Fault Diagnosis Method for Mechanical and Electrical Equipment in Smart Hydropower Plants

Abstract

The present invention discloses a fault diagnosis method for electromechanical equipment in intelligent hydropower plants. The present invention relates to the technical field of intelligent hydropower plants, and solves the problem that the original fault diagnosis method is not comprehensive and cannot determine the specific cause of the corresponding fault based on the relevant characteristics of the fault. The present invention confirms the rotation meshing process of the hydro-generator set, identifies whether the rotation meshing process is normal. If the meshing is normal, it then confirms the power generation parameters of the subsequent magnetic induction coil during the power generation process, and synchronously conducts electromotive force analysis to evaluate whether there is a consistency feature in the difference of the generated electromotive force, so as to evaluate whether the magnetic induction coil is normal during operation and thus evaluate the cause of the fault. By adopting this processing method, not only can the faults existing in the hydropower plant be effectively confirmed, but also the cause of the fault can be quickly locked, achieving a more accurate fault diagnosis effect and improving the comprehensiveness of the fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent hydropower plants, and specifically to a fault diagnosis method for electromechanical equipment for intelligent hydropower plants. Background Art

[0002] An intelligent hydropower plant is a new type of hydropower plant that, based on traditional hydropower plants, utilizes advanced information technologies (such as the Internet of Things, big data, cloud computing, artificial intelligence, etc.) to achieve intelligent operation, management, and maintenance of hydropower plants; it can comprehensively perceive, automatically control, intelligently diagnose, and optimize decisions on the equipment status, power generation process, environmental information, etc. of hydropower plants, thereby improving the safety, reliability, economy, and environmental friendliness of hydropower plants.

[0003] The application with the publication number CN111076962B discloses a fault diagnosis method for electromechanical equipment for intelligent hydropower plants, abbreviated as a two-way diagnosis method; forward diagnosis, based on the diagnosis direction of the health feature model, calls the health feature model in the database; reverse diagnosis, based on the diagnosis direction of the fault rule sample expert knowledge base, calls the fault tree (FTA) model in the database. Through internal program algorithms, functions such as fault feature extraction, principal component analysis, and pattern matching are realized to complete the fault diagnosis of electromechanical equipment. The two-way diagnosis method makes full use of the characteristic parameters, measuring point data, and expert knowledge base of electromechanical equipment to identify and diagnose the status and faults of electromechanical equipment, providing a technical basis for users' maintenance decisions. The invention makes full use of industrial big data, meets the requirements of intelligent manufacturing of electromechanical equipment in intelligent hydropower plants, has an intelligent and advanced logical reasoning mechanism, and a high diagnostic accuracy.

[0004] During the process of fault diagnosis and treatment of the internal unit equipment of the hydropower plant, generally based on the operating parameters associated with the corresponding hydropower plant, it is used to evaluate whether there are faults in its unit equipment, so as to make a fault determination. However, the original fault diagnosis method is not comprehensive and cannot determine the specific causes of the corresponding faults based on the relevant characteristics of the faults and display the signals, resulting in the need for on-site detection by later operators, causing too long maintenance time. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a fault diagnosis method for electromechanical equipment for intelligent hydropower plants, which solves the problem that the original fault diagnosis method is not comprehensive and cannot determine the specific causes of the corresponding faults based on the relevant characteristics of the faults.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A fault diagnosis method for electromechanical equipment for intelligent hydropower plants includes the following steps:

[0007] Step 1: From the historical completed data, confirm the different power generations associated with different speeds of the water turbine units within a unit time, and based on the average power generation per speed confirmed for the corresponding speed, confirm and record the power generation average interval associated with the corresponding speed. The specific method is as follows:

[0008] S11: From the historical completed data, confirm the different power generations corresponding to different speeds within a unit time. Use K i = power generation ÷ speed to confirm its associated average power generation K i , and then based on several groups of different associated average power generations K i confirmed for the same speed, select the minimum value and the maximum value from them, and confirm the power generation average interval for the corresponding several groups of different associated average power generations K i ;

[0009] S12: For different speeds of the water turbine units, confirm and record different power generation average intervals;

[0010] Step 2: Based on the different power generation average intervals associated with different speeds, monitor the speed and power generation generated by the hydraulic unit equipment within a unit time, and check the results of the real-time monitoring to evaluate whether the hydraulic unit equipment generates electricity normally. If it is normal, continue to monitor; if it is abnormal, directly generate an abnormal signal. The specific sub-steps are as follows:

