A method and system for early warning of fan equipment failure based on data analysis

Abstract

The present invention discloses a method and system for early warning of wind turbine equipment faults based on data analysis, which relates to the technical field of fault early warning. The main scheme is: through multi-source data collection, including power operation, mechanical, environmental and fault data; based on real-time power operation data, wind turbine operation-related indexes are calculated, and a comprehensive operation status score is obtained; the main mechanical data is used to calculate the blade fatigue, transmission compliance and structural resonance risk index, and then a comprehensive mechanical status score is obtained; the power and environmental data are combined to calculate the electrical insulation aging index and the electrical environment score; the fault impact score is obtained according to the fault data; the preset threshold is used to compare each score to determine whether to issue an early warning and generate a maintenance priority report.

Description

Technical Field

[0001] The present invention relates to the technical field of fault early warning, and in particular to a method and system for early warning of fan equipment faults based on data analysis. Background Art

[0002] With the increasing global demand for renewable energy, wind power generation has been widely used and developed as a clean and sustainable form of energy. As the core component of the wind power generation system, the reliability and stability of wind turbine equipment are directly related to the power generation efficiency and economic benefits of the wind farm. However, wind turbine equipment is exposed to harsh natural environments for a long time and is easily affected by factors such as mechanical wear, electrical failures, and material aging, resulting in frequent equipment failures, affecting power generation efficiency and safety.

[0003] Traditional wind turbine operation and maintenance models mostly rely on regular maintenance and inspection by staff to avoid equipment failures. This is inefficient and cannot fully reflect the operating status of the wind turbine. There are problems such as one-sided evaluation indicators and lack of systematic integration. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the deficiencies in the prior art, the present invention provides a method and system for early warning of wind turbine equipment faults based on data analysis. By collecting the real-time power operation data, mechanical data, environmental data and fault data of the wind turbine, a comprehensive operation status score, a comprehensive mechanical status score, an electrical environment score and a fault impact score are calculated based on these data. Preset thresholds are used to compare the scores to determine whether to issue an early warning and generate a maintenance priority report. These scores are also used to construct a fault assessment model to simulate the health index of the wind turbine and rank it. This solves the problems of one-sided, lack of systematicness and foresight in traditional wind turbine assessments, and achieves a comprehensive, dynamic and accurate assessment of the health status of the wind turbine, early warning of faults, and improved wind turbine operation and maintenance management level and reliability.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A wind turbine equipment fault early warning method based on data analysis, comprising the following steps:

[0008] Step 1: Collect the wind turbine's real-time power operation data, wind turbine mechanical data, environmental data, and fault data;

[0009] Step 2: Calculate the wind turbine health index (FHI) based on real-time power operation data i , fan power fluctuation index FPF i and frequency stability index FSI i , and further calculate the comprehensive operating status score ASD of the fani ;

[0010] Calculate the fan blade fatigue damage index FBD based on the fan body mechanical data i and fan mechanical transmission compliance index FMT i , according to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i ; According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i ;

[0011] Calculate the wind turbine electrical insulation aging index EAI based on real-time power operation data and environmental data i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i , and further calculate the comprehensive operating electrical environment score FCE of the fan i ;

[0012] Calculate the wind turbine fault impact score DVH based on fault data i ;

[0013] Step 3: Preset the wind turbine scoring threshold set and calculate the wind turbine comprehensive operating status score ASD i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i The wind turbine scores are compared with the thresholds in the threshold set to determine whether to issue an early warning. When an early warning is issued, an analysis report of the wind turbine maintenance priority is generated based on different early warning information.

[0014] In the preferred embodiment of the above-mentioned method for early warning of fan equipment failure based on data analysis: calculating the comprehensive operating status score ASD of the fan i The method is:

[0015] Real-time power operation data including wind turbine vibration amplitude reference value XIR i , real-time vibration amplitude value XER i , cumulative fan downtime TFA i , total fan operating time TAI i , the wind turbine's current actual power generation PAC i and wind turbine rated power PRT i;

[0016] Calculate the wind turbine operation health index (FHI) based on real-time power operation data i , based on the following formula:

[0017]

[0018] Among them, XER i is the real-time vibration amplitude value of the i-th fan, XIR i is the vibration amplitude reference value of the i-th fan, TFA i is the cumulative downtime of the i-th wind turbine, TAI i is the total operating time of the i-th fan, PAC i is the actual power generation of the i-th wind turbine, PRT i is the rated power of the i-th wind turbine, i is the serial number corresponding to different wind turbines, and its value is [1, n]; n is the number of wind turbines, and its value is a positive integer;

[0019] Real-time power operation data also includes the average power generation of wind turbines

[0020] According to the actual power generation capacity of the wind turbine i，j Average power generation of wind turbines Calculate the wind turbine power fluctuation index FPF i , based on the following formula:

[0021]

[0022] Among them, PAC i，j is the actual power generation of the i-th wind turbine at the j-th sampling moment, j is the sequence number corresponding to different sampling moments, and its value is [1, m]; m is the number of sampling moments, and its value is a positive integer;

[0023] Real-time power operation data also includes wind turbine real-time frequency FSR i and rated frequency FSN i ;

[0024] According to the fan real-time frequency FSR i and rated frequency FSN i Calculate the frequency stability index FSI i , based on the following formula:

[0025]

[0026] Among them, FSR i is the real-time frequency of the i-th fan, FSN i is the rated frequency of the i-th fan, β iFSR i -FSN i ) weight factor, γ i for The weight factor, δ i for The weight factor of , d is the differential operator, used to represent the derivative of the variable, and dt represents the differential of time;

[0027] According to the fan health index FHI i , fan power fluctuation index FPF i and frequency stability index FSI i Calculate the comprehensive operating status score ASD of the fan i , based on the following formula:

[0028]

[0029] In the preferred embodiment of the above-mentioned method for early warning of fan equipment failure based on data analysis: calculating the fatigue damage index FBD of the fan blade i and fan mechanical transmission compliance index FMT i The method is:

[0030] Mechanical data of the wind turbine including the number of stress cycles the blades are subjected to NIL i，g , fatigue life times NFI of blade material i，g , the stress amplitude ZXC borne by the blade i，g and the symmetrical cyclic fatigue limit value ZXV of the blade material i，g ;

[0031] According to the number of stress cycles the blade is subjected to NIL i，g , fatigue life times NFI of blade material i,g , the stress amplitude ZXC borne by the blade i，g and the symmetrical cyclic fatigue limit value ZXV of the blade material i，g Calculation of FBD index of wind turbine blades i , based on the following formula:

[0032]

[0033] Among them, NIL i，g NFI is the number of stress cycles that the blades of the i-th wind turbine endure under the g-th working condition, i，g is the fatigue life of the blade material under the g-th working condition of the i-th fan, ZXC i，g is the stress amplitude of the blade under the g-th working condition of the i-th wind turbine, ZXV i，g is the symmetrical cyclic fatigue limit value of the blade material under the g-th working condition of the i-th wind turbine, Ti，g is the influence coefficient of the g-th working condition of the i-th fan, g is the serial number corresponding to different stress working conditions, and its value is [1, q]; q is the number of stress working conditions, and its value is a positive integer; y is the fatigue strength index of the material, FBD i is the blade fatigue damage index of the i-th wind turbine;

[0034] The mechanical data of the wind turbine also includes the real-time torque CVB of the transmission components i，l , reference torque CVN i，l , the input angular velocity WTU of the transmission system i , output angular velocity WTI i , the resistance torque generated by energy loss during transmission FGJ i And the transmission system theoretical output force FTA i ;

[0035] According to the real-time torque CVB of the transmission components i，l , reference torque CVN i，l , the input angular velocity WTU of the transmission system i , output angular velocity WTI i , the resistance torque generated by energy loss during transmission FGJ i And the transmission system theoretical output force FTA i Calculate the fan mechanical transmission compliance index FMT i , based on the following formula:

[0036]

[0037] Among them, CVB i，l is the real-time torque of the lth transmission component of the i-th fan, CVN l is the reference torque of the lth transmission component of the i-th wind turbine, WTI i is the input angular velocity of the drive system of the i-th wind turbine, WTU i is the output angular velocity of the transmission system of the i-th fan, FGJi is the resistance torque equivalent to the energy loss generated during the transmission process of the i-th fan, and FTA is the i is the theoretical output force FTA of the transmission system of the i-th fan i , l is the serial number corresponding to different transmission components, and its value is [1, d]; d is the number of transmission components, and its value is a positive integer, FMT i is the mechanical transmission compliance index of the i-th fan.

