CN110212574B - Wind power control parameter coordination setting method considering virtual inertia - Google Patents

Wind power control parameter coordination setting method considering virtual inertia Download PDF

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CN110212574B
CN110212574B CN201910479763.1A CN201910479763A CN110212574B CN 110212574 B CN110212574 B CN 110212574B CN 201910479763 A CN201910479763 A CN 201910479763A CN 110212574 B CN110212574 B CN 110212574B
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余娟
刘珏麟
杨知方
黄俊凯
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Chongqing University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention discloses a wind power control parameter coordination setting method considering virtual inertia, which mainly comprises the following steps: 1) setting control parameters to be optimized
Figure DDA0002083425910000014
And power system sensitivity threshold.2) And establishing a wind power generation control parameter coordination optimization model. 3) And solving the wind power generation control parameter coordination optimization model by using an optimization algorithm. 4) Comparative sensitivity I3And a sensitivity threshold, if I3<Then delete the current control parameter
Figure DDA0002083425910000015
And proceeds to step 5. If I3If not, the current control parameter is reserved
Figure DDA0002083425910000011
And proceeds to step 5. 5) Determining the control parameter of the kth update
Figure DDA0002083425910000012
Whether or not the corresponding inter-region oscillation damping ratio is satisfied
Figure DDA0002083425910000013
If yes, the iteration is terminated, and all control parameters are output. y is less than or equal to k. The method can be widely applied to setting the wind power control parameters considering the stability of the power system.

Description

Wind power control parameter coordination setting method considering virtual inertia
Technical Field
The invention relates to the field of electric power systems and automation thereof, in particular to a wind power control parameter coordination setting method considering virtual inertia.
Background
In order to cope with global climate deterioration, wind power will gradually replace the traditional synchronous power generation system. However, wind power systems are generally considered to be low inertia systems or inertialess systems, which present significant challenges to the stabilization of the power system. To address this problem, many countries require wind power to provide a virtual inertia response to support system stability. The current research on virtual inertia control is mainly focused on frequency stability: the frequency attenuation speed of the system is reduced by increasing the virtual inertia, so that the frequency response performance of the system is improved. However, industrial and academic research has found that virtual inertia control of wind power may interfere with the stability of small signals. From the perspective of the power grid, the virtual inertia is added to comprehensively consider different dimensions of the system stability problem, including frequency stability and small signal stability. For frequency stability, it requires a minimum virtual inertia level. And the control parameters of the wind power, including virtual inertia control, rotor-side inverter control and the like, need to be coordinated and set to improve the stability of the small signal. However, at present, the control parameters of wind power are mainly set according to the stability and the requirements of the power generation system, and the stability of a power grid is not fully considered. In consideration of the rapid development of the wind power generation technology, the coordination and setting of control parameters capable of supporting the stability of different dimensions of the system need to be further researched.
Disclosure of Invention
The present invention is directed to solving the problems of the prior art.
The technical scheme adopted for achieving the purpose of the invention is that the wind power control parameter coordination setting method considering the virtual inertia mainly comprises the following steps:
1) setting control parameters to be optimized
Figure BDA0002083425890000011
And a power system sensitivity threshold. j is 1, 2. And n is the number of control parameters. k is initially 0.
The control parameters mainly comprise a virtual inertia control parameter Hω、
Virtual inertia control parameter HωThe following constraints are satisfied:
Figure BDA0002083425890000021
virtual inertia control parameter HωHas an initial value of Hωmin。
2) And establishing a wind power generation control parameter coordination optimization model.
Further, the main steps of establishing the wind power generation control parameter coordination optimization model are as follows:
2.1) establishing a small signal analysis model of the power system, which is respectively shown as a formula 1 to a formula 3:
Figure BDA0002083425890000022
in the formula, x is a state variable set of the power system, and mainly comprises the rotating speed and the power angle of the synchronous generator. u is the power system voltage and phase angle set. f is a time derivative function.
Figure BDA0002083425890000023
Is the derivative of x over time.
0＝g(x,u)。(3)
In the formula, g is a power flow balance equation of the power system.
Figure BDA0002083425890000024
In the formula, AsIs a state equation of the power system. And deltax is the power system state variable deviation.
Wherein the system state equation AsAs follows:
As＝(A-BD-1C)。 (5)
in the formula, A, B, C, D is a parameter equation.
Parametric equation A, B, C, D is shown in equations 5 through 8, respectively:
Figure BDA0002083425890000031
in the formula, x0Is the system state equilibrium point.
Figure BDA0002083425890000032
Figure BDA0002083425890000033
Figure BDA0002083425890000034
2.2) State matrix A for Power SystemsSolving is carried out to obtain a state matrix A of the power systemsIs characterized byRoot lambdai＝σi±jwi. i is 1,2, …, l. l is the power system state matrix AsAnd the total number of the feature roots is the total number of the working modes of the power system. SigmaiIs a characteristic root λiThe real part of (a). jwiIs a characteristic root λiThe imaginary part of (1).
Based on the characteristic root λiAnd calculating to obtain the damping ratio Zeta of the power systemiNamely:
Figure BDA0002083425890000035
damping ratio ζ of power systemiThe constraints of (2) are as follows:
ζi＞0。 (11)
2.3) writing the state variables x associated with the conventional synchronous generator into the set of state variables xSGIn (1), writing a state variable x related to wind power generation into a state variable set xWThus, equation 4 is blocked as follows:
Figure BDA0002083425890000036
in the formula, equation of state As22The elements in (1) comprise wind power control parameters.
Wherein, the state variable set xSGAs follows:
xSG＝[,ω,Eq′,E′d]T。 (13)
in the formula, the power angle is set for the synchronous generator. ω is the set of synchronous generator speeds. E'q、E′dIs the set of quadrature and direct electromotive forces of the synchronous generator. OmegarThe r-th synchronous generator speed.
Set of state variables xWAs follows:
in the formula, x1、x2、x3、x4The intermediate variable is controlled by the rotor converter of the wind power system. i.e. idr、iqrThe rotor current of the wind power system.