[0011] S21: Real-time monitor the speed of the water turbine unit in the hydraulic unit equipment, and calibrate the real-time monitored speed as ZS k , where k represents different moments, and the interval between different adjacent moments is 5 minutes. Then monitor the power generation associated with the corresponding moment and calibrate it as FD k . Based on the power generation average interval associated with the current speed ZS k , confirm the two groups of power generation parameters associated with the current speed ZS k . Presume the power generation average interval is [Q1, Q2], where Q1 is the initial value of the power generation average interval and Q2 is the end value of the power generation average interval. Use: CL1 = ZS k ×Q1 to confirm the first group of power values CL1, and then use CL2 = ZS k ×Q2 to confirm the second group of power values CL2;

[0012] S22: Based on the confirmed first group of power values CL1 and the second group of power values CL2, determine the power interval associated with this speed ZS k , and identify whether its FD k satisfies: FD k ∈ power interval. If it is satisfied, it means that the hydraulic unit equipment generates electricity normally at the current moment, and continuous monitoring can be carried out; if it is not satisfied, it means that the hydraulic unit equipment generates electricity abnormally at the current moment, and an abnormal signal is directly generated;

[0013] Step 3: Based on the confirmed abnormal signal, define a set of processing cycles, monitor the rotational speed of the water turbine generator shaft and the magnetic induction axis speed within this processing cycle, and confirm whether the gear unit in this hydropower plant is operating normally based on the rotational speed ratio associated with the two at the same moment. The specific method is as follows:

[0014] S31: Based on the confirmed abnormal signal, define a set of processing cycles T, where T is a preset value. Calibrate the rotational speed of the water turbine generator shaft monitored at different times within this processing cycle T as Jz o , and calibrate the monitored magnetic induction axis speed as Zc o , where o represents different times within this processing cycle T, and confirm the rotational speed ratio ZB associated with the corresponding time o , where ZB o = Zc o ÷ Jz o , and confirm the rotational speed ratio ZB associated with the corresponding time o ;

[0015] S32: Confirm whether the rotational speed ratios ZB confirmed at several times within this processing cycle T o are all the same ratio. If they are all the same ratio, it means that the gear unit in this hydropower plant is operating normally, and proceed to Step 4 for further analysis; if they are not the same ratio, it means that the gear unit in this hydropower plant is operating abnormally, and generate a gear unit transmission abnormal signal for display;

[0016] Step 4: After confirming that the gear unit in the hydropower plant is operating normally, determine the corresponding electromotive force change curve by analyzing the electromotive force change brought about by the rotation of the magnetic induction axis, and then generate the standard electromotive force standard line associated with this process according to the preset standard theoretical formula. Combine and compare the electromotive force change curve with the electromotive force standard line to identify whether the magnetic induction coil unit in the hydropower plant is operating normally. The specific method is as follows:

[0017] S41: Redefine a set of monitoring cycles, where the monitoring cycle is a preset cycle. Monitor the magnetic induction axis speed and the real-time generated electromotive force associated within this monitoring cycle. Based on the different magnetic induction axis speeds and the corresponding electromotive forces monitored within this monitoring cycle, generate the electromotive force change curve. The horizontal coordinate of this change curve is the time line, and its vertical coordinate axis is the corresponding electromotive force. Different electromotive forces are associated with different magnetic induction axis speeds;

[0018] S42: Calibrate the magnetic induction axis speeds associated with different times within this monitoring cycle as V p , where p represents different times within this monitoring cycle, and use E p = BLV p sinθ to confirm the corresponding Vp The associated standard electromotive force E p , where B is the magnetic field strength, L is the conductor length, and θ is the angle between the conductor movement direction and the magnetic field direction. When the coil is cut by the magnetic induction axis for one circle, the generated induced electromotive force is related to the movement speed v of the conductor, and B, L, and θ are all fixed values and preset values. Based on the different standard electromotive forces E confirmed at different times p , generate the electromotive force standard line associated within this monitoring period. Place this electromotive force standard line and the electromotive force change curve in the same two-dimensional coordinate system, and identify the same-point potential difference Cz located within the electromotive force standard line and the electromotive force change curve at the same time p , and confirm several groups of the identified same-point potential differences Cz p Whether they are all the same difference. If so, generate a display of the abnormal operation signal of the magnetic induction coil unit;

[0019] If several groups of the identified same-point potential differences Cz p do not belong to the same difference, directly generate a display of the abnormal power fluctuation signal.