[0038] In the preferred embodiment of the above-mentioned method for early warning of wind turbine equipment failure based on data analysis: calculating the wind turbine structural resonance risk index FSR i The method is:

[0039] According to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i , based on the following formula:

[0040]

[0041] In the preferred embodiment of the above-mentioned method for early warning of fan equipment failure based on data analysis: Calculate the comprehensive score of the fan mechanical state FBH i The method is:

[0042] According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i , based on the following formula:

[0043]

[0044] In the preferred solution of the above-mentioned method for early warning of fan equipment failure based on data analysis: Calculate the comprehensive operating electrical environment score FCE of the fan i The method is:

[0045] Real-time power operation data also includes the real-time insulation resistance RIN of the wind turbine i , Rated insulation resistance RIS i , Real-time operating voltage UOP i and rated voltage URA i ;

[0046] Environmental data includes the real-time temperature value TEN of the fan operating environment i and reference temperature value TRF i ;

[0047] According to the real-time insulation resistance RIN i , Rated insulation resistance RIS i , Real-time operating voltage UOP i 、Rated voltage URA i , Real-time temperature value TEN of the fan operating environment i and reference temperature TRF i Calculate the fan electrical insulation aging index EAI i , based on the following formula:

[0048]

[0049] Among them, RINi is the real-time insulation resistance of the i-th wind turbine, RIS i is the rated insulation resistance of the i-th fan, TEN i is the real-time temperature value of the i-th fan, TRF i is the reference temperature value of the i-th fan, UOP i is the real-time operating voltage of the i-th wind turbine, URA i is the rated voltage of the i-th fan;

[0050] Environmental data also includes real-time wind speed VWI at the turbine hub height i , rated wind speed VRA i , Real-time air density PAI of the fan operating environment i and standard air density PST i ;

[0051] According to the real-time wind speed VWI at the hub height of the wind turbine i , rated wind speed VRA i , Real-time air density PAI of the fan operating environment i , standard air density PST i , the wind turbine's current actual power generation PAC i and rated power generation PRT i Calculate the wind turbine power generation efficiency impairment index EEP i , based on the following formula:

[0052]

[0053] Among them, VWI i is the real-time wind speed of the i-th wind turbine, VRA i is the rated wind speed of the i-th wind turbine, PAI i is the real-time air density of the ith fan, PST i is the standard air density of the ith fan, PAC i is the actual power generation of the i-th wind turbine, PRT i is the rated power of the i-th wind turbine, EEP i is the power generation efficiency impairment index of the i-th wind turbine;

[0054] Real-time power operation data also includes the effective value of harmonic current TJK i And the fundamental current effective value TPM i ;

[0055] Environmental data also includes the real-time relative humidity value THU of the fan operating environment i and rated relative humidity THA i ;

[0056] According to the effective value of harmonic current TJK i , fundamental current effective value TPM i , Real-time relative humidity value THU of the fan operating environment i and rated relative humidity THA i Calculate the harmonic distortion index EHD of the wind turbine electrical system i , based on the following formula:

[0057]

[0058] in, is the power factor of the fan electrical system; THU i is the real-time relative humidity value of the i-th fan, THA i is the rated relative humidity value of the i-th fan, TJK i is the effective value of the harmonic current of the i-th wind turbine, TPM i is the effective value of the fundamental current of the i-th wind turbine, EHD i is the harmonic distortion index of the electrical system of the i-th wind turbine;

[0059] According to the fan electrical insulation aging index EAI i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i Calculate the comprehensive operating electrical environment score (FCE) of the fan i , based on the following formula:

[0060] FCE i =ω1×EAI i +EEP i ×ω2+EHD i ×ω3

[0061] Among them, ω1 is the fan electrical insulation aging index EAI i The weight coefficient is 0.2 to 0.4; ω2 is the wind turbine power generation efficiency impairment index EEP i The weight coefficient is 0.3 to 0.4; ω3 is the harmonic distortion index EHD of the fan electrical system i The weight coefficient is 0.3~0.4; and ω1+ω2+ω3=1.

[0062] In the preferred embodiment of the above-mentioned method for early warning of fan equipment failure based on data analysis: calculating the fan failure impact score DVH i The method is:

[0063] Fault data includes the number of fan failures NFM i , total running time DYT i, mean repair time TMT i and mean time between failures (TMF) i ;

[0064] According to the number of fan failures NFM i , total running time DYT i , mean repair time TMT i , mean time between failures TMF i and the cumulative downtime of the fan TFA i Calculating the Fan Failure Impact Score (DVH) i , based on the following formula:

[0065]

[0066] Among them, NFM i is the number of failures of the i-th fan, DYT i is the total operating time of the i-th wind turbine, TMT i is the mean time to repair the fault of the i-th wind turbine, TMF i is the mean time between failures of the i-th fan, TFA i is the cumulative downtime of the i-th fan, DVH i Score the fault impact of the i-th wind turbine.

[0067] In the preferred embodiment of the above-mentioned method for early warning of fan equipment failure based on data analysis, the method for determining whether to issue an early warning is:

[0068] The wind turbine scoring thresholds include a mild warning threshold TD and a severe warning threshold TW, wherein the severe warning threshold TW is greater than the mild warning threshold TD;

[0069] The comprehensive operating status score of the wind turbine is ASD i Compared with the mild warning threshold TD and the severe warning threshold TW, the warning criteria are as follows:

[0070]

[0071] The wind turbine scoring threshold also includes a mild warning threshold QA and a severe warning threshold QW, where the severe warning threshold QW is greater than the mild warning threshold QA;

[0072] The comprehensive score of the fan mechanical condition FBH i Compared with the mild warning threshold QA and the severe warning threshold QW, the warning criteria are as follows:

[0073]

[0074] The wind turbine scoring threshold also includes a mild warning threshold AZ and a severe warning threshold AS, wherein the severe warning threshold AS is greater than the mild warning threshold AZ;

[0075] The comprehensive operating electrical environment score FCE of the fan i Compared with the mild warning threshold AZ and the severe warning threshold AS, the warning criteria are as follows:

[0076]

[0077] The wind turbine scoring threshold also includes a mild warning threshold XD and a severe warning threshold XC, wherein the severe warning threshold XC is greater than the mild warning threshold XD;

[0078] DVH i Compared with the mild warning threshold XD and the severe warning threshold XC, the warning criteria are as follows:

[0079]

[0080] Among them, among the same type of warnings, the maintenance priority of severe warnings is higher than that of mild warnings.

[0081] In the preferred embodiment of the above-mentioned method for early warning of fan equipment failure based on data analysis:

[0082] Also includes step 4: scoring ASD based on the comprehensive operating status of the wind turbine i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i Calculate the fan health index NUL i ; According to the fan health index NUL of different fans i Rank the health of wind turbines;

[0083] Among them, the calculation of fan health index NUL i The formula is as follows:

[0084] NUL i =ASD i +FBH i +FCE i +DVH i .

