Wind power plant frequency stability cluster modeling method considering active adjustment capability

Abstract

The invention discloses a wind power plant frequency stability cluster modeling method considering active adjustment capability, and relates to the field of power system frequency adjustment and wind power plant control, and the method comprises the steps: employing a Jensen wake flow model to describe the wake flow effect in a wind power plant, calculating the actual wind speed of a fan according to a shielding coefficient, and calculating the actual wind speed of the fan according to the shielding coefficient; the method comprises the following steps: establishing a wind power plant loss model and an improved thermal power generating unit frequency control model, introducing an inertia regulation factor and a frequency modulation influence coefficient, establishing a function relationship between a power grid frequency and a fan power tracking coefficient, introducing an S-Function module to update system parameters in real time, completing modeling of a wind power plant frequency stability cluster, and realizing frequency analysis and control. According to the method, the wake effect is considered by adopting a multi-fan equivalent aggregation method, an improved suboptimal power point tracking control strategy is introduced to enable the fans to dynamically respond to frequency changes, an improved wind-thermal power cooperative frequency control model and a real-time parameter correction mechanism are established, and the accuracy and timeliness of frequency control are improved.

Description

Technical Field

[0001] The invention relates to the field of power system frequency regulation and wind farm control, and in particular to a wind farm frequency stability cluster modeling method considering active regulation capability. Background Art

[0002] Frequency is an important indicator for measuring the quality of power in the power grid. It is closely related to the balance between the output power of the generator set and the power demanded by the load during the operation of the system. Normally, the power generation power and the load power of the power grid are in balance, and the system frequency is maintained near the rated value. However, when the system experiences load fluctuations, unit failures and other events, this balance is broken, resulting in fluctuations in the grid frequency. If the system frequency deviates from the normal range for a long time or over a large range, it will affect the operating performance of the electrical equipment, and may even cause equipment damage, resulting in greater economic and social losses.

[0003] In the traditional operation mode, wind turbines are mainly responsible for providing energy to the power grid, and do not involve auxiliary services such as system frequency regulation. With the increase in the proportion of wind power in the power grid, traditional conventional generators are gradually replaced by wind power and photovoltaic generators with uncertain characteristics. This change significantly reduces the resources available for frequency regulation in the system, thus posing a challenge to the stable operation of the system. Therefore, it is particularly necessary to explore the possibility of using wind turbines as system frequency regulation resources and study their specific mechanisms for participating in system frequency control.

[0004] At present, there are many technologies for wind turbines to participate in grid frequency control. However, the current technology has the following two main shortcomings: First, the current technology mostly uses the frequency response characteristics of wind farms through single-machine models, ignoring the differences and correlations of wind turbines in wind farms or wind farm clusters. In fact, affected by the wake effect, the operating status of each wind turbine in a wind farm is not completely consistent, resulting in different frequency regulation capabilities. Therefore, using a single-machine equivalent model of a wind farm to analyze its frequency response process will bring certain errors. Secondly, the current technology is mostly based on high-order nonlinear mathematical models to simulate the frequency transient response process of wind turbines. For large-scale power grids, this modeling technology will bring huge computing pressure and it is difficult to meet the timeliness of real-time stability analysis.

[0005] Therefore, there is an urgent need for a cluster modeling method that can effectively improve the frequency stability regulation accuracy and response speed of wind farms and make full use of the active regulation capabilities of wind turbines to cope with frequency fluctuations and improve the stability and reliability of the power grid. Summary of the invention

[0006] In view of the problems existing in the existing wind farm frequency stability adjustment method, the present invention is proposed.

[0007] Therefore, the problem to be solved by the present invention is how to improve the accuracy and response speed of frequency stability regulation of a wind farm and enhance its adaptability to frequency fluctuations.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In the first aspect, an embodiment of the present invention provides a wind farm frequency stability cluster modeling method that takes into account active regulation capability, which includes: using the Jensen wake model to characterize the wake effect in the wind farm, and calculating the actual wind speed received by the wind turbine according to the shielding coefficient; establishing a wind farm loss model and an improved thermal power unit frequency control model according to the actual wind speed; based on the traditional maximum power point tracking MPPT method, introducing an inertia regulation factor and a frequency regulation influence coefficient, and establishing a functional relationship between the grid frequency and the wind turbine power tracking coefficient; constructing a wind power and thermal power coordinated frequency control model according to the functional relationship, introducing an S-Function module to update system parameters in real time, and completing the modeling of the wind farm frequency stability cluster.

[0010] As a preferred solution of the wind farm frequency stability cluster modeling method considering active regulation capability described in the present invention, the improved thermal power unit frequency control model includes a speed regulator model, a prime mover model and a generator-load model; the wind power and thermal power coordinated frequency control model includes the following steps: when the system power fluctuates, a fluctuation signal ΔP is generated i Enter the system parameters and update the parameters in real time; if the differential signal detector detects signal fluctuations, the control signal CTRL is transmitted to the S-Function module by improving the frequency control model of the thermal power unit, combined with the unit output value ΔP stored in the model sampling module before the disturbance occurs G_sam , calculate the real-time spare capacity and ramp rate of the system; perform the first frequency modulation on the power system by improving the transfer function of the frequency control model of the thermal power unit; perform the second frequency modulation on the power system by comparing the control module, the ramp control module and the feedback branch; at the same time, the fan frequency response module receives the system frequency change signal, changes the fan pitch angle parameter and the blade tip speed ratio parameter through the control model, and changes the fan output; input the output response power of the wind farm loss model and the improved thermal power unit frequency control model into the generator-load module, output the frequency increment signal, and change the frequency of the system; execute the above steps repeatedly to continuously respond to the system frequency fluctuation; wherein, ΔP i is the unbalanced power between the thermal power unit in state i and the system, ΔP G_sam It is the unit output value.

