Fan power smoothing method based on hybrid weighted filtering and active disturbance rejection variable pitch

Abstract

The invention provides a hybrid weighted filtering and active disturbance rejection variable pitch-based fan power smoothing method, and belongs to the technical field of wind power generation control. The technical problem that the output power of the wind driven generator fluctuates under the working conditions of high-wind-speed turbulence and wind-speed sudden-drop turbulence is solved. According to the technical scheme, the method comprises the following steps that S1, a nonlinear mathematical model of the permanent magnet direct-driven wind power generation system is established; s2, generating a smooth power reference instruction; s3, calculating to obtain a corresponding smooth reference rotating speed instruction; s4, a linear active disturbance rejection variable pitch control algorithm based on the Lyapunov stability is adopted; and S5, comprehensive power smoothing of the wind driven generator under the complex turbulence working condition is realized. According to the method, the defect of insufficient adaptability of a fixed bandwidth controller under a time-varying working condition is overcome, and the power smoothing effect and the operation reliability of a fan under complex turbulence are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generation control, and in particular to a wind turbine power smoothing method based on hybrid weighted filtering and active disturbance rejection variable pitch. BACKGROUND

[0002] With the large-scale development and high proportion of grid connection of wind power generation, large wind turbines have become new power supply units with power active support capability. When the wind turbine operates at and above the rated wind speed, it faces complex working conditions such as high-frequency turbulence and wind speed sudden drop, which leads to significant oscillation and rapid drop of its output power and other power quality problems. The effective suppression and smoothing of these problems are directly related to the operation life of the wind turbine and the stable operation of the power grid, and also put forward urgent needs for the effect and reliability of the "turbine power smoothing control technology suitable for complex turbulence working conditions".

[0003] In the research of wind turbine power smoothing control, the existing technology mainly focuses on the power reference instruction generation and variable pitch execution control. In the instruction generation link, the model-based filtering method such as extended Kalman filter has good dynamic tracking performance, but it will follow the power drop in extreme working conditions and lacks effective smoothing mechanism. The time domain filtering method such as recursive mean filter has strong smoothing characteristics, but its inherent phase lag will sacrifice the dynamic response speed of the system and affect the wind energy capture efficiency. The single filtering architecture lacks a mechanism for adaptive adjustment according to the operating conditions, and it is difficult to achieve a dynamic balance between fast tracking and effective smoothing.

[0004] In the execution control link, the traditional linear active disturbance rejection control estimates and compensates the total disturbance of the system through the extended state observer, and shows better robustness than the traditional PI controller. However, the observer bandwidth of the traditional active disturbance rejection control is usually a fixed parameter. The fixed bandwidth parameter cannot maintain optimal performance in complex turbulence working conditions: when facing wind speed sudden change and other large dynamic processes, low bandwidth configuration will lead to slow response and insufficient disturbance estimation; while in the case of high-frequency turbulence, high bandwidth configuration is easy to amplify measurement noise and cause actuator oscillation.

[0005] At present, for the power smoothing control of wind turbines in medium and high wind speed range, the existing technology has the following defects: first, there is no coordinated control architecture suitable for complex turbulence and wind speed sudden drop working conditions; second, the filtering method cannot achieve adaptive balance between dynamic response and fluctuation suppression; third, the controller parameters are fixed, lacking the ability to adapt to time-varying working conditions, resulting in difficult to achieve optimal system-level performance.

[0006] Therefore, there is a need for a power smoothing control method that can adapt to complex turbulence working conditions and effectively coordinate power instruction smoothing and variable pitch execution control to achieve global optimization. SUMMARY

[0007] The application provides a fan power smoothing method based on hybrid weighted filtering and active disturbance rejection variable pitch to solve the technical problem of fan output power fluctuation under complex turbulent flow conditions.

