A power smoothing method for wind turbine rotor kinetic energy control

Abstract

The present invention discloses a power smoothing method for wind turbine rotor kinetic energy control, including: calculating the fluctuation smoothing effect of the wind turbine under maximum power tracking control according to the wind turbine parameters, and calculating the inherent filtering time constant; designing a first-order low-pass filter to digitally smooth the maximum wind power; considering the loss caused by non-maximum power tracking control operation, designing a dual-input single-output fuzzy logic controller based on the speed and speed change rate, the input is the speed and speed change rate, and the output is the proportional coefficient k; giving the wind turbine a suitable active reference value to smooth the power fluctuation, the active reference value is set to the first-order low-pass filter designed by the maximum wind power cascade, and the coefficient k is multiplied into the active reference value to ensure the stable operation of the wind turbine. The present invention can ensure the normal and stable operation of the wind turbine system while smoothing the output power fluctuation.

Description

Technical Field

[0001] The present invention relates to the field of wind power technology, and in particular to a power smoothing method for controlling kinetic energy of a wind turbine rotor. Background Art

[0002] In order to build a cleaner and more economical energy structure, wind energy conversion systems are gaining more and more attention. However, with the continuous increase in wind power penetration, the instability of output power has become a serious problem that leads to abnormal grid conditions and restricts wind power generation. Since wind energy is proportional to the cube of wind speed, the output power of wind turbines fluctuates with changes in wind speed. In order to improve the grid-connected power quality of wind power generation systems and meet grid connection requirements, the output power of wind turbines needs to be as smooth as possible. Smoothing the output power fluctuations of wind turbines is of great significance for ensuring the stability of grid operation, especially for isolated island system scenarios where the grid is relatively weak.

[0003] At present, the methods for smoothing the output power fluctuation of wind turbines can be roughly divided into two categories. The first category is called indirect power control strategy, which is implemented through energy storage systems (ESS) including batteries, flywheels, supercapacitors, etc. The essence of this method is to use the fast charging and discharging characteristics of energy storage devices to compensate for the fluctuating output power of wind power to achieve a stable active power output. However, the introduction of such energy storage devices will greatly increase the operation and maintenance costs of the entire wind power generation system. The second category is direct power control strategy. This strategy aims to use the wind turbine's own regulation ability for power smoothing. Compared with indirect power control, no additional energy storage device is required, reducing system costs. The wind turbine's own power smoothing capability consists of the following parts: DC bus voltage control, pitch angle control and rotor kinetic energy control. Rotor kinetic energy control treats the wind turbine's rotor as a virtual energy storage device, and uses the storage and release of the rotor's rotational kinetic energy to smooth the system output power. Compared with the other two methods, it avoids problems such as excessive DC voltage fluctuations and mechanical stress caused by frequent pitch changes. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a power smoothing method for wind turbine rotor kinetic energy control based on low-pass filtering and fuzzy logic based on rotor kinetic energy control. By analyzing the transfer function of the traditional maximum power tracking method, a first-order low-pass filter is reasonably designed to smooth power fluctuations. At the same time, a fuzzy controller is designed to generate a proportional coefficient k to take into account the power shortage caused by non-maximum power tracking control (MPPT) operation, ensuring the normal and stable operation of the wind turbine system while smoothing the output power fluctuations.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The embodiment of the present invention proposes a method for smoothing the power of a wind turbine rotor kinetic energy control. The smoothing method is based on the rotor kinetic energy control and smoothes the output power of the wind turbine based on low-pass filtering and fuzzy logic. The smoothing method includes the following steps:

[0007] S1: Calculate the fluctuation smoothing effect of the fan under maximum power tracking control based on the fan parameters and calculate the inherent filtering time constant;

[0008] S2: Design a first-order low-pass filter to digitally smooth the maximum wind power, combining the smoothing effect and smoothing ability so that its time constant is greater than the inherent filter time constant of the maximum power tracking control while maintaining the same order of magnitude as the inherent filter time constant;

[0009] S3: Considering the power shortage caused by non-maximum power tracking control operation, a dual-input single-output fuzzy logic controller is designed based on the speed and speed change rate. The input is the speed and speed change rate, and the output is the proportional coefficient k;

[0010] S4: A suitable active reference value is given to the wind turbine to smooth power fluctuations. The active reference value is set to a first-order low-pass filter designed for the maximum wind power cascade, and the coefficient k is multiplied into the active reference value to ensure stable operation of the wind turbine.

