• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    A method implemented using at least one processor module (124), comprising receiving a plurality of operating parameters (128) corresponding to a plurality of wind turbines (102) and obtaining a plurality of source sound power values corresponding to the plurality of wind turbines (102). The method further includes obtaining a reception sound pressure value (132) corresponding to a receiving location (104) and estimating an attenuation pattern based on the plurality of sound power values. source (130) and the receiving sound pressure value (132). The attenuation model described herein includes a plurality of attenuation coefficients. The method also includes determining at least one wind turbine setpoint (134) corresponding to at least one wind turbine (102) among the plurality of wind turbines (102), based on the plurality of attenuation coefficients and the plurality of wind turbine operating parameters (128).
    公开号:FR3024558A1
    申请号:FR1557377
    申请日:2015-07-31
    公开日:2016-02-05
    发明作者:Akshay Krishnamurty Ambekar；Vishal Cholapadi Ravindra；Jean Benoit Philippe Petit；Kalpit Vikrambhai Desai
    申请人:General Electric Co；
    IPC主号:
    专利说明:

    [0001] SYSTEM AND METHOD FOR OPTIMIZING THE OPERATION OF A WIND PARK A system and method for the optimized operation of wind turbines of a wind farm are described. More particularly, the operating setpoint values for a plurality of wind turbines are determined to limit as much as possible the energy losses in the wind farm while operating the plurality of wind turbines so as to comply with the regulations limiting the noise at the wind farm. several reception locations. Wind energy is one of the least usable sources of energy that is least damaging to ecosystems. Wind turbines installed on masts are driven by the wind to produce electricity. Ordinarily, a modern wind turbine has one or more rotor blades to intercept the kinetic energy of the wind and transmits kinetic energy to rotate a shaft coupling the rotor blades with a multiplier or alternator. The alternator converts the mechanical energy into electrical energy and the electrical energy is distributed over a network.
    [0002] Wind turbines produce aerodynamic noise due to the rotation of the rotor blades in the air. In the immediate vicinity is meant periodic sound pulses due to the amplitude modulation of the aerodynamic noise. Regulations issued by official bodies limit the maximum noise level (expressed in decibels - dB) for noise emissions by wind turbines operating around residential areas or other inhabited areas. Near field noise from a wind turbine is specified and measured on the basis of IEC 61400-11. Far-field noise estimates, ranging from about 1 km to 4 km, are determined from near-field noise using noise propagation models. Noise propagation models are unreliable, especially on complex terrain or during crosswind propagation. To account for the shortcomings of noise propagation models, conservative estimates of noise are taken into account to ensure compliance with official regulations, so that wind turbines operate at suboptimal setpoints. The far-field aerodynamic noise emitted by wind turbines can be reduced by lowering the rated power of all wind turbines in a wind farm. Lowering the rated power of wind turbines can be achieved by reducing the speed of the blades of the wind turbines or by controlling the pitch angle of the blades. However, lowering the nominal power of wind turbines tends to reduce the power generation of the wind farm. There is a need to improve techniques to control the operation of the wind farms' wind turbines. According to one aspect of the present invention, there is provided a method. The method includes receiving a plurality of operating parameters corresponding to the plurality of wind turbines and obtaining a plurality of sound power values of the source corresponding to the plurality of wind turbines. The method further includes obtaining a reception sound pressure value corresponding to the receiving location and estimating an attenuation pattern based on the plurality of source sound power values and the reception sound pressure value. The attenuation model presented here includes a plurality of attenuation coefficients. The method also includes determining, based on the plurality of attenuation coefficients and the plurality of wind turbine operating parameters, at least one wind turbine setpoint corresponding to at least one of the plurality of wind turbines. wind turbines. According to one aspect of the present invention, there is provided a system. The system includes at least one processor module and a memory module coupled to a communication bus. The system further includes a signal acquisition module configured to receive a plurality of wind turbine operating parameters corresponding to a plurality of wind turbines and to obtain a plurality of source sound power values corresponding to the plurality of wind turbines. . The signal acquisition module is also designed to obtain a reception sound pressure value corresponding to the receiving location. The system includes an attenuation pattern creation module communicating with the signal acquisition module and configured to estimate a plurality of attenuation coefficients based on the plurality of source sound power values and the value receiving sound pressure. The system also includes a fleet control optimization module communicating with the attenuation model creation module and adapted to determine, based on the plurality of attenuation coefficients and the plurality of wind turbine operating parameters ( s), at least one setpoint corresponding to at least one wind turbine among the plurality of wind turbines.
    [0003] The signal acquisition module and / or the attenuation model creation module and / or the system park optimization module is / are stored in the memory module and is / are executable (s) by the processor module (s). The invention will be better understood from the following description of an embodiment taken by way of nonlimiting example and illustrated by the appended drawings in which: - Figure 1 is a schematic representation of a system for optimizing the operation of a plurality of wind turbines according to an exemplary embodiment; Figure 2 is a linear model for estimating noise at a receiver, according to an exemplary embodiment; Figure 3 is a flowchart of a constrained optimization technique according to an exemplary embodiment; and FIG. 4 is a flowchart illustrating a method for determining operating setpoints for the plurality of wind turbines, according to an exemplary embodiment.