[0011] As a preferred solution of the wind farm frequency stability cluster modeling method considering active regulation capability described in the present invention, the first frequency regulation includes the following steps: the speed regulator model is a speed regulator input system frequency deviation Δf i And the power regulation capacity ΔP that the thermal power unit can provide to the system i,CG , generating a control signal to be transmitted to the prime mover model to change the valve opening; the prime mover model is to input the transfer function parameter of the hot section charging delay of the reheat steam turbine, the control signal of the steam valve increase or decrease, generate a mechanical power increment and input it to the generator-load model; the transfer function parameter includes the reheat coefficient F of the reheater i,HP , steam turbine time constant T i,CH and the reheater inertia time constant T i,RH The generator-load model inputs the mechanical power increment of the prime mover model and the generator electromagnetic power increment into the generator-load model, and the output is the generator frequency increment.

[0012] As a preferred solution of the wind farm frequency stability cluster modeling method considering active regulation capability described in the present invention, the second frequency regulation includes the following steps: i,a And the regulation capacity ΔP required by the system i,r After being transmitted to the comparison control module, they enter the abs module to obtain the absolute value; the adjustment capacity ΔP required by the system i,r The input is sent to the limiting module for processing, and the output value and the system regulation capacity ΔP i,a The comparison result is transmitted to the climbing control module, and the system uses the equivalent climbing rate K i The linear coefficient increases or decreases the unit output power until the output power is equal to the power regulation capacity ΔP required for the system to restore normal frequency. i,m So far; the relevant calculation formula of the improved thermal power unit frequency control model is as follows:

[0013]

[0014] Among them, N G,N is the set of thermal power units in normal operation when the system is in state i, Δf i is the system frequency deviation, ΔP i,a To adjust the system capacity, R i is the unit power regulation of the generator set, P j,max is the maximum output power of the jth thermal power unit, P j,min is the minimum output power of the jth thermal power unit, P j is the real-time output power of the jth thermal power unit, H j is the inertia constant of the jth thermal power unit, Rj is the speed regulation coefficient of the jth thermal power unit, K j is the ramp rate of the jth thermal power unit.

[0015] As a preferred solution of the wind farm frequency stability cluster modeling method considering active regulation capability described in the present invention, the wind power and thermal power coordinated frequency control model includes the following steps: according to the traditional maximum power point tracking MPPT method, the inertia factor is introduced to analyze the relationship between the grid frequency change rate and the wind turbine active power change, and the power tracking coefficient is corrected; the functional relationship between the grid frequency and the power tracking coefficient in the suboptimal power point tracking mode is established, and the specific formula is as follows:

[0016]

[0017] Among them, K o is the power tracking coefficient in the suboptimal power point tracking mode, K bast is the maximum power tracking factor, f oppt is the inertia factor of the system frequency, k t is the inertia coefficient of the equivalent synchronous unit, ω d0 is the initial change in fan speed, Δω t is the rotor speed change of the equivalent synchronous unit, and p is the pole pair number of the doubly fed induction generator set.

[0018] Set up the control model to obtain the system frequency change, calculate the frequency adjustment factor and inertia adjustment factor in real time; use the corrected power tracking coefficient to adjust the fan output power, define the frequency adjustment influence coefficient, and combine the inertia adjustment factor Δf oppt , calculate the power tracking coefficient in the improved suboptimal power point tracking mode; the specific formula of the frequency modulation influence coefficient is as follows:

[0019]

[0020] Among them, E real is the actual available kinetic energy, E avail To specify the kinetic energy for safe operation, ω min is the starting speed of the rotor, Δf is the grid frequency deviation, ω d is the change in fan speed, ω max is the starting speed of the rotor in the constant power zone.

[0021] The control model includes a high-pass filter and a low-pass filter.

[0022] As a preferred solution of the wind farm frequency stability cluster modeling method considering active regulation capability described in the present invention, the characterization includes the following steps: inputting wind farm parameters into the Jensen wake model, dividing the aggregation index using the wind turbine aggregation method, and outputting the wind speed V received by the downstream wind turbine. d , the specific formula is as follows:

[0023]

[0024] Among them, V d is the wind speed received by the downstream fan, C T,i is the thrust coefficient of the i-th fan T, V na is the ambient wind speed of the wind farm, n is the total number of wind turbines upstream of the i-th wind turbine, ξ ij is the coefficient of the jth downstream wind turbine affected by the wake effect of the ith upstream wind turbine, r u is the radius of the fan blade u, S shadow is the shadow area of ​​the downstream fan shaded by the upstream fan in the wind direction, S0 is the initial area of ​​the downstream fan shaded by the upstream fan in the wind direction, r i is the radius of the i-th fan, and the specific formula is as follows:

[0025] r i =r i +α w x;

[0026] Among them, α w is the drop coefficient, and x is the distance between the two fans along the wind direction.

[0027] As a preferred solution of the wind farm frequency stability cluster modeling method considering active regulation capability described in the present invention, the wind turbine aggregation method includes the following steps: according to the shielding coefficient, the wind turbines are divided into several sets according to the division principle, respectively marked as set 1 to set m, and the wind turbines are grouped and aggregated in turn; the wind turbines in the same set m are aggregated using the K-means clustering method to form wind turbine equivalents and as an aggregated single wind turbine model, the system is introduced to participate in frequency control; if the number of wind turbines in set 1 to set m-1 after aggregation is k groups, the specific formula for calculating the wind speed of the i-th wind turbine in the set is as follows:

[0028]

[0029] Among them, V k,k∈Set 2 is the wind speed of the i-th wind turbine in the set of k groups, ε 1,k is the wake effect of the wind turbine in cluster 1 on the wind speed of the typhoon wind turbine in cluster 2, C T,Groupl is the thrust coefficient, ζ i,kis the coefficient of group k affected by the wake effect of the i-th wind turbine.