[0011] Furthermore, in step S1, the process of calculating the fluctuation smoothing effect of the fan under the maximum power tracking control according to the fan parameters and calculating the inherent filtering time constant includes the following steps:

[0012] S11, assuming that the motion equation of the wind turbine is:

[0013]

[0014] In formula (1), J is the moment of inertia of the rotor, ω is the angular velocity of the rotor, and T w is the mechanical torque of the wind turbine, T e is the electromagnetic torque of the generator;

[0015] S12, expressed in terms of power, can be rewritten as:

[0016]

[0017] In formula (2), P w is the mechanical power of the wind turbine, P e is the electromagnetic power of the generator; P w The expression is:

[0018]

[0019] Where ρ is the air density, R is the fan blade radius, Cp is the wind energy utilization coefficient, which is determined by the tip speed ratio λ and the pitch angle β; the tip speed ratio λ is defined as follows:

[0020] λ=ωR / v (4)

[0021] In formula (4), v is the wind speed;

[0022] S13, below the rated wind speed, the wind turbine adopts maximum power tracking control, the pitch angle is set to 0, and the wind energy utilization coefficient C p Only related to the tip speed ratio λ; when the optimal tip speed ratio λ is obtained opt When the wind energy utilization coefficient reaches the maximum value C pmax ; The maximum wind power that a wind turbine can capture is:

[0023]

[0024] Under maximum power tracking control, the electromagnetic power P e The reference value is set as:

[0025]

[0026] In formula (6), K opt It is the maximum power tracking coefficient, which is related to the wind turbine parameters;

[0027] S14, for P w and P e Linearization is performed separately to obtain:

[0028]

[0029]

[0030] Where v = v0, ω = ω0 represents the steady-state value of the parameter;

[0031] S15, small perturbation linearization analysis of equation (2) is performed to obtain:

[0032]

[0033] S16, substitute equation (7) and equation (8) into (9), and perform Laplace transformation on both sides of the equation to obtain the transfer function from wind speed to rotation speed under maximum power tracking control:

[0034]

[0035] Under the maximum power tracking control, from the rotation speed angle, the system acts as a first-order low-pass filter for the wind speed fluctuation. From formula (10), it can be seen that the filtering time constant is proportional to the moment of inertia and the maximum power tracking coefficient K optAnd it is inversely proportional to the steady-state speed ω0; since the output electromagnetic power is proportional to the cube of the speed, according to formula (6), the output power will also be smoothed compared to the maximum wind power.

[0036] Furthermore, in step S3, the process of designing a dual-input single-output fuzzy logic controller based on the speed and the speed change rate includes:

[0037] The speed is divided into seven intervals: maximum, very large, large, medium, small, very small and extremely small, which are represented by EB, VB, B, M, S, VS and ES respectively;

[0038] The speed change rate is divided into two intervals, positive and negative, represented by P and N;

[0039] The output of the fuzzy controller is a proportional coefficient k less than 1, the range is set to [0.8, 1], and it is also divided into seven intervals: EB, VB, B, M, S, VS and ES.

[0040] Further, in step S4, the designed low-pass filter and fuzzy control output k are cascaded after the maximum wind power, and the wind turbine output active power reference value is given as:

[0041]

[0042] The invention discloses a power smoothing method for wind turbine rotor kinetic energy control based on low-pass filtering and fuzzy logic, comprising the following steps: S1: calculating the fluctuation smoothing effect of the wind turbine under the traditional maximum power tracking (MPPT) control according to the wind turbine parameters, and calculating the inherent filtering time constant; S2: designing a first-order low-pass filter to digitally smooth the maximum wind power, so that its time constant is greater than the inherent time constant of the MPPT control, so as to obtain a better smoothing effect. However, considering the limited kinetic energy storage of the wind turbine, the low-pass filter parameters should be considered in combination with the smoothing effect and the smoothing ability; S3: since a larger increase and decrease in the speed is required to realize the storage and release of the rotational kinetic energy, the loss caused by non-MPPT operation must be considered, and a dual-input single-output fuzzy logic controller is designed based on the speed and the speed change rate, and the output is the proportional coefficient k; S4: giving the wind turbine a suitable active reference value to smooth the power fluctuation, the reference value is set as the first-order low-pass filter designed by the maximum wind power cascade, and the power shortage coefficient k generated by the fuzzy controller is multiplied into the active reference value to ensure the stable operation of the wind turbine.

