Wind-solar power generation power system frequency modulation control method based on improved fuzzy control

Abstract

The invention discloses a wind-solar power generation power system frequency modulation control method based on improved fuzzy control, and by applying the technical scheme provided by the invention and utilizing a fuzzy control technology, a fuzzy controller can be designed to realize power regulation and stabilization of a new energy power generation system. The fuzzy controller can make a control decision according to the fuzzy relationship between the input and output variables of the system, thereby realizing the adjustment and control of the generator set. Through application of the fuzzy controller, power fluctuation can be reduced, and stability and reliability of the system can be improved. Meanwhile, aiming at power standby control and rotor kinetic energy control with a fixed droop coefficient, the droop control coefficient of the doubly-fed wind turbine generator is researched, fuzzy control is introduced into a load shedding control coefficient and the droop control coefficient, system control parameters are dynamically changed according to different working conditions, the frequency modulation capability of the doubly-fed wind turbine generator is fully exerted, and the control precision of the doubly-fed wind turbine generator is improved. And the system frequency stability is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of control of wind-solar power generation systems, and in particular to a frequency modulation control method of wind-solar power generation systems based on improved fuzzy control. Background Art

[0002] The development of renewable energy and the construction of a new power system dominated by renewable energy are an inevitable trend. Renewable energy sources such as wind and solar are connected to the grid through converters. The low inertia and weak damping characteristics of these new power systems are becoming increasingly prominent, significantly weakening the power system's inertial response capability. Currently, most renewable energy power stations suffer from large power fluctuations and poor stability, seriously threatening the safe and stable operation of the power grid and creating significant difficulties in its control and operation. Fuzzy control technology can be used to design fuzzy controllers to achieve power regulation and stability in renewable energy power generation systems. Fuzzy controllers make control decisions based on the fuzzy relationship between the system's input and output variables, thereby regulating and controlling the generator sets. The application of fuzzy controllers can reduce power fluctuations, improve system stability and reliability, and address current frequency regulation control issues in wind and solar power generation systems. Summary of the Invention

[0003] In order to solve the problems existing in the frequency regulation control of wind and solar power generation power systems, the present invention provides a frequency regulation control method for wind and solar power generation power systems based on improved fuzzy control, comprising the following steps:

[0004] S1, obtain the total frequency and output value of the wind turbine generator set;

[0005] S2, obtain the total frequency change rate of the photovoltaic generator set;

[0006] S3, inputting the frequency change rate and output value of the wind turbine generator set into the first fuzzy controller to obtain the total active power increment required for frequency regulation of the wind turbine generator set;

[0007] S4, inputting the total frequency change rate of the photovoltaic generator set into the second fuzzy controller to obtain the total active power increment required for frequency regulation of the photovoltaic generator set;

[0008] S5, allocating the active power increments of the wind turbine generator set and the photovoltaic generator set to obtain a power adjustment plan for the wind turbine generator set and the photovoltaic generator set;

[0009] In S6, the wind turbine generator set and the photovoltaic generator set adjust their operating states according to the power adjustment plan, thereby realizing frequency regulation control of the wind and solar power generation system.

[0010] Furthermore, the wind turbine generator set is one or more doubly-fed wind turbine generator sets.

[0011] Furthermore, the photovoltaic power generation group is one or more bipolar photovoltaic grid-connected inverters.

[0012] Furthermore, the method for obtaining the total frequency of the wind turbine generator set in S1 is:

[0013]

[0014] in,

[0015]

[0016]

[0017] Where f1(t) is the time curve of the wind turbine frequency, ω n is the undamped natural frequency of the system; ω d is the damped oscillation frequency, H and D are the inertia time constant and damping coefficient of the wind turbine generator set; R is the adjustment coefficient; T RH F is the reheating time constant of the prime mover of the wind turbine generator set; HP is the work ratio of the prime mover of the wind turbine.

[0018] Furthermore, the method for obtaining the output value of the wind turbine generator set in S1 is:

[0019]

[0020] Where P1 is the output value of the wind turbine, ρ is the air density, R is the blade radius, V m is the wind speed, β best is the optimal pitch angle, ω best is the optimal rotor speed.

[0021] Furthermore, the method for obtaining the total frequency deviation rate of the photovoltaic power generation group in S2 is:

[0022]

[0023] Where, f2(t) is the time curve of the wind turbine frequency, f d is the frequency response action value; k is the frequency adjustment coefficient; P n is the maximum output power of the photovoltaic generator set under any illumination, P3 is the initial power value, P4 is the active power of the photovoltaic generator set,

[0024] The total frequency deviation rate of the photovoltaic power generation group at time t is the derivative of the time curve of the frequency of the photovoltaic power generation group at time t.

[0025] Furthermore, the control method of the first fuzzy controller in S3 is:

[0026]

[0027] Where ΔP1 is the total active power increment required for frequency regulation of the wind turbine generator set, P1 is the output value of the wind turbine generator set, V m is the wind speed, β best is the optimal pitch angle, Δβ is the change in pitch angle, ω best is the optimal rotor speed, and Δω is the change in rotor speed.