    [0004] Embodiments of a method and system for optimizing the operation of a plurality of wind turbines of a wind farm include receiving a plurality of operating parameters of the plurality of wind turbines. Embodiments further include obtaining a plurality of source sound power values corresponding to the plurality of wind turbines and at least one receiving sound pressure value corresponding to the receiving location. An attenuation model is estimated from the plurality of sound power values and at least one receiving sound pressure value, the attenuation model including a plurality of attenuation coefficients. At least one wind turbine setpoint corresponding to at least one wind turbine of the plurality of wind turbines is determined from the plurality of attenuation coefficients and from the plurality of wind turbine operating parameters at the wind turbine. using an optimization technique under constraints. A power value delivered by the wind turbine (s) is changed according to the wind turbine set point (s). Figure 1 is a schematic representation of a system 100 for optimizing the operation of a wind farm 138 according to an exemplary embodiment. The system 100 communicates with a plurality of wind turbines 102 and with a plurality of locations 104 for receiving the wind farm 138. Each of the various wind turbines 102 includes a nacelle 106 enclosing a multiplier 110 coupled to an alternator 112. The nacelle 106 also contains a controller 114 communicating with the multiplier 110, and the alternator 112. Each of the different wind turbines 102 has one or more blades 108 and each wind turbine is mounted on a mast 116. In an exemplary embodiment, each of the different wind turbines 102 is equipped with a near field microphone to acquire a source sound power value of the corresponding wind turbine. A plurality of sound power values 130 of sources corresponding to the plurality of wind turbines are produced. The wind farm 138 comprises at least one receiving location 105 equipped with a far-field microphone to obtain a reception sound pressure value. Generally, the embodiments of the invention described herein include a plurality of reception sound pressure values 132 acquired by a plurality of far-field microphones disposed at the plurality of receiving locations 104. In the wind farm 138, each of the different receiving locations 104 receives acoustic contributions from one or more of the different wind turbines 102. The term "acoustic power value" as used herein refers to an electrical parameter representing the "sound pressure level". (NPA) "produced by the wind turbine or received at the receiving location. Measurements of sound pressure values at the receiving locations are referred to herein as "receiving sound pressure measurements". Estimates of reception sound pressure values are referred to as "reception sound pressure estimates". Measurements of source sound power values are used interchangeably herein with the term "source sound power measurements". Estimates of source sound power values are used interchangeably with the term "source sound power estimates". The system 100 includes a signal acquisition module 118, an attenuation model creation module 120, a park control optimization module 122, at least one processor module 124, and a memory module 126. The modules of the system 100 communicate with each other via a communication bus 136. The signal acquisition module 118 communicates with the wind farm 138 and is designed to receive measurement data from the wind farm. The fleet control optimization module 122 communicates with the wind farm 138 and is designed to provide control data for optimized operation of at least one wind turbine. The signal acquisition module 118 receives a plurality of operating parameters 128 corresponding to the plurality of wind turbines 102. The operating parameters 128 include, in no way limiting, the speed 140 of the rotor, the speed 142 of the wind and a or more angles of setting 144 of the rotor blades corresponding to each of the different wind turbines. The signal acquisition module also contains the plurality of source sound power values 130 corresponding to the plurality of wind turbines and the receiving sound pressure value (s) 132 corresponding to the receiving location / locations . In one embodiment, the plurality of source power values 130 and the receiving sound pressure value (s) 132 are measured by permanent hardware equipment including a plurality of field microphones disposed in the plurality of wind turbines. 102 and at the receiving location / locations 104. In another embodiment, the plurality of source sound power values 130 and the receiving sound pressure value (s) 132 are measured by hardware devices. temporary. In one embodiment, the temporary hardware devices include the plurality of microphones set up for a fortnight each quarter or semester.