[0030] The division principle is that wind turbines in the same set do not affect each other, and the wake effect on the wind turbines in set i only comes from the wind turbines in set 1 to set i-1; the wind farm parameters include the geographical location of the wind turbines, natural wind speed and natural wind direction.

[0031] In the second aspect, an embodiment of the present invention provides a wind farm frequency stability cluster modeling system that takes into account active regulation capability, which includes: a calculation module, which is used to characterize the wake effect in the wind farm using the Jensen wake model, and calculate the actual wind speed received by the wind turbine according to the shielding coefficient; an establishment module, which is used to establish a wind farm loss model and an improved thermal power unit frequency control model according to the actual wind speed; an inertia regulation factor module, which introduces an inertia regulation factor and a frequency regulation influence coefficient based on the traditional maximum power point tracking MPPT method, and establishes a functional relationship between the grid frequency and the wind turbine power tracking coefficient; a wind power and thermal power coordinated frequency control model module, which constructs a wind power and thermal power coordinated frequency control model according to the functional relationship, introduces an S-Function module to update system parameters in real time, and completes the modeling of the wind farm frequency stability cluster.

[0032] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the wind farm frequency stability cluster modeling method considering the active regulation capability as described in the first aspect of the present invention are implemented.

[0033] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the wind farm frequency stability cluster modeling method considering the active regulation capability as described in the first aspect of the present invention are implemented.

[0034] The beneficial effects of the present invention are as follows: the present invention takes into account the wake effect by adopting a multi-wind turbine equivalent aggregation method, introduces an improved suboptimal power point tracking control strategy to enable the wind turbine to dynamically respond to frequency changes, and establishes an improved wind-thermal power coordinated frequency control model and a real-time parameter correction mechanism to improve the accuracy and timeliness of frequency control; while ensuring accuracy, the simulation time is greatly shortened, the calculation burden is reduced, and the overall stability and reliability of the power system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0036] Figure 1 This is a flow chart of the wind farm frequency stability cluster modeling method considering active regulation capability in Example 1.

[0037] Figure 2 This is a Jensen wake model diagram of the wind farm frequency stability cluster modeling method considering active regulation capability in Example 1.

[0038] Figure 3 This is a frequency control model diagram of thermal power units considering operation constraints in the wind farm frequency stability cluster modeling method considering active regulation capability in Example 1.

[0039] Figure 4 This is a structural diagram of a doubly-fed induction wind turbine generator according to a wind farm frequency stability cluster modeling method taking active regulation capability into consideration in Example 1.

[0040] Figure 5 This is a diagram of the improved suboptimal power point tracking control model of wind turbines in the wind farm frequency stability cluster modeling method considering active regulation capability in Example 1.

[0041] Figure 6 This is a diagram of the improved suboptimal power point tracking control model of wind turbines in the wind farm frequency stability cluster modeling method considering active regulation capability in Example 1.

[0042] Coordinated frequency control model of wind power and thermal power based on improved suboptimal power point tracking.

[0043] Figure 7 This is a diagram of the IEEE-RTS79 system adjusted by the present invention for the wind farm frequency stability cluster modeling method considering active regulation capability according to Example 2.

[0044] Figure 8 This is a grid structure diagram of the test system of the wind farm frequency stability cluster modeling method considering active regulation capability in Example 2.

[0045] Fig. 9 This is a comparison of the output active power of the wind farm frequency stability cluster modeling method considering the active regulation capability in Example 2.

[0046] Fig.10 This is a rotor speed comparison diagram of the wind farm frequency stability cluster modeling method considering active regulation capability in Example 2.

[0047] Fig.11 This is a pitch angle comparison diagram of the wind farm frequency stability cluster modeling method considering the active regulation capability in Example 2. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0051] Example 1

[0052] Reference Figure 1 to Figure 6 , which is the first embodiment of the present invention, and provides a wind farm frequency stability cluster modeling method considering active regulation capability, including:

[0053] S1: The Jensen wake model is used to characterize the wake effect in the wind farm, and the actual wind speed received by the wind turbine is calculated based on the shielding coefficient.

[0054] Specifically, Figure 2 As shown in the Jensen wake model, V d is the wind speed of the downstream fan, V na is the ambient wind speed of the wind farm, r u is the radius of the fan blade u, x is the distance between the two fans along the wind direction, S shadow is the shadow area of ​​the downstream fan shaded by the upstream fan in the wind direction, S0 is the initial area of ​​the downstream fan shaded by the upstream fan in the wind direction, α w is the reduction coefficient, r i is the radius of the i-th wind turbine; the Jensen wake model decreases with the wake drop coefficient α w As the flow rate increases, the affected range of downstream fans will increase accordingly.

[0055] Furthermore, the wind farm parameters are input into the Jensen wake model, and the aggregation index is divided using the wind turbine aggregation method to output the wind speed V of the downstream wind turbine.d , the specific formula is as follows:

[0056]

[0057] Among them, V d is the wind speed received by the downstream fan, C T,i is the thrust coefficient of the i-th fan T, V na is the ambient wind speed of the wind farm, n is the total number of wind turbines upstream of the i-th wind turbine, ξ ij is the coefficient of the jth downstream wind turbine affected by the wake effect of the ith upstream wind turbine, r u is the radius of the fan blade u, S shadow is the shadow area of ​​the downstream fan shaded by the upstream fan in the wind direction, S0 is the initial area of ​​the downstream fan shaded by the upstream fan in the wind direction, r i is the radius of the i-th fan, and the specific formula is as follows:

[0058] r i =r i +α w x;

[0059] Among them, α w is the drop coefficient, and x is the distance between the two fans along the wind direction.

[0060] Furthermore, the wind turbine aggregation method includes the following steps: according to the shielding coefficient, the wind turbines are divided into several sets according to the division principle, marked as set 1 to set m respectively, and the wind turbines are grouped and aggregated in turn.