[0043] The beneficial effects of the present invention are:

[0044] The fan output power smoothing method based on low-pass filtering and fuzzy logic proposed in the present invention analyzes the transfer function of the traditional MPPT method and reasonably designs the time constant of the first-order low-pass filter to make the power smoothing more targeted and effective. The shortage coefficient k is generated by fuzzy logic to ensure normal and stable operation under non-MPPT conditions. This method realizes the storage and release of kinetic energy and rotational kinetic energy by controlling the speed, without the need for energy storage devices, which can greatly reduce the cost of smoothing power fluctuations and has high feasibility and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the fuzzy logic reasoning mechanism in a specific implementation manner of the present invention.

[0046] Figure 2 It is a schematic diagram of the membership function of the fuzzy controller in a specific implementation manner of the present invention.

[0047] Figure 3 It is a block diagram of the fan rotor kinetic energy control based on low-pass filtering and fuzzy logic in a specific implementation manner of the present invention.

[0048] Figure 4 It is a block diagram of the machine-side vector control of the wind turbine in a specific implementation manner of the present invention.

[0049] Figure 5 Schematic diagram of a given set of wind speed sequences.

[0050] Figure 6 This is a comparison chart of the output power under the control method proposed by the present invention and the output power under traditional MPPT control.

[0051] Figure 7 Schematic diagram of the fan rotor speed waveform under the control method proposed in the present invention.

[0052] Figure 8 Schematic diagram of the waveform of the fuzzy controller output k under the control mode proposed in the present invention.

[0053] Fig. 9 It is a flow chart of a power smoothing method for controlling kinetic energy of a wind turbine rotor in a specific implementation manner of the present invention. DETAILED DESCRIPTION

[0054] The present invention will now be described in further detail with reference to the accompanying drawings.

[0055] It should be noted that the terms such as "upper", "lower", "left", "right", "front", "back", etc. cited in the invention are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments in their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0056] Fig. 9 FIG. 1 is a flow chart of a method for smoothing the power of a fan rotor kinetic energy control in a specific embodiment of the present invention. Fig. 9 The embodiment of the present invention proposes a method for smoothing the power of a wind turbine rotor kinetic energy control. The method is based on the rotor kinetic energy control and smoothes the output power of the wind turbine based on low-pass filtering and fuzzy logic, and includes the following steps:

[0057] S1: Calculate the fluctuation smoothing effect of the fan under maximum power tracking control based on the fan parameters and calculate the inherent filtering time constant.

[0058] S2: Design a first-order low-pass filter to digitally smooth the maximum wind power, combining the smoothing effect and smoothing ability so that its time constant is greater than the inherent filter time constant of the maximum power tracking control while maintaining the same order of magnitude as the inherent filter time constant.

[0059] S3: Considering the losses caused by non-maximum power tracking control operation, a dual-input single-output fuzzy logic controller is designed based on the speed and speed change rate. The input is the speed and speed change rate, and the output is the proportional coefficient k.

[0060] S4: A suitable active reference value is given to the wind turbine to smooth power fluctuations. The active reference value is set to a first-order low-pass filter designed for the maximum wind power cascade, and the coefficient k is multiplied into the active reference value to ensure stable operation of the wind turbine.

[0061] The preferred technical solution of this embodiment is further described in detail below with reference to the accompanying drawings and examples.

[0062] In step S1, the process of calculating the fluctuation smoothing effect of the fan under the maximum power tracking control according to the fan parameters and calculating the inherent filtering time constant includes the following steps:

[0063] S11, assuming that the motion equation of the wind turbine is:

[0064]

[0065] In formula (1), J is the moment of inertia of the rotor, ω is the angular velocity of the rotor, and T w is the mechanical torque of the wind turbine, T e is the electromagnetic torque of the generator.

[0066] S12, expressed in terms of power, can be rewritten as:

[0067]

[0068] In formula (2), P w is the mechanical power of the wind turbine, P eis the electromagnetic power of the generator; P w The expression is:

[0069]

[0070] Where ρ is the air density, R is the fan blade radius, C p is the wind energy utilization coefficient, which is determined by the tip speed ratio λ and the pitch angle β; the tip speed ratio λ is defined as follows:

[0071] λ=ωR / v (4)

[0072] In formula (4), v is the wind speed.

