Method and system for monitoring torsional deformation of blades of wind generating set

Abstract

The invention relates to the technical field of fan blades, in particular to a method and system for monitoring torsional deformation of blades of a wind generating set. Comprising the following steps: setting anchoring points of each fan blade in a single wind turbine generator, and constructing an anchoring plane according to position parameters of all the anchoring points; a plurality of monitoring points are set according to the fan blade parameters, and position parameters of all the monitoring points are obtained according to preset monitoring time nodes; generating the torsion amount of each fan blade according to the position parameters of all the monitoring points, and judging whether an early warning instruction is generated or not according to all the torsion amounts; a plurality of anchoring points are arranged in a single wind turbine generator, so that an anchoring plane is constructed, two monitoring points are arranged on a single fan blade, so that two dynamic calibration straight lines are constructed, and the torsion amount of the single fan blade is generated according to the included angle between the two calibration straight lines. Therefore, real-time monitoring and early warning of the torsion state of the fan blade are realized, and the operation efficiency of a wind turbine generator is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of wind turbine blades, and in particular to a method and system for monitoring torsional deformation of blades of a wind turbine generator set. Background Art

[0002] Blades are important components for wind turbines to achieve the conversion of wind energy and mechanical energy. The performance and power generation efficiency of the whole machine are largely affected by the condition of the blades. The blades are subjected to large loads, operate in harsh environments, and are corroded by wind and rain, which will affect the life of the blades. In recent years, wind turbine blade fracture accidents caused by excessive torsional loads have frequently occurred at home and abroad. Once the blades are damaged, the repair process is very complicated and will reduce the working efficiency of the wind turbine.

[0003] At present, there is no real-time online monitoring system for blade torsion deformation in the wind power industry. Since wind turbine blades need to experience various complex wind conditions and various operating conditions during actual operation, laboratory-related tests alone cannot completely and effectively reduce the blade operating load and fatigue level, and thus cannot effectively increase the blade service life. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides a monitoring method and system for the torsional deformation of wind turbine blades, aiming to achieve real-time monitoring and early warning of the torsion amount of wind turbine blades, avoid operational failures caused by blade torsion, and ensure the operating efficiency of the wind turbine.

[0005] In some embodiments of the present application, multiple anchor points are set in a single wind turbine group to construct an anchor plane, and two monitoring points are set on a single wind turbine blade to construct two dynamic calibration lines. The torsion amount of a single wind turbine blade is generated according to the angle between the two calibration lines, thereby achieving real-time monitoring of the torsion state of the wind turbine blade and timely warning of the risk of excessive blade torsion, thereby ensuring the operating efficiency of the wind turbine group.

[0006] In some embodiments of the present application, by dynamically adjusting the data acquisition parameters of each monitoring time node, accurate monitoring of wind turbine blades in various operating states is achieved, the diagnostic accuracy of the torsion of wind turbine blades is improved, and potential operating risks of wind turbine blades are warned in a timely manner to avoid interference caused by environmental factors and wind turbine operating factors, thereby ensuring the stable operation of wind turbines.

[0007] In some embodiments of the present application, a method for monitoring torsional deformation of blades of a wind turbine generator set is provided, characterized in that it includes: Set the anchor points of each wind turbine blade in a single wind turbine and construct the anchor plane according to the position parameters of all anchor points; Set multiple monitoring points according to the fan blade parameters, and obtain the position parameters of each monitoring point according to the preset monitoring time node; Generate the torsion amount of each wind turbine blade according to the position parameters of all monitoring points, and determine whether to generate an early warning instruction according to all the torsion amounts; Among them, when setting the anchor point of each wind turbine blade, it includes: Establish the fan blade sequence A of a single wind turbine, A=(a1, a2…a i …a n ), where a i is the i-th wind turbine blade; n is the number of wind turbine blades in a single wind turbine; According to the fan blade sequence A, establish the anchor point sequence B, B = (b1, b2…b i …b n ), where b i is the anchor point of the i-th wind turbine blade.

[0008] In some embodiments of the present application, multiple monitoring points are set according to the fan blade parameters, including: Set a in sequence according to the number of fan blades A i The target fan blades; Setting a first monitoring point and a second monitoring point of a target wind turbine blade; Setting a sub-association relationship between the first monitoring point, the second monitoring point and the anchor point of the target wind turbine blade; Set the monitoring points of each fan blade in sequence; Construct an anchor point-monitoring point association mapping table based on all sub-association relationships.

[0009] In some embodiments of the present application, obtaining the location parameters of each monitoring point according to a preset monitoring time node includes: Obtain the fan operating parameters and environmental parameters of the current monitoring time node, and generate the monitoring evaluation value c of the current monitoring time node; Set the collection period of the current monitoring time node according to the monitoring evaluation value c; Construct multiple collection time nodes within the collection cycle and establish a collection time node sequence T, T=(t1, t2…t i …t m ), where t i is the ith collection time node in the collection cycle; m is the number of collection time nodes in the collection cycle; According to the fan blade sequence A, ai is set as the target fan blade in sequence; Obtaining position parameters of a first monitoring point and a second monitoring point of a target fan blade at each acquisition time node, and generating a monitoring data packet of the target fan blade at a current monitoring time node; Generate monitoring data packets for each wind turbine blade in turn.