Figure BDA0002083425890000042
The stator flux linkage is a stator flux linkage of a wind power system. x is the number ofPLL、PLLIntermediate variables are controlled for the phase locked loop. x is the number ofωIntermediate variables are controlled for virtual inertia.
The wind power control parameter equation P is as follows:
p＝[Hω,KP_PLL,KI_PLL,KP1,KI1,KP2,KI2,KP3,KI3,KP4,KI4]T。 (15)
in the formula, HωAnd controlling parameters for the virtual inertia. KP_PLL、KI_PLLThe parameters are controlled for the phase locked loop. KP1、KI1、KP2、KI2、KP3、KI3、KP4、KI4And controlling parameters for the rotor converter.
2.4) establishing a frequency constraint based on the power system inertia, as shown in equations 16 to 19:
the frequency index related to inertia has mainly frequency change rate
Figure BDA0002083425890000043
And a deviation Δ f of the frequency, which can be expressed in relation to the system inertia as:
Figure BDA0002083425890000044
in the formula,. DELTA.PdIs the amount of interference of the system. DLIs the load damping coefficient. t is the interference duration. HtotalIs the total inertia of the system.
Wherein the deviation Δ f of the frequency satisfies the following constraint condition:
-Δfmax≤Δf(p)≤Δfmax(17)
in the formula,. DELTA.fmaxThe maximum deviation in frequency.
Rate of change of frequency
Figure BDA0002083425890000051
As follows:
Figure BDA0002083425890000052
wherein the rate of change of frequency
Figure BDA0002083425890000053
The following constraints are satisfied:
Figure BDA0002083425890000054
in the formula (I), the compound is shown in the specification,
Figure BDA0002083425890000055
is the maximum rate of change of frequency.
Wherein, the total inertia of the system is HtotalAs follows:
Figure BDA0002083425890000056
in the formula, HSGIs the synchronous machine inertia time constant. HωIs an inertia time constant simulated by wind power virtual inertia control parameters. SSGIs the capacity of the synchronous machine generator. N is a radical ofSGIs the total number of synchronous machine generators. SWIs the capacity of wind power α is any synchronous machine generator.
Order to
Figure BDA0002083425890000057
Then equations 17 and 19 are unified as follows:
-b≤h(p)≤b (21)
in the formula (II)
Figure BDA0002083425890000058
When the temperature of the water is higher than the set temperature,
Figure BDA0002083425890000059
when h (p) ═ Δ f, b ═ Δ fmax。
2.5) based on the steps 2.1 to 2.4, the wind power generation control parameter coordination optimization model is as follows:
maxζ(p)inter-area。 (22)
in the formula, max represents a maximum value. Zeta (p)inter-areaThe inter-area oscillation damping ratio.
The constraints of the wind power generation control parameter coordination optimization model are respectively shown in formulas 23 to 25, namely:
s.t.ζ(p)＞0。 (23)
pmin≤p≤pmax。 (24)
-b≤h(p)≤b。 (25)
where ζ (p) is a set of system damping ratios. p is a set of control parameters. p is a radical ofminAnd pmaxLower and upper limits for the control parameter. h (p) is a frequency function related to the virtual inertia control parameter. b is a frequency stability index.
3) Solving the wind power generation control parameter coordination optimization model by using an optimization algorithm so as to obtain the damping ratio zeta of the power systemiAnd the control parameter updated at the k time
Figure BDA0002083425890000061
Sensitivity I between3。k＝0，1，2，3…。
Further, the main steps of solving the wind power generation control parameter coordination optimization model by using the optimization algorithm are as follows:
3.1) calculating the System State equation AsAnd a control parameter pjSensitivity I between1Namely:
Figure BDA0002083425890000062
3.2) based on equation 26, electricityCharacteristic value lambda of force systemiAnd a control parameter pjSensitivity I between2The equation of the relationship (A) is as follows:
Figure BDA0002083425890000063
in the formula, viIs the left eigenvector u of the i-th working mode of the power systemiIs the right eigenvector of the i-th class of operating mode of the power system.
3.3) carrying out derivation on the formula 12, and calculating to obtain the damping ratio zeta of the power systemiAnd a control parameter pjSensitivity I between3Namely:
Figure BDA0002083425890000071
in the formula, Re (×) represents a real part. Im (×) denotes the imaginary part.
4) Comparative sensitivity I3And a sensitivity threshold, if I3<Then delete the current control parameter
Figure BDA0002083425890000072
And proceeds to step 5. If I3If not, the current control parameter is reserved
Figure BDA0002083425890000073
And proceeds to step 5.
5) Determining the control parameter of the kth update
Figure BDA0002083425890000074
Whether or not the corresponding inter-region oscillation damping ratio is satisfied
Figure BDA0002083425890000075
If not, let k equal to k +1 and use the last reserved control parameter
Figure BDA0002083425890000076
Sensitivity of (1)3Updating control parameters to optimize direction
Figure BDA0002083425890000077
And returns to step 3. If yes, the iteration is terminated, and all control parameters are output. y is less than or equal to k.
Further, the control parameter of the k-th update
Figure BDA0002083425890000078
As follows:
Figure BDA0002083425890000079
in the formula, mujIs the step size.
The technical effect of the present invention is undoubted. Taking wind power as an example, based on small signal analysis and sensitivity analysis, the invention provides a wind power control parameter coordination setting method giving consideration to both frequency stability and small signal stability. The invention aims to improve the frequency stability and small interference stability of the system at the same time through the coordination optimization of the wind power control parameters, and provides a basis for setting the wind power control parameters for the entry point from the stable operation requirement of the power system. The control parameter coordination optimization setting model established by the invention can improve the small signal stability of the system on the premise of ensuring the frequency stability of the system through the optimization setting of the wind power control parameters, so that the system stability requirements of different dimensions are met.