[0073] S13, Generally speaking, when the wind speed is below the rated wind speed, the wind turbine adopts the maximum power tracking control, the pitch angle is set to 0, and the wind energy utilization coefficient C p Only related to the tip speed ratio λ; when the optimal tip speed ratio λ is obtained opt When the wind energy utilization coefficient reaches the maximum value C pmax ; The maximum wind power that a wind turbine can capture is:

[0074]

[0075] Under maximum power tracking control, the electromagnetic power P e The reference value is set as:

[0076]

[0077] In formula (6), K opt It is the maximum power tracking coefficient, which is related to the fan parameters.

[0078] S14, due to the inertia of the fan, the output power under traditional MPPT control has a certain smoothing effect. w and P e Linearization is performed separately to obtain:

[0079]

[0080]

[0081] Where v=v0, ω=ω0 represents the steady-state value of the parameter.

[0082] S15, small perturbation linearization analysis of equation (2) is performed to obtain:

[0083]

[0084] S16, substitute equation (7) and equation (8) into (9), and perform Laplace transformation on both sides of the equation to obtain the transfer function from wind speed to rotation speed under maximum power tracking control:

[0085]

[0086] It can be seen that under the maximum power tracking control, from the perspective of speed, the system plays the role of a first-order low-pass filter for the fluctuation of wind speed. From formula (10), it can be seen that the filtering time constant is proportional to the moment of inertia and the maximum power tracking coefficient K opt And inversely proportional to the steady-state speed ω0. Since the output electromagnetic power is proportional to the cube of the speed, according to formula (6), the output power will also be smoothed compared to the maximum wind power.

[0087] For example, the fan parameters in the example are as follows: ρ = 1.225, R = 35, β = 0, C pmax =0.48,λ opt =8.1, K opt =91281.4, J = 257000. After calculation, when the wind speed is 6m / s, the MPPT inherent filtering time constant is 0.676s; when the wind speed is 12m / s, the MPPT inherent filtering time constant is 0.676s.

[0088] In step S2, the designed first-order low-pass filter time constant τ is determined according to the following principle: the larger the time constant τ, the more obvious the power smoothing effect. In order to obtain a smoother output power than MPPT, τ should be greater than the inherent time constant of MPPT in formula (9). At the same time, it should be taken into account that the rotational kinetic energy of the wind turbine rotor is limited, and the smoothing ability is also limited. An excessively large filter time constant will aggravate the oscillation of the speed, especially when the wind speed drops rapidly, there will be a risk of instability. In summary, the selection of τ should be considered in combination with the smoothing effect and smoothing ability. While being greater than the inherent time constant of MPPT, it should be maintained at the same order of magnitude. For the above example, the final selected value is τ = 1s.

[0089] In step S3, the wind energy capture loss caused by the speed deviation from the maximum power point needs to be considered, otherwise it will cause the output reference to be greater than the actual input, causing the wind turbine to lose stability. A fuzzy controller is used to infer and generate a proportional coefficient k less than 1. The fuzzy reasoning mechanism is constructed with the speed and the speed change rate as input, and the output of the fuzzy controller is a proportional coefficient k less than 1. Since a larger increase and decrease in speed is required to realize the storage and release of rotational kinetic energy, the loss caused by non-MPPT operation must be considered. A dual-input single-output fuzzy logic controller is designed based on the speed and the speed change rate. The output is the proportional coefficient k, and the fuzzy reasoning mechanism is as follows: Figure 1As shown. The speed is divided into seven intervals: "extremely large", "very large", "large", "medium", "small", "very small" and "extremely small", which are represented by "EB", "VB", "B", "M", "S", "VS" and "ES" respectively. The speed change rate is divided into two intervals: "positive" and "negative", which are represented by "P" and "N". The fuzzy controller output is in the range of [0.8, 1], which is also divided into seven intervals. The membership function is as follows Figure 2 shown. Figure 8 Schematic diagram of the waveform of the fuzzy controller output k under the control mode proposed in the present invention.

[0090] In step S4, the designed low-pass filter and fuzzy control output k are cascaded after the maximum wind power, and the given wind turbine output active power reference value is:

[0091]

[0092] Given the speed control block diagram Figure 3 As shown, the generator side rectifier vector control block diagram is as follows Figure 4 The waveform of the example used is shown in Figure 5-7 As shown, Figure 5 This is a comparison diagram of the output power under the method of the present invention and the traditional MPPT method. It can be seen that a smoother output power can be obtained under the control method of the present invention. Figure 6 This is a comparison chart of the rotor speed under the method of the present invention and the traditional MPPT method. The method achieves the storage and release of rotational kinetic energy by accelerating / decelerating the rotor speed to a greater extent, so as to smooth out the output power fluctuation. Figure 7 This is the waveform of the fuzzy controller output k under the method of the present invention. The proposed fuzzy reasoning mechanism can effectively respond to the power shortage caused by non-MPPT operation according to the speed state of the wind turbine, taking into account the power generation efficiency and stability, and maintaining the normal and stable operation of the wind turbine.