[0085] The present invention also discloses a fan equipment fault early warning system based on data analysis, comprising:

[0086] Data acquisition module, used to collect real-time power operation data of the wind turbine, wind turbine body mechanical data, environmental data and fault data;

[0087] Scoring calculation module, used to calculate the wind turbine health index (FHI) based on real-time power operation data i , fan power fluctuation index FPF i and frequency stability index FSI i , and further calculate the comprehensive operating status score ASD of the fan i ;

[0088] Calculate the fan blade fatigue damage index FBD based on the fan body mechanical data i and fan mechanical transmission compliance index FMT i , according to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i ; According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i ;

[0089] Calculate the wind turbine electrical insulation aging index EAI based on real-time power operation data and environmental data i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i , and further calculate the comprehensive operating electrical environment score FCE of the fan i ;

[0090] Calculate the wind turbine fault impact score DVH based on fault data i ;

[0091] The early warning module is used to preset the wind turbine scoring threshold set and score the wind turbine comprehensive operating status ASD i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i The wind turbine scores are compared with the thresholds in the threshold set to determine whether to issue an early warning. When an early warning is issued, an analysis report of the wind turbine maintenance priority is generated based on different early warning information.

[0092] (3) Beneficial effects

[0093] The present invention provides a method and system for early warning of fan equipment failure based on data analysis, which has the following beneficial effects:

[0094] (1) By collecting the real-time power operation data, mechanical data, environmental data and fault data of the wind turbine, a rich and detailed data foundation is provided for subsequent accurate evaluation and analysis, avoiding one-sided evaluation due to missing data and making the understanding of the wind turbine status more complete and accurate.

[0095] (2) By calculating the comprehensive operating status score, mechanical status comprehensive score, comprehensive operating electrical environment score and fault impact score of the fan, the status of the fan in various aspects such as operation, machinery, electrical environment and fault impact can be quantified comprehensively and meticulously, which helps to accurately grasp the health status of the fan and discover potential problems in advance, providing a reliable basis for subsequent early warning, maintenance priority determination, fault assessment and health ranking, effectively ensuring the stable operation of the fan and reducing operation and maintenance costs and fault risks.

[0096] (3) Through the preset threshold comparison and early warning mechanism, the wind turbine scores can be monitored in real time, abnormalities can be detected in time and early warnings can be issued, effectively preventing failures and reducing safety risks. In addition, maintenance priority analysis reports can be generated based on early warnings, which can reasonably allocate maintenance resources and improve operation and maintenance efficiency and pertinence. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 The figure is a schematic diagram of the working steps of a method for early warning of fan equipment failure based on data analysis according to the present invention. DETAILED DESCRIPTION

[0098] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0099] See also Figure 1 The present invention provides a method for early warning of fan equipment failure based on data analysis, comprising the following steps:

[0100] Step 1: Collect the wind turbine's real-time power operation data, wind turbine mechanical data, environmental data, and fault data.

[0101] When using, combine the content of step 1:

[0102] By collecting the wind turbine's real-time power operation data, wind turbine mechanical data, environmental data, and fault data, a rich and detailed data foundation is provided for subsequent accurate evaluation and analysis, avoiding one-sided evaluation due to missing data and making the understanding of the wind turbine status more complete and accurate.

[0103] Step 2: Calculate the wind turbine health index (FHI) based on real-time power operation data i , fan power fluctuation index FPF i and frequency stability index FSI i , and further calculate the comprehensive operating status score ASD of the fan i .

[0104] Calculate the fan blade fatigue damage index FBD based on the fan body mechanical data i and fan mechanical transmission compliance index FMT i , according to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i ; According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i .

[0105] Calculate the wind turbine electrical insulation aging index EAI based on real-time power operation data and environmental data i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i , and further calculate the comprehensive operating electrical environment score FCE of the fan i .

[0106] Calculate the wind turbine fault impact score DVH based on fault data i .

[0107] Step 201: Calculate the comprehensive operating status score ASD of the wind turbine i The method is:

[0108] Real-time power operation data including wind turbine vibration amplitude reference value XIR i , real-time vibration amplitude value XER i , cumulative fan downtime TFA i , total fan operating time TAI i , the wind turbine's current actual power generation PAC i and wind turbine rated power PRT i .

[0109] It should be noted that the fan vibration amplitude reference value XIR i represents the vibration amplitude reference value of the i-th fan, which is obtained from the design specification provided by the fan manufacturer; the real-time vibration amplitude value XER irepresents the real-time vibration amplitude value of the i-th fan, which is measured in real time by the vibration sensor installed on the fan; the cumulative downtime of the fan TFA i The cumulative downtime of the i-th fan is obtained by recording the total downtime of the fan from the start of operation to the current moment through the fan control system; the total operating time of the fan TAI i It represents the total operating time of the i-th wind turbine, which is obtained by recording the total time from the start of the wind turbine operation to the current moment through the wind turbine control system; the current actual power generation power of the wind turbine PAC i The actual power generated by the i-th wind turbine is measured in real time by the power sensor installed in the wind turbine power generation system; the rated power generated by the wind turbine PRT i represents the rated power of the i-th wind turbine, which is obtained from the technical specifications provided by the wind turbine manufacturer.

[0110] Calculate the wind turbine operation health index (FHI) based on real-time power operation data i , based on the following formula:

[0111]

[0112] Where i is the serial number corresponding to different fans, and its value is [1, n]; n is the number of fans, and its value is a positive integer.

[0113] It should be noted that in this formula: the denominator Taking into account the difference between the real-time vibration amplitude value of the fan and the reference value of the vibration amplitude, the closer the real-time vibration amplitude value is to the reference value of the vibration amplitude, the smaller the value of this part is, indicating that the vibration stability of the fan is better, and the numerator is Taking into account the ratio of the cumulative downtime of the fan to the total operating time, the lower the ratio of the fan downtime to the total operating time, the larger the value of this part, indicating that the fan availability is higher. The ratio of the wind turbine's current actual power generation to its rated power generation is taken into account. When the actual power generation is close to or exceeds the rated power generation, the larger the value of this part is, the higher the power generation efficiency of the wind turbine is.

[0114] Real-time power operation data also includes the average power generation of wind turbines

[0115] It should be noted that the average power generation of wind turbines The average power generated by the wind turbine is obtained by averaging the actual power generated by the wind turbine at multiple sampling moments.

[0116] According to the actual power generation capacity of the wind turbine i,j Average power generation of wind turbines Calculate the wind turbine power fluctuation index FPF i, based on the following formula:

[0117]

[0118] Among them, PAC i,j is the actual power generation of the i-th wind turbine at the j-th sampling moment, j is the sequence number corresponding to different sampling moments, and its value is [1, m]; m is the number of sampling moments, and its value is a positive integer.

[0119] It should be noted that in this formula: The actual power generation PAC of the i-th wind turbine at each sampling moment is calculated i,j Average power generation The larger the squared sum of deviations, the greater the power fluctuation of the fan. Get the fan power fluctuation index FPF i .

[0120] Real-time power operation data also includes wind turbine real-time frequency FSR i and rated frequency FSN i .

[0121] It should be noted that the fan real-time frequency FSR i Represents the real-time frequency measurement value of the i-th fan, which is obtained by real-time measurement of the frequency sensor installed in the fan system; rated frequency FSN i represents the rated frequency of the i-th fan, which can be obtained from the fan's technical specifications.