[0020] The present invention provides a fault diagnosis method for electromechanical equipment for intelligent hydropower plants. Compared with the prior art, it has the following beneficial effects:

[0021] The present invention monitors the operation parameters of the electromechanical equipment in the hydropower plant, identifies the power generation average quantity interval associated with its operation parameters from the historical completed data, and then based on the specific monitoring and verification results, identifies whether this electromechanical equipment is operating normally and conducts numerical evaluation to perform fault detection, so as to achieve a better fault diagnosis and treatment effect. The accuracy of its fault diagnosis can be effectively guaranteed, which is determined based on the data characteristics in the historical completed data, and can effectively achieve a more accurate fault diagnosis effect;

[0022] When there is an abnormal operation in the unit equipment in the hydropower plant, first confirm the rotation meshing process of the water turbine unit, identify whether its rotation meshing process is normal. If the meshing is normal, then confirm the power generation parameters of the subsequent magnetic induction coil during the power generation process, and synchronously conduct electromotive force analysis to evaluate whether there is a consistency characteristic in the difference of the generated electromotive force, so as to evaluate whether the magnetic induction coil is operating normally during operation and evaluate its fault cause. By using this processing method, not only can it effectively confirm that there is a fault in the hydropower plant, but also quickly lock the fault cause at the same time, and can achieve a more accurate fault diagnosis effect and improve the comprehensiveness of its fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic flow chart of the method of the present invention;

[0024] Figure 2This is a schematic flowchart for determining abnormalities in the magnetic induction coil of the present invention. Specific embodiments

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] First embodiment

[0027] Please refer to Figure 1 , this application provides a method for diagnosing faults in electromechanical equipment for intelligent hydropower plants. When the electromechanical equipment is diagnosing faults, the corresponding water turbine unit rotates based on hydraulic potential energy. When it rotates, it drives the corresponding main shaft to rotate. When the main shaft rotates, it drives the relevant magnetic induction shaft to rotate based on the internal reduction gear unit or acceleration gear unit, and the magnetic induction shaft then performs magnetic induction line cutting movement to complete the corresponding power generation process, including the following steps:

[0028] Step 1: From the historical completed data, confirm the different power generations associated with the rotational speeds of different water turbine units per unit time, and based on the average power generation per rotational speed confirmed corresponding to the rotational speed, confirm the power generation average interval associated with the corresponding rotational speed and record it. Specifically, during the normal rotation of the water turbine unit, different rotational speeds will generate different power generations, so the average rotational speed associated with power generation when the corresponding rotational speed generates power can be determined, that is, the power generation average interval associated with it. Among them, the specific sub-steps for confirmation are:

[0029] S11: From the historical completed data, confirm the different power generations corresponding to different rotational speeds per unit time, and use K i = power generation ÷ rotational speed (the power generation and rotational speed correspond one by one) to confirm the associated power generation average K i , and then based on several groups of different associated power generation averages K i confirmed at the same rotational speed, select the minimum value and the maximum value from them, and confirm the power generation average interval of the corresponding several groups of different associated power generation averages K i ;

[0030] S12. For different speeds of the water turbine unit, confirm different power generation average value intervals and record them. Specifically, its power generation average value belongs to a specific characteristic interval corresponding to the speed. Based on this characteristic interval, the associated evaluation criteria can be determined preferentially. Under normal circumstances, the magnitude of the power generation depends on the specific speed of the corresponding water turbine unit. Therefore, based on the corresponding relationship between such values, the corresponding average value interval can be locked, which is convenient for later numerical evaluation to conduct fault detection, so as to achieve a better fault diagnosis and treatment effect.

[0031] Step 2. Based on the different power generation average value intervals associated with different speeds, monitor the speed and power generation generated by the hydraulic unit equipment per unit time, and check the results of the real-time monitoring, and evaluate whether the hydraulic unit equipment generates electricity normally. If it is normal, continue to monitor. If it is abnormal, directly generate an abnormal signal. The specific sub-steps for evaluation are as follows:

[0032] S21. Real-time monitor the speed of the water turbine unit in the hydraulic unit equipment, and calibrate the real-time monitored speed as ZS k , where k represents different moments, and the interval between different adjacent moments is 5 minutes. Then monitor the power generation associated with the corresponding moment and calibrate it as FD k , based on the power generation average value interval associated with the current speed ZS k , confirm the two groups of power generation parameters associated with its current speed ZS k , and formulate the power generation average value interval as [Q1, Q2], where Q1 is the initial value of the power generation average value interval and Q2 is the end value of the power generation average value interval. Use: CL1 = ZS k ×Q1 to confirm the first group of power values CL1, and then use CL2 = ZS k ×Q2 to confirm the second group of power values CL2;