[0008] The application idea of the application is as follows: firstly, an adaptive hybrid weighted filtering algorithm based on power instruction differential variance is designed: the overshoot is obtained by calculating the instantaneous change rate of the power instruction and comparing it with a preset threshold; the overshoot is smoothly mapped into the weight coefficient of improved mean value filtering by using an exponential function, and the outputs of extended Kalman filtering and recursive mean value filtering are dynamically fused to generate a smooth power reference instruction; secondly, an improved linear active disturbance rejection variable pitch controller based on Lyapunov stability is designed: a Lyapunov function containing state estimation error and bandwidth estimation error is constructed, and the bandwidth of the extended state observer is dynamically adjusted by designing an adaptive law to ensure the global stability of the system and improve the disturbance estimation accuracy.

[0009] To achieve the above application purpose, the technical scheme adopted by the application is as follows: a fan power smoothing method based on hybrid weighted filtering and active disturbance rejection variable pitch comprises the following steps. Step S1: a nonlinear mathematical model of a permanent magnet direct drive wind power generation system is established; Step S2: an adaptive hybrid weighted filtering algorithm based on power instruction differential variance is used to generate a smooth power reference instruction: the instantaneous change rate of the power instruction is calculated in real time, the overshoot is obtained by comparing it with a preset threshold, and the overshoot is mapped into the weight coefficient of improved mean value filtering by using an exponential function, and the outputs of extended Kalman filtering and improved recursive mean value filtering are weighted and fused; Step S3: according to the smooth power reference instruction obtained in step S2, the corresponding smooth reference speed instruction is calculated in combination with the mapping relationship between the rotor kinetic energy and the power; Step S4: a linear active disturbance rejection variable pitch control algorithm based on Lyapunov stability is used to perform variable pitch tracking control, the deviation between the smooth reference speed instruction and the actual speed is taken as the input of the controller, a Lyapunov function containing state estimation error and bandwidth estimation error is constructed, a bandwidth adaptive law is designed to dynamically adjust the bandwidth of the extended state observer, and the total disturbance of the system estimated by the observer is used for feedforward compensation to generate a reference pitch angle. Step S5: Combine the generation of the smooth reference speed command in step S3 with the linear active disturbance rejection variable pitch control in step S4, through the coordination of front-end command optimization and back-end robust control, to realize the comprehensive power smoothing of the wind turbine under complex turbulent conditions.

[0010] Further, in step S1, the mechanical power captured by the wind turbine in the permanent magnet direct drive wind power generation system P w and the mechanical torque T w can be expressed as: (1) wherein, is the air density, is the wind turbine blade radius, is the wind speed, is the wind energy utilization coefficient, is the tip speed ratio, is the pitch angle, is the wind speed variation rate influence coefficient, is the rotor angular velocity, t is the time, d is the differential operator; The wind energy utilization coefficient is a key parameter reflecting the aerodynamic conversion efficiency of the wind turbine, and its value depends on the nonlinear coupling relationship between the tip speed ratio and the pitch angle , which can be expressed as: (2) wherein, is an intermediate variable, e is the Euler number.

[0011] Further, in step S2, the instantaneous change rate of the power command at the adjacent sampling time is calculated: (3) In equation (3), is the electromagnetic power command at the current time, is the electromagnetic power command at the previous time, is the sampling period, is the rated power.

[0012] The instantaneous change rate is compared with the preset power change rate threshold to calculate the overshoot : (4) The overshoot Smooth mapping to weight interval, calculate real-time weight coefficient of recursive mean filter For: (5) In formula (5), is the upper limit value of weight, is the control sensitivity coefficient.

[0013] According to the real-time weight coefficient , the extended Kalman filter output is weighted and fused with the recursive mean filter output to generate the final smooth power reference value For: (6) Where, is the weight coefficient, that is, the above .

[0014] Further, in step S3, according to the smooth power reference value , the smooth reference speed of the rotor kinetic energy control is obtained For: (7); Where, is the moment of inertia.

[0015] Further, in step S4, the bandwidth self-tuning mechanism based on Lyapunov stability is realized as follows: a) Define the state estimation error and bandwidth estimation error as: (8); Where, is the speed change, is the speed differential change, is the total disturbance of the permanent magnet direct drive wind power generation system, , , are respectively , , observation estimates of , is the observer bandwidth, is the ideal bandwidth, is the bandwidth estimation error, , , are respectively , , and , , deviation between

[0016] b) Construct a Lyapunov function V that includes the state estimation error and the bandwidth estimation error: (9) In equation (9), For adaptive rate and .