[0061] It should be noted that the division principle is that the wind turbines in the same set do not affect each other, and the wake effect on the wind turbines in set i only comes from the wind turbines in sets 1 to i-1; the wind farm parameters include the geographical location of the wind turbines, the natural wind speed and the natural wind direction. The wind turbines in set 1 are not blocked by other wind turbines and are all aggregated into set 1; in set 1, the wind speeds received by all wind turbines are the ambient wind speeds, and the thrust coefficients C of these wind turbines are T,Groupl are assumed to be consistent.

[0062] Specifically, the K-means aggregation method is used to aggregate the wind turbines in set 2; after aggregation, the wind turbines in set 2 are divided into several groups. Since the wind turbines in the same set have similar ε 1,k , so the wind speeds of each wind turbine are similar. Similarly, it can be assumed that the thrust coefficients of the wind turbines in the same set are the same. The wind speed of set 2 affected by the wind turbines in set 1 is calculated as follows:

[0063]

[0064] Among them, Vna is the ambient wind speed of the wind farm, V k,k∈Set2 is the wind speed of the i-th wind turbine in the set of k groups, ε 1,k is the wake effect of the wind turbine in cluster 1 on the wind speed of the typhoon wind turbine in cluster 2, C T,Groupl is the thrust coefficient, ζ i,k is the coefficient of group k affected by the wake effect of the i-th wind turbine.

[0065] It should be noted that since the parameter is ε 1,k The only parameter that affects the wind speed of wind turbines in set 2 is [ε 1,k ] 1×1 Aggregate indicator vector;

[0066] Furthermore, the K-means clustering method is used to aggregate the wind turbines in the same set m, forming wind turbine equivalents and serving as an aggregated single wind turbine model, and introducing the system to participate in frequency control; repeat the operation process and iterate through all sets; if the number of wind turbines in sets 1 to m-1 after aggregation is k, the specific formula for calculating the wind speed of the i-th wind turbine in the set is as follows:

[0067]

[0068] Among them, V k,k∈Set 2 is the wind speed of the i-th wind turbine in the set of k groups, ε 1,k is the wake effect of the wind turbine in cluster 1 on the wind speed of the typhoon wind turbine in cluster 2, C T,Groupl is the thrust coefficient, ζ i,k is the coefficient of group k affected by the wake effect of the i-th wind turbine.

[0069] Furthermore, the wind speeds of wind turbines in the set m area are all affected by the wind turbines in the count(m-1) group. The wind turbines in the set m area are aggregated using a multidimensional index vector, which is expressed as follows:

[0070] [ε 1,k ,ε 2,k ,···,ε count(m-1),k ] count(m-1)×1 .

[0071] It should be noted that after the aggregation is completed, several groups of wind turbines classified by the wind speeds they are subjected to can be obtained. The wind turbines in the same group are equivalent to an aggregated single wind turbine model and introduced into the system to participate in frequency control.

[0072] S2: According to the actual wind speed, a wind farm loss model and an improved thermal power unit frequency control model are established.

[0073] Specifically, the frequency control model of thermal power units is improved as follows: Figure 3 As shown, the system is in state i, ΔP i is the unbalanced power between the thermal power unit in state i and the system, Δf i is the system frequency deviation, H i is the inertia constant of the system, D i The load unit regulates the power, R i is the unit power regulation of the generator set, B i is the frequency deviation factor, K i is the proportional integral constant of the system's secondary frequency modulation, F i,HP is the reheat coefficient of the reheater, T i,G is the speed regulator time constant, T i,CH is the steam turbine time constant, T i,RH is the reheater inertia time constant, ΔP i,m The power regulation capacity required for the system to restore normal frequency, ΔP i,CG It refers to the power regulation capacity that thermal power units can provide to the system.

[0074] Furthermore, the frequency control model of the thermal power unit is improved, including the speed regulator model, the prime mover model and the generator-load model; by improving the transfer function of the frequency control model of the thermal power unit, the power system is frequency regulated for the first time; the first frequency regulation includes the following steps: the speed regulator model is to input the system frequency deviation Δf through the speed regulator i And the power regulation capacity ΔP that the thermal power unit can provide to the system i,CG , generating a control signal and transmitting it to the prime mover model to change the valve opening; when the load suddenly increases or decreases, the electromagnetic power and input power of the generator set are no longer the same, causing the system frequency to change. The speed governor of the generator can adjust the valve opening of the prime mover and the output power of the generator, so that the frequency of the power system can be stabilized at a new balance point, realizing the primary frequency regulation of the power system. Figure 3 As shown, the governor transfer function contains the unit regulation power of the generator set 1 / R i , speed regulator time constant 1 / 1+sT i,G And the addition module between the two functions.

[0075] Furthermore, the prime mover model inputs the parameters in the transfer function of the hot section charging delay of the reheat steam turbine into the control signal of the increase or decrease of the steam valve, generates a mechanical power increment and inputs it into the generator-load model; wherein, the output power of the prime mover depends on the valve opening, the prime mover intake is changed by changing the position of the steam valve, and the mechanical power input to the generator is changed by the prime mover intake; the parameters of the transfer function include the reheat coefficient F of the reheater i,HP , steam turbine time constant T i,CHand the reheater inertia time constant T i,RH ; Include the reheat coefficient F of the reheater in the transfer function i,HP , steam turbine time constant T i,CH and the reheater inertia time constant T i,RH Parameters.

[0076] Specifically, the generator-load model inputs the mechanical power increment of the prime mover model and the generator electromagnetic power increment into the generator-load model, and outputs the generator frequency increment; when the load of the power system changes, the output electromagnetic torque of the generator changes, causing the mechanical torque and the electromagnetic torque to be in an unbalanced state, so it is necessary to change the mechanical torque of the generator so that the mechanical torque and the electromagnetic torque reach a balanced state.