The algorithm provided by the invention establishes a relational expression between an optimization target and an optimization variable through sensitivity, and selects the sensitivity as an algorithm optimization direction, so that a control parameter coordination optimization setting model can be solved. And the control parameters are screened through sensitivity, optimization variables irrelevant to the target function are eliminated, the calculation burden is reduced, and the extracted model can be converged efficiently.
The method can be widely applied to wind power control parameter setting considering the stability of the power system, and can be popularized to coordination optimization setting of other renewable energy control parameters.
Drawings
FIG. 1 is a process flow diagram;
FIG. 2 is a diagram of a test system;
FIG. 3 is a schematic diagram of a method convergence process;
FIG. 4 is a graph of simulation results for the system frequency of case 1;
FIG. 5 is a graph of simulation results for the system frequency of case 2;
FIG. 6 is a graph of simulation results for the system frequency of case 3;
FIG. 7 is a diagram of the simulation results of the system power angle of case 1;
fig. 8 is a diagram of the simulation result of the system power angle of case 2;
fig. 9 is a diagram showing the simulation result of the system power angle of case 3.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1 to 3, the wind power control parameter coordination setting method considering virtual inertia mainly includes the following steps:
1) setting control parameters to be optimized
Figure BDA0002083425890000091
And a power system sensitivity threshold. j is 1, 2. And n is the number of control parameters. k is initially 0.
The control parameters mainly comprise a virtual inertia control parameter Hω、
Virtual inertia control parameter HωThe following constraints are satisfied:
Figure BDA0002083425890000092
virtual inertia control parameter HωHas an initial value of Hωmin。
Alternative H in this exampleωLower limit of as being virtualThe initial values of the control parameters are to be managed to ensure the frequency stability. The initial values of the other control parameters are set by the constraint equation (24).
2) And establishing a wind power generation control parameter coordination optimization model.
Further, the main steps of establishing the wind power generation control parameter coordination optimization model are as follows:
2.1) establishing a small signal analysis model of the power system, which is respectively shown as a formula 1 to a formula 3:
Figure BDA0002083425890000093
in the formula, x is a set of power system state variables. u is the power system voltage and phase angle set. f is a time derivative function.
Figure BDA0002083425890000094
Is the derivative of x over time.
0＝g(x,u)。 (3)
In the formula, g is a power flow balance equation of the power system.
Figure BDA0002083425890000095
In the formula, AsIs a state equation of the power system. And deltax is the power system state variable deviation.
Wherein the system state equation AsAs follows:
As＝(A-BD-1C)。 (5)
in the formula, A, B, C, D is a parameter equation.
Parametric equation A, B, C, D is shown in equations 5 through 8, respectively:
Figure BDA0002083425890000101
in the formula, x0Is the system state equilibrium point.
Figure BDA0002083425890000102
Figure BDA0002083425890000103
Figure BDA0002083425890000104
2.2) State matrix A for Power SystemsSolving is carried out to obtain a state matrix A of the power systemsCharacteristic root λ ofi＝σi±jwi. i is 1,2, …, l. l is the power system state matrix AsAnd the total number of the feature roots is the total number of the working modes of the power system. SigmaiIs a characteristic root λiThe real part of (a). jwiIs a characteristic root λiThe imaginary part of (1).
Based on the characteristic root λiAnd calculating to obtain the damping ratio Zeta of the power systemiNamely:
Figure BDA0002083425890000105
according to the small signal analysis principle, when the real part of the characteristic root is smaller than 0, the system is stable, the real part of the characteristic root is far away from the virtual axis, and the virtual part is close to the real axis, so that the small interference stability of the system is facilitated. Therefore, when damping ratio ζiThe greater the damping ratio, the more stable the system is at > 0.
Damping ratio ζ of power systemiThe constraints of (2) are as follows:
ζi＞0。 (11)
2.3) writing the state variables x associated with the conventional synchronous generator into the set of state variables xSGIn (1), writing a state variable x related to wind power generation into a state variable set xWThus, equation 4 is blocked as follows:
Figure BDA0002083425890000111
in the formula, equation of state As22The elements in (1) comprise wind power control parameters.
The relational expression of the wind power control parameter p and the system damping ratio zeta (p) can be obtained through the formula 12, so that the objective function and the constraint related to the small signal stability are obtained. It should be noted that the relationship is a multidimensional complex non-analytic equation.
The dynamic equation of the invention considers the virtual inertia control, the rotor converter control and the phase-locked loop control of the wind power system, but the invention is not limited to the above control, and the specific dynamic equation can be set according to the requirement. The synchronous machine dynamic equations employ a conventional fourth order model. Thus, the set of state variables xSGAs follows:
xSG＝[,ω,Eq′,E′d]T。 (13)
in the formula, the power angle is set for the synchronous generator. ω is the set of synchronous generator speeds. E'q、E′dIs the set of quadrature and direct electromotive forces of the synchronous generator.
Set of state variables xWAs follows:
Figure BDA0002083425890000112
in the formula, x1、x2、x3、x4The intermediate variable is controlled by the rotor converter of the wind power system. i.e. idr、iqrThe rotor current of the wind power system.
Figure BDA0002083425890000113
The stator flux linkage is a stator flux linkage of a wind power system. x is the number ofPLL、PLLIntermediate variables are controlled for the phase locked loop. x is the number ofωIntermediate variables are controlled for virtual inertia. OmegarThe r-th synchronous generator speed.
The wind power control parameter equation p is as follows:
p＝[Hω,KP_PLL,KI_PLL,KP1,KI1,KP2,KI2,KP3,KI3,KP4,KI4]T。 (15)
in the formula, HωAnd controlling parameters for the virtual inertia. KP_PLL、KI_PLLThe parameters are controlled for the phase locked loop. KP1、KI1、KP2、KI2、KP3、KI3、KP4、KI4And controlling parameters for the rotor converter.
2.4) establishing a frequency constraint based on the power system inertia, as shown in equations 16 to 19:
the deviation Δ f of the frequency is as follows:
the frequency index related to inertia has mainly frequency change rate
Figure BDA0002083425890000121
And the deviation of the frequency, Δ f, which can be expressed in relation to the system inertia as:
Figure BDA0002083425890000122
in the formula,. DELTA.PdIs the amount of interference of the system. DLIs the load damping coefficient. t is the interference duration. HtotalIs the total inertia of the system.