[0093] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for smoothing the power of a fan rotor kinetic energy control, characterized in that: The smoothing method is based on rotor kinetic energy control and smoothes the output power of the fan based on low-pass filtering and fuzzy logic. The smoothing method includes the following steps: S1: Calculate the fluctuation smoothing effect of the fan under maximum power tracking control based on the fan parameters and calculate the inherent filtering time constant; S2: Design a first-order low-pass filter to digitally smooth the maximum wind power, combining the smoothing effect and smoothing ability so that its time constant is greater than the inherent filter time constant of the maximum power tracking control while maintaining the same order of magnitude as the inherent filter time constant; S3: Considering the power shortage caused by non-maximum power tracking control operation, a dual-input single-output fuzzy logic controller is designed based on the speed and speed change rate. The input is the speed and speed change rate, and the output is the proportional coefficient k; S4: Give the wind turbine a suitable active reference value to smooth the power fluctuation. The active reference value is set as a first-order low-pass filter designed for the maximum wind power cascade, and the coefficient k is multiplied into the active reference value to ensure the stable operation of the wind turbine. In step S1, the process of calculating the fluctuation smoothing effect of the fan under the maximum power tracking control according to the fan parameters and calculating the inherent filtering time constant includes the following steps: S11, assuming that the motion equation of the wind turbine is: In formula (1), J is the moment of inertia of the rotor, ω is the angular velocity of the rotor, and T w is the mechanical torque of the wind turbine, T e is the electromagnetic torque of the generator; S12, expressed in terms of power, can be rewritten as: In formula (2), P w is the mechanical power of the wind turbine, P e is the electromagnetic power of the generator; P w The expression is: Where ρ is the air density, R is the fan blade radius, C p is the wind energy utilization coefficient, which is determined by the tip speed ratio λ and the pitch angle β; the tip speed ratio λ is defined as follows: λ=ωR / v (4) In formula (4), v is the wind speed; S13, below the rated wind speed, the wind turbine adopts maximum power tracking control, the pitch angle is set to 0, and the wind energy utilization coefficient C p Only related to the tip speed ratio λ; when the optimal tip speed ratio λ is obtained opt When the wind energy utilization coefficient reaches the maximum value C pmax ; The maximum wind power that a wind turbine can capture is: Under maximum power tracking control, the electromagnetic power P e The reference value is set as: In formula (6), K opt It is the maximum power tracking coefficient, which is related to the wind turbine parameters; S14, for P w and P e Linearization is performed separately to obtain: Where v = v0, ω = ω0 represents the steady-state value of the parameter; S15, small perturbation linearization analysis of equation (2) is performed to obtain: S16, substitute equation (7) and equation (8) into (9), and perform Laplace transformation on both sides of the equation to obtain the transfer function from wind speed to rotation speed under maximum power tracking control: Under the maximum power tracking control, from the rotation speed angle, the system acts as a first-order low-pass filter for the wind speed fluctuation. From formula (10), it can be seen that the filtering time constant is proportional to the moment of inertia and the maximum power tracking coefficient K opt And inversely proportional to the steady-state speed ω0; Since the output electromagnetic power under maximum power tracking control is proportional to the cube of the rotational speed, according to formula (6), the output power will also be smoothed compared to the maximum wind power.

2. The method for controlling power smoothing of a wind turbine rotor kinetic energy according to claim 1, characterized in that: In step S3, the process of designing a dual-input single-output fuzzy logic controller based on the speed and the speed change rate includes: The speed is divided into seven intervals: maximum, very large, large, medium, small, very small and extremely small, which are represented by EB, VB, B, M, S, VS and ES respectively; The speed change rate is divided into two intervals, positive and negative, represented by P and N; The output of the fuzzy controller is a power shortage coefficient k less than 1, whose range is set to [0.8,1] and is also divided into seven intervals: EB, VB, B, M, S, VS and ES.

3. The method for controlling power smoothing of a wind turbine rotor kinetic energy according to claim 1, characterized in that: In step S4, the designed low-pass filter and fuzzy control output k are cascaded after the maximum wind power, and the wind turbine output active power reference value is given as:

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