[0077] Furthermore, by comparing the control module, the ramp control module and the feedback branch, the power system is frequency-regulated for the second time; the second frequency regulation includes the following steps: adjusting the system capacity ΔP i,a And the regulation capacity ΔP required by the system i,r After being transmitted to the comparison control module, they enter the abs module to obtain the absolute value; the adjustment capacity ΔP required by the system i,r The input is sent to the limiting module for processing, and the output value and the system regulation capacity ΔP i,a The comparison result is transmitted to the climbing control module, and the system uses the equivalent climbing rate K i The linear coefficient increases or decreases the unit output power until the output power is equal to the power regulation capacity ΔP required for the system to restore normal frequency. i,m until.

[0078] Furthermore, the relevant calculation formula of the frequency control model of the thermal power unit is as follows:

[0079]

[0080] Among them, N G,N is the set of thermal power units in normal operation when the system is in state i, Δf i is the system frequency deviation, ΔP i,a To adjust the system capacity, R i is the unit power regulation of the generator set, P j,max is the maximum output power of the jth thermal power unit, P j,min is the minimum output power of the jth thermal power unit, P j is the real-time output power of the jth thermal power unit, H j is the inertia constant of the jth thermal power unit, R j is the speed regulation coefficient of the jth thermal power unit, K j is the ramp rate of the jth thermal power unit.

[0081] Specifically, the general structure of DFIG-WT is as follows Figure 4 As shown, the converted power P g The specific formula is as follows:

[0082]

[0083] Where ρ is the air density, A is the area covered by the fan rotation, v is the wind speed, R is the radius of the fan blade, C g is the wind energy utilization coefficient, where is a function of the wind turbine tip speed ratio and pitch angle. The specific formula is as follows:

[0084]

[0085] Among them, ω b is the angular velocity of the fan blade, α is the pitch angle, c1, c2, c3, c4, c5 and c6 are constant values, and γ is the fan blade tip speed.

[0086] Furthermore, when the pitch angle α is 0, the function C composed of the fan tip speed ratio and the pitch angle is g There exists an optimal fan blade tip speed γ best , which can make the wind energy utilization coefficient C g Get the maximum value of wind energy utilization coefficient C gmax Based on this, the maximum output power of the fan is calculated as follows:

[0087]

[0088] Among them, P max is the maximum output power, C gmax is the maximum value of wind energy utilization coefficient, ω b is the angular velocity of the fan blade, ρ is the air density, ω is the fan rotor speed, K best is the maximum power tracking factor.

[0089] Furthermore, DFIG-WT generally operates in the maximum power point tracking (MPPT) mode under normal circumstances. This mode can adjust the rotor current measuring converter and pitch angle control system according to different environmental wind speeds, track the maximum power point, and realize the maximum output power capture. The specific formula of output power is as follows:

[0090]

[0091] Among them, ω g is the real-time speed of the fan rotor, ω0 is the starting speed of the rotor in the MPPT zone, ω minis the starting speed of the rotor, ω1 is the starting speed of the rotor in the constant speed zone, ω max is the starting speed of the rotor in the constant power zone.

[0092] Specifically, after the DFIT-WT unit is subjected to external disturbance, the rotor speed changes from the initial change of the fan speed ω d0 Converted into the total change of fan speed ω d0 +Δω d , the change of rotor kinetic energy during the process can be expressed as:

[0093]

[0094] Where, ΔE DFIG is the change in the rotor kinetic energy during the process, J WT is the equivalent inertia constant of the fan, ω d0 is the initial change of fan speed.

[0095] Furthermore, if the fan unit is equivalent to a synchronous unit, when the rotor speed is changed from ω t0 Transformed into ω t0 +Δω t , the specific formula for the equivalent synchronous unit rotor kinetic energy change is as follows:

[0096]

[0097] Among them, J t is the moment of inertia of the equivalent synchronous unit of the fan, Δω t is the rotor speed change of the equivalent synchronous unit, ω t0 is the initial change in rotor speed of the equivalent synchronous unit.

[0098] Furthermore, the change in the kinetic energy of the intermediate rotor is combined with the change in the kinetic energy of the equivalent synchronous unit rotor, and the specific formula is as follows:

[0099]

[0100] Among them, J t is the moment of inertia of the equivalent synchronous unit of the fan, Δω t is the rotor speed change of the equivalent synchronous unit, ω t0 is the initial change in rotor speed of the equivalent synchronous unit.

[0101] It should be noted that in the frequency response process, the change in speed is small relative to the rotor speed itself and can be ignored, that is, 2ω d0 Δω d , 2ω t0 Δω t , the formula can be simplified to:

[0102]

[0103] Among them, k t is the inertia coefficient of the equivalent synchronous unit.

[0104] S3: Based on the traditional maximum power point tracking MPPT method, the inertia adjustment factor and frequency modulation influence coefficient are introduced to establish the functional relationship between the grid frequency and the wind turbine power tracking coefficient.

[0105] Specifically, according to the traditional maximum power point tracking MPPT method, the inertia factor is introduced to analyze the relationship between the grid frequency change rate and the wind turbine active power change, and the power tracking coefficient is corrected; the inertia factor f related to the system frequency is introduced oppt Maximum power tracking coefficient K best Dynamic adjustment is performed, the specific formula is as follows:

[0106]

[0107] Among them, K O is the power tracking coefficient in suboptimal power point tracking mode.

[0108] Furthermore, considering the grid frequency deviation Δf t , Fan speed change Δω d and the speed change of the equivalent synchronous unit Δω t The relationship between the grid frequency and the power tracking coefficient in the suboptimal power point tracking mode is established. The specific formula is as follows:

[0109]

[0110] Among them, K o is the power tracking coefficient in the suboptimal power point tracking mode, K bast is the maximum power tracking factor, f oppt is the inertia factor of the system frequency, k t is the inertia coefficient of the equivalent synchronous unit, ω d0 is the initial change in fan speed, Δω t is the rotor speed change of the equivalent synchronous unit, and p is the pole pair number of the doubly fed induction generator set.