[0017] c) Design a bandwidth adaptive law and differentiate it with respect to the Lyapunov function V: (10) In equation (10), The derivative of V, for The derivative, for The derivative, for The derivative, for The derivative of .

[0018] To meet Choose the adaptive law to cancel out the remainder: (11) As can be seen from the observation error, All Higher-order terms, if the observer converges, satisfy the following: At this point, you can select Thus, the observer bandwidth is obtained. for: (12) In equation (12), This is the initial value for the observer bandwidth.

[0019] d) Based on the control objective The control law is designed as follows: (13) In equation (13), for The derivative, It is a constant. For proportional gain, This is the integral gain.

[0020] Meanwhile, the present invention proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed, it implements the steps of the method described in the present invention.

[0021] In addition, the application provides a computer readable storage medium, which stores a computer program configured to realize the steps of the method of the application when called by a processor.

[0022] Finally, the application provides a computer program product comprising computer programs / instructions, characterized in that the computer programs / instructions realize the steps of the method of the application when executed by a processor.

[0023] Compared with the prior art, the application has the following beneficial effects: 1. The method of the application is specifically used to cope with the fluctuation of the output power of a wind turbine under high wind speed turbulent flow and wind speed sudden drop turbulent flow conditions, generates a smooth power instruction through an adaptive hybrid weighted filtering algorithm, and realizes robust tracking control in combination with a linear active disturbance rejection variable pitch controller; the method dynamically fuses the outputs of an extended Kalman filter and an improved mean filter through an adaptive weight distribution mechanism based on the differential variance of the power instruction, so as to balance the contradiction between power smoothing and dynamic response; and the bandwidth of an extended state observer in the active disturbance rejection controller is adjusted in real time through a bandwidth self-tuning mechanism based on Lyapunov stability, so as to enhance the adaptive capacity and global stability of the system to time-varying disturbances.

[0024] 2. The wind turbine power smoothing control method of the application realizes the whole process optimization from power instruction generation to variable pitch execution through the construction of a control architecture in which an adaptive hybrid weighted filtering algorithm and a linear active disturbance rejection variable pitch controller are optimized cooperatively.

[0025] 3. The application effectively solves the contradiction between dynamic response and fluctuation suppression of a single filtering strategy, overcomes the insufficient adaptability of a fixed bandwidth controller under time-varying conditions, and significantly improves the power smoothing effect and operation reliability of the wind turbine under complex turbulent flow. BRIEF DESCRIPTION OF DRAWINGS

[0026] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, illustrate the application together with the embodiments thereof, and explain the application without limiting the application.

[0027] Figure 1 The figure is a flowchart of the method in embodiment 1 of the application.

[0028] Figure 2 The figure is a control block diagram of the adaptive weighted hybrid filtering rotor kinetic energy in embodiment 1 of the application.

[0029] Figure 3 The figure is a control block diagram of the bandwidth self-tuning linear active disturbance rejection variable pitch in embodiment 1.

[0030] Figure 4 The figure is a simulation result diagram of high wind speed turbulent flow in embodiment 2 of the application.