    [0005] In one exemplary embodiment, the plurality of source sound power values 130 are estimated from the plurality of operating parameters 128. For each wind turbine, the rotor speed 140, the wind speed 142, and the wind angles Calibration 144 is measured and a wind turbine model is used to estimate the source sound power value corresponding to the wind turbine. In one embodiment, an empirical model is used to model the wind turbine. In some embodiments, at least one reception sound pressure value 132 is estimated from the plurality of source sound power values 130. In one embodiment, a linear model is used to estimate at least one reception sound pressure 132. The operation of the linear model for producing the reception sound pressure estimate is explained in a subsequent paragraph. In one embodiment, the signal acquisition module 118 is stored in the memory module and is executable by the processor module (s) 124. In another embodiment, the module signal acquisition 118 is a standalone hardware module adapted to receive the plurality of operating parameters 128, a plurality of source sound power values 130 and the reception sound pressure value (s) 132. The noise attenuation model 120 communicates with the signal acquisition module 118 and is designed to estimate a plurality of attenuation coefficients. In some embodiments, the noise attenuation pattern is created from the plurality of source sound power values 130 and from the receiving sound pressure value (s) 132. The models attenuation determined from input and output data are referred to herein as "data-oriented models". In such an embodiment, a reception pattern producing reception sound pressure estimates is determined using techniques such as machine learning and statistical regression techniques. The reception model is based on the relative geometry of the plurality of wind turbines 102 and the location / locations of reception 104. The accuracy of the reception model is validated by a cross-validation technique. In another embodiment, an empirical parametric model such as the ISO 9613-2 model is used to estimate at least one reception sound pressure value 132. In yet another embodiment, a hybrid model combining the empirical parametric model and a data-oriented model for estimating the reception sound pressure value (s) 132. In an exemplary embodiment, a least squares linear technique is used to determine the plurality of attenuation coefficients of the linear model. . In another exemplary embodiment, a nonlinear technique such as a non-linear least squares technique or a Levenberg-Marquardt method is used to estimate parameter values of the empirical parametric model. In one embodiment, the acoustic attenuation pattern generator module 120 is stored in the memory module and is executable by the processor module (s) 124. In another embodiment, the noise attenuation template creation module 120 is a stand-alone hardware module designed to estimate a plurality of attenuation coefficients based on the plurality of source sound power values and the plurality of reception sound pressure values. In one embodiment, the plurality of attenuation coefficients are determined every six hours or twelve hours. In another embodiment, the plurality of attenuation coefficients are determined once a day. In other possible embodiments, the plurality of attenuation coefficients are determined once a week or once a month. The fleet control optimization module 122 communicates with the noise attenuation model generator module 120 and is adapted to determine, based on the plurality of attenuation coefficients and the plurality of wind turbine operating parameters. at least one setpoint 134 corresponding to at least one wind turbine among the plurality of wind turbines.
    [0006] The terms "setpoint" and "wind turbine setpoint" used here interchangeably refer to the operating conditions of a wind turbine. In an exemplary embodiment, a plurality of setpoints corresponding to the plurality of wind turbines are determined using a real-time constrained optimization technique for the prevailing environmental conditions. The ambient conditions discussed herein include, but are not limited to wind direction, wind speed, and plurality of attenuation coefficients. The different setpoint values can be used to modify power values delivered by the corresponding wind turbines. In one embodiment, the park control optimization module 122 is stored in the memory module and is executable by the processor module (s) 124. In another embodiment, the module The controller 122 is a dedicated hardware module designed to perform a constrained optimization maximizing a combined power value delivered by the plurality of wind turbines by maintaining the plurality of receiving sound pressure values below that value. a regulatory threshold. The processor module (s) 124 comprises / comprise at least one arithmetic and logic unit, a microprocessor, a polyvalent controller or a series of processors for performing the desired calculations. In one embodiment, the role of the processor module (s) 124 may be limited to the reception of the operating parameters 128. In another embodiment, the role of the module (s) of processor (s) 124 is limited to the determination of the set value (s) wind turbine (s). In some exemplary embodiments, the role of the processor module (s) would include one or more of the functions of the signal acquisition module 118, the sound attenuation model generator module 120 and the fleet control optimization module 122. Although the processor module 12 is shown as a separate component, there may be a processor located at the same location or integrated in one or more of the modules 118, 120, 122. Alternatively, the module processor 124 may be local or remote, for example based on central server or cloud, the communication bus 136 may be wired, radio or combining these two types. The memory module 126 may be a non-transitory storage medium. For example, the memory module 126 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, a flash memory device, or other memory devices. In one embodiment, the memory module 126 may include a non-volatile memory or similar permanent storage device, media such as a hard disk drive, a floppy disk drive, a CD ROM device (CD -ROM), a versatile digital read-only disk device (DVD-ROM), a versatile digital disk device with random access memory (DVD-RAM), a rewritable multi-purpose digital disc device (DVD-RW), a flash memory or other non-volatile storage devices. In a specific embodiment, a computer-readable non-transitory storage medium contains instructions for allowing the processor module (s) 124 to determine the operating setpoint values for the plurality of wind turbines. . Figure 2 is a linear model 200 for estimating receive noise according to an exemplary embodiment. The linear model 200 constitutes an attenuation model for estimating the reception sound pressure value Prj corresponding to the jth reception location among M reception locations of a wind farm comprising N wind turbines. Each of the different source sound power values 202, 204, 206 is attenuated and delayed before being combined by an adder 220 to create the receiving sound pressure value 222 corresponding to the jth receiving location. The different attenuation coefficients 208, 210, 212 and the different delays 214, 216, 218 respectively correspond to the plurality of sound power values of sources 202, 204, 206.