Wherein the deviation Δ f of the frequency satisfies the following constraint condition:
-Δfmax≤Δf(p)≤Δfmax(17)
in the formula,. DELTA.fmaxThe maximum deviation in frequency.
Rate of change of frequency
Figure BDA0002083425890000123
As follows:
Figure BDA0002083425890000124
wherein the rate of change of frequency
Figure BDA0002083425890000125
Satisfies the following constraint stripsA piece:
Figure BDA0002083425890000126
in the formula (I), the compound is shown in the specification,
Figure BDA0002083425890000127
is the maximum rate of change of frequency.
Wherein, the total inertia of the system is HtotalAs follows:
Figure BDA0002083425890000131
in the formula, HSGIs the synchronous machine inertia time constant. HωIs an inertia time constant simulated by wind power virtual inertia control parameters. SSGIs the capacity of the synchronous machine generator. N is a radical ofSGIs the total number of generators of the synchronous machine. SWIs the capacity of wind power α is any synchronous machine generator.
Order to
Figure BDA0002083425890000132
Then equations 17 and 19 are unified as follows:
-b≤h(p)≤b (21)
in the formula (II)
Figure BDA0002083425890000133
When the temperature of the water is higher than the set temperature,
Figure BDA0002083425890000134
when h (p) ═ Δ f, b ═ Δ fmax。
2.5) for small signal stability, the basic requirement for small signal stability is that all damping ratios of the system should be greater than zero. Furthermore, the damping ratio should be as large as possible in case other stability requirements are met, in order to improve the stability of the small signal. However, it is almost impossible to increase all damping ratios simultaneously. Therefore, the present invention selects the damping ratio of the inter-region oscillation mode as the maximization target. Of course, the damping ratio of other modes may be set as the objective function, as desired.
For frequency stability, the frequency constraint of inertia should be satisfied. Finally, in order to ensure the stable operation of the wind generating set, the constraints of each control parameter should be considered.
In order to coordinate the stability requirements, the wind power generation control parameter coordination optimization model is as follows:
maxζ(p)inter-area。 (22)
in the formula, max represents a maximum value. Zeta (p)inter-areaThe inter-area oscillation damping ratio.
The constraints of the wind power generation control parameter coordination optimization model are respectively shown in formulas 23 to 25, namely:
s.t.ζ(p)＞0。 (23)
pmin≤p≤pmax。 (24)
-b≤h(p)≤b。 (25)
where ζ (p) is a set of system damping ratios. p is a set of control parameters. p is a radical ofminAnd pmaxLower and upper limits for the control parameter. h (p) is a frequency function related to the virtual inertia control parameter. b is a frequency stability index.
3) In the control parameter coordination optimization model, the relation between the damping ratio and the control parameter is difficult to analyze and express, so that the coordination optimization model is difficult to solve, and the sensitivity can intuitively express the relation between the damping ratio and the control parameter. Therefore, the invention introduces sensitivity to solve the control parameter coordination optimization model.
Solving the wind power generation control parameter coordination optimization model by using an optimization algorithm so as to obtain the damping ratio zeta of the power systemiAnd the control parameter updated at the k time
Figure BDA0002083425890000141
Sensitivity I between3。k＝0，1，2，3…。
Further, the main steps of solving the wind power generation control parameter coordination optimization model by using the optimization algorithm are as follows:
3.1) calculating the System State equation AsAnd a control parameter pjSensitivity I between1Namely:
Figure BDA0002083425890000142
3.2) Power System eigenvalue λ based on equation 26iAnd a control parameter pjSensitivity I between2The equation of the relationship (A) is as follows:
Figure BDA0002083425890000143
in the formula, viIs the left eigenvector u of the i-th working mode of the power systemiIs the right eigenvector of the i-th class of operating mode of the power system.
3.3) carrying out derivation on the formula 12, and calculating to obtain the damping ratio zeta of the power systemiAnd a control parameter pjSensitivity I between3Namely:
Figure BDA0002083425890000151
in the formula, Re (×) represents a real part. Im (×) denotes the imaginary part.
This sensitivity may represent the contribution of the control parameter to the optimization objective, equation 22. Since the sensitivity reflects the gradient of the optimization problem, the present invention uses the sensitivity calculated by equation 28 to set the search direction.
4) Comparative sensitivity I3And a sensitivity threshold, if I3<Then delete the current control parameter
Figure BDA0002083425890000152
And proceeds to step 5. If I3If not, the current control parameter is reserved
Figure BDA0002083425890000153
And proceeds to step 5.
5) Determining the control parameter of the kth update
Figure BDA0002083425890000154
Whether or not the corresponding inter-region oscillation damping ratio is satisfied
Figure BDA0002083425890000155
If not, let k equal to k +1 and use the last reserved control parameter
Figure BDA0002083425890000161
Sensitivity of (1)3Updating control parameters to optimize direction
Figure BDA0002083425890000162
And returns to step 3. If yes, the iteration is terminated, and all control parameters are output. y is less than or equal to k.
Further, the control parameter of the k-th update
Figure BDA0002083425890000163
As follows:
Figure BDA0002083425890000164
in the formula, mujIs the step size.
Example 2:
the wind power control parameter coordination setting method considering the virtual inertia mainly comprises the following steps:
1) setting control parameters to be optimized
Figure BDA0002083425890000165
And a power system sensitivity threshold. j is 1, 2. And n is the number of control parameters. k is initially 0.
2) Establishing a wind power generation control parameter coordination optimization model, and calculating to obtain an inter-area oscillation damping ratio
Figure BDA0002083425890000166
3) Solving the wind power generation control parameter coordination optimization model by using an optimization algorithm so as to obtain the damping ratio zeta of the power systemiAnd a firstk updated control parameters
Figure BDA0002083425890000167
Sensitivity I between3。k＝0，1，2，3…。
4) Comparative sensitivity I3And a sensitivity threshold, if I3<Then delete the current control parameter
Figure BDA0002083425890000168
And proceeds to step 5. If I3If not, the current control parameter is reserved
Figure BDA0002083425890000169
And proceeds to step 5.