[0122] According to the fan real-time frequency FSR i and rated frequency FSN i Calculate the frequency stability index FSI i , based on the following formula:

[0123]

[0124] Among them, β i FSR i -FSN i ) weight factor, γ i for The weight factor, δ i for The weight factor is measured by the frequency sensor under different wind speed and wind direction conditions. The frequency data is calculated by the least squares method to calculate the minimum, maximum and average values ​​of the frequency stability index and the actual operation stability error of the wind turbine, which is β i , γ i and δ i; d is the differential operator, used to represent the derivative of the variable, and dt represents the differential of time.

[0125] It should be noted that in this formula: (FSR i -FSN i ) represents the difference between the real-time frequency measurement value of the i-th fan and the rated frequency. This difference reflects the degree to which the current frequency of the fan deviates from the rated frequency. Represents the frequency change rate of the i-th fan. The larger the frequency change rate, the more drastic the dynamic change of the fan frequency, which may affect the stability of the system. It represents the second-order frequency change rate of the i-th fan, reflecting the acceleration of the frequency change, which is important for judging whether the fan frequency is tending to be stable or the fluctuation is intensified. i , γ i and δ i are weight factors related to the frequency of the i-th wind turbine, which are used to adjust the importance of different factors in the overall frequency stability assessment, and the frequency stability index FSI i It is a quantitative indicator used to evaluate the frequency stability of wind turbines. The smaller the value, the better the frequency stability of the wind turbine; the larger the value, the worse the frequency stability of the wind turbine, which may affect the normal operation of the wind turbine and even the stability of the power grid.

[0126] According to the fan health index FHI i , fan power fluctuation index FPF i and frequency stability index FSI i Calculate the comprehensive operating status score ASD of the fan i , based on the following formula:

[0127]

[0128] It should be noted that in this formula: FHI i The higher the index value, the better the health of the fan. (1-FPF i ) indicates the stability of the fan power. The smaller the power fluctuation index, the greater the 1-FPF i The larger the value, the more stable the fan power is. i The higher the value, the more stable the fan frequency; add these three data and divide by three to get the fan comprehensive operating status score ASD i , the denominator is 3, which plays a normalization role and allows for intuitive comparison and evaluation of the operating status of the fans.

[0129] Step 202: Calculate the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i The method is:

[0130] Mechanical data of the wind turbine including the number of stress cycles the blades are subjected to NIL i，g , fatigue life times NFI of blade material i，g , the stress amplitude ZXC borne by the blade i，g and the symmetrical cyclic fatigue limit value ZXV of the blade material i，g .

[0131] It should be noted that the number of stress cycles that the blade is subjected to is NIL i，g It represents the number of stress cycles that the blades of the i-th wind turbine are subjected to under the g-th working condition, which is obtained by real-time monitoring of the stress sensors installed on the wind turbine blades during the operation of the wind turbine; the fatigue life number NFI of the blade material i，g Indicates the fatigue life of the blade material under the g-th working condition of the i-th wind turbine, provided by the supplier of the blade material; the stress amplitude ZXC borne by the blade i，g It represents the stress amplitude of the blade under the g-th working condition of the i-th wind turbine. The stress changes of the blade under the starting, stable operation and shutdown conditions are measured by the stress sensor, and the average values ​​under the three working conditions are calculated and added together; the symmetrical cyclic fatigue limit value ZXV of the blade material is obtained. i，g The symmetrical cyclic fatigue limit value (SCFL) of the blade material under the g-th operating condition of the i-th wind turbine is determined by performing a fatigue test on the blade material. During the test, the stress amplitude is gradually increased until fatigue failure occurs. The stress amplitude at this time is recorded as the SCFL.

[0132] According to the number of stress cycles the blade is subjected to NIL i，g , fatigue life times NFI of blade material i,g , the stress amplitude ZXC borne by the blade i,g and the symmetrical cyclic fatigue limit value ZXV of the blade material i，g Calculation of FBD index of wind turbine blades i , based on the following formula:

[0133]

[0134] Among them, T i，g is the influence coefficient of the g-th working condition of the i-th wind turbine, which is obtained by adding the average values ​​of blade fatigue damage under wind speed, wind direction, temperature and other working conditions. g is the serial number corresponding to different stress working conditions, which is [1, q]; q is the number of stress working conditions, which is a positive integer; y is the fatigue strength index of the material, which is obtained by performing constant amplitude fatigue and variable amplitude fatigue tests on the blade material, testing the number of cycles when the material fails, and calculating the average value using the fatigue life formula. i is the blade fatigue damage index of the i-th wind turbine.

[0135] It should be noted that in this formula: It represents the ratio of the stress amplitude of the blade to the symmetrical cyclic fatigue limit of the blade material, reflecting the degree of stress borne by the blade under the g-th working condition relative to its fatigue limit. It represents the ratio of the number of stress cycles to the fatigue life of the material. The more stress cycles, the greater the fatigue damage of the blade. The above two parts are multiplied by the influence coefficient to obtain the fan blade fatigue damage index FBD i .

[0136] The mechanical data of the wind turbine also includes the real-time torque CVB of the transmission components i，l , reference torque CVN i，l , the input angular velocity WTU of the transmission system i , output angular velocity WTI i , the resistance torque generated by energy loss during transmission FGJ i And the transmission system theoretical output force FTA i .

[0137] It should be noted that the real-time torque CVB i，l The real-time torque of the l-th transmission component of the i-th fan is measured by the torque sensor in real time; the reference torque CVN i，l The reference torque of the lth transmission component of the i-th fan is obtained from the technical specifications; the input angular velocity WTU i The input angular velocity of the drive system of the i-th wind turbine is measured by the angular velocity sensor; the output angular velocity WTI i represents the output angular velocity of the transmission system of the i-th fan, which is measured by the angular velocity sensor; the resistance torque equivalent force FGJi represents the resistance torque equivalent force generated by the energy loss during the transmission process of the i-th fan, which is calculated according to the law of conservation of energy; the theoretical output force FTA of the transmission system i represents the theoretical output force of the transmission system of the i-th fan, which is obtained from the technical specifications.

[0138] According to the real-time torque CVB of the transmission components i，l , reference torque CVN i，l , the input angular velocity WTU of the transmission system i , output angular velocity WTI i , the resistance torque generated by energy loss during transmission FGJ i And the transmission system theoretical output force FTA i Calculate the fan mechanical transmission compliance index FMT i , based on the following formula:

[0139]

[0140] Among them, l is the serial number corresponding to different transmission components, and its value is [1, d]; d is the number of transmission components, and its value is a positive integer. i is the mechanical transmission compliance index of the i-th fan.

[0141] It should be noted that in this formula: By calculating the deviation between the real-time torque of each transmission component and the reference torque and summing these deviations, the larger the deviation, the more likely the transmission component is to be subjected to additional load or to be in a non-ideal working state; It represents the ratio of the input angular velocity to the output angular velocity of the transmission system, Indicates the proportion of energy loss in the theoretical output force of the transmission system, The larger the value, the smaller the energy loss and the higher the efficiency of the transmission system.

[0142] Step 203: Calculate the wind turbine structural resonance risk index FSR i The method is:

[0143] According to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i , based on the following formula:

[0144]

[0145] It should be noted that in this formula: The ratio of the absolute value of the difference between the mechanical transmission compliance index and the blade fatigue damage index to the blade fatigue damage index was calculated. i With FBD i When approaching, The value of is small, The larger the value of FMT, the better the mechanical transmission compliance of the wind turbine matches the blade fatigue damage, and the lower the risk of structural resonance. i With FBD i When the difference is large, The value of is larger, The smaller the value, the less likely it is that the mechanical transmission compliance of the wind turbine does not match the blade fatigue damage, and the structural resonance risk is relatively high. By multiplying the above ratio with the blade fatigue damage index and then multiplying it by the inverse of the mechanical transmission compliance index, the wind turbine structural resonance risk index FSR is obtained. i; The larger the blade fatigue damage index, the more serious the blade fatigue damage and the higher the structural resonance risk may be; when the mechanical transmission flexibility is good, it helps to reduce the structural resonance risk index; when the mechanical transmission flexibility is poor, the structural resonance risk index will increase.