[0031] Figure 5 Figure 3 is a simulation result diagram of the wind speed sudden drop turbulent flow condition in the embodiment 3 of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. Of course, the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0033] Embodiment 1: see Figure 1 The technical scheme of the present embodiment is a wind turbine power smoothing method based on hybrid weighted filtering and active disturbance variable pitch, which comprises the following steps: Step S1: establishing a nonlinear mathematical model of the permanent magnet direct drive wind power generation system; Step S2: generating a smoothed power reference instruction by using an adaptive hybrid weighted filtering algorithm based on the differential variance of the power instruction: calculating the instantaneous change rate of the power instruction in real time, obtaining the overshoot by comparing with the preset threshold, and mapping the overshoot to the weight coefficient of the improved mean value filtering by using the exponential function, and weighting and fusing the output of the extended Kalman filter and the output of the improved recursive mean value filter; Step S3: calculating the corresponding smoothed reference speed instruction according to the smoothed power reference instruction obtained in step S2, in combination with the mapping relationship between the rotor kinetic energy and the power; Step S4: executing variable pitch tracking control by using a linear active disturbance variable pitch control algorithm based on Lyapunov stability, taking the deviation between the smoothed reference speed instruction and the actual speed as the input of the controller, constructing a Lyapunov function containing the state estimation error and the bandwidth estimation error, designing a bandwidth adaptive law to dynamically adjust the bandwidth of the extended state observer, and using the total disturbance estimated by the observer for feedforward compensation to generate a reference pitch angle; Step S5: combining the generation link of the smoothed reference speed instruction in step S3 with the link of the linear active disturbance variable pitch control in step S4, and realizing the comprehensive power smoothing of the wind turbine under complex turbulent flow conditions through the cooperation of the front-end instruction optimization and the back-end robust control.

[0034] Further, in the step S1, the mechanical power P w and the mechanical torque T w can be expressed as: (1) wherein, is the air density, is the radius of the wind turbine blade, is the wind speed, is the wind energy utilization coefficient, is the tip speed ratio, is the pitch angle, is the wind speed variation rate influence coefficient, is the rotor angular velocity, t is the time, d is the differential operator.

[0035] Wind energy utilization coefficient is a key parameter reflecting the aerodynamic conversion efficiency of the wind turbine, and its value depends on the nonlinear coupling relationship between the tip speed ratio and the pitch angle , which can be expressed as: (2) where, is an intermediate variable, e is the Euler number.

[0036] Further, in the step S2, the instantaneous change rate of the power command at the adjacent sampling time is calculated : (3) where, is the electromagnetic power command at the current time, is the electromagnetic power command at the previous time, is the sampling period, is the rated power.

[0037] The instantaneous change rate is compared with the preset power change rate threshold to calculate the overshoot : (4) The overshoot is smoothly mapped to the weight interval using an exponential function, and the real-time weight coefficient of recursive mean filtering is calculated is: (5) where, is the upper limit value of the weight, is the control sensitivity coefficient.

[0038] According to the real-time weight coefficient , the extended Kalman filter (EKF) output and the recursive mean filter (RMF) output are weighted and fused to generate the final smoothed power reference value is: (6) wherein, is a weight coefficient, i.e. .

[0039] Further, in the step S3, the smooth power reference value is obtained according to the rotor kinetic energy control is: (7); wherein, is the moment of inertia.

[0040] The adaptive weighted hybrid filter rotor kinetic energy control block diagram formed by the step S2 and the step S3 is shown in Fig. 2. Figure 2

[0041] Further, in the step S4, the bandwidth self-tuning mechanism based on Lyapunov stability is realized as follows: a) define the state estimation error and the bandwidth estimation error as: (8); wherein, is the rotor speed variation, is the rotor speed differential variation, is the total disturbance of the permanent magnet direct drive wind power generation system, , , are respectively , , the observation estimation value of is the observer bandwidth, is the ideal bandwidth, is the bandwidth estimation error, , , are respectively , , and , , the deviation between

[0042] b) construct a Lyapunov function V containing the state estimation error and the bandwidth estimation error: (9) wherein, is the adaptive rate and .

[0043] c) design the bandwidth adaptive law, derive the Lyapunov function V: (10) wherein,​ derivative of V, derivative of derivative of derivative of derivative of

[0044] to satisfy , the adaptive law is selected to offset the residual term: (11) From the observation error, are high-order small terms of , if the observer converges, satisfies , and then the observer bandwidth is: (12) wherein, is the initial value of the observer bandwidth.

[0045] d) According to the control target , the control law is designed as: (13): wherein, derivative of , is a constant, is a proportional gain, is an integral gain.

[0046] The bandwidth self-tuning linear active disturbance rejection variable pitch (ILADRC) control block diagram formed in step S4 is shown in Figure 3 .