[0111] Furthermore, the relationship between the grid frequency change rate and the active power change of the wind turbine is analyzed, and the inertia adjustment factor Δf is introduced. oppt Maximum power tracking coefficient K best Adjustment is made; based on the equivalent formula for the change in kinetic energy of the synchronous unit rotor, the specific formula for the change in active power when the fan rotor speed changes is as follows:

[0112]

[0113] Where ΔP DEIG is the change in active power.

[0114] It should be noted that the control model includes a high-pass filter and a low-pass filter; according to the above power tracking method, Figure 5 As shown in the figure, the control block diagram of DFIG-WT using the improved suboptimal power point tracking operation mode is given. When the system frequency changes, the control model uses the rotor speed ω that the controller has sampled and stored before the change through the power tracking coefficient formula in the suboptimal power point tracking mode. d0 Calculate the frequency adjustment factor f oppt In addition, based on the rotor speed ω before the frequency change d0 , through the inertia adjustment factor Δf oppt Calculation is performed, and the control model is based on the power tracking coefficient K in the improved suboptimal power point tracking mode I-OPPT Formula to correct fan power tracking coefficient K best , to achieve the adjustment of fan power.

[0115] Specifically, the inertia time constant T of the equivalent synchronous unit is introduced t The equivalent synchronous unit moment of inertia J t The relationship between them is as follows:

[0116]

[0117] Among them, T t is the inertia time constant of the equivalent synchronous unit, J t is the equivalent synchronous unit moment of inertia, P b is the selected benchmark value.

[0118] Furthermore, according to the change in active power and the relationship formula, the change in wind turbine output power can be obtained. The specific formula is as follows:

[0119]

[0120] Where ΔP DEIG is the change in active power, T t is the inertia time constant of the equivalent synchronous unit, J t is the equivalent synchronous unit moment of inertia, P b is the selected reference value, ω t0 is the initial change in rotor speed of the equivalent synchronous unit.

[0121] Furthermore, the fan output power variation is optimized, and the specific formula is as follows:

[0122]

[0123] in, is the change in fan output power after normalization. It is the change of fan speed after normalization.

[0124] Specifically, considering that the synchronous speed per unit value and the grid frequency per unit value are equal, the change in wind turbine output power after further deduction is as follows:

[0125]

[0126] Among them, f t * To optimize the grid frequency after processing.

[0127] Furthermore, the specific formula of the frequency modulation influence coefficient is as follows:

[0128]

[0129] Among them, E real is the actual available kinetic energy, E avail To specify the kinetic energy for safe operation, ω min is the starting speed of the rotor, Δf is the grid frequency deviation, ω d is the change in fan speed, ω max is the starting speed of the rotor in the constant power zone.

[0130] Furthermore, the inertia adjustment factor Δf is introduced oppt , improve the power tracking coefficient K in suboptimal power point tracking mode I-OPPT for:

[0131]

[0132] K I-OPPT =K best (f oppt -Δf oppt );

[0133] Among them, K I-OPPT To improve the power tracking coefficient in suboptimal power point tracking mode, Δf oppt is the inertia adjustment factor.

[0134] S4: Construct a wind power and thermal power coordinated frequency control model based on the functional relationship, introduce the S-Function module to update the system parameters in real time, and complete the modeling of the wind farm frequency stability cluster.

[0135] Specifically, Figure 6As shown in Figure 2, the wind power and thermal power coordinated frequency control model includes the following steps: When the system power fluctuates, a fluctuation signal ΔP is generated. i Enter the system parameters and update the parameters in real time; if the differential signal detector detects signal fluctuations, the control signal CTRL is transmitted to the S-Function module by improving the frequency control model of the thermal power unit, combined with the unit output value ΔP stored in the model sampling module before the disturbance occurs G_sam , calculate the system’s real-time spare capacity and ramp rate.

[0136] Furthermore, through the first frequency modulation and the second frequency modulation, the fan frequency response module receives the system frequency change signal, changes the fan pitch angle parameter and the blade tip speed ratio parameter through the control model, and changes the fan output; the output response power of the wind farm loss model and the improved thermal power unit frequency control model is input into the generator-load module, and the frequency increment signal is output to change the system frequency; the above steps are executed cyclically to continuously respond to the system frequency fluctuation; wherein, ΔP i is the unbalanced power between the thermal power unit in state i and the system, ΔP G_sam It is the unit output value.

[0137] Furthermore, this embodiment also provides a wind farm frequency stability cluster modeling system that takes into account active regulation capability, including: a calculation module, which is used to use the Jensen wake model to characterize the wake effect in the wind farm, and calculate the actual wind speed received by the wind turbine according to the shielding coefficient; an establishment module, which is used to establish a wind farm loss model and an improved thermal power unit frequency control model according to the actual wind speed; an inertia regulation factor module, which introduces the inertia regulation factor and the frequency regulation influence coefficient based on the traditional maximum power point tracking MPPT method, and establishes a functional relationship between the grid frequency and the wind turbine power tracking coefficient; a wind power and thermal power coordinated frequency control model module, which constructs a wind power and thermal power coordinated frequency control model according to the functional relationship, introduces the S-Function module to update the system parameters in real time, and completes the modeling of the wind farm frequency stability cluster.

[0138] This embodiment also provides a computer device, which is suitable for the case of a wind farm frequency stability cluster modeling method considering active regulation capability, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the wind farm frequency stability cluster modeling method considering active regulation capability as proposed in the above embodiment.

[0139] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0140] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the following steps are implemented: the Jensen wake model is used to characterize the wake effect in the wind farm, and the actual wind speed received by the wind turbine is calculated according to the shielding coefficient; according to the actual wind speed, a wind farm loss model and an improved thermal power unit frequency control model are established; based on the traditional maximum power point tracking MPPT method, an inertia adjustment factor and a frequency modulation influence coefficient are introduced to establish a functional relationship between the grid frequency and the wind turbine power tracking coefficient; according to the functional relationship, a wind power and thermal power coordinated frequency control model is constructed, and an S-Function module is introduced to update system parameters in real time to complete the modeling of the wind farm frequency stability cluster.