[0146] Step 204: Calculate the comprehensive score of the fan mechanical status FBH i The method is:

[0147] According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i , based on the following formula:

[0148]

[0149] It should be noted that in this formula: In the case of FSR i The smaller the value, The smaller the value of The larger the value of , the lower the risk of structural resonance, which has a positive contribution to the overall score; In the case of FMT i The larger the value, The larger the value of , the better the mechanical transmission compliance, which has a positive contribution to the comprehensive score; In the case of FBD i The smaller the value, The larger the value, the smaller the blade fatigue damage, which has a positive contribution to the comprehensive score. The three parts are multiplied to obtain the comprehensive score of the wind turbine mechanical condition FBH. i , the higher the score, the better the mechanical condition of the fan.

[0150] Step 205: Calculate the comprehensive operating electrical environment score FCE of the fan i The method is:

[0151] Real-time power operation data also includes the real-time insulation resistance RIN of the wind turbine i , rated insulation resistance RISi, real-time operating voltage UOP i and rated voltage URA i .

[0152] It should be noted that the real-time insulation resistance RIN of the fan i Indicates the real-time insulation resistance of the i-th fan, which is obtained by measuring the insulation resistance of the fan in real time using an insulation resistance tester; Rated insulation resistance RIS iIndicates the rated insulation resistance of the i-th fan, obtained from the fan's technical specifications; real-time operating voltage UOP i Indicates the real-time operating voltage of the i-th fan, which is measured in real time by the voltage sensor; the rated voltage URA i represents the rated voltage of the i-th fan, which is obtained from the fan's technical specifications.

[0153] Environmental data includes the real-time temperature value TEN of the fan operating environment i and reference temperature TRF i .

[0154] It should be noted that the real-time temperature value TEN i Indicates the real-time temperature value of the i-th fan, which is measured in real time by the temperature sensor; the reference temperature value TRF i It represents the reference temperature value of the i-th fan, which is obtained from the technical specifications of the fan.

[0155] According to the real-time insulation resistance RIN i , Rated insulation resistance RIS i , Real-time operating voltage UOP i 、Rated voltage URA i , Real-time temperature value TEN of the fan operating environment i and reference temperature TRF i Calculate the fan electrical insulation aging index EAI i , based on the following formula:

[0156]

[0157] It should be noted that in this formula: Related to insulation resistance, when the real-time insulation resistance is close to the rated insulation resistance, the value increases, indicating that the insulation condition is better; Related to temperature, when the real-time temperature is close to the reference temperature, the value is close to 1, indicating that the temperature has little effect on insulation; Related to voltage, when the real-time operating voltage is close to the rated voltage, the value is close to 1, indicating that the voltage has little effect on insulation; multiplying the three parts together gives the fan electrical insulation aging index EAI i The higher the index, the higher the risk of aging of the fan electrical insulation.

[0158] Environmental data also includes real-time wind speed VWI at the turbine hub height i , rated wind speed VRA i , Real-time air density PAI of the fan operating environment i and standard air density PST i .

[0159] It should be noted that the real-time wind speed VWI i Indicates the real-time wind speed of the i-th wind turbine, which is measured in real time by an anemometer; the rated wind speed VRA i represents the rated wind speed of the i-th fan, which is obtained from the fan's technical specifications; the real-time air density PAI i Indicates the real-time air density of the i-th fan, which is measured in real time by the air density sensor; standard air density PST i It represents the standard air density of the i-th fan, which is obtained according to the international standard value.

[0160] According to the real-time wind speed VWI at the hub height of the wind turbine i , rated wind speed VRA i , Real-time air density PAI of the fan operating environment i , standard air density PST i , the wind turbine's current actual power generation PAC i and rated power generation PRT i Calculate the wind turbine power generation efficiency impairment index EEP i , based on the following formula:

[0161]

[0162] Among them, PAC i is the actual power generation of the i-th wind turbine, PRT i is the rated power of the i-th wind turbine, EEP i is the power generation efficiency impairment index of the i-th wind turbine.

[0163] It should be noted that in this formula: Indicates the difference between the actual power generation of the wind turbine and the rated power generation. When the actual power generation of the wind turbine is close to the rated power generation, the value is small, indicating that the power generation efficiency is high. Taking into account the relationship between real-time wind speed and rated wind speed, this value is larger when the real-time wind speed is close to the rated wind speed, indicating that the wind speed has less impact on power generation efficiency; Taking into account the impact of air density on power generation efficiency, when the real-time air density is close to the standard air density, the value is close to 1, indicating that the air density has little impact on power generation efficiency; multiplying the three parts together gives the wind turbine power generation efficiency impairment index EEP i .

[0164] Real-time power operation data also includes the effective value of harmonic current TJK i And the fundamental current effective value TPM i .

[0165] It should be noted that the effective value of harmonic current TJK iThe effective value of the harmonic current of the i-th wind turbine is measured by the current transformer and the power quality analyzer; the effective value of the fundamental current TPM i It represents the effective value of the fundamental current of the i-th wind turbine, which is measured by the current transformer and power quality analyzer.

[0166] Environmental data also includes the real-time relative humidity value THU of the fan operating environment i and rated relative humidity THA i .

[0167] It should be noted that the real-time relative humidity value THU i Represents the real-time relative humidity value of the i-th fan, which is measured in real time by the humidity sensor; the rated relative humidity value THA i represents the rated relative humidity value of the i-th fan, which is obtained from the fan's technical specifications.

[0168] According to the effective value of harmonic current TJK i , fundamental current effective value TPM i , Real-time relative humidity value THU of the fan operating environment i and rated relative humidity THA i Calculate the harmonic distortion index EHD of the wind turbine electrical system i , based on the following formula:

[0169]

[0170] in, is the power factor of the wind turbine electrical system, measured by a power factor meter, EHD i is the harmonic distortion index of the electrical system of the i-th wind turbine.

[0171] It should be noted that in this formula: The size of can reflect the efficiency of electric energy utilization. When the power factor is low and the real-time relative humidity deviates greatly from the rated value, A large value means that the electrical system has poor energy utilization in an undesirable humidity environment; When the effective value of the harmonic current is larger than the effective value of the fundamental current, this part is large, indicating that the harmonic distortion is serious; multiplying the two parts together gives the harmonic distortion index EHD of the wind turbine electrical system i The higher the index, the more the fan electrical system is affected by humidity and the more serious the harmonic distortion is, which will have an adverse effect on the fan operation and the life of the electrical equipment.

[0172] According to the fan electrical insulation aging index EAI i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHDi Calculate the comprehensive operating electrical environment score (FCE) of the fan i , based on the following formula:

[0173] FCE i =ω1×EAI i +EEP i ×ω2+EHD i ×ω3

[0174] Among them, ω1 is the fan electrical insulation aging index EAI i The weight coefficient is based on the fan electrical insulation aging index EAI i Comprehensive operating electrical environment score FCE for wind turbines i The degree of influence is determined by the value of 0.2 to 0.4; ω2 is the wind turbine power generation efficiency impairment index EEP i The weight coefficient is based on the wind turbine power generation efficiency impairment index EEP i Comprehensive operating electrical environment score FCE for wind turbines i The influence degree is determined by the value of 0.3 to 0.4; ω3 is the harmonic distortion index EHD of the fan electrical system i The weight coefficient is based on the harmonic distortion index EHD of the wind turbine electrical system. i Comprehensive operating electrical environment score FCE for wind turbines i The degree of influence is determined, and the value is 0.3~0.4; and ω1+ω2+ω3=1.