[0028] Furthermore, the control method of the second fuzzy controller in S4 is:

[0029]

[0030] Where ΔP1 is the total active power increment required for frequency regulation of the photovoltaic generator set, H pv is the virtual inertia time constant, is the total frequency change rate of the photovoltaic generator set.

[0031] A frequency regulation control method for wind and solar power generation systems based on improved fuzzy control is applied. Fuzzy control technology can be used to design a fuzzy controller to achieve power regulation and stability in renewable energy power generation systems. The fuzzy controller can make control decisions based on the fuzzy relationship between the system's input and output variables, thereby achieving regulation and control of the generator set. The application of the fuzzy controller can reduce power fluctuations and improve system stability and reliability. Furthermore, the droop control coefficient of doubly-fed wind turbines is studied for power reserve control and fixed-droop rotor kinetic energy control. Fuzzy control is introduced into the load shedding and droop control coefficients. System control parameters are dynamically adjusted according to different operating conditions, fully utilizing the frequency regulation capability of the doubly-fed wind turbines and effectively improving system frequency stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings in the specification, which constitute a part of this application, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 The figure is a flow chart of the frequency regulation control method of wind-solar power generation system based on improved fuzzy control according to the present invention. DETAILED DESCRIPTION

[0034] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to embodiments and drawings.

[0035] Example 1

[0036] This embodiment is based on the wind and solar power generation system frequency control method of improved fuzzy control, such as Figure 1 As shown, the following steps are included:

[0037] S1, obtain the total frequency and output value of the wind turbine generator set;

[0038] S2, obtain the total frequency change rate of the photovoltaic generator set;

[0039] S3, inputting the frequency change rate and output value of the wind turbine generator set into the first fuzzy controller to obtain the total active power increment required for frequency regulation of the wind turbine generator set;

[0040] S4, inputting the total frequency change rate of the photovoltaic generator set into the second fuzzy controller to obtain the total active power increment required for frequency regulation of the photovoltaic generator set;

[0041] S5, allocating the active power increments of the wind turbine generator set and the photovoltaic generator set to obtain a power adjustment plan for the wind turbine generator set and the photovoltaic generator set;

[0042] In S6, the wind turbine generator set and the photovoltaic generator set adjust their operating states according to the power adjustment plan, thereby realizing frequency regulation control of the wind and solar power generation system.

[0043] Furthermore, the wind turbine generator set is one or more doubly-fed wind turbine generator sets.

[0044] Furthermore, the photovoltaic power generation group is one or more bipolar photovoltaic grid-connected inverters.

[0045] Furthermore, the method for obtaining the total frequency of the wind turbine generator set in S1 is:

[0046]

[0047] in,

[0048]

[0049] Where f1(t) is the time curve of the wind turbine frequency, ω n is the undamped natural frequency of the system; ω d is the damped oscillation frequency, H and D are the inertia time constant and damping coefficient of the wind turbine generator set; R is the adjustment coefficient; T RH F is the reheating time constant of the prime mover of the wind turbine generator set; HP is the work ratio of the prime mover of the wind turbine.

[0050] Furthermore, the method for obtaining the output value of the wind turbine generator set in S1 is:

[0051]

[0052] Where P1 is the output value of the wind turbine, ρ is the air density, R is the blade radius, V m is the wind speed, β best is the optimal pitch angle, ω best is the optimal rotor speed.

[0053] Furthermore, the method for obtaining the total frequency deviation rate of the photovoltaic power generation group in S2 is:

[0054]

[0055] Where, f2(t) is the time curve of the wind turbine frequency, f d is the frequency response action value; k is the frequency adjustment coefficient; P n is the maximum output power of the photovoltaic generator set under any illumination, P3 is the initial power value, P4 is the active power of the photovoltaic generator set,

[0056] The total frequency deviation rate of the photovoltaic power generation group at time t is the derivative of the time curve of the frequency of the photovoltaic power generation group at time t.

[0057] Furthermore, the control method of the first fuzzy controller in S3 is:

[0058]

[0059] Where ΔP1 is the total active power increment required for frequency regulation of the wind turbine generator set, P1 is the output value of the wind turbine generator set, V m is the wind speed, β best is the optimal pitch angle, Δβ is the change in pitch angle, ω best is the optimal rotor speed, and Δω is the change in rotor speed.

[0060] Furthermore, the control method of the second fuzzy controller in S4 is:

[0061]

[0062] Where ΔP1 is the total active power increment required for frequency regulation of the photovoltaic generator set, H pv is the virtual inertia time constant, is the total frequency change rate of the photovoltaic generator set.