[0010] In some embodiments of the present application, generating a monitoring evaluation value c of a current monitoring time node includes: c=e1*Q1*[ (β 1i *p 1i )]+e2*Q2*[ (β 2i *p 2i )]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first weight coefficient; Q2 is the preset second weight coefficient; is the number of environmental disturbance indicators β 1i is the influencing factor of the i-th environmental disturbance index; p 1i is the reference value of the i-th environmental disturbance index at the current monitoring time node; 2 is the number of operating disturbance indicators; β 2i is the influencing factor of the i-th operation disturbance index; p 2i It is the reference value of the i-th operating disturbance index at the current monitoring time node.

[0011] In some embodiments of the present application, when generating the torsion amount of each fan blade, it includes: Obtain the monitoring data packet of the target wind turbine blade at the current feedback time node; Set t in sequence according to the number of acquisition time nodes T i Collect time nodes for the target; A first target monitoring point, a second target monitoring point and a target anchor point are set according to the target wind turbine blade; Generate a first calibration straight line according to position parameters of the first target monitoring point and the second target monitoring point at the target acquisition time node; generating a second calibration straight line according to the position parameters of the target anchor point at the current acquisition time node and the anchor plane; Generate a torsion amount of a target wind turbine blade at a target acquisition time node according to a first calibration straight line and a second calibration straight line; Generate the torsion amount of the target wind turbine blade at each acquisition time node in sequence; Establish the torsion amount series F of the target fan blade at the current feedback time node; F=(f1,f2…f i …f m ), where f i is the torsion amount of the target fan blade at the i-th acquisition time node; The torsion amount series of each wind turbine blade at the current monitoring time node is established in sequence.

[0012] In some embodiments of the present application, when determining whether to generate a warning instruction based on the total amount of torsion, it includes: Presetting a first torsion amount threshold F1 and a second torsion amount threshold F2, and F1 < F2; Generating an average torsion value Δf according to the torsion amount sequence F of the target wind turbine blade; If Δf < F1, no warning instruction is generated for the target wind turbine blade at the current feedback time node; If F1 ≤ Δf < F2, it is determined whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node; If Δf ≥ F2, a secondary warning instruction is generated for the target wind turbine blade at the current feedback time node.

[0013] In some embodiments of the present application, determining whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node includes: Generating the operation risk value g of the target wind turbine blade at the current monitoring time node according to the torsion amount sequence F; g = e3 * Q3 * (f i - Δf) 2 + e4 * Q4 * Y(i) * (f i - f'); Wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; f' is the safe torsion amount of the target wind turbine blade at the current monitoring time node; Y(i) is a selection coefficient; if (fi - f') > 0, Y(i) = 1; if (fi - f') < 0, Y(i) = 0; Presetting a first operation risk value threshold G1 and a second operation risk value threshold G2; If g < G1, no warning instruction is generated; If G1 ≤ g < G2, a primary warning instruction is generated; If g ≥ G2, a secondary warning instruction is generated.

[0014] In some embodiments of the present application, a monitoring system for wind turbine blade torsional deformation is provided, including: A central control unit, configured to set the anchor points of each wind turbine blade in a single wind turbine generator set, and construct an anchor plane according to the position parameters of all the anchor points; The central control unit is further configured to set a plurality of monitoring points according to the wind turbine blade parameters; A monitoring unit, including a plurality of monitoring sub - modules, the monitoring unit is configured to collect the position parameters of each monitoring point and each anchor point; The central control unit comprises: A first processing module, used for constructing an anchor plane according to position parameters of all anchor points; The second processing module is used to obtain the position parameters of each monitoring point according to the preset monitoring time node, and generate the torsion amount of each wind turbine blade according to the position parameters of all monitoring points; A first warning module, used to determine whether to generate a warning instruction according to the total torsion amount; The third processing module is used to establish a fan blade sequence A of a single wind turbine group, A=(a1, a2…a i …a n ), where a i is the i-th wind turbine blade; n is the number of wind turbine blades in a single wind turbine; The third processing module is also used to establish an anchor point sequence B according to the fan blade sequence A, B=(b1, b2...b i …b n ), where b i is the anchor point of the i-th wind turbine blade; The fourth processing module is used to set a according to the fan blade sequence A in sequence i The target fan blades; Setting a first monitoring point and a second monitoring point of a target wind turbine blade; Setting a sub-association relationship between the first monitoring point, the second monitoring point and the anchor point of the target wind turbine blade; Set the monitoring points of each fan blade in sequence; Construct an anchor point-monitoring point association mapping table based on all sub-association relationships.