[0175] It should be noted that in this formula: the fan electrical insulation aging index EAI i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i Multiply it by its weight coefficient and then add it to get the comprehensive operating electrical environment score FCE of the fan i , when FCE i When the value is low, it means that the electrical operating environment of the fan is good; when FCE i A high value indicates that the electrical operating environment of the fan is poor.

[0176] Step 206: Calculate the wind turbine failure impact score DVH i The method is:

[0177] Fault data includes the number of fan failures NFM i , total running time DYT i , mean repair time TMT i and mean time between failures (TMF) i .

[0178] It should be noted that the number of failures NFMi Indicates the number of failures of the i-th fan, obtained through the fan fault recording system; the total operating time DYT i represents the total operating time of the i-th wind turbine, which is obtained through the wind turbine operating time recording system; the mean repair time TMT i The mean time to repair the fault of the i-th wind turbine is obtained by recording the repair time of each fault of the wind turbine and then calculating the average value; the mean time between failures TMF i It represents the mean time between failures of the i-th fan, which is obtained by calculating the operating time interval between two adjacent failures of the fan and then finding the average value.

[0179] According to the number of fan failures NFM i , total running time DYT i , mean repair time TMT i , mean time between failures TMF i and the cumulative downtime of the fan TFA i Calculating the Fan Failure Impact Score (DVH) i , based on the following formula:

[0180]

[0181] Among them, TFA i is the cumulative downtime of the i-th fan, DVH i Score the fault impact of the i-th wind turbine.

[0182] It should be noted that in this formula: Indicates the frequency of fan failure. The more failures occur and the shorter the total operating time, the higher the failure frequency. Indicates the ratio of the cumulative downtime of the fan to the total operating time. The longer the cumulative downtime and the shorter the total operating time, the larger the value of this part, and the greater its contribution to the fan fault impact score; Considering the relationship between the repair time and the time between failures of the fan, when the mean repair time is longer than the mean time between failures, its value is smaller, indicating that the recovery ability of the fan after failure is poor; the three are multiplied to obtain the fan failure impact score DVH i .

[0183] When using, combine the contents of steps 201 to 206:

[0184] By calculating the comprehensive operating status score, mechanical status comprehensive score, comprehensive operating electrical environment score and fault impact score of the fan, the status of the fan in various aspects such as operation, mechanics, electrical environment and fault impact can be comprehensively and meticulously quantified. This helps to accurately grasp the health status of the fan and discover potential problems in advance, providing a reliable basis for subsequent early warning, maintenance priority determination, fault assessment and health ranking, and effectively ensuring the stable operation of the fan.

[0185] Step 3: Preset the wind turbine scoring threshold set and calculate the wind turbine comprehensive operating status score ASD i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i The wind turbine scores are compared with the thresholds in the threshold set to determine whether to issue an early warning. When an early warning is issued, an analysis report of the wind turbine maintenance priority is generated based on different early warning information.

[0186] Step 301: The method for presetting the wind turbine scoring threshold set is:

[0187] The wind turbine scoring thresholds include the mild warning threshold TD, severe warning threshold TW, mild warning threshold QA, severe warning threshold QW, mild warning threshold AZ, severe warning threshold AS, mild warning threshold XD and severe warning threshold XC.

[0188] The mild warning threshold TD is obtained by collecting historical operating data of the fan under normal and stable operating conditions, calculating the comprehensive operating status score of the fan at multiple time points, and calculating the average value, which is used as the mild warning threshold TD; the severe warning threshold TW is obtained by collecting historical fault data when different faults occur, calculating the comprehensive operating status score of the fan at multiple time points, and calculating the average value, which is used as the severe warning threshold TW.

[0189] The mild warning threshold QA is obtained by collecting historical operating data of the fan under normal and stable operating conditions, calculating the comprehensive score of the fan mechanical status at multiple time points, and calculating the average value as the mild warning threshold QA; the severe warning threshold QW is obtained by collecting historical fault data when different faults occur, calculating the comprehensive score of the fan mechanical status at multiple time points, and calculating the average value as the severe warning threshold QW.

[0190] The mild warning threshold AZ is obtained by collecting historical operating data of the fan under normal and stable operating conditions, calculating the comprehensive operating electrical environment score of the fan at multiple time points, and calculating the average value as the mild warning threshold AZ; the severe warning threshold AS is obtained by collecting historical fault data when different faults occur, calculating the comprehensive operating electrical environment score of the fan at multiple time points, and calculating the average value as the severe warning threshold AS.

[0191] The mild warning threshold XD is obtained by collecting historical operating data of the fan under normal and stable operating conditions, calculating the fan fault impact score at multiple time points, and calculating the average value as the mild warning threshold XD; the severe warning threshold XC is obtained by collecting historical fault data when different faults occur, calculating the fan fault impact score at multiple time points, and calculating the average value as the severe warning threshold XC.

[0192] Step 302: The method for determining whether to issue an early warning is:

[0193] The comprehensive operating status score of the wind turbine is ASD i Compared with the mild warning threshold TD and the severe warning threshold TW, the warning criteria are as follows:

[0194]

[0195] The comprehensive score of the fan mechanical condition FBH i Compared with the mild warning threshold QA and the severe warning threshold QW, the warning criteria are as follows:

[0196]

[0197] The comprehensive operating electrical environment score FCE of the fan i Compared with the mild warning threshold AZ and the severe warning threshold AS, the warning criteria are as follows:

[0198]

[0199] DVH i Compared with the mild warning threshold XD and the severe warning threshold XC, the warning criteria are as follows:

[0200]

[0201] Among them, among the same type of warnings, the maintenance priority of severe warnings is higher than that of mild warnings.

[0202] The analysis report may include: the various scores of the wind turbine, the time when the warning is triggered, the corresponding score data and the abnormality type, and the maintenance priority according to severe warning and mild warning.

[0203] When using, combine the contents of step 301 to step 302:

[0204] Through the preset threshold comparison and early warning mechanism, the wind turbine scores can be monitored in real time, abnormalities can be detected in time and early warnings can be issued, effectively preventing failures and reducing safety risks. A maintenance priority analysis report can be generated based on the early warning. The report includes the various scores of the wind turbine, records the time when the early warning is triggered, the corresponding score data and the type of abnormality, and sorts the maintenance priorities into high, medium and low. Maintenance resources can be reasonably allocated to improve the efficiency and pertinence of operation and maintenance.

[0205] Step 4: Score ASD based on the comprehensive operating status of the wind turbine i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i Calculate the fan health index NUL i ; According to the fan health index NUL of different fans i Rank the health of your wind turbines.

[0206] Step 401: Score the ASD based on the comprehensive operating status of the wind turbine i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i The formula for calculating the fan health index NUL is as follows:

[0207] NUL i =ASD i +FBH i +FCE i +DVH i .

[0208] It should be noted that the principle of this formula is: by adding these four scoring indicators, the fan health index is obtained. The higher the index, the more problems the fan may have during operation and the worse the health condition; the lower the index, the better the operating status of the fan and the better the health condition.

[0209] By calculating the fan health index, we can achieve a forward-looking prediction of the fan health status, plan maintenance plans in advance, avoid failures, ensure the continuous and stable operation of the fan, and provide data support for fan performance optimization and upgrades.

[0210] Step 402: Based on the fan health index NUL of different fans i Rank the health of your wind turbines.