[0047] Example 2: According to the control strategy adopted in the present application, a wind turbine power output simulation is carried out, and the wind turbine simulation parameters adopted are: , , , , , , the rated wind speed is 10.5 m / s, and the rated power is 6 MW. The high wind speed turbulence shown in Figure 4 (a) is used for simulation, and the power output shown in Figure 4 (b), Figure 4 (c) the rotor speed, and Figure 4 (d) the pitch angle can be obtained. Figure 4 (e) represents the adaptive weight coefficient in the simulation process, Figure 4 ​​​​​​(f) represents the observer bandwidth during the simulation.

[0048] As can be seen from Figure 4 It can be seen that, under high wind speed turbulence, the present application generates a smooth power instruction through adaptive hybrid weighted filtering, and combines a bandwidth self-tuning linear active disturbance rejection variable pitch controller to realize significant smoothing of power output and stable tracking of rotor speed, while the observer bandwidth and the filtering weight can be dynamically self-adaptively adjusted according to the working condition. Figure 4 (b) shows the power output comparison of the three methods under high wind speed turbulence: the traditional PI control has significant fluctuations and poor output stability; the extended Kalman filter combined with fixed bandwidth active disturbance rejection control (EKF-ADRC) has improved, but still has certain fluctuations; and the proposed method has the most stable output power and the smallest fluctuations.

[0049] Example 3: The fan parameters in the simulation are the same as those in Example 2, and the simulation is carried out under the wind speed sudden drop turbulence condition shown in Figure 5 (a), and the power output, Figure 5 (b) shown, Figure 5 (c) shown rotor speed, and Figure 5 (d) shown pitch angle. Figure 5 (e) represents the adaptive weight coefficient under the wind speed sudden drop turbulence condition, Figure 5 (f) represents the observer bandwidth change during the simulation.

[0050] As can be seen from Figure 5 (b), under the wind speed sudden drop turbulence condition, the control scheme adopted by the present application can maintain the rated power output, and the power fluctuation is significantly reduced compared with the other control strategies when the wind speed suddenly drops below the rated wind speed, so the present application can ensure the smoothness of the power output and the stable operation of the system.

[0051] Example 4: The present embodiment proposes an electronic system, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method steps of the present application.

[0052] Example 5: The present embodiment proposes a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the method of the present application, which will not be described here again.

[0053] Example 6: The present embodiment proposes a computer program product comprising computer programs / instructions, wherein the computer programs / instructions are executed by a processor to implement the steps of the method of the present application, which will not be described here again.

[0054] It should be noted that the processing flow of the embodiments 4-6 corresponds to the specific steps of the method provided in the embodiment 1 of the present application, and has the corresponding functions and beneficial effects of the method. Technical details not described in detail in the present embodiment can be referred to the method provided in the embodiment 1 of the present application.

[0055] Program code for carrying out the methods of the present application can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, causes the machine to perform the functions / operations specified in the flow diagrams and / or block diagrams. The program code can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0056] The above description is merely that of the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of the present application.

Claims

1. A wind turbine power smoothing method based on hybrid weighted filtering and active disturbance rejection pitch control, characterized in that, Includes the following steps: Step S1: Establish a nonlinear mathematical model of the permanent magnet direct-drive wind power generation system; Step S2: An adaptive hybrid weighted filtering algorithm based on the differential variance of power command is adopted to generate a smooth power reference command: the instantaneous rate of change of the power command is calculated in real time, the overshoot is obtained by comparing it with a preset threshold, and the overshoot is mapped to the weight coefficient of the improved mean filter using an exponential function. The output of the extended Kalman filter and the output of the improved recursive mean filter are weighted and fused. Step S3: Based on the smoothed power reference command obtained in step S2, and combined with the mapping relationship between rotor kinetic energy and power, calculate the corresponding smoothed reference speed command; Step S4: A linear active disturbance rejection pitch control algorithm based on Lyapunov stability is adopted to perform pitch tracking control. The deviation between the smooth reference speed command and the actual speed is used as the controller input. A Lyapunov function containing state estimation error and bandwidth estimation error is constructed. A bandwidth adaptive law is designed to dynamically adjust the bandwidth of the extended state observer. The total system disturbance estimated by the observer is used for feedforward compensation to generate the reference pitch angle. Step S5: Combine the generation of the smooth reference speed command described in step S3 with the linear active disturbance rejection pitch control described in step S4. Through the synergy of front-end command optimization and back-end robust control, the comprehensive power smoothing of the wind turbine under complex turbulent conditions is achieved.

