponding to the risk duration is set as the cooling abnormality rate; Compare and analyze the cooling abnormality rate with the preset cooling abnormality rate threshold value stored in the internal data entry: If the ratio between the cooling abnormality rate and the preset cooling abnormality rate threshold is less than 1, no signal is generated; If the ratio between the cooling abnormality rate and the preset cooling abnormality rate threshold is greater than or equal to 1, a feedback instruction is generated; When the cooling evaluation unit generates a feedback instruction: TT1: obtain the real-time water flow velocity value and the average water temperature rise rate in the cooling pipe of the target generator within the time threshold, and label the real-time water flow velocity value and the average water temperature rise rate as SL and SW respectively; TT2: According to the formula A cooling performance evaluation coefficient L is obtained, wherein a1 represents a preset error factor coefficient, a1 is greater than zero, a cooling performance evaluation coefficient curve of the target generator within the time threshold is obtained, and then a change trend value of the cooling performance evaluation coefficient curve is obtained, and the change trend value of the cooling performance evaluation coefficient curve is set as a dynamic risk value DT; TT3: Obtain the sub-region block corresponding to the maximum temperature within the time threshold, and mark it as the analysis region block, and at the same time obtain the heating rate characteristic curve of the analysis region block within the time threshold, and then obtain the area enclosed by the heating rate characteristic curve and the X-axis, and set the part of the area enclosed by the heating rate characteristic curve and the X-axis that exceeds the preset area threshold as the temperature rise out of control value SK, and at the same time obtain the real-time warning response value SX of the warning agency within the time threshold; TT4: According to the formula The alarm management evaluation coefficient R is obtained, where f1, f2 and f3 are the preset proportional factor coefficients of the dynamic risk value, the temperature rise out of control value and the real-time warning response value, respectively, and f4 is the preset correction factor coefficient. f1, f2, f3 and f4 are all greater than zero. The alarm management evaluation coefficient R is compared and analyzed with the preset alarm management evaluation coefficient interval stored in the internal input: If the alarm management evaluation coefficient R is greater than the maximum value in the preset alarm management evaluation coefficient interval, a first-level warning signal is generated; if the alarm management evaluation coefficient R belongs to the preset alarm management evaluation coefficient interval, a second-level warning signal is generated; if the alarm management evaluation coefficient R is less than the minimum value in the preset alarm management evaluation coefficient interval, a third-level warning signal is generated.

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