5) Determining the control parameter of the kth update
Figure BDA00020834258900001610
Whether or not the corresponding inter-region oscillation damping ratio is satisfied
Figure BDA00020834258900001611
If not, let k equal to k +1 and use the last reserved control parameter
Figure BDA00020834258900001612
Sensitivity of (1)3Updating control parameters to optimize direction
Figure BDA00020834258900001613
And returns to step 2. If yes, the iteration is terminated, and all control parameters are output. y is less than or equal to k.
Example 3:
the wind power control parameter coordination setting method considering the virtual inertia mainly comprises the following steps of embodiment 2, wherein the wind power generation control parameter coordination optimization model is established mainly by the following steps:
1) establishing a small signal analysis model of the power system, which is respectively shown as a formula 1 to a formula 3:
Figure BDA0002083425890000171
in the formula, x is a state variable set of the power system, mainly including the rotation speed, the power angle and the like of the synchronous generator, and is specifically determined by variables required by a small signal analysis model of the power system. u is the power system voltage and phase angle set. f is a time derivative function, i.e. a differential function.
Figure BDA0002083425890000172
Is the derivative, i.e., the differential, of x with respect to time.
0＝g(x,u)。 (2)
In the formula, g is a power flow balance equation of the power system.
Figure BDA0002083425890000173
In the formula, AsIs a state equation of the power system. And deltax is the power system state variable deviation.
Wherein the system state equation AsAs follows:
As＝(A-BD-1C)。 (4)
in the formula, A, B, C, D is a parameter equation.
Parametric equation A, B, C, D is shown in equations 5 through 8, respectively:
Figure BDA0002083425890000174
in the formula, x0Is the system state equilibrium point.
Figure BDA0002083425890000175
Figure BDA0002083425890000176
Figure BDA0002083425890000181
2) To the power system state matrix AsSolving is carried out to obtain a state matrix A of the power systemsCharacteristic root λ ofi＝σi±jwi. i is 1,2, …, l. l is the power system state matrix AsAnd the total number of the feature roots is the total number of the working modes of the power system.
Based on the characteristic root λiAnd calculating to obtain the damping ratio Zeta of the power systemiNamely:
Figure BDA0002083425890000182
damping ratio ζ of power systemiThe constraints of (2) are as follows:
ζi＞0。 (10)
3) writing state variables x associated with a conventional synchronous generator into a set of state variables xSGIn (1), writing a state variable x related to wind power generation into a state variable set xWThus, equation 4 is blocked as follows:
Figure BDA0002083425890000183
in the formula, equation of state As22The elements in (1) comprise wind power control parameters.
Wherein, the state variable set xSGAs follows:
xSG＝[,ω,Eq′,E′d]T。 (12)
in the formula, the power angle is set for the synchronous generator. ω is the set of synchronous generator speeds. E'q、E′dIs the set of quadrature and direct electromotive forces of the synchronous generator.
Set of state variables xWAs follows:
Figure BDA0002083425890000184
in the formula, x1、x2、x3、x4Is windIntermediate variable of the rotor converter control of the electrical system.
idr、iqrThe rotor current of the wind power system.
Figure BDA0002083425890000185
The stator flux linkage is a stator flux linkage of a wind power system. x is the number ofPLL、PLLIntermediate variables are controlled for the phase locked loop. x is the number ofωIntermediate variables are controlled for virtual inertia.
The wind power control parameter equation P is as follows:
p＝[Hω,KP_PLL,KI_PLL,KP1,KI1,KP2,KI2,KP3,KI3,KP4,KI4]T。 (14)
in the formula, HωAnd controlling parameters for the virtual inertia. KP_PLL、KI_PLLThe parameters are controlled for the phase locked loop. KP1、KI1、KP2、KI2、KP3、KI3、KP4、KI4And controlling parameters for the rotor converter.
4) Based on the inertia of the power system, frequency constraints are established, as shown in equations 16 to 19:
the deviation Δ f of the frequency is as follows:
the frequency index related to inertia has mainly frequency change rate
Figure BDA0002083425890000191
And the deviation of the frequency, Δ f, which can be expressed in relation to the system inertia as:
Figure BDA0002083425890000192
in the formula,. DELTA.PdIs the amount of interference of the system. DLIs the load damping coefficient. t is the interference duration. HtotalIs the total inertia of the system.
Wherein the deviation Δ f of the frequency satisfies the following constraint condition:
-Δfmax≤Δf(p)≤Δfmax(16)
in the formula,. DELTA.fmaxThe maximum deviation in frequency.
Rate of change of frequency
Figure BDA0002083425890000193
As follows:
Figure BDA0002083425890000194
wherein the rate of change of frequency
Figure BDA0002083425890000195
The following constraints are satisfied:
Figure BDA0002083425890000196
in the formula (I), the compound is shown in the specification,
Figure BDA0002083425890000197
is the maximum rate of change of frequency.
Wherein, the total inertia of the system is HtotalAs follows:
Figure BDA0002083425890000201
in the formula, HSGIs the synchronous machine inertia time constant. HωIs an inertia time constant simulated by wind power virtual inertia control parameters. SSGIs the capacity of the synchronous machine generator. N is a radical ofSGIs the number of synchronous machine generators. SWIs the capacity of wind power.
Order to
Figure BDA0002083425890000202
Then equations 17 and 19 are unified as follows:
-b≤h(p)≤b (20)
in the formula (II)
Figure BDA0002083425890000203
When the temperature of the water is higher than the set temperature,
Figure BDA0002083425890000204
when h (p) ═ Δ f, b ═ Δ fmax。
5) Based on the steps 2.1 to 2.4, the wind power generation control parameter coordination optimization model is as follows:
maxζ(p)inter-area。 (21)
in the formula, max represents a maximum value. Zeta (p)inter-areaThe inter-area oscillation damping ratio.