[0015] In some embodiments of the present application, the second processing module is further used to: Obtain the fan operating parameters and environmental parameters of the current monitoring time node, and generate the monitoring evaluation value c of the current monitoring time node; Set the collection period of the current monitoring time node according to the monitoring evaluation value c; Construct multiple collection time nodes within the collection cycle and establish a collection time node sequence T, T=(t1, t2…t i …t m ), where t i is the ith collection time node in the collection cycle; m is the number of collection time nodes in the collection cycle; According to the fan blade sequence A, ai is set as the target fan blade in sequence; Obtaining position parameters of a first monitoring point and a second monitoring point of a target fan blade at each acquisition time node, and generating a monitoring data packet of the target fan blade at a current monitoring time node; Obtain the monitoring data packet of the target wind turbine blade at the current feedback time node; Set ti as the target acquisition time node in sequence according to the acquisition time node sequence T; Set the first target monitoring point, the second target monitoring point and the target anchoring point according to the target wind turbine blade; Generate the first calibration line according to the position parameters of the first target monitoring point and the second target monitoring point at the target acquisition time node; Generate the second calibration line according to the position parameter of the target anchoring point at the current acquisition time node and the anchoring plane; Generate the torsion amount of the target wind turbine blade at the target acquisition time node according to the first calibration line and the second calibration line; Generate the torsion amounts of the target wind turbine blade at each acquisition time node in sequence; Establish a torsion amount sequence F of the target wind turbine blade at the current feedback time node; F = (f1, f2…fi…fm), where fi is the torsion amount of the target wind turbine blade at the i-th acquisition time node; Establish the torsion amount sequences of each wind turbine blade at the current monitoring time node in sequence.

[0016] In some embodiments of the present application, the first warning module is further configured to: Preset a first torsion amount threshold F1 and a second torsion amount threshold F2, and F1 < F2; Generate an average torsion value Δf according to the torsion amount sequence F of the target wind turbine blade; If Δf < F1, no warning instruction is generated for the target wind turbine blade at the current feedback time node; If F1 ≤ Δf < F2, it is determined whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node; If Δf ≥ F2, a secondary warning instruction is generated for the target wind turbine blade at the current feedback time node; Determining whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node includes: Generate the operation risk value g of the target wind turbine blade at the current monitoring time node according to the torsion amount sequence F; g = e3 * Q3 * (f i - Δf) 2 + e4 * Q4 * Y(i) * (f i - f'); Among them, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; f' is the safe torsional amount of the target wind turbine blade at the current monitoring time node; Y(i) is a selection coefficient; if (fi - f') > 0, Y(i) = 1; if (fi - f') < 0, Y(i) = 0; Preset a first operating risk value threshold G1 and a second operating risk value threshold G2; If g < G1, no warning command is generated; If G1 ≤ g < G2, a first-level warning command is generated; If g ≥ G2, a second-level warning command is generated.

[0017] Compared with the prior art, the monitoring method and system for wind turbine blade torsional deformation in the embodiments of the present application have the following beneficial effects: By setting multiple anchor points within a single wind turbine unit to construct an anchor plane, and setting two monitoring points on a single wind turbine blade to construct two dynamic calibration lines, the torsional amount of a single wind turbine blade is generated according to the included angle between the two calibration lines, so as to realize real-time monitoring of the torsional state of the wind turbine blade, timely warn of the risk of excessive blade torsion, and thus ensure the operating efficiency of the wind turbine unit.

[0018] By dynamically adjusting the data acquisition parameters at each monitoring time node, accurate monitoring of the wind turbine blade under various operating conditions is realized, the diagnostic accuracy of the torsional amount of the wind turbine blade is improved, potential operating risks of the wind turbine blade are timely warned, and interference caused by environmental factors and wind turbine operating factors is avoided. Ensure the stable operation of the wind turbine unit. Description of the Drawings

[0019] Figure 1 It is a schematic flow chart of a monitoring method for wind turbine blade torsional deformation in a preferred embodiment of the embodiments of the present application. Detailed Embodiments

[0020] The following will further describe in detail the specific embodiments of the present application with reference to the drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0021] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present application.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0024] like Figure 1 As shown, a method for monitoring torsional deformation of blades of a wind turbine generator set according to a preferred embodiment of the present application includes: S101: setting anchor points of each wind turbine blade in a single wind turbine group, and constructing an anchor plane according to position parameters of all anchor points; S102: setting a plurality of monitoring points according to the parameters of the wind turbine blades, and obtaining position parameters of each monitoring point according to a preset monitoring time node; S103: generating the torsion amount of each wind turbine blade according to the position parameters of all monitoring points, and determining whether to generate an early warning instruction according to all the torsion amounts; Among them, when setting the anchor point of each wind turbine blade, it includes: Establish the fan blade sequence A of a single wind turbine, A=(a1, a2…a i …a n ), where a i is the i-th wind turbine blade; n is the number of wind turbine blades in a single wind turbine; According to the fan blade sequence A, establish the anchor point sequence B, B = (b1, b2…b i …b n ), where b i is the anchor point of the i-th wind turbine blade.