[0141] In summary, the present invention adopts a multi-wind turbine equivalent aggregation method to consider the wake effect, introduces an improved suboptimal power point tracking control strategy to enable the wind turbine to dynamically respond to frequency changes, and establishes an improved wind-thermal power coordinated frequency control model and a real-time parameter correction mechanism to improve the accuracy and timeliness of frequency control; while ensuring accuracy, the simulation time is greatly shortened, the calculation burden is reduced, and the overall stability and reliability of the power system are improved.

[0142] Example 2

[0143] Reference Figure 7 to Figure 11 , which is the second embodiment of the present invention, and this embodiment provides a wind farm frequency stability cluster modeling method considering active regulation capability. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0144] Specifically, the modified IEEE-RTS79 system is as follows Figure 7As shown, the present invention uses it to verify the validity of the model. The rated power of each wind turbine is set to 20MW, and the penetration rate of wind power is set to 15%. The wind speed of the wind farm environment is 14.1m / s, the wake effect attenuation coefficient is 0.06, and the load is 3780MW.

[0145] Furthermore, the specific number of various types of units connected to the system is shown in Table 1. The wind turbines are distributed in 8 rows and 7 columns, the rated frequency of the power grid is 50 Hz, and the load damping factor is 70 MW / Hz.

[0146] Table 1 System generator amount

[0147] U12 U20 U50 U76 U100 U155 U197 U350 U400 Wind power penetration rate Fan Total Power 6 5 6 4 3 2 4 2 3 12％ 56 4064MW

[0148] Furthermore, in addition to substituting the simplified model of the wind turbine mentioned in the method for calculation, this example also puts the standard AG-DFIG model in Simulink into the same power grid for calculation and compares the results. Since the research content of this invention is mainly focused on the field of frequency control, the example mainly compares the active power of the wind turbine, the rotor speed of the wind turbine, and the blade pitch angle.

[0149] Specifically, the power grid used for simulation is as follows: Figure 8 The research scenario is that the wind speed of the fan is constant at 15.2m / s.

[0150] Further, such as Fig. 9 , Fig.10 and Fig.11 As shown, it can be seen that the results of the standard simplified model and the standard simulation model are basically the same, and compared with the refined simulation model, the simulation time of the simplified model proposed by the present invention is shortened by 69%. In summary, the present invention has better performance in terms of computational burden and running speed while ensuring that the model has high accuracy.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wind farm frequency stability cluster modeling method considering active regulation capability, characterized by: include, The Jensen wake model is used to characterize the wake effect in the wind farm, and the actual wind speed to which the wind turbine is subjected is calculated based on the shielding coefficient. According to the actual wind speed, a wind farm loss model and an improved thermal power unit frequency control model are established; Based on the traditional maximum power point tracking MPPT method, the inertia adjustment factor and frequency modulation influence coefficient are introduced to establish the functional relationship between the grid frequency and the wind turbine power tracking coefficient; A wind power and thermal power coordinated frequency control model is constructed according to the functional relationship, and an S-Function module is introduced to update system parameters in real time to complete the modeling of the wind farm frequency stability cluster.

2. The wind farm frequency stability cluster modeling method considering active regulation capability according to claim 1, characterized in that: The improved thermal power unit frequency control model includes a speed regulator model, a prime mover model and a generator-load model; the wind power and thermal power coordinated frequency control model includes the following steps: When the system power fluctuates, a fluctuation signal ΔP is generated. i Enter the system parameters to update the parameters in real time; If the differential signal detector detects signal fluctuations, the control signal CTRL is transmitted to the S-Function module by improving the frequency control model of the thermal power unit, and the unit output value ΔP stored in the model sampling module before the disturbance occurs is combined with the G_sam , calculate the real-time spare capacity and ramp rate of the system; By improving the transfer function of the frequency control model of thermal power units, the power system is frequency regulated for the first time. By comparing the control module, the ramp control module and the feedback branch, the power system is frequency-regulated for the second time; At the same time, the fan frequency response module receives the system frequency change signal, and changes the fan pitch angle parameter and blade tip speed ratio parameter through the control model to change the fan output; Input the output response power of the wind farm loss model and the improved thermal power unit frequency control model into the generator-load module, output a frequency increment signal, and change the frequency of the system; The above steps are executed cyclically to continuously respond to system frequency fluctuations; Among them, ΔP i is the unbalanced power between the thermal power unit in state i and the system, ΔP G_sam It is the unit output value.

3. The wind farm frequency stability cluster modeling method considering active regulation capability as claimed in claim 2, characterized in that: The first frequency regulation includes the following steps: the speed regulator model is to input the system frequency deviation Δf i And the power regulation capacity ΔP that the thermal power unit can provide to the system i，CG , generating a control signal to be transmitted to the prime mover model to change the valve opening; the prime mover model is to input the transfer function parameter of the hot section charging delay of the reheat steam turbine, the control signal of the steam valve increase or decrease, generate a mechanical power increment and input it to the generator-load model; the transfer function parameter includes the reheat coefficient F of the reheater i,HP , steam turbine time constant T i,CH and the reheater inertia time constant T i,RH The generator-load model inputs the mechanical power increment of the prime mover model and the generator electromagnetic power increment into the generator-load model, and the output is the generator frequency increment.