The constraints of the wind power generation control parameter coordination optimization model are respectively shown in formulas 23 to 25, namely:
s.t.ζ(p)＞0。 (22)
pmin≤p≤pmax。 (23)
-b≤h(p)≤b。 (24)
where ζ (p) is a set of system damping ratios. p is a set of control parameters. p is a radical ofminAnd pmaxLower and upper limits for the control parameter. h (p) is a frequency function related to the virtual inertia control parameter. b is a frequency stability index.
Example 4:
the wind power control parameter coordination setting method considering the virtual inertia mainly comprises the following steps of the embodiment 2, wherein the main steps of solving the wind power generation control parameter coordination optimization model by using an optimization algorithm are as follows:
1) computing system equation of state AsAnd a control parameter pjSensitivity I between1Namely:
Figure BDA0002083425890000211
2) based on equation 26, the characteristic value λ of the power systemiAnd a control parameter pjSensitivity I between2The equation of the relationship (A) is as follows:
Figure BDA0002083425890000212
in the formula, viIs the left eigenvector u of the i-th working mode of the power systemiIs the right eigenvector of the i-th class of operating mode of the power system.
3) The equation 12 is derived, and the damping ratio zeta of the power system is obtained through calculationiAnd control parameters
pjSensitivity I between3Namely:
Figure BDA0002083425890000221
in the formula, Re (×) represents a real part. Im (×) denotes the imaginary part.
Example 5:
the experiment for verifying the wind power control parameter coordination setting method considering the virtual inertia mainly comprises the following steps:
1) a test system as shown in fig. 2, i.e., an IEEE4 machine 2 regional system joining a wind farm, was constructed. The system information is as follows:
a synchronous generator: gen1-Gen4 is a synchronous generator, wherein Gen3 is a balancing machine, the specific data are shown in Table 1, the line length is shown in FIG. 2, and other parameters are IEEE4 machine 2 region system standard calculation parameters.
TABLE 1 synchronous Generator related parameters
Synchronous generator Capacity (MVA) Generator output (MW) Time constant of inertia(s)
Gen1,Gen2 500 350 2.5
Gen3,Gen4 900 700 4.175
Wind power plant: a wind power plant with 200 fans is connected to the position of the No. 6 bus, the stator side capacity of each fan is 3.6MW, and the total generated energy of the wind power plant is 720 MW. The parameters of the wind turbine generator set are as follows: stator resistance Rs0.00449 p.u.; stator leakage reactance Ls0.09241 p.u.; rotor resistance Rr0.00549 p.u.; rotor leakage reactance Lr0.09955 p.u.; mutual inductance Lm3.95279 p.u.; fan inertia time constant Hr4.02 p.u.; virtual inertia control time constant Tω＝0.01p.u.。
Controlling parameters: the initial values and the feasible ranges (which can be obtained through experiments or experience) of the wind power control parameters are shown in table 2. To simplify the calculation, assume KP1＝KP3，TP1＝TP3，KP2＝KP4，TP2＝TP4。
TABLE 2 initial values and feasible ranges of wind power related control parameters
Control parameter KP1,KP3 TP1,TP3 KP2,KP4
Initial value 4 0.1 0.0496
Feasible range [0.01,15] [0.001,1] [0.01,5]
Control parameter TP2,TP4 KP_PLL KI_PLL
Initial value 0.0128 73.6 333.3
Feasible range [0.001,1] [0,100] [0,500]
The disturbance of the system is set to 60MW loss load, the maximum frequency change rate is set to 0.5Hz/s, the maximum frequency deviation is set to 0.15Hz, and the standard frequency is 50 Hz. Therefore, the temperature of the molten metal is controlled,
Figure BDA0002083425890000231
Δfmax＝0.003p.u.。
the constraints for ensuring frequency stability are as follows:
Hω≥max{0.2397,0.0264}＝0.2397 (1)
thus, HωIs set to 0.2397p.u.
The method is simulated by the Digsilent/Power Factory. Three cases were tested:
case 1: test system without consideration of virtual inertia control
Case 2: test system considering virtual inertia control before optimization
Case 3: the optimized test system considering the virtual inertia control
2) Algorithm performance testing
Damping ratio ζ (p)inter-areaInitial sensitivity to control parameters is shown in Table 3, KP_PLLAnd KI_PLLIs less than the threshold value of 1e-4Therefore, they are considered to be related to the objective function ζ (p)inter-areaThe relationship is weak, and the method is eliminated in the optimization process. When the sensitivity of other control parameters is lower than 1e-4In time, the corresponding control parameters are also rejected.
TABLE 3 initial sensitivity of wind-power related control parameters
Control parameter KP1,KP3 TP1,TP3 KP2,KP4 TP2,TP4
Initial sensitivity -0.00036 0.01213 0.004842 -0.01877
Control parameter KP_PLL KI_PLL Kω
Initial sensitivity 1e-08 1e-09 -0.00339
The convergence process is shown in fig. 3. It can be seen that the algorithm proposed by the present invention can converge in 27 iterations. The algorithm is proved to have good convergence.
(3) Optimizing results
The optimized wind power control parameters are shown in table 4.
TABLE 4 optimization results of wind power related control parameters
Control parameter KP1,KP3 TP1,TP3 KP2,KP4 TP2,TP4
Optimizing results 0.1528 0.2718 0.1161 0.0016
Control parameter KP_PLL KI_PLL Kω
Optimizing results 73.6 333.3 3.0124
The system frequency simulation results of cases 1-3 are shown in fig. 4-6, respectively. It can be seen that virtual inertia control is beneficial for frequency stabilization. Case 1 has a maximum frequency deviation exceeding 0.15Hz, which threatens the safe operation of the power system. After adding the virtual inertia control, the maximum frequency deviation of case 2 and case 3 is reduced to be within 0.15 Hz. In fig. 4 to 6, the dotted line indicates the frequency constraint under general conditions, and the ordinate is the system frequency and the abscissa is the number of iterations.