[0025] Specifically, a single wind turbine generator set preferably has three wind turbine blades, and the specific number can be set according to actual equipment parameters of the wind turbine generator set.

[0026] Specifically, the anchor point of the wind turbine blade is calibrated at the 0° blade root angle and as close to the hub as possible. A GPS positioning antenna is set at each anchor point to obtain the position parameters of each anchor point and generate the spatial positioning coordinates of each anchor point based on the preprocessing results of each position parameter.

[0027] Specifically, the spatial positioning coordinates of all anchor points in a single wind turbine are connected in sequence to form an anchor plane.

[0028] Specifically, multiple monitoring points are set according to the wind turbine blade parameters, including: Set a in sequence according to the number of fan blades A i The target fan blades; Setting a first monitoring point and a second monitoring point of a target wind turbine blade; Setting a sub-association relationship between the first monitoring point, the second monitoring point and the anchor point of the target wind turbine blade; Set the monitoring points of each fan blade in sequence; Construct an anchor point-monitoring point association mapping table based on all sub-association relationships.

[0029] Specifically, two symmetrical positions at the front edge and the rear edge of the target wind turbine blade 1 / 4 close to the blade tip are set as the first monitoring point and the second monitoring point, and GPS positioning antennas are installed at the first monitoring point and the second monitoring point.

[0030] Specifically, a first calibration line is constructed according to the real-time positions of the two monitoring points; a straight line passing through the anchor point and parallel to the opposite side of the anchor point in the anchor plane is set as the second calibration line; and the torsion amount of the target wind turbine blade is monitored in real time by calculating the angle between the first calibration line and the second calibration line.

[0031] It can be understood that in the above embodiment, a plurality of anchor points are set in a single wind turbine to construct an anchor plane, and two monitoring points are set on a single wind turbine blade to construct two dynamic calibration lines. The torsion amount of a single wind turbine blade is generated according to the angle between the two calibration lines, thereby realizing real-time monitoring of the torsion state of the wind turbine blade, improving the monitoring efficiency of the torsion state of the wind turbine blade, and ensuring the safe operation of the wind turbine.

[0032] In a preferred embodiment of the present application, the location parameters of each monitoring point are obtained according to a preset monitoring time node, including: Obtain the fan operating parameters and environmental parameters of the current monitoring time node, and generate the monitoring evaluation value c of the current monitoring time node; Set the collection period of the current monitoring time node according to the monitoring evaluation value c; Construct multiple collection time nodes within the collection cycle and establish a collection time node sequence T, T=(t1, t2…t i …t m ), where t i is the ith collection time node in the collection cycle; m is the number of collection time nodes in the collection cycle; According to the fan blade sequence A, ai is set as the target fan blade in sequence; Obtaining position parameters of a first monitoring point and a second monitoring point of a target wind turbine blade at each acquisition time node, and generating a monitoring data packet of the target wind turbine blade at a current monitoring time node; Generate monitoring data packets for each wind turbine blade in turn.

[0033] Specifically, the larger the monitoring evaluation value, the greater the possibility of short-term twisting of the current wind turbine blades. The data collection frequency of the current monitoring time node is dynamically adjusted according to the monitoring evaluation value, thereby eliminating the interference of occasional short-term twisting of the fan blades on the diagnosis results and improving the monitoring accuracy of the fan blade twisting amount.

[0034] Specifically, the larger the monitoring evaluation value, the longer the corresponding collection period, and the collected data can better eliminate the interference of short-term torsional interference.

[0035] Specifically, the time interval between two adjacent collection time nodes is a fixed value, and its specific value can be set according to historical parameters. Specifically, the monitoring evaluation value c of the current monitoring time node is generated, including: c=e1*Q1*[ (β 1i *p 1i )]+e2*Q2*[ (β 2i *p 2i )]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first weight coefficient; Q2 is the preset second weight coefficient; is the number of environmental disturbance indicators β 1i is the influencing factor of the i-th environmental disturbance index; p 1i is the reference value of the i-th environmental disturbance index at the current monitoring time node; 2 is the number of operating disturbance indicators; β 2i is the influencing factor of the i-th operation disturbance index; p 2i It is the reference value of the i-th operating disturbance index at the current monitoring time node.

[0036] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient and the second fixed coefficient, so that each parameter is within the same value range.

[0037] Specifically, environmental interference indicators include but are not limited to parameters such as wind speed, wind volume, and wind direction changes; operational interference indicators include but are not limited to parameters such as pitch change, operational flutter, and the material of wind turbine blades.

[0038] It is understandable that in the above embodiment, by dynamically adjusting the data collection parameters of each monitoring time node, accurate monitoring of the wind turbine blades under various operating conditions is achieved, the diagnostic accuracy of the wind turbine blade torsion is improved, and the potential operating risks of the wind turbine blades are warned in time to avoid interference caused by environmental factors and wind turbine operating factors, thereby ensuring the stable operation of the wind turbine.