[0211] The wind turbine health index ranking facilitates horizontal comparison of the health status of different wind turbines, quickly identifies wind turbines in poor health, prioritizes maintenance and servicing, optimizes the operation and maintenance management strategy of the entire wind turbine fleet, and improves the reliability and operating efficiency of the entire wind turbine system.

[0212] On the other hand, the present invention also discloses a wind turbine equipment fault early warning system based on data analysis, comprising:

[0213] Data acquisition module, used to collect real-time power operation data of the wind turbine, wind turbine body mechanical data, environmental data and fault data;

[0214] Scoring calculation module, used to calculate the wind turbine health index (FHI) based on real-time power operation data i , fan power fluctuation index FPF i and frequency stability index FSI i , and further calculate the comprehensive operating status score ASD of the fan i ;

[0215] Calculate the fan blade fatigue damage index FBD based on the fan body mechanical data i and fan mechanical transmission compliance index FMT i , according to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i ; According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i ;

[0216] Calculate the wind turbine electrical insulation aging index EAI based on real-time power operation data and environmental data i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i , and further calculate the comprehensive operating electrical environment score FCE of the fan i ;

[0217] Calculate the wind turbine fault impact score DVH based on fault data i ;

[0218] The early warning module is used to preset the wind turbine scoring threshold set and score the wind turbine comprehensive operating status ASD i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE iand wind turbine failure impact score DVH i The wind turbine scores are compared with the thresholds in the threshold set to determine whether to issue an early warning. When an early warning is issued, an analysis report of the wind turbine maintenance priority is generated based on different early warning information.

[0219] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0220] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0221] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A wind turbine equipment fault early warning method based on data analysis, characterized by: The following steps are involved: Step 1: Collect the wind turbine's real-time power operation data, wind turbine mechanical data, environmental data, and fault data; Step 2: Calculate the wind turbine health index (FHI) based on real-time power operation data i , fan power fluctuation index FPF i and frequency stability index FSI i , and further calculate the comprehensive operating status score ASD of the fan i Among them, the real-time power operation data includes the fan vibration amplitude reference value XIR i , real-time vibration amplitude value XER i , cumulative fan downtime TFA i , total fan operating time TAI i , the wind turbine's current actual power generation PAC i and wind turbine rated power PRT i ; Calculate the wind turbine operation health index (FHI) based on real-time power operation data i , based on the following formula: Among them, XER i is the real-time vibration amplitude value of the i-th fan, XIR i is the vibration amplitude reference value of the i-th fan, TFA i is the cumulative downtime of the i-th wind turbine, TAI i is the total operating time of the i-th fan, PAC i is the actual power generation of the i-th wind turbine, PRT i is the rated power of the i-th wind turbine, i is the serial number corresponding to different wind turbines, and its value is [1, n]; n is the number of wind turbines, and its value is a positive integer; Calculate the fan blade fatigue damage index FBD based on the fan body mechanical data i and fan mechanical transmission compliance index FMT i , according to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i ; According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i ; Calculate the wind turbine electrical insulation aging index EAI based on real-time power operation data and environmental data i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i , and further calculate the comprehensive operating electrical environment score FCE of the fan i ; Calculate the wind turbine fault impact score DVH based on fault data i ; Step 3: Preset the wind turbine scoring threshold set and calculate the wind turbine comprehensive operating status score ASD i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i The wind turbine scores are compared with the thresholds in the threshold set to determine whether to issue an early warning. When an early warning is issued, an analysis report of the wind turbine maintenance priority is generated based on different early warning information.

2. The method for early warning of fan equipment failure based on data analysis according to claim 1, characterized in that: Calculate the comprehensive operating status score ASD of the fan i The method is: Real-time power operation data also includes the average power generation of wind turbines According to the actual power generation capacity of the wind turbine i,j Average power generation of wind turbines Calculate the wind turbine power fluctuation index FPF i , based on the following formula: Among them, PAC i,j is the actual power generation of the i-th wind turbine at the j-th sampling moment, j is the sequence number corresponding to different sampling moments, and its value is [1, m]; m is the number of sampling moments, and its value is a positive integer; Real-time power operation data also includes wind turbine real-time frequency FSR i and rated frequency FSN i ; According to the fan real-time frequency FSR i and rated frequency FSN i Calculate the frequency stability index FSI i , based on the following formula: Among them, FSR i is the real-time frequency of the i-th fan, FSN i is the rated frequency of the i-th fan, β i FSR i -FSN i ) weight factor, γ i for The weight factor, δ i for The weight factor, d is the differential operator, used to represent the derivative of the variable, and dt represents the time differential; According to the fan health index FHI i , fan power fluctuation index FPF i and frequency stability index FSI i Calculate the comprehensive operating status score ASD of the fan i , based on the following formula:

3. The method for early warning of fan equipment failure based on data analysis according to claim 2, characterized in that: Calculation of FBD index of wind turbine blades i and fan mechanical transmission compliance index FMT i The method is: Mechanical data of the wind turbine including the number of stress cycles the blades are subjected to NIL i,g , fatigue life times NFI of blade material i,g , the stress amplitude ZXC borne by the blade i,g and the symmetrical cyclic fatigue limit value ZXV of the blade material i,g ; According to the number of stress cycles the blade is subjected to NIL i,g , fatigue life times NFI of blade material i,g , the stress amplitude ZXC borne by the blade i,g and the symmetrical cyclic fatigue limit value ZXV of the blade material i,g Calculation of FBD index of wind turbine blades i , based on the following formula: Among them, NIL i,g NFI is the number of stress cycles that the blades of the i-th wind turbine endure under the g-th working condition, i,g is the fatigue life of the blade material under the g-th working condition of the i-th fan, ZXC i,g is the stress amplitude of the blade under the g-th working condition of the i-th wind turbine, ZXV i,g is the symmetrical cyclic fatigue limit value of the blade material under the g-th working condition of the i-th wind turbine, τ i,g is the influence coefficient of the g-th working condition of the i-th fan, g is the serial number corresponding to different stress working conditions, and its value is [1, q]; q is the number of stress working conditions, and its value is a positive integer; y is the fatigue strength index of the material, FBD i is the blade fatigue damage index of the i-th wind turbine; The mechanical data of the wind turbine also includes the real-time torque CVB of the transmission components i,l , reference torque CVN i,l , the input angular velocity WTU of the transmission system i , output angular velocity WTI i , the resistance torque generated by energy loss during transmission FGJ i And the transmission system theoretical output force FTA i ; According to the real-time torque CVB of the transmission components i,l , reference torque CVN i,l , the input angular velocity WTU of the transmission system i , output angular velocity WTI i , the resistance torque generated by energy loss during transmission FGJ i And the transmission system theoretical output force FTA i Calculate the fan mechanical transmission compliance index FMT i , based on the following formula: Among them, CVB i,l is the real-time torque of the lth transmission component of the i-th fan, CVN l is the reference torque of the lth transmission component of the i-th wind turbine, WTI i is the input angular velocity of the drive system of the i-th wind turbine, WTU i is the output angular velocity of the transmission system of the i-th fan, FGJ i is the resistance torque equivalent to the energy loss during the transmission process of the i-th fan, FTA i is the theoretical output force FTA of the transmission system of the i-th fan i , l is the serial number corresponding to different transmission components, and its value is [1, d]; d is the number of transmission components, and its value is a positive integer, FMT i is the mechanical transmission compliance index of the i-th fan.