The damping ratio of the interval oscillation modes of cases 1 to 3 is shown in table 5, the simulation results of the system power angle are shown in fig. 7 to 9, the ordinate is the system power angle, and the abscissa is the iteration number, in this embodiment, the virtual inertia control improves the frequency stability of the system, but reduces the small signal stability of the system. Compared with case 1 and case 2, after the virtual inertia control is added, the damping ratio of the inter-area oscillation mode is reduced to-0.0037 from 0.1057, so that the power angle oscillation is diverged. After the wind power parameters are optimized by the method of the invention, case 3 obviously improves the small signal stability of the test system. The damping ratio of the inter-region oscillation mode increases to 0.0549 and the power angle oscillation converges within 15 seconds.
TABLE 5 results of optimization of inter-region oscillation modes
Test case Case 1 Case 2 Case 3
ζ(p)inter-area 0.1057 -0.0369 0.0549
From the experimental results, it can be seen that: the wind power control parameter coordination setting method considering the virtual inertia can meet the requirement of system frequency stability, improve the small interference stability of the system and have good convergence.
In summary, the invention provides a wind power control parameter coordination setting method. A control parameter coordination optimization model which simultaneously meets the requirements of frequency stability and small signal stability is established, and the stability of small signals is improved by taking the damping ratio of the inter-region oscillation mode as an objective function. Meanwhile, the frequency stability of the system is ensured by considering the inertia frequency constraint. To solve the optimization model, sensitivity analysis is introduced into the optimization algorithm so that the proposed model can be solved. Example research shows that the method can effectively improve the stability of different dimensions of the system only by optimizing the control parameters of the wind power, and can provide guidance for setting the control parameters of the renewable energy source considering the operation of the power system.
Example 6:
the wind power control parameter coordination setting method considering the virtual inertia mainly comprises the following steps: firstly, a wind power generation control parameter coordination optimization model is established. On the basis of small signal analysis, the damping ratio of the inter-area oscillation mode is used as an objective function to reflect the global stability of the power grid. The damping ratio of the other modes acts as a constraint to ensure the stability of each element of the system. And sets constraints of individual control parameters according to the stable operation of the wind power generation. Meanwhile, a group of virtual inertia parameter constraints related to frequency are deduced through frequency stability indexes (such as frequency change rate, frequency offset and the like), so that the requirement of frequency stability is met. And then, an optimization algorithm is provided to solve the optimization model. And analyzing the sensitivity between the inter-region oscillation mode damping ratio and the wind power generation control parameter to obtain the relation between the small signal stability and the control parameter, so as to eliminate the parameter irrelevant to the objective function and determine the optimization direction of the optimization algorithm. The coordinated optimization model can be converged accurately and rapidly by introducing sensitivity, and the efficiency and effectiveness of the algorithm are ensured. Finally, the simulation is carried out on the IEEE four-machine two-region system under the environment of the Digsilent/Power Factory software, and the fact that the method can effectively guide the setting of the wind Power control parameters is proved, the frequency stability is guaranteed, and meanwhile the small signal stability is improved.

Claims (5)

1. The wind power control parameter coordination setting method considering the virtual inertia is characterized by mainly comprising the following steps of:
1) setting control parameters to be optimized
Figure FDA0002560126750000011
And a power system sensitivity threshold; j ═ 1,2,. n; n is the number of control parameters; k is initially 0;
2) establishing a wind power generation control parameter coordination optimization model, and calculating to obtain an inter-area oscillation damping ratio
Figure FDA0002560126750000012
3) Solving the wind power generation control parameter coordination optimization model by using an optimization algorithm so as to obtain the damping ratio zeta of the power systemiAnd the control parameter updated at the k time
Figure FDA0002560126750000013
Sensitivity I between3；k＝0，1，2，3…；
4) Comparative sensitivity I3And a sensitivity threshold, if I3<Then delete the current control parameter
Figure FDA0002560126750000014
And go to step 5; if I3If not, the current control parameter is reserved
Figure FDA0002560126750000015
And go to step 5;
5) determining the control parameter of the kth update
Figure FDA0002560126750000016
Whether or not the corresponding inter-region oscillation damping ratio is satisfied
Figure FDA0002560126750000017
If not, let k equal to k +1 and use the last reserved control parameter
Figure FDA0002560126750000018
Sensitivity of (1)3Updating control parameters to optimize direction
Figure FDA0002560126750000019
And returning to the step 2; if yes, terminating iteration and outputting all control parameters; y is less than or equal to k.