    [0007] A system of linear equations is obtained from the linear model 200, it is represented in the form of the following matrix equation: A = Ax = b where PsN (tiN) b = power Prj (t1) Pr (t2) Pri (tK) 5 x = F '1 / Ali 11A2 j of (1) where Ps1 (t11) Ps2 (t12) Ps1 (t2N) 0 acoustic 11ANi (2) source Ps1 (t21) Ps1 (t22) Psi (tKN) of Psi (txi) Ps1 (tic2) the value Psi denotes corresponding to the jth wind turbine among the N wind turbines, Prj denotes the reception sound pressure value corresponding to the jth receiving location among M receiving locations, Ail denotes the attenuation coefficient corresponding to a transmission path from the wind turbine i to the receiving location j. The time stamp tk indicates the time of the value of the reception sound pressure and the time index tki is a time stamp given by: tki = tk - dt (i, j) (3) where At (i, j) designates the propagation delay between the wind turbine and the jth receiving location. In an exemplary embodiment, the different source sound power values Psi and the different reception sound pressure values Prj correspond to an average frequency octave frequency. In one embodiment, the linear equation system (2) is obtained for eight octave bands. It should be emphasized here that the technique is not limited to eight octave bands and that in other embodiments band frequency analysis, including one-third octave bands or narrow band is used. The different attenuation coefficients of the vector x correspond to the jth reception location. In one embodiment, the different attenuation coefficients are obtained using a least squares method. The different attenuation coefficients corresponding to each of the other reception locations can be determined in the same way by choosing the matrix A and the vector b of equation (1) corresponding to the other reception locations. In one embodiment, the reception model is for determining an attenuation coefficient Ai; corresponding to the index i of the wind turbine and to the index j of the receiving location in the form: Aij = a (Prj) I (4) a (Psi) a (PsÙlt = t1-At (i, j) Psi) t = t1 where a (Pri) / a (Psi) is a partial derivative of the receiving acoustic pressure Pd with respect to the acoustic power of Psi source obtained analytically or empirically using disruptions. In one embodiment, the empirical parametric model such as a far-field noise propagation model ISO 9613-2 is used to determine the plurality of attenuation coefficients. In the ISO 9613-2 model the different attenuation coefficients for a given wind turbine and a receiving location for a given octave band are given by: Aij (CO) = Aij_known (CO) Aij_unknown (CO) (5) where the term Au_known is due to geometric divergence and atmospheric absorption predicted with precision. The term AiLunknown corresponds to the contribution of unpredictable components and the term w designates the angular frequency corresponding to the octave band at medium frequency f. Equation (2) can be modified from Equation (5) and the different attenuation coefficients can be determined by a modified linear equation. The different attenuation coefficients determined from the modified matrix equation (1) have a better numerical stability. Figure 3 is a flowchart 300 of a constrained optimization technique according to an exemplary embodiment. The constrained optimization technique presented here determines the different wind turbine setpoint values corresponding to the different wind turbines of the wind farm according to the prevailing environmental conditions. In an exemplary embodiment, at least one wind turbine setpoint corresponding to at least one of the plurality of wind turbines is determined using the constrained optimization technique. The technique for determining at least one wind turbine set point includes maximizing a combined power value delivered by the plurality of wind turbines as an objective function 302 of the constrained optimization. The objective function is given by: f (x) = maxre P - (I (xi, xi 1 = 1 WL (6) where xi is a wind turbine setpoint or a group of setpoints for the wind turbine, Pwi is the power delivered by the wind mill and Vi is the wind speed value corresponding to the wind mill .. In one embodiment, the wind turbine setpoint is selected from a plurality of low noise operation modes ( FBR) The term reduced noise operation (FBR) used herein refers to a pre-established condition of wind turbine operation that causes a particular maximum level of noise emission at the wind turbine. reduce the number of parameters to be optimized and ensure that a feasibility solution can be obtained from optimization under constraints The technique to determine the setpoint value (s) also includes the choice of a regulatory threshold for a location as optimization constraint 304. The optimization constraint includes limiting each of the different reception sound pressure values. The function constraints c (x) is given by: x, 8 1 rn (7) C (X) = Li.l L f = 1 Aii.fst, Vi) <Ci (Vi) where Aiff denotes the coefficient of attenuation in an octave band at medium frequency f, P sil 'is the source acoustic power value of the ith wind turbine in the mid-frequency octave band f. The symbol Cj designates the regulatory threshold corresponding to the jth receiving location and Vj designates the wind speed at the Jth receiving location. The constant Nj designates the number of wind turbines contributing to the noise at the Jth receiving location and the index j denotes one of the M receiving locations. The various attenuation coefficients, estimated as previously explained by the noise attenuation estimation module, are used in the optimization technique 306. The constrained optimization requires partial derivatives of the plurality of powers delivered and the plurality of source sound power values. In one embodiment, the partial derivatives are determined from the 308 wind turbine performance maps. In another possible embodiment, aerodynamic and aeroacoustic simulations of wind turbine rotors can be used to determine the partial derivatives. In another embodiment, the