[0039] In a preferred embodiment of the present application, when generating the torsion amount of each fan blade, it includes: Obtain the monitoring data packet of the target wind turbine blade at the current feedback time node; Set t in sequence according to the number of acquisition time nodes T i Collect time nodes for the target; A first target monitoring point, a second target monitoring point and a target anchor point are set according to the target wind turbine blade; Generate a first calibration straight line according to position parameters of the first target monitoring point and the second target monitoring point at the target acquisition time node; generating a second calibration straight line according to the position parameters of the target anchor point at the current acquisition time node and the anchor plane; Generate a torsion amount of a target wind turbine blade at a target acquisition time node according to a first calibration straight line and a second calibration straight line; Generate the torsion amount of the target wind turbine blade at each acquisition time node in sequence; Establish the torsion amount series F of the target fan blade at the current feedback time node; F=(f1,f2…f i …f m ), where f i is the torsion amount of the target fan blade at the i-th acquisition time node; The torsion amount series of each wind turbine blade at the current monitoring time node is established in sequence.

[0040] Specifically, the angle between the first calibration straight line and the second calibration straight line is the torsion angle of the target wind turbine blade, and the corresponding torsion amount is generated according to the torsion angle.

[0041] Specifically, when all the torsion quantities are used to determine whether to generate a warning instruction, the following are included: Preset a first torsional quantity threshold F1 and a second torsional quantity threshold F2, and F1 < F2; Generate an average torsional value Δf according to the torsional quantity sequence F of the target wind turbine blade; If Δf < F1, the target wind turbine blade does not generate a warning instruction at the current feedback time node; If F1 ≤ Δf < F2, determine whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node; If Δf ≥ F2, the target wind turbine blade generates a secondary warning instruction at the current feedback time node.

[0042] Specifically, the first torsional quantity threshold and the second torsional quantity threshold can be set according to historical parameters.

[0043] Specifically, the secondary warning instruction means that the torsional quantity of the target wind turbine blade at the current monitoring time node has exceeded the torsional threshold, and the wind turbine unit needs to be shut down for maintenance immediately to avoid the problem of excessive torsion and fracture.

[0044] Specifically, determining whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node includes: Generate the operation risk value g of the target wind turbine blade at the current monitoring time node according to the torsional quantity sequence F; g = e3 * Q3 * (f i - Δf) 2 + e4 * Q4 * Y(i) * (f i - f'); Wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; f' is the safe torsional quantity of the target wind turbine blade at the current monitoring time node; Y(i) is a selection coefficient; if (fi - f') > 0, Y(i) = 1; if (fi - f') < 0, Y(i) = 0; Preset a first operation risk value threshold G1 and a second operation risk value threshold G2; If g < G1, no warning instruction is generated; If G1 ≤ g < G2, a primary warning instruction is generated; If g ≥ G2, a secondary warning instruction is generated.

[0045] Specifically, all parameters in the model are normalized by presetting the third fixed coefficient and the fourth fixed coefficient, so that each parameter in the model is within the same value range.

[0046] Specifically, f' can be set according to the current environmental parameters, wind turbine operating parameters and monitoring parameters of the remaining blades of the same wind turbine. If the twist amount of the target wind turbine blade is lower than f', it means that the target wind turbine blade is in a normal state.

[0047] Specifically, the larger the risk assessment value, the greater the possibility that the target wind turbine blade has a potential operating failure.

[0048] Specifically, the first-level warning instruction means that the current target wind turbine blades are twisted, and the working parameters of the wind turbine need to be adjusted in time to avoid an increase in the amount of twist. At the same time, when the wind turbine is shut down, timely maintenance is carried out to eliminate potential failure risks.

[0049] It can be understood that in the above embodiments, by diagnosing each abnormally twisted wind blade, the potential failure risk of the wind blade is predicted in time, the maintenance efficiency of the wind blade is improved, and the safe operation of the wind turbine is ensured.

[0050] Based on another preferred embodiment of a method for monitoring torsional deformation of a blade of a wind turbine generator set in any of the above preferred embodiments, this preferred embodiment provides a system for monitoring torsional deformation of a blade of a wind turbine generator set, comprising: The central control unit is used to set the anchor points of each wind turbine blade in a single wind turbine unit and to construct the anchor plane according to the position parameters of all anchor points; The central control unit is also used to set multiple monitoring points according to the parameters of the fan blades; A monitoring unit, including a plurality of monitoring submodules, the monitoring unit is used to collect position parameters of each monitoring point and each anchor point; The central control unit includes: A first processing module, used for constructing an anchor plane according to position parameters of all anchor points; The second processing module is used to obtain the position parameters of each monitoring point according to the preset monitoring time node, and generate the torsion amount of each wind turbine blade according to the position parameters of all monitoring points; A first warning module, used to determine whether to generate a warning instruction according to the total torsion amount; The third processing module is used to establish a fan blade sequence A of a single wind turbine group, A=(a1, a2…a i …a n ), where a i is the i-th wind turbine blade; n is the number of wind turbine blades in a single wind turbine; The third processing module is also used to establish an anchor point sequence B according to the fan blade sequence A, B=(b1, b2…b i …b n ), where b iis the anchor point of the i-th wind turbine blade; The fourth processing module is used to set a according to the fan blade sequence A in sequence i The target fan blades; Setting a first monitoring point and a second monitoring point of a target wind turbine blade; Setting a sub-association relationship between the first monitoring point, the second monitoring point and the anchor point of the target wind turbine blade; Set the monitoring points of each fan blade in sequence; Construct an anchor point-monitoring point association mapping table based on all sub-association relationships.