2. The wind power control parameter coordination setting method considering the virtual inertia according to claim 1, characterized in that the main steps of establishing the wind power generation control parameter coordination optimization model are as follows:
2.1) establishing a small signal analysis model of the power system, which is respectively shown as a formula 1 to a formula 3:
Figure FDA00025601267500000110
in the formula, x is a state variable set of the power system and mainly comprises the rotating speed and the power angle of the synchronous generator; u is a set of power system voltages and phase angles; f is a time derivative function;
Figure FDA0002560126750000021
is the derivative of x over time;
0＝g(x,u)； (2)
in the formula, g is a power flow balance equation of the power system;
Figure FDA0002560126750000022
in the formula, AsIs a state equation of the power system; Δ x is the power system state variable deviation;
wherein the system state equation AsAs follows:
As＝(A-BD-1C)； (4)
in the formula, A, B, C, D is a parameter equation;
the parameter equation A, the parameter equation B, the parameter equation C and the parameter equation D are respectively shown in the formula 5 to the formula 8:
Figure FDA0002560126750000023
in the formula, x0Is the system state balance point;
Figure FDA0002560126750000024
Figure FDA0002560126750000025
Figure FDA0002560126750000026
2.2) equation of state A for electric Power SystemsSolving to obtain a state equation A of the power systemsCharacteristic root λ ofi＝σi±jwi(ii) a 1,2, …, l; l is the state equation A of the power systemsThe total number of the feature roots, namely the total number of the working modes of the power system; sigmaiIs a characteristic root λiThe real part expression form of (a); jwiIs a characteristic root λiThe imaginary representation form of (a);
based on the characteristic root λiAnd calculating to obtain the damping ratio Zeta of the power systemiNamely:
Figure FDA0002560126750000031
damping ratio ζ of power systemiThe constraints of (2) are as follows:
ζi＞0； (10)
2.3) writing the state variables x associated with the conventional synchronous generator into the set of state variables xSGIn (1), writing a state variable x related to wind power generation into a state variable set xWThus, equation 3 is blocked as follows:
Figure FDA0002560126750000032
in the formula, equation of state As22The elements in (1) comprise wind power control parameters;
wherein, the state variable set xSGAs follows:
xSG＝[,ω,E′q,E′d]T； (12)
in the formula, the power angle is a synchronous generator power angle set; omega is the set of synchronous generator rotational speeds; e'q、E′dThe method comprises the steps of (1) collecting quadrature-axis electromotive force and direct-axis electromotive force of a synchronous generator;
set of state variables xWAs follows:
Figure FDA0002560126750000033
in the formula, x1、x2、x3、x4The intermediate variable is controlled by a rotor converter of the wind power system; i.e. idr、iqrThe rotor current of the wind power system;
Figure FDA0002560126750000034
a stator flux linkage of a wind power system; x is the number ofPLL、PLLControlling an intermediate variable for the phase locked loop; x is the number ofωControlling an intermediate variable for the virtual inertia; omegarThe r-th synchronous generator speed;
the wind power control parameter equation P is as follows:
p＝[Hω,KP_PLL,KI_PLL,KP1,KI1,KP2,KI2,KP3,KI3,KP4,KI4]T； (14)
in the formula, HωControlling parameters for the virtual inertia; kP_PLL、KI_PLLControlling parameters for the phase-locked loop; kP1、KI1、KP2、KI2、KP3、KI3、KP4、KI4Control parameters for the rotor converter;
2.4) establishing frequency constraints based on the inertia of the power system, as shown in equations 15 to 18:
the deviation Δ f of the frequency is as follows:
Figure FDA0002560126750000041
in the formula,. DELTA.PdIs the amount of interference of the system; dLIs the load damping coefficient; t is the interference duration; htotalIs the total inertia of the system;
wherein the deviation Δ f of the frequency satisfies the following constraint condition:
-Δfmax≤Δf(p)≤Δfmax(16)
in the formula,. DELTA.fmaxIs the maximum deviation of the frequency;
rate of change of frequency
Figure FDA0002560126750000042
As follows:
Figure FDA0002560126750000043
wherein the rate of change of frequency
Figure FDA0002560126750000044
The following constraints are satisfied:
Figure FDA0002560126750000045
in the formula (I), the compound is shown in the specification,
Figure FDA0002560126750000046
is the maximum rate of change of frequency;
wherein, the total inertia of the system is HtotalAs follows:
Figure FDA0002560126750000047
in the formula, HSGIs the synchronous machine inertia time constant; hωIs caused by windInertia time constants of electric virtual inertia control parameter simulation; sSGIs the capacity of the synchronous machine generator; n is a radical ofSGIs the total number of synchronous machine generators; sWα is any synchronous generator;
order to
Figure FDA0002560126750000051
Δ f, then equations 16 and 18 are unified as follows:
-b≤h(p)≤b (20)
in the formula (II)
Figure FDA0002560126750000052
When the temperature of the water is higher than the set temperature,
Figure FDA0002560126750000053
when h (p) ═ Δ f, b ═ Δ fmax；
2.5) based on the steps 2.1 to 2.4, the wind power generation control parameter coordination optimization model is as follows:
max ζ(p)inter-area； (21)
in the formula, max represents a maximum value; zeta (p)inter-areaThe inter-area oscillation damping ratio;
the constraints of the wind power generation control parameter coordination optimization model are respectively shown in formulas 22 to 24, namely:
s.t.ζ(p)＞0； (22)
pmin≤p≤pmax； (23)
-b≤h(p)≤b； (24)
where ζ (p) is the system damping ratio set; p is a control parameter set; p is a radical ofminAnd pmaxLower and upper limits for the control parameter; h (p) is a frequency function related to a virtual inertia control parameter; b is a frequency stability index.
3. The wind power control parameter coordination setting method considering the virtual inertia according to claim 1, wherein the main steps of solving the wind power generation control parameter coordination optimization model by using an optimization algorithm are as follows:
1) computing system equation of state AsAnd a control parameter pjSensitivity I between1Namely:
Figure FDA0002560126750000061
2) based on equation 25, the characteristic value λ of the power systemiAnd a control parameter pjSensitivity I between2The equation of the relationship (A) is as follows:
Figure FDA0002560126750000062
in the formula, viIs the left eigenvector u of the i-th working mode of the power systemiIs the right eigenvector of the i-th class working mode of the power system;
3) the equation 11 is derived, and the damping ratio zeta of the power system is obtained through calculationiAnd a control parameter pjSensitivity I between3Namely:
Figure FDA0002560126750000063
in the formula, Re (×) represents a real part; im (×) denotes the imaginary part.
4. The wind power control parameter coordination setting method considering virtual inertia according to claim 1, characterized in that control parameters
Figure FDA0002560126750000064
As follows:
Figure FDA0002560126750000071
in the formula, mujIs the step size.
5. The test of claim 1The wind power control parameter coordination setting method considering the virtual inertia is characterized in that the control parameters mainly comprise a virtual inertia control parameter Hω；
Virtual inertia control parameter HωThe following constraints are satisfied:
Figure FDA0002560126750000072
virtual inertia control parameter HωHas an initial value of Hωmin；ΔPdIs the amount of interference of the system; dLIs the load damping coefficient; t is the interference duration; hSGIs the synchronous machine inertia time constant; sSGIs the capacity of the synchronous machine generator; n is a radical ofSGIs the total number of the generators of the synchronous machine, α is the generator of any synchronous machine, deltafmaxIs the maximum deviation of the frequency;
Figure FDA0002560126750000073
is the maximum rate of change of frequency.
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