[0063] A simulation model was constructed to verify the frequency regulation control method for a wind-solar power generation system. The model used a wind turbine with a rated output power of 1012 MW, a wind speed of 7 m / s, a synchronous generator with a rated output power of 1298 MW, 510 MW of active power consumed by local loads, and 1000 MW of DC power transmission. When the load increased by 150 MW, if wind turbines were not involved in power system frequency regulation, the minimum system frequency would reach 49.3 Hz, threatening the stability and safety of the power system frequency. However, when wind turbines participated in power system frequency regulation, the control method of this embodiment enabled the wind turbines to generate an additional 50 MW of active power, supporting power system frequency stability. When the wind turbines ended frequency regulation, the system entered a new steady-state with a frequency consistent with the steady-state value without wind turbines. This indicates that virtual inertia control effectively supports grid frequency stability, increases system inertia, and reduces the fluctuation amplitude of system frequency under load fluctuations. This embodiment was used to control the frequency regulation of a wind-solar power generation system.

[0064] In addition, photovoltaic power generation was verified using a single-unit equivalent model. The rated output power of the photovoltaic power generation was 500MW, the rated output power of the synchronous generator set was 1300MW, the active power consumed by the local load was 500MW, the DC transmission power was 1000MW, and the voltage level was 330kV. When the load increased by 100MW, if the photovoltaic power generation did not participate in the power system frequency regulation, the minimum system frequency would reach 49.7Hz, posing certain risks to the safe and stable operation of the power system. When photovoltaic power generation participates in the power system frequency regulation, the generator generates an additional 100MW of active power to support the stability of the power system frequency. When the photovoltaic power generation ends the frequency regulation, the system enters a new steady-state with the same steady-state frequency value as when the photovoltaic generator does not participate in the frequency regulation. This provides good support for the system frequency, improves the system inertia, and reduces the fluctuation amplitude of the system frequency under load fluctuations.

[0065] The above are merely specific embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A frequency control method for wind-solar power generation system based on improved fuzzy control, characterized in that: The following steps are involved: S1, obtaining the total frequency and output value of the wind turbine generator set; S2, obtain the total frequency change rate of the photovoltaic generator set; S3, inputting the frequency change rate and output value of the wind turbine generator set into the first fuzzy controller to obtain the total active power increment required for frequency modulation of the wind turbine generator set; S4, inputting the total frequency change rate of the photovoltaic power generation group into the second fuzzy controller to obtain the total active power increment required for frequency regulation of the photovoltaic power generation group; S5, allocating the active power increments of the wind turbine generator set and the photovoltaic generator set to obtain a power adjustment plan for the wind turbine generator set and the photovoltaic generator set; S6, the wind turbine generator set and the photovoltaic generator set adjust their operating states according to the power adjustment plan, thereby realizing the frequency regulation control of the wind and solar power generation system.

2. The wind-solar power generation system frequency modulation control method based on improved fuzzy control as claimed in claim 1, characterized in that: The wind turbine generator set is one or more double-fed wind turbine generator sets.

3. The wind-solar power generation system frequency modulation control method based on improved fuzzy control as claimed in claim 1, characterized in that: The photovoltaic power generation group is one or more bipolar photovoltaic grid-connected inverters.

4. The wind-solar power generation system frequency modulation control method based on improved fuzzy control as claimed in claim 1, characterized in that: The method for obtaining the total frequency of the wind turbine generator set in S1 is: in, Where f1(t) is the time curve of the wind turbine frequency, ω n is the undamped natural frequency of the system; ω d is the damped oscillation frequency, H and D are the inertia time constant and damping coefficient of the wind turbine generator set; R is the adjustment coefficient; T RH F is the reheating time constant of the prime mover of the wind turbine generator set; HP is the work ratio of the prime mover of the wind turbine.

5. The wind-solar power generation system frequency modulation control method based on improved fuzzy control as claimed in claim 1, characterized in that: The method for obtaining the output value of the wind turbine generator set in S1 is: Where P1 is the output value of the wind turbine, ρ is the air density, R is the blade radius, V m is wind speed, β best is the optimal pitch angle, ω best is the optimum rotor speed.

6. The wind-solar power generation system frequency modulation control method based on improved fuzzy control as claimed in claim 1, characterized in that: The method for obtaining the total frequency deviation rate of the photovoltaic power generation group in S2 is: Where f2(t) is the time curve of the wind turbine frequency, f d is the frequency response action value; k is the frequency adjustment coefficient; P n is the maximum output power of the photovoltaic generator set under any illumination, P3 is the initial power value, P4 is the active power of the photovoltaic generator set, The total frequency deviation rate of the photovoltaic power generation group at time t is the derivative of the time curve of the frequency of the photovoltaic power generation group at time t.

7. The wind-solar power generation system frequency modulation control method based on improved fuzzy control as claimed in claim 1, characterized in that: The control method of the first fuzzy controller in S3 is: Where ΔP1 is the total active power increment required for frequency modulation of the wind turbine generator set, P1 is the output value of the wind turbine generator set, V m is wind speed, β best is the optimal pitch angle, Δβ is the change in pitch angle, ω best is the optimal rotor speed, and Δω is the change in rotor speed.

8. The wind-solar power generation system frequency modulation control method based on improved fuzzy control as claimed in claim 1, characterized in that: The control method of the second fuzzy controller in S4 is: Where ΔP1 is the total active power increment required for frequency modulation of the photovoltaic generator set, H pv is the virtual inertia time constant, is the total frequency change rate of the photovoltaic generator set.

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