[0051] Specifically, the monitoring submodule is preferably a GPS positioning antenna.

[0052] In a preferred embodiment of the present application, the second processing module is also used for: Obtain the fan operating parameters and environmental parameters of the current monitoring time node, and generate the monitoring evaluation value c of the current monitoring time node; Set the collection period of the current monitoring time node according to the monitoring evaluation value c; Construct multiple collection time nodes within the collection cycle and establish a collection time node sequence T, T=(t1, t2…t i …t m ), where t i is the ith collection time node in the collection cycle; m is the number of collection time nodes in the collection cycle; According to the fan blade sequence A, ai is set as the target fan blade in sequence; Obtaining position parameters of a first monitoring point and a second monitoring point of a target fan blade at each acquisition time node, and generating a monitoring data packet of the target fan blade at a current monitoring time node; Obtain the monitoring data packet of the target wind turbine blade at the current feedback time node; According to the collection time node sequence T, ti is set as the target collection time node in sequence; A first target monitoring point, a second target monitoring point and a target anchor point are set according to the target wind turbine blade; Generate a first calibration straight line according to position parameters of the first target monitoring point and the second target monitoring point at the target acquisition time node; generating a second calibration straight line according to the position parameters of the target anchor point at the current acquisition time node and the anchor plane; Generate a torsion amount of a target wind turbine blade at a target acquisition time node according to a first calibration straight line and a second calibration straight line; Generate the torsion amount of the target wind turbine blade at each acquisition time node in sequence; Establish the torsion amount series F of the target fan blade at the current feedback time node; F = (f1, f2…fi…fm), where fi is the torsion amount of the target wind turbine blade at the i-th acquisition time node; Successively establish the torsion amount sequences of each wind turbine blade at the current monitoring time node.

[0053] In the preferred embodiment of the present application, the first warning module is further configured to: Preset a first torsion amount threshold F1 and a second torsion amount threshold F2, and F1 < F2; Generate an average torsion value Δf according to the torsion amount sequence F of the target wind turbine blade; If Δf < F1, no warning instruction is generated for the target wind turbine blade at the current feedback time node; If F1 ≤ Δf < F2, it is determined whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node; If Δf ≥ F2, a secondary warning instruction is generated for the target wind turbine blade at the current feedback time node; Determining whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node includes: Generate the operation risk value g of the target wind turbine blade at the current monitoring time node according to the torsion amount sequence F; g = e3 * Q3 * (f i - Δf) 2 + e4 * Q4 * Y(i) * (f i - f'); Wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3Wie is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; f' is the safe torsion amount of the target wind turbine blade at the current monitoring time node; Y(i) is a selection coefficient; if (fi - f') > 0, Y(i) = 1; if (fi - f') < 0, Y(i) = 0; Preset a first operation risk value threshold G1 and a second operation risk value threshold G2; If g < G1, no warning instruction is generated; If G1 ≤ g < G2, a primary warning instruction is generated; If g ≥ G2, a secondary warning instruction is generated.

[0054] According to the first concept of the present application, multiple anchor points are set in a single wind turbine to construct an anchor plane, and two monitoring points are set on a single wind turbine blade to construct two dynamic calibration lines. The torsion amount of a single wind turbine blade is generated according to the angle between the two calibration lines, thereby achieving real-time monitoring of the torsion state of the wind turbine blade and timely warning of the risk of excessive blade torsion, thereby ensuring the operating efficiency of the wind turbine.

[0055] According to the second concept of the present application, by dynamically adjusting the data collection parameters of each monitoring time node, accurate monitoring of wind turbine blades under various operating conditions is achieved, the diagnostic accuracy of wind turbine blade torsion is improved, and potential operating risks of wind turbine blades are warned in a timely manner to avoid interference caused by environmental factors and wind turbine operating factors, thereby ensuring the stable operation of wind turbines.

[0056] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring the torsional deformation of blades of a wind turbine generator set, characterized in that: Including: Set the anchor points of each wind turbine blade in a single wind turbine, and construct an anchor plane according to the position parameters of all anchor points; Set multiple monitoring points according to the wind turbine blade parameters, and obtain the position parameters of each monitoring point according to the preset monitoring time nodes; Generate the torsion amount of each wind turbine blade according to the position parameters of all monitoring points, and judge whether to generate a warning instruction according to all torsion amounts; Among them, when setting the anchor points of each wind turbine blade, it includes: Establish the fan blade sequence A of a single wind turbine, A=(a1, a2…a i …a n ), where a i is the i-th wind turbine blade; n is the number of wind turbine blades in a single wind turbine; According to the fan blade sequence A, establish the anchor point sequence B, B = (b1, b2…b i …b n ), where b i is the anchor point of the i-th wind turbine blade.