partial derivatives are determined by the different values of power delivered and the different source sound power values. The derivative of the objective function is given by: axi-axi (xi.VI) af _ apwi (8) and the derivative of the function under constraints is given by: (x, v) 8 aps ,, f oxi f-1 AU 0X1 Vi) The partial derivative of the objective function is called here "objective gradient" and the partial derivative of the constraint function is called here "Jacobian constraints". The objective gradient and the Jacobian constraints are determined, 310, 312, according to partial derivatives and attenuation constants. An optimization under constraints in which the Equation (7) exerts constraints on the objective function of the Equation (6) is realized, 314, according to the objective gradient and the Jacobian constraints respectively of the Equation (8) and Equation (9) to determine the optimum setpoint value 316 for each windmill wind turbine. In one embodiment, a direct search method is used to determine the value of the partial derivatives. In another embodiment, the value of the partial derivatives is extracted from a memory location. In some embodiments, a gradient descent method is used to determine the optimum setpoint. In other possible embodiments, any other digital technique including, but not limited to, the convex programming method and the stochastic method, may also be used. Fig. 4 is a flowchart 400 illustrating a method for optimizing the operation of the wind farm according to an exemplary embodiment. The method comprises receiving a value of wind speed, a value of angular speed of the rotor in revolutions per minute (rpm), at least one calibration angle corresponding to each wind turbine of the wind farm 402. The different Source sound power values are obtained, 404, using a plurality of microphones disposed on the plurality of wind turbines of the wind farm. The different reception sound pressure values are obtained, 406, using a plurality of microphones arranged at the different reception locations. An attenuation model is determined (9) from the plurality of source sound power values, the different reception sound pressure values, and the operating parameters of the plurality of wind turbines 408. The attenuation model 410 comprises a plurality of attenuation coefficients corresponding to each of the different octave frequency bands. In one embodiment, the attenuation pattern 410 is determined from the different source sound power values and the different reception sound pressure values obtained by measurements calculated from step 408. In a given embodiment In another embodiment, the attenuation model 410 is re-estimated from step 418, when the ambient conditions of the wind farm have changed. The process of re-estimation of the attenuation model 418 will be explained in more detail in a subsequent paragraph.
    [0008] In one embodiment, the receiving sound pressure value at each of the different receiving locations is predicted from the attenuation model and different source sound power values 412. The constrained optimization is performed to determine an optimum setpoint for at least one wind turbine among the different wind turbines, 414. In one embodiment, the constrained optimization, 414, is performed once per minute. In another embodiment, constrained optimization 414 is performed once every ten minutes. The optimization is performed according to the different reception sound pressure values predicted in step 412. In another embodiment, the optimization is performed according to the plurality of reception sound pressure values measured during from step 406. The optimum setpoint is communicated to the controller located in the wind turbine (s) and the operating parameters of the wind turbine (s) are adjusted according to the optimum setpoint communicated. . The power value delivered by the wind turbine (s) is changed according to the new set of operating parameters. In other possible embodiments, the adjustment of the rotor speed and / or the setting of the stall angle is changed according to the new set of operating parameters. In some embodiments where the reception sound pressure value is predicted, the different measured reception sound pressure values are compared with the different predicted corresponding values, 416. In one embodiment, a plurality of difference values between different estimated reception sound pressure values and the different measured reception sound pressure values are determined. Each of the different values of differences is compared with a predetermined threshold. When all the difference values are below the predetermined threshold, the different reception sound pressure values obtained by measurements correspond to the plurality of reception acoustic pressure values obtained by estimation. In this case, the re-estimation of the attenuation model is not necessary and the unchanged attenuation model 420 is identical to the attenuation model 410. In an exemplary embodiment, the comparison 416 is performed once. every five minutes. In another embodiment, the comparison 416 is performed once per minute. If at least one of the different difference values exceeds a predetermined threshold, the received sound pressure value obtained by the measurement does not correspond to the reception sound pressure value obtained by the estimate. In this case, the different attenuation coefficients of the attenuation model are re-estimated, 418. The attenuation model of 410 is updated using the plurality of attenuation coefficients re-estimated during the Step 418. In one embodiment, the predetermined threshold is provided by a user and received by the signal acquisition module. In some embodiments, the re-estimation of the different attenuation coefficients 418 is initiated from a few consecutive comparisons in step 416. In an example of such an embodiment, the re-estimation 418 is initiated from five consecutive comparisons 416 of the reception sound pressure value obtained by the measurement with the reception sound pressure value obtained by the estimate.