[0033] S22. Based on the confirmed first group of power values CL1 and the second group of power values CL2, determine the power interval associated with this speed ZS k , and identify whether its FD k meets: FD k ∈ power interval. If it meets, it means that the hydraulic unit equipment generates electricity normally at the current moment, and continuous monitoring can be carried out. If it does not meet, it means that the hydraulic unit equipment generates electricity abnormally at the current moment, and an abnormal signal is directly generated;

[0034] Specifically, when the water turbine unit rotates, it will generate corresponding power generation. If the corresponding speed and the associated power generation have a large difference in the past historical data, then in the actual processing process, based on the specific display of the corresponding power generation value, it can be evaluated whether the mechanical and electrical equipment in the hydropower plant operates normally, so as to conduct a comprehensive evaluation, and then conduct a fault diagnosis and investigation on the specific equipment associated with the hydropower plant;

[0035] Step 3: Based on the confirmed abnormal signal, define a set of processing cycles, monitor the rotational speed of the water turbine unit shaft and the magnetic induction shaft speed within this processing cycle, and based on the rotational speed ratio associated with the two at the same moment, confirm whether the gear unit in this hydropower plant is operating normally. Specifically, when the gear unit is operating, it can accelerate or decelerate. The accelerating gear unit or decelerating gear unit is set in advance by the operator. After setting, when the water turbine unit shaft rotates, it will drive the driven gear associated with the corresponding gear unit to rotate. If the gears mesh normally, it can drive the magnetic induction shaft to rotate normally. When the gear unit operates normally, the ratio of rotation generated should be constant. Among them, the specific method to confirm whether the gear unit is operating normally is as follows:

[0036] S31: Based on the confirmed abnormal signal, define a set of processing cycles T, where T is a preset value, and its specific value is determined by the operator according to experience. Generally, T takes a value of 10 min. Calibrate the rotational speed of the water turbine unit shaft monitored at different moments within this processing cycle T as Jz o , and calibrate the monitored magnetic induction shaft speed as Zc o , where o represents different moments within this processing cycle T, and confirm the rotational speed ratio ZB associated with the corresponding moment o , where ZB o = Zc o ÷Jz o , and confirm the rotational speed ratio ZB associated with the corresponding moment o ;

[0037] S32: Confirm whether the rotational speed ratios ZB confirmed at several moments within this processing cycle T o are all the same ratio. If they are all the same ratio, it means that the gear unit in this hydropower plant is operating normally, and proceed to Step 4 for further analysis. If there are other rotational speed ratios that do not belong to the same ratio, it means that the gear unit in this hydropower plant is operating abnormally, and generate a gear unit transmission abnormal signal for display;

[0038] Specifically, when there is abnormal meshing or tooth damage in the corresponding gear unit, it will cause abnormal transmission of the corresponding gear shaft. When the transmission is abnormal, it will cause abnormal transmission ratio, resulting in abnormal rotation of the corresponding magnetic induction shaft, leading to insufficient or reduced power generation. Therefore, it can be directly determined whether there is an abnormality in the gear unit. Based on the corresponding rotational speed ratio, confirm the ratio change between the magnetic induction shaft and the corresponding water turbine unit shaft. If there is abnormal tooth engagement or abnormal transmission, it will cause abnormal transmission ratio during the actual transmission process, thus affecting the corresponding power generation process.

[0039] Second Embodiment

[0040] In the specific implementation process of this embodiment, compared with the first embodiment, this embodiment mainly focuses on the electromotive force analysis of the magnetic induction coil to evaluate whether there are any abnormal or short-circuited related line segments in the magnetic induction coil. It also includes the following steps:

[0041] Step 4: After confirming that the gear unit in the hydropower plant is operating normally, by analyzing the change in electromotive force brought about by the rotation of the magnetic induction axis, determine the corresponding electromotive force change curve. Then, based on the preset standard theoretical formula, generate the standard electromotive force standard line associated with this process. Combine and compare the electromotive force change curve with the electromotive force standard line to identify whether the magnetic induction coil unit in the hydropower plant is operating normally. The specific method of identification is as follows:

[0042] S41. Combine Figure 2 , re-determine a set of monitoring periods. The monitoring period is the preset period, and its specific value is determined by the operator according to experience, generally taking 10 minutes, which is the same as the time value of the limited period. Monitor the rotational speed of the magnetic induction axis and the real-time generated electromotive force associated with this monitoring period (the electromotive force is the voltage generated during the corresponding cutting process and can be directly monitored, with the unit of volt). Based on the different rotational speeds of the magnetic induction axis and the corresponding electromotive forces monitored within this monitoring period, generate the electromotive force change curve. The horizontal coordinate of this change curve is the time line, and its vertical coordinate axis is the corresponding electromotive force. Different electromotive forces are associated with different rotational speeds of the magnetic induction axis;

[0043] S42. Calibrate the rotational speed of the magnetic induction axis associated with different moments within this monitoring period as V p , where p represents different moments within this monitoring period. Use E p = BLV p sinθ to confirm the standard electromotive force E p associated with the corresponding V p , where B is the magnetic field strength, L is the conductor length, and θ is the angle between the direction of conductor movement and the magnetic field direction. When the coil is cut by the magnetic induction axis for one circle, the generated induced electromotive force is related to the movement speed v of the conductor, and B, L, and θ are all fixed values and preset values, determined by the relevant operator according to experience. Based on the different standard electromotive forces E p confirmed at different moments, generate the electromotive force standard line associated with this monitoring period. Place this electromotive force standard line and the electromotive force change curve in the same two-dimensional coordinate system, and identify the same-point potential difference Cz p located within the electromotive force standard line and the electromotive force change curve at the same moment, and confirm the identified several groups of same-point potential differences Cz pAre they all the same difference? If so, generate a display of abnormal operation signals of the magnetic induction coil unit. If not, it means there are relevant power fluctuations, resulting in specific fluctuations in the actual electromotive force, and directly generate a display of abnormal power fluctuation signals;

[0044] Specifically, based on the confirmed abnormal operation signals of the magnetic induction coil unit, relevant operators will conduct a short - circuit detection on the magnetic induction coil units associated within the corresponding magnetic induction coil units of the hydropower plant, evaluate whether there are short - circuit abnormal problems in such coil units, and handle them in a timely manner. If there are abnormal power fluctuation situations, power debugging will be carried out to identify whether there are jitters or other situations when the corresponding magnetic induction axis cuts the magnetic induction line, so as to lock the specific reasons for the corresponding power fluctuations, and conduct relevant processing and maintenance to ensure the normal operation of this hydropower plant.

[0045] The third embodiment

[0046] In the specific implementation process of this embodiment, it includes all the implementation processes of the above - mentioned two groups of embodiments.

[0047] Some data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well - known to those skilled in the art.

[0048] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for diagnosing faults of mechanical and electrical equipment for intelligent hydropower plants, characterized in that, It includes the following steps: Step 1: From the historical completed data, confirm the different power generations associated with different water turbine unit speeds within a unit time, and based on the average power generation per rotation confirmed for the corresponding speed, confirm the power generation average interval associated with the corresponding speed and record it; Step 2: Based on the different power generation average intervals associated with different speeds, monitor the speed and power generation of the hydraulic unit equipment within a unit time, and check the results of real-time monitoring to evaluate whether the hydraulic unit equipment is generating electricity normally. If it is normal, continue to monitor; if it is abnormal, directly generate an abnormal signal; Step 3: Based on the confirmed abnormal signal, define a set of processing cycles, monitor the rotational speed of the water turbine unit shaft and the rotational speed of the magnetic induction shaft within this processing cycle, and based on the rotational speed ratio associated with the two at the same moment, confirm whether the gear unit in this hydropower plant is operating normally. The specific method is as follows: S31. Based on the confirmed abnormal signal, define a set of processing cycles T, where T is a preset value, and calibrate the rotational speeds of the water turbine unit shaft monitored at different times within this processing cycle T as Jz o , and calibrate the monitored rotational speed of the magnetic induction axis as Zc o , where o represents different times within this processing cycle T, and confirm the rotational speed ratio ZB associated with the corresponding time o , and for ZB o =Zc o ÷Jz o , confirm the rotational speed ratio ZB associated with the corresponding time o ; S32. Confirm the rotational speed ratio ZB confirmed at several moments within the current processing cycle T o Whether they are all the same ratio. If they are all the same ratio, perform Step Four for further analysis. When they are all the same ratio, it means that the gear unit in this hydropower plant is operating normally. If there are other rotational speed ratios that do not belong to the same ratio, it means that the gear unit in this hydropower plant is operating abnormally; Step 4: After confirming that the gear unit in the hydropower plant is operating normally, determine the corresponding electromotive force change curve by analyzing the change in electromotive force brought about by the rotation of the magnetic induction shaft, and then generate the standard electromotive force standard line associated with this process according to the preset standard theoretical formula. Combine and compare the electromotive force change curve with the electromotive force standard line to identify whether the magnetic induction coil unit in the hydropower plant is operating normally. The specific method is as follows: S41: Re-determine a set of monitoring cycles, the monitoring cycle is the preset cycle, monitor the rotational speed of the magnetic induction shaft and the real-time generated electromotive force associated with this monitoring cycle. Based on the different rotational speeds of the magnetic induction shaft and the corresponding electromotive force monitored within this monitoring cycle, generate its electromotive force change curve. The horizontal coordinate of this change curve is the time line, and its vertical coordinate is the corresponding electromotive force. Different electromotive forces are associated with different rotational speeds of the magnetic induction shaft; S42. Calibrate the rotational speed of the magnetic induction axis associated with different moments within this monitoring period as V p , where p represents different moments within this monitoring period, and use E p = BLV p sinθ to confirm the corresponding standard electromotive force E p associated with V p , where B is the magnetic field strength, L is the conductor length, and θ is the angle between the conductor movement direction and the magnetic field direction. When the coil is cut by the magnetic induction axis for one circle, the generated induced electromotive force is related to the movement speed v of the conductor, and B, L, and θ are all fixed values and preset values. Based on the different standard electromotive forces E p confirmed at different moments, generate the electromotive force standard line associated with this monitoring period. Place this electromotive force standard line and the electromotive force change curve in the same two-dimensional coordinate system, and identify the same-point potential difference Cz p located within the electromotive force standard line and the electromotive force change curve at the same moment, and confirm the identified several groups of same-point potential differences Cz p Whether they are all the same difference. If so, generate a display of the abnormal operation signal of the magnetic induction coil unit.