2. The method for monitoring the torsional deformation of blades of a wind turbine generator set according to claim 1, characterized in that: Set multiple monitoring points according to the wind turbine blade parameters, including: Set a in sequence according to the number of fan blades A i The target fan blades; Set the first monitoring point and the second monitoring point of the target wind turbine blade; Set the sub-association relationship between the first monitoring point and the second monitoring point and the anchor point of the target wind turbine blade; Set the monitoring points of each wind turbine blade in sequence; Construct an anchor point-monitoring point association mapping table according to all sub-association relationships.

3. The method for monitoring the torsional deformation of blades of a wind turbine generator set according to claim 2, characterized in that: Obtain the position parameters of each monitoring point according to the preset monitoring time nodes, including: Obtain the wind turbine operation parameters and environmental parameters at the current monitoring time node, and generate a monitoring evaluation value c at the current monitoring time node; Set the acquisition period at the current monitoring time node according to the monitoring evaluation value c; Construct multiple collection time nodes within the collection cycle and establish a collection time node sequence T, T=(t1, t2…t i …t m ), where t i is the ith collection time node in the collection cycle; m is the number of collection time nodes in the collection cycle; Set ai as the target wind turbine blade in sequence according to the wind turbine blade sequence A; Obtain the position parameters of the first monitoring point and the second monitoring point of the target wind turbine blade at each acquisition time node, and generate a monitoring data packet of the target wind turbine blade at the current monitoring time node; Generate the monitoring data packets of each wind turbine blade in sequence.

4. The method for monitoring the torsional deformation of blades of a wind turbine generator set according to claim 3, characterized in that: Generate the monitoring evaluation value c at the current monitoring time node, including: c=e1*Q1*[ (β 1i *p 1i )]+e2*Q2*[ (β 2i *p 2i )]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first weight coefficient; Q2 is the preset second weight coefficient; is the number of environmental disturbance indicators β 1i is the influencing factor of the i-th environmental disturbance index; p 1i is the reference value of the i-th environmental disturbance index at the current monitoring time node; 2 is the number of operating disturbance indicators; β 2i is the influencing factor of the i-th operation disturbance index; p 2i It is the reference value of the i-th operating disturbance index at the current monitoring time node.

5. The method for monitoring the torsional deformation of blades of a wind turbine generator set according to claim 3, characterized in that: When generating the torsion amount of each wind turbine blade, it includes: Obtain the monitoring data packet of the target wind turbine blade at the current feedback time node; Set t in sequence according to the number of acquisition time nodes T i Collect time nodes for the target; Set the first target monitoring point, the second target monitoring point and the target anchor point according to the target wind turbine blade; Generate a first calibration line according to the position parameters of the first target monitoring point and the second target monitoring point at the target acquisition time node; Generate a second calibration line according to the position parameter of the target anchor point at the current acquisition time node and the anchor plane; Generate the torsion amount of the target wind turbine blade at the target acquisition time node according to the first calibration line and the second calibration line; Generate the torsion amounts of the target wind turbine blade at each acquisition time node in sequence; Establish a torsion amount sequence F of the target wind turbine blade at the current feedback time node; F=(f1,f2…f i …f m ), where f i is the torsion amount of the target fan blade at the i-th acquisition time node; Establish the torsion amount sequences of each wind turbine blade at the current monitoring time node in sequence.

6. The method for monitoring torsional deformation of blades of a wind turbine generator set according to claim 5, characterized in that: When judging whether to generate a warning instruction according to all torsion amounts, it includes: Preset a first torsion amount threshold F1 and a second torsion amount threshold F2, and F1 < F2; Generate an average torsion value Δf according to the torsion amount sequence F of the target wind turbine blade; If Δf < F1, no warning instruction is generated for the target wind turbine blade at the current feedback time node; If F1 ≤ Δf < F2, judge whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node; If Δf ≥ F2, a secondary warning instruction is generated for the target wind turbine blade at the current feedback time node.

7. The method for monitoring torsional deformation of blades of a wind turbine generator set according to claim 6, characterized in that: When judging whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node, it includes: Generate the operation risk value g of the target wind turbine blade at the current monitoring time node according to the torsion amount sequence F; g=e3*Q3*[ (f i -Δf) 2 ]+e4*Q4*[ Y(i)*(f i -f')]; Among them, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; f' is the safe torsional amount of the target wind turbine blade at the current monitoring time node; Y(i) is a selection coefficient; if (fi - f') > 0, Y(i) = 1; if (fi - f') < 0, Y(i) = 0; Preset the first operating risk value threshold G1 and the second operating risk value threshold G2; If g < G1, no warning instruction is generated; If G1 ≤ g < G2, a first-level warning instruction is generated; If g ≥ G2, a second-level warning instruction is generated.