    [0009] When one or more of the different difference values exceeds the predetermined threshold in each of the five consecutive comparisons, a re-estimation of the attenuation model is initiated. Embodiments using a predetermined attenuation pattern are referred to herein as "offline processes". In an exemplary embodiment of the offline process, steps 402, 404, 406, 408, 410, 414 are used. A plurality of previously calculated attenuation patterns and corresponding ambient conditions are stored in a memory. Attenuation model 410 is selected from the plurality of attenuation models previously calculated from the measured ambient condition. Embodiments frequently determining the attenuation pattern, at regular intervals, based on the plurality of source sound power values and the plurality of receiving sound pressure values are referred to herein as "on-line methods". Online processes include a re-estimation of the attenuation model at regular intervals. In an exemplary embodiment, the on-line method includes additional steps 412, 416, 418. If the plurality of reception sound pressure measurements do not match the plurality of reception sound pressure estimates, a plurality estimate of attenuation model 416 is launched. In an exemplary embodiment, the re-estimation of the attenuation model 416 is performed once every six hours. In other embodiments, the re-estimation of the attenuation template 416 is performed once a day, once a week, or once a month. The exemplary embodiments described herein provide a system and method for optimizing the operation of a plurality of wind turbines operating in a wind farm. Operation optimization for noise-constrained wind farms results in an increase in annual energy output (AEP) of 2% to 4% compared to prior art solutions which employ setpoint values of wind turbine operation that do not change over time. The far-field noise modeling described here is based on directly received field measurement data to improve the quality of the estimate of the plurality of noise attenuation coefficients. The constrained optimization technique using the plurality of noise attenuation coefficients determines the setpoint value (s) with greater degrees of confidence and precision. The described embodiments allow the use of simpler, physics-based noise mitigation models instead of complex models that require many simulations. The objectives or advantages described above may not necessarily all be achieved with any particular embodiment. Thus, for example, those skilled in the art will understand that the systems and techniques described herein may be implemented or executed in a manner that achieves or enhances an advantage or benefit package explained herein without necessarily achieving other objectives or benefits that may be presented or suggested here.
    权利要求:
    Claims (21)
    [0001]
    REVENDICATIONS1. A method (400) comprising: receiving (402) a plurality of operating parameters corresponding to a plurality of wind turbines; obtaining (404) a plurality of source sound power values corresponding to the plurality of wind turbines; obtaining (406) a reception sound pressure value corresponding to a receiving location; determining (408) an attenuation pattern based on the plurality of source sound power values and the receiving sound pressure value, the attenuation pattern comprising a plurality of attenuation coefficients; and determining (414) at least one wind turbine setpoint corresponding to at least one of the plurality of wind turbines, based on the plurality of attenuation coefficients and the plurality of wind turbine operating parameters. .
    [0002]
    2. Method (400) according to claim 1, further comprising the modification of a power value delivered by the wind turbine (s) by modifying at least one of the various operating parameters and the wind turbine set point value. the corresponding wind turbine.
    [0003]
    The method (400) of claim 1, wherein the plurality of operating parameters comprises a stall angle and / or a wind speed value and / or a rotor speed value, corresponding to each of the individual wind turbines.
    [0004]
    The method (400) of claim 1, wherein obtaining (404) the plurality of source sound power values comprises an estimate of the different source sound power values according to the different operating parameters of the source. wind turbine (s) and a wind turbine model.
    [0005]
    The method (400) of claim 1, wherein obtaining (406) the reception sound pressure value comprises obtaining a plurality of reception sound pressure values corresponding to a plurality of receiving locations. .
    [0006]
    The method (400) of claim 5, wherein obtaining (406) the plurality of reception sound pressure values comprises receiving a plurality of reception sound pressure measurements transmitted by a plurality of arranged microphones. in the plurality of receiving locations.
    [0007]
    The method (400) of claim 6, wherein obtaining (406) the plurality of reception sound pressure values comprises determining a plurality of pressure estimates.
    [0008]
    The method (400) of claim 7, wherein the determining (408) comprises: determining a plurality of difference values between the plurality of reception sound pressure estimates and the plurality of acoustic sound pressure measurements. reception; and modifying the plurality of attenuation coefficients according to the plurality of difference values if at least one of the different difference values exceeds a predetermined threshold.
    [0009]
    The method (400) of claim 1, wherein the determining (408) comprises determining the plurality of attenuation coefficients by a least squares method.
    [0010]
    The method (400) of claim 1, wherein the determining (408) comprises determining the plurality of attenuation coefficients according to a far-field noise propagation model.
    [0011]
    11. Method (400) according to claim 1, wherein the determination (414) of the value (s) setpoint is based on a constrained optimization technique, the constrained optimization technique submitting the pressure value. receiving acoustic signal at a constraint below a regulatory threshold and maximizing a combined power value delivered by the plurality of wind turbines.