4. The method for early warning of fan equipment failure based on data analysis according to claim 3, characterized in that: Calculate the wind turbine structural resonance risk index FSR i The method is: According to the fan blade fatigue damage index FBC i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i , based on the following formula:

5. The method for early warning of fan equipment failure based on data analysis according to claim 4, characterized in that: Calculate the comprehensive score of the fan mechanical condition FBH i The method is: According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i , based on the following formula:

6. The method for early warning of fan equipment failure based on data analysis according to claim 5, characterized in that: Calculate the comprehensive operating electrical environment score (FCE) of the fan i The method is: Real-time power operation data also includes the real-time insulation resistance RIN of the wind turbine i , Rated insulation resistance RIS i , Real-time operating voltage UOP i and rated voltage URA i ; Environmental data includes the real-time temperature value TEN of the fan operating environment i and reference temperature value TRF i ; According to the real-time insulation resistance RIN i , Rated insulation resistance RIS i , Real-time operating voltage UOP i 、Rated voltage URA i , Real-time temperature value TEN of the fan operating environment i and reference temperature value TRF i Calculate the fan electrical insulation aging index EAI i , based on the following formula: Among them, RIN i is the real-time insulation resistance of the i-th wind turbine, RIS i is the rated insulation resistance of the i-th fan, TEN i is the real-time temperature value of the i-th fan, TRF i is the reference temperature value of the i-th fan, UOP i is the real-time operating voltage of the i-th wind turbine, URA i is the rated voltage of the i-th fan; Environmental data also includes real-time wind speed VWI at the turbine hub height i , rated wind speed VRA i , Real-time air density PAI of the fan operating environment i and standard air density PST i ; According to the real-time wind speed VWI at the hub height of the wind turbine i , rated wind speed VRA i , Real-time air density PAI of the fan operating environment i , standard air density PST i , the wind turbine's current actual power generation PAC i and rated power generation PRT i Calculate the wind turbine power generation efficiency impairment index EEP i , based on the following formula: Among them, VWI i is the real-time wind speed of the i-th wind turbine, VRA i is the rated wind speed of the i-th wind turbine, PAI i is the real-time air density of the ith fan, PST i is the standard air density of the ith fan, PAC i is the actual power generation of the i-th wind turbine, PRT i is the rated power of the i-th wind turbine, EEP i is the power generation efficiency impairment index of the i-th wind turbine; Real-time power operation data also includes the effective value of harmonic current TJK i And the fundamental current effective value TPM i ; Environmental data also includes the real-time relative humidity value THU of the fan operating environment i and rated relative humidity THA i ; According to the effective value of harmonic current TJK i , fundamental current effective value TPM i , Real-time relative humidity value THU of the fan operating environment i and rated relative humidity THA i Calculate the harmonic distortion index EHD of the wind turbine electrical system i , based on the following formula: in, is the power factor of the fan electrical system; THU i is the real-time relative humidity value of the i-th fan, THA i is the rated relative humidity value of the i-th fan, TJK i is the effective value of the harmonic current of the i-th wind turbine, TPM i is the effective value of the fundamental current of the i-th wind turbine, EHD i is the harmonic distortion index of the electrical system of the i-th wind turbine; According to the fan electrical insulation aging index EAI i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i Calculate the comprehensive operating electrical environment score (FCE) of the fan i , based on the following formula: FCE i =ω1×EAI i +EEP i ×ω2+EHD i ×ω3 Among them, ω1 is the fan electrical insulation aging index EAI i The weight coefficient is 0.2 to 0.4; ω2 is the wind turbine power generation efficiency impairment index EEP i The weight coefficient is 0.3 to 0.4; ω3 is the harmonic distortion index EHD of the fan electrical system i The weight coefficient is 0.3~0.4; and ω1+ω2+ω3=1.

7. The method for early warning of fan equipment failure based on data analysis according to claim 6, characterized in that: Calculating the Fan Failure Impact Score (DVH) i The method is: Fault data includes the number of fan failures NFM i , total running time DYT i , mean repair time TMT i and mean time between failures (TMF) i ; According to the number of fan failures NFM i , total running time DYT i , mean repair time TMT i , mean time between failures TMF i and the cumulative downtime of the fan TFA i Calculating the Fan Failure Impact Score (DVH) i , based on the following formula: Among them, NFM i is the number of failures of the i-th fan, DYT i is the total operating time of the i-th wind turbine, TMT i is the mean time to repair the fault of the i-th wind turbine, TMF i is the mean time between failures of the i-th fan, TFA i is the cumulative downtime of the i-th fan, DVH i Score the fault impact of the i-th wind turbine.

8. The method for early warning of fan equipment failure based on data analysis according to claim 7, characterized in that: The method for determining whether to issue an early warning is: The wind turbine scoring thresholds include a mild warning threshold TD and a severe warning threshold TW, wherein the severe warning threshold TW is greater than the mild warning threshold TD; The comprehensive operating status score of the wind turbine is ASD i Compared with the mild warning threshold TD and the severe warning threshold TW, the warning criteria are as follows: The wind turbine scoring threshold also includes a mild warning threshold QA and a severe warning threshold QW, where the severe warning threshold QW is greater than the mild warning threshold QA; The comprehensive score of the fan mechanical condition FBH i Compared with the mild warning threshold QA and the severe warning threshold QW, the warning criteria are as follows: The wind turbine scoring threshold also includes a mild warning threshold AZ and a severe warning threshold AS, wherein the severe warning threshold AS is greater than the mild warning threshold AZ; The comprehensive operating electrical environment score FCE of the fan i Compared with the mild warning threshold AZ and the severe warning threshold AS, the warning criteria are as follows: The wind turbine scoring threshold also includes a mild warning threshold XD and a severe warning threshold XC, wherein the severe warning threshold XC is greater than the mild warning threshold XD; DVH i Compared with the mild warning threshold XD and the severe warning threshold XC, the warning criteria are as follows: Among them, among the same type of warnings, the maintenance priority of severe warnings is higher than that of mild warnings.

9. The method for early warning of fan equipment failure based on data analysis according to claim 8, characterized in that: Also includes step 4: scoring ASD based on the comprehensive operating status of the wind turbine i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i Calculate the fan health index NUL i ; According to the fan health index NUL of different fans i Rank the health of wind turbines; Among them, the calculation of fan health index NUL i The formula is as follows: ZERO i =ASD i +FBH i +FCE i +DVH i 。 10. A wind turbine equipment failure early warning system based on data analysis, characterized by: include: Data acquisition module, used to collect real-time power operation data of the wind turbine, wind turbine body mechanical data, environmental data and fault data; Scoring calculation module, used to calculate the wind turbine health index (FHI) based on real-time power operation data i , fan power fluctuation index FPF i and frequency stability index FSI i , and further calculate the comprehensive operating status score ASD of the fan i ; Calculate the fan blade fatigue damage index FBD based on the fan body mechanical data i and fan mechanical transmission compliance index FMT i , according to the fan blade fatigue damage index FBD i and fan mechanical transmission compliance index FMT i Calculate the wind turbine structural resonance risk index FSR i ; According to the fan blade fatigue damage index FBD i , fan mechanical transmission compliance index FMT i and wind turbine structural resonance risk index FSR i Calculate the comprehensive score of the fan mechanical condition FBH i ; Calculate the wind turbine electrical insulation aging index EAI based on real-time power operation data and environmental data i , Wind turbine power generation efficiency impairment index EEP i and wind turbine electrical system harmonic distortion index EHD i , and further calculate the comprehensive operating electrical environment score FCE of the fan i ; Calculate the wind turbine fault impact score DVH based on fault data i ; The early warning module is used to preset the wind turbine scoring threshold set and score the wind turbine comprehensive operating status ASD i , Comprehensive score of fan mechanical status FBH i , Fan Comprehensive Operation Electrical Environment Score FCE i and wind turbine failure impact score DVH i Compare them with the thresholds in the wind turbine scoring threshold set to determine whether to issue an early warning; When an early warning is issued, an analysis report of the wind turbine maintenance priority is generated based on different early warning information.

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