8. A monitoring system for torsional deformation of blades of a wind turbine generator set, using the monitoring method for torsional deformation of blades of a wind turbine generator set as claimed in any one of claims 1 to 7, characterized in that: Including: A central control unit for setting the anchor points of each wind turbine blade in a single wind turbine unit and constructing an anchor plane according to the position parameters of all the anchor points; The central control unit is also used to set a plurality of monitoring points according to the wind turbine blade parameters; A monitoring unit includes a plurality of monitoring sub-modules, and the monitoring unit is used to collect the position parameters of each monitoring point and each anchor point; The central control unit includes: A first processing module for constructing an anchor plane according to the position parameters of all the anchor points; A second processing module for obtaining the position parameters of each monitoring point according to the preset monitoring time node and generating the torsional amount of each wind turbine blade according to the position parameters of all the monitoring points; A first warning module for judging whether to generate a warning instruction according to all the torsional amounts; The third processing module is used to establish a fan blade sequence A of a single wind turbine group, A=(a1, a2…a i …a n ), where a i is the i-th wind turbine blade; n is the number of wind turbine blades in a single wind turbine; The third processing module is also used to establish an anchor point sequence B according to the fan blade sequence A, B=(b1, b2...b i …b n ), where b i is the anchor point of the i-th wind turbine blade; The fourth processing module is used to set a according to the fan blade sequence A in sequence i The target fan blades; Set the first monitoring point and the second monitoring point of the target wind turbine blade; Set the sub-association relationship between the first monitoring point and the second monitoring point and the anchor point of the target wind turbine blade; Set the monitoring points of each wind turbine blade in sequence; Construct an anchor point - monitoring point association mapping table according to all the sub-association relationships.

9. The monitoring system for torsional deformation of blades of a wind turbine generator set according to claim 8, characterized in that: The second processing module is also used for: Obtaining the wind turbine operation parameters and environmental parameters at the current monitoring time node and generating a monitoring evaluation value c at the current monitoring time node; Setting the acquisition period at the current monitoring time node according to the monitoring evaluation value c; Construct multiple collection time nodes within the collection cycle and establish a collection time node sequence T, T=(t1, t2…t i …t m ), where t i is the ith collection time node in the collection cycle; m is the number of collection time nodes in the collection cycle; Sequentially set ai as the target wind turbine blade according to the wind turbine blade sequence A; Obtaining the position parameters of the first monitoring point and the second monitoring point of the target wind turbine blade at each acquisition time node and generating a monitoring data packet of the target wind turbine blade at the current monitoring time node; Obtaining the monitoring data packet of the target wind turbine blade at the current feedback time node; Sequentially set ti as the target acquisition time node according to the acquisition time node sequence T; Set the first target monitoring point, the second target monitoring point and the target anchor point according to the target wind turbine blade; Generating a first calibration line according to the position parameters of the first target monitoring point and the second target monitoring point at the target acquisition time node; Generating a second calibration line according to the position parameter of the target anchor point at the current acquisition time node and the anchor plane; Generating the torsional amount of the target wind turbine blade at the target acquisition time node according to the first calibration line and the second calibration line; Sequentially generate the torsional amounts of the target wind turbine blade at each acquisition time node; Establish a torsional amount sequence F of the target wind turbine blade at the current feedback time node; F = (f1, f2... fi... fm), where fi is the torsional amount of the target wind turbine blade at the i-th acquisition time node; Establish the torsion quantity sequence of each wind turbine blade at the current monitoring time node in sequence.

10. The monitoring system for torsional deformation of blades of a wind turbine generator set according to claim 9, characterized in that: The first warning module is further configured to: Preset a first torsion quantity threshold F1 and a second torsion quantity threshold F2, and F1 < F2; Generate an average torsion value Δf according to the torsion quantity sequence F of the target wind turbine blade; If Δf < F1, no warning instruction is generated for the target wind turbine blade at the current feedback time node; If F1 ≤ Δf < F2, it is judged whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node; If Δf ≥ F2, a secondary warning instruction is generated for the target wind turbine blade at the current feedback time node; Judging whether to generate a warning instruction according to the operation risk value g of the target wind turbine blade at the current feedback time node includes: Generating an operation risk value g of the target wind turbine blade at the current monitoring time node according to the torsion quantity sequence F; g=e3*Q3*[ (f i -Δf) 2 ]+e4*Q4*[ Y(i)*(f i -f')]; Wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3Wie is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; f' is the safe torsion quantity of the target wind turbine blade at the current monitoring time node; Y(i) is a selection coefficient; if (fi - f') > 0, Y(i) = 1; if (fi - f') < 0, Y(i) = 0; Preset a first operation risk value threshold G1 and a second operation risk value threshold G2; If g < G1, no warning instruction is generated; If G1 ≤ g < G2, a primary warning instruction is generated; If g ≥ G2, a secondary warning instruction is generated.

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