    [0012]
    A system (100) comprising: at least one processor module (124) and a memory module (126) coupled to a communication bus (136); a signal acquisition module (118) adapted to receive a plurality of wind turbine operating parameters (128) corresponding to a plurality of wind turbines (102); obtaining a plurality of source sound power values (130) corresponding to the plurality of wind turbines (102); and obtaining a reception sound pressure value (132) corresponding to a receiving location (104); an attenuation pattern generator module (120) communicating with the signal acquisition module (118) and adapted to estimate a plurality of attenuation coefficients from the plurality of source sound power values (130) and the reception sound pressure value (132); and a park control optimization module (122) communicating with the attenuation model generator module (120) and arranged to determine at least one setpoint value (134) corresponding to at least one wind turbine (102) among the plurality of wind turbines (102) according to the plurality of attenuation coefficients and the plurality of wind turbine operating parameters (128); the signal acquisition module (118) and / or the attenuation model generator module (120) and / or the park control optimization module (122) is / are stored in the module memory (126) and may / may be executed by the processor module (s) (124).
    [0013]
    The system (100) of claim 12, wherein the fleet control optimization module (122) is further adapted to modify a power value delivered by the wind turbine (s) (102) by changing the minus one of the different operating parameters (128) and the wind turbine set point (134) of the corresponding wind turbine (102).
    [0014]
    The system (100) of claim 12, wherein the signal acquisition module (118) is adapted to receive a rotor speed value (140) and / or a wind speed value (142) and / or a stall angle (144) corresponding to each of the different wind turbines (102).
    [0015]
    The system (100) of claim 12, wherein the signal acquisition module (118) is further adapted to estimate the plurality of source sound power values (130) according to the plurality of operating parameters. (128) wind turbine (s) and a wind turbine model.
    [0016]
    The system (100) of claim 15, wherein the signal acquisition module (118) is further adapted to receive a plurality of reception sound pressure measurements of a plurality of microphones disposed on the plurality of wind turbines (102) and the plurality of receiving locations (104).
    [0017]
    The system (100) of claim 16, wherein the signal acquisition module (118) is further adapted to determine a plurality of reception sound pressure estimates based on the plurality of acoustic power values of sources (130) and the attenuation model.
    [0018]
    The system (100) of claim 17, wherein the attenuation model generator module (120) is adapted to: determine a plurality of difference values between the plurality of reception sound pressure estimates and the plurality reception sound pressure measurements; and modifying the plurality of attenuation coefficients according to the plurality of difference values if at least one of the different difference values exceeds a predetermined threshold.
    [0019]
    The system (100) of claim 12, wherein the attenuation model generator module (120) comprises a far-field noise propagation model.
    [0020]
    The system (100) of claim 12, wherein the fleet control optimization module (122) is designed to perform a constrained optimization maximizing a combined power value delivered by the plurality of submitting wind turbines (102). the reception sound pressure value (132) at constraints below a regulatory threshold.
    [0021]
    A wind farm (138) having a plurality of wind turbines (102) communicating with the signal acquisition module (118) of the system (100) according to claim 12.10
    类似技术:
    公开号 | 公开日 | 专利标题
    FR3024558A1|2016-02-05|
    EP2876302B1|2019-10-23|Method for controlling and monitoring a wind turbine by estimating wind speed using a lidar sensor
    US9551322B2|2017-01-24|Systems and methods for optimizing operation of a wind farm
    US9797377B2|2017-10-24|System and method for controlling a wind farm
    DK3263889T3|2019-03-25|SYSTEM AND PROCEDURE FOR ASSESSING PERFORMANCE LEVEL OF A WINDMILL PARK
    FR3024502A1|2016-02-05|
    FR3031817A1|2016-07-22|METHOD FOR CORRECTING THE CALCULATION OF A FLIGHT CHARACTERISTIC OF A PLANE BY TAKING INTO ACCOUNT OF THE VERTICAL WIND, METHOD OF CALCULATING THE COEFFICIENT OF TRAINING
    US20100080703A1|2010-04-01|System and method for wind condition estimation
    US10247171B2|2019-04-02|System and method for coordinating wake and noise control systems of a wind farm
    CA2807912C|2018-06-26|Method for optimizing the control of a free turbine power package for an aircraft, and control for implementing same