2. The electromechanical equipment fault diagnosis method for intelligent hydropower plants according to claim 1, characterized in that In the above Step 1, the specific method for confirming the power generation average interval associated with the corresponding speed is: S11. From the historical completed data, confirm the different power generations corresponding to different rotational speeds within a unit time. Use K i = power generation ÷ rotational speed to confirm the average associated power generation K i . Then, based on several groups of different average associated power generations K i confirmed at the same rotational speed, select the minimum value and the maximum value from them, and confirm the average power generation interval corresponding to several groups of different average associated power generations K i ; S12: For different water turbine unit speeds, confirm different power generation average intervals and record them.

3. The electromechanical equipment fault diagnosis method for intelligent hydropower plants according to claim 1, wherein In the above Step 2, the specific sub-steps for evaluating whether the hydraulic unit equipment is generating electricity normally are: S21. Real-time monitor the rotational speed of the water turbine unit in the hydraulic unit equipment, and calibrate the real-time monitored rotational speed as ZS k , where k represents different moments, then monitor the generated electricity associated with the corresponding moments and calibrate it as FD k , based on the current rotational speed ZS k , confirm the two sets of power generation parameters associated with its current rotational speed ZS k , draw up the power generation average amount interval as [Q1, Q2], where Q1 is the initial value of the power generation average amount interval and Q2 is the end value of the power generation average amount interval, and use: CL1 = ZS k ×Q1 to confirm the first set of power values CL1, and then use CL2 = ZS k ×Q2 to confirm the second set of power values CL2; S22. Determine the rotational speed ZS based on the confirmed first set of power values CL1 and the second set of power values CL2 k of the associated power range and identify its FD k to check whether: FD k ∈ the power range. If satisfied, it means that the hydro-generating unit equipment is operating normally at the current moment, and continuous monitoring can be carried out 4. The method for diagnosing mechanical and electrical equipment failures for intelligent hydropower plants according to claim 3, wherein, The interval between different adjacent moments is 5 minutes.

5. The method for diagnosing faults of electromechanical equipment for intelligent hydropower plants according to claim 3, characterized in that If FD k FD is not satisfied k ∈ the power range, it indicates that the hydropower unit equipment generates electricity abnormally at the current moment, and an abnormal signal is directly generated.

6. The method for diagnosing faults of electromechanical equipment for intelligent hydropower plants according to claim 1, wherein In the above Step S32, if there are other rotational speed ratios that do not belong to the same ratio, generate a gear unit transmission abnormal signal for display.

7. The electromechanical equipment fault diagnosis method for intelligent hydropower plants according to claim 1, characterized in that, In the step S42, if several groups of same-point position differences Cz p do not belong to the same difference, a power fluctuation abnormal signal is directly generated for display.

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