4. The wind farm frequency stability cluster modeling method considering active regulation capability according to claim 2, characterized in that: The second frequency modulation comprises the following steps: Adjust the system capacity ΔP i,a And the regulation capacity ΔP required by the system i,r After being transmitted to the comparison control module, they enter the abs module to obtain the absolute value; The regulation capacity ΔP required by the system i,r The input is sent to the limiting module for processing, and the output value and the system regulation capacity ΔP i,a Make a comparison; The comparison result is transmitted to the climbing control module, and the system uses the equivalent climbing rate K i The linear coefficient increases or decreases the unit output power until the output power is equal to the power regulation capacity ΔP required for the system to restore normal frequency. i,m until; The relevant calculation formula of the improved thermal power unit frequency control model is as follows: Among them, N G,N is the set of thermal power units in normal operation when the system is in state i, Δf i is the system frequency deviation, ΔP i,a To adjust the capacity of the system, R i is the unit power regulation of the generator set, P j,max is the maximum output power of the jth thermal power unit, P j,min is the minimum output power of the jth thermal power unit, P j is the real-time output power of the jth thermal power unit, H j is the inertia constant of the jth thermal power unit, R j is the speed regulation coefficient of the jth thermal power unit, K j is the ramp rate of the jth thermal power unit.

5. The wind farm frequency stability cluster modeling method considering active regulation capability as claimed in claim 2, characterized in that: The wind power and thermal power coordinated frequency control model comprises the following steps: According to the traditional maximum power point tracking MPPT method, the inertia factor is introduced to analyze the relationship between the grid frequency change rate and the wind turbine active power change, and the power tracking coefficient is corrected; Establish the functional relationship between the grid frequency and the power tracking coefficient in the suboptimal power point tracking mode. The specific formula is as follows: Among them, K o is the power tracking coefficient in the suboptimal power point tracking mode, K bast is the maximum power tracking factor, f oppt is the inertia factor of the system frequency, k t is the inertia coefficient of the equivalent synchronous unit, ω d0 is the initial change in fan speed, Δω t is the rotor speed variation of the equivalent synchronous unit, p is the pole pair number of the doubly-fed induction generator set; Set the control model to obtain the system frequency change and calculate the frequency adjustment factor and inertia adjustment factor in real time; Use the corrected power tracking coefficient to adjust the fan output power, define the frequency modulation influence coefficient, and combine the inertia adjustment factor Δf oppt , calculate the power tracking coefficient in the improved suboptimal power point tracking mode; The specific formula of the frequency modulation influence coefficient is as follows: Among them, E real is the actual available kinetic energy, E avail To specify the kinetic energy for safe operation, ω min is the starting speed of the rotor, Δf is the grid frequency deviation, ω d is the change in fan speed, ω max is the starting speed of the rotor in the constant power zone; The control model includes a high-pass filter and a low-pass filter.

6. The wind farm frequency stability cluster modeling method considering active regulation capability according to claim 1, characterized in that: The characterization comprises the following steps: The wind farm parameters are input into the Jensen wake model, and the aggregation index is divided using the wind turbine aggregation method to output the wind speed V received by the downstream wind turbine. d , the specific formula is as follows: Among them, V d is the wind speed of the downstream fan, C T,i is the thrust coefficient of the i-th fan T, V na is the ambient wind speed of the wind farm, n is the total number of wind turbines upstream of the i-th wind turbine, ξ ij is the coefficient of the jth downstream wind turbine affected by the wake effect of the ith upstream wind turbine, r u is the radius of the fan blade u, S shadow is the shadow area of ​​the downstream fan shaded by the upstream fan in the wind direction, S0 is the initial area of ​​the downstream fan shaded by the upstream fan in the wind direction, r i is the radius of the i-th fan, and the specific formula is as follows: r i ＝r i +α w x； Among them, α w is the drop coefficient, and x is the distance between the two fans along the wind direction.

7. The wind farm frequency stability cluster modeling method considering active regulation capability according to claim 6, characterized in that: The fan polymerization method comprises the following steps: According to the shielding coefficient, the wind turbines are divided into several sets according to the division principle, which are marked as set 1 to set m, and the wind turbines are grouped and aggregated in turn; The K-means clustering method is used to aggregate wind turbines in the same set m, forming wind turbine equivalents and using them as aggregated single wind turbine models, and introducing the system to participate in frequency control; If the number of wind turbines in set 1 to set m-1 after aggregation is k, the specific formula for calculating the wind speed of the i-th wind turbine in the set is as follows: Among them, V k,k∈Set2 is the wind speed of the i-th wind turbine in the set of k groups, ε 1,k is the wake effect of the wind turbine in cluster 1 on the wind speed of the typhoon wind turbine in cluster 2, C T,Groupl is the thrust coefficient, ζ i,k is the coefficient of group k affected by the wake effect of the i-th wind turbine; The division principle is that wind turbines in the same set do not affect each other, and the wake effect on the wind turbines in set i only comes from wind turbines in sets 1 to i-1; The wind farm parameters include the geographical location of wind turbines, natural wind speed and natural wind direction.

8. A wind farm frequency stability cluster modeling system considering active regulation capability, based on the wind farm frequency stability cluster modeling method considering active regulation capability as claimed in any one of claims 1 to 7, characterized in that: include, The calculation module is used to characterize the wake effect in the wind farm using the Jensen wake model and calculate the actual wind speed experienced by the wind turbine based on the shielding coefficient; Establishing a module for establishing a wind farm loss model and improving a frequency control model of a thermal power unit according to the actual wind speed; The inertia adjustment factor module introduces the inertia adjustment factor and frequency modulation influence coefficient based on the traditional maximum power point tracking MPPT method, and establishes the functional relationship between the grid frequency and the wind turbine power tracking coefficient; The wind power and thermal power coordinated frequency control model module constructs the wind power and thermal power coordinated frequency control model according to the functional relationship, introduces the S-Function module to update the system parameters in real time, and completes the modeling of the wind farm frequency stability cluster.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the wind farm frequency stability cluster modeling method considering active regulation capability according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the wind farm frequency stability cluster modeling method considering active regulation capability according to any one of claims 1 to 7 are implemented.

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