    CA2892654C|2020-04-14|Control device for an engine
    US20210262441A1|2021-08-26|Method for determining the available power of a wind park, and corresponding wind park
    JP2017506311A|2017-03-02|Method and system for improving energy capture efficiency of an energy capture device
    EP3642647A1|2020-04-29|Method for acquiring and modelling an incident wind field by means of a lidar sensor
    US20180363632A1|2018-12-20|Offset Toggle Method for Wind Turbine Operation
    FR3073496A1|2019-05-17|SYSTEM AND METHOD FOR DIAGNOSING A ROTOR IMBALANCE OF A WINDMILL
    EP3438448A1|2019-02-06|Wind farm control
    CA3059484A1|2020-05-12|Process for determining an induction factor for a wind turbine equipped with a laser remote sensor
    FR3035982A1|2016-11-11|METHOD FOR MONITORING AN AIRCRAFT ENGINE IN OPERATION DURING A FLIGHT
    CA3108124A1|2021-08-10|Method for determining wind speed in the plane of the rotor of a wind turbine
    FR3065804B1|2019-06-21|METHOD FOR EVALUATING THE ACOUSTIC IMPACT OF A VIRTUAL WIND FARM
    FR3032176A1|2016-08-05|DEVICE FOR MONITORING A POWER TRANSMISSION SYSTEM OF AN AIRCRAFT, AN AIRCRAFT PROVIDED WITH SAID DEVICE AND THE METHOD USED
    CA3088429C|2021-09-07|Estimating the clearance between a tower and foundations of a wind turbine
    EP3862563A1|2021-08-11|Method for determining the direction of the wind by means of a laser remote detection sensor
    FR3032574A1|2016-08-12|METHOD OF COMMUNICATING A VOICE MESSAGE COMPRISING A TRANSCRIPTION STEP OF A DETERMINED PORTION OF AN AUDIO SIGNAL CORRESPONDING TO THIS VOICE MESSAGE AND DEVICE FOR IMPLEMENTING IT
    同族专利:
    公开号 | 公开日
    DE102015112595A1|2016-02-04|
    US10711766B2|2020-07-14|
    CA2898915A1|2016-01-31|
    FR3024558B1|2020-02-14|
    US20160032894A1|2016-02-04|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    SE0200237D0|2002-01-29|2002-01-29|Abb Ab|Apparatus and method for operation of a power generating plant|
    US7346469B2|2005-03-31|2008-03-18|General Electric Company|System and method for sensor data validation|
    US20070031237A1|2005-07-29|2007-02-08|General Electric Company|Method and apparatus for producing wind energy with reduced wind turbine noise|
    GB0724366D0|2007-12-14|2008-01-23|Univ York|Environment modelling|
    EP2337952B1|2008-09-30|2012-11-07|Vestas Wind Systems A/S|Control of wind park noise emission|
    WO2010061255A2|2008-11-01|2010-06-03|Clipper Windpower, Inc.|Active blade pitch control for reduction of wind turbine noise or loads|
    US20100135798A1|2009-02-10|2010-06-03|General Electric Company|Wind turbine noise controls|
    US7896613B2|2009-06-03|2011-03-01|General Electric Company|System and method for wind turbine noise control and damage detection|
    US8215907B2|2009-09-30|2012-07-10|General Electric Company|Method and apparatus for controlling acoustic emissions of a wind turbine|
    US20110223018A1|2010-12-21|2011-09-15|Prashant Srinivasan|Control System, Wind Farm, And Methods Of Optimizing The Operation Of A Wind Turbine|
    US8232663B2|2011-06-30|2012-07-31|General Electric Company|Controlling noise generated by wind turbines in a wind farm by de-synchronization of wind turbine rotors|
    CN103797244B|2011-08-16|2017-09-12|维斯塔斯风力系统集团公司|Noise acoustic monitoring system for wind turbine|
    US8304926B2|2011-09-16|2012-11-06|General Electric Company|Wind turbine sound management|
    US8258643B2|2011-10-11|2012-09-04|General Electric Company|Method and system for control of wind turbines|
    EP2599996B1|2011-12-02|2016-06-29|Vestas Wind Systems A/S|Controlling of noise emission of a wind park|
    HU1200197A2|2012-04-03|2013-10-28|Budapesti Mueszaki Es Gazdasagtudomanyi Egyetem|Method and arrangement for real time source-selective monitoring and mapping of enviromental noise|WO2017174089A1|2016-04-07|2017-10-12|Vestas Wind Systems A/S|Control of a wind turbine taking noise into account|
    US10247171B2|2016-06-14|2019-04-02|General Electric Company|System and method for coordinating wake and noise control systems of a wind farm|
    EP3732361B1|2017-12-29|2022-03-02|Vestas Wind Systems A/S|Wind turbine design method|
    CN110500234B|2018-05-18|2020-07-03|北京金风科创风电设备有限公司|Method and device for noise control of wind generating set|
    DE102018005329A1|2018-07-05|2020-01-09|Senvion Gmbh|Method and control for operating a plurality of wind energy plants|
    法律状态:
    2016-07-26| PLFP| Fee payment|Year of fee payment: 2 |
    2017-07-26| PLFP| Fee payment|Year of fee payment: 3 |
    2018-06-21| PLFP| Fee payment|Year of fee payment: 4 |
    2019-01-25| PLSC| Search report ready|Effective date: 20190125 |
    2019-06-21| PLFP| Fee payment|Year of fee payment: 5 |
    2020-06-23| PLFP| Fee payment|Year of fee payment: 6 |
    2021-06-23| PLFP| Fee payment|Year of fee payment: 7 |
    优先权:
    申请号 | 申请日 | 专利标题
    IN3768CHE2014|2014-07-31|
    IN3768CH2014|2014-07-31|
    [返回顶部]