2. The wind turbine power smoothing method based on hybrid weighted filtering and active disturbance rejection pitch control according to claim 1, characterized in that: In step S1, the mechanical power captured by the wind turbine in the permanent magnet direct-drive wind power generation system P w and mechanical torque T w Represented as: （1）； in, P w The mechanical power captured by the wind turbine. T w The mechanical torque captured by the wind turbine, air density, The radius of the wind turbine blades. For wind speed, The wind energy utilization coefficient, For the tip speed ratio, The pitch angle is the propeller angle. The influence coefficient of wind speed change rate. The rotor angular velocity, t For time, d It is a differential operator; Wind energy utilization coefficient The aerodynamic efficiency of a fan is reflected in its value, which depends on the tip speed ratio. and propeller pitch angle The nonlinear coupling relationship is expressed as: （2）； in, As an intermediate variable, e It is the Euler number.

3. The wind turbine power smoothing method based on hybrid weighted filtering and active disturbance rejection pitch control according to claim 2, characterized in that: In step S2, the instantaneous rate of change of the power command at adjacent sampling times is calculated. : （3）； in, The instantaneous rate of change This refers to the electromagnetic power command at the current moment. The electromagnetic power command from the previous moment. The sampling period is Rated power; The instantaneous rate of change Compared with the preset power change rate threshold Compare and calculate the overshoot. : （4）； The overshoot was measured using an exponential function. Smooth mapping to the weight interval, and calculate the real-time weight coefficients of the improved recursive mean filter. for: （5）； in, This is the upper limit of the weight. To control the sensitivity coefficient; According to the real-time weighting coefficient For the extended Kalman filter output With improved recursive mean filter output Weighted fusion is performed to generate the final smoothed power reference value. for: （6）； in, As a smooth power reference value, The weighting coefficient is the one in formula (5). .

4. The wind turbine power smoothing method based on hybrid weighted filtering and self-disturbance rejection pitch control according to claim 3, characterized in that: In step S3, based on the smoothed power reference value Smooth reference speed obtained by combining rotor kinetic energy control for: （7）； in, Let be the moment of inertia.

5. The wind turbine power smoothing method based on hybrid weighted filtering and active disturbance rejection pitch control according to claim 1, characterized in that: In step S4, the bandwidth self-tuning mechanism based on Lyapunov stability is implemented as follows: a) Define the state estimation error and bandwidth estimation error as: （8）； in, This represents the change in rotational speed. This is the differential change in rotational speed. This represents the total disturbance of a permanent magnet direct-drive wind power generation system. , , They are respectively , , The observed estimates, For the observer bandwidth, For ideal bandwidth, For bandwidth estimation error, , , They are respectively , , and , , Deviation between; b) Construct a Lyapunov function V that includes the state estimation error and bandwidth estimation error: （9）； in, For adaptive rate, and ; c) Design a bandwidth adaptive law and differentiate it with respect to the Lyapunov function V: （10）； in, The derivative of V, for The derivative, for The derivative, for The derivative, for The derivative; To meet Choose the adaptive law to cancel out the remainder: （11）； From the observation error, we know that All Higher-order terms, if the observer converges, satisfy the following: Select The observer bandwidth is obtained. for: （12）； in, This is the initial value for the observer bandwidth; d) Based on the control objective The control law is designed as follows: （13）； in, In order to control the target, for The derivative, It is a constant. For proportional gain, This is the integral gain.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed, it implements the steps of the method as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is configured to implement the steps of the method according to any one of claims 1 to 5 when invoked by a processor.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 5.

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