CN117375018A - Online assessment method and assessment terminal for wind farm stability region and stability margin - Google Patents
Online assessment method and assessment terminal for wind farm stability region and stability margin Download PDFInfo
- Publication number
- CN117375018A CN117375018A CN202311225675.1A CN202311225675A CN117375018A CN 117375018 A CN117375018 A CN 117375018A CN 202311225675 A CN202311225675 A CN 202311225675A CN 117375018 A CN117375018 A CN 117375018A
- Authority
- CN
- China
- Prior art keywords
- stability
- impedance
- wind
- parameter
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims description 61
- 230000010355 oscillation Effects 0.000 claims abstract description 51
- 230000006870 function Effects 0.000 claims abstract description 46
- 238000011156 evaluation Methods 0.000 claims abstract description 33
- 230000014509 gene expression Effects 0.000 claims abstract description 15
- 238000004458 analytical method Methods 0.000 claims description 29
- 239000011159 matrix material Substances 0.000 claims description 17
- 238000013507 mapping Methods 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 7
- 238000010801 machine learning Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 2
- URWAJWIAIPFPJE-YFMIWBNJSA-N sisomycin Chemical compound O1C[C@@](O)(C)[C@H](NC)[C@@H](O)[C@H]1O[C@@H]1[C@@H](O)[C@H](O[C@@H]2[C@@H](CC=C(CN)O2)N)[C@@H](N)C[C@H]1N URWAJWIAIPFPJE-YFMIWBNJSA-N 0.000 claims description 2
- 238000004088 simulation Methods 0.000 abstract description 8
- 238000010248 power generation Methods 0.000 abstract description 5
- 238000012360 testing method Methods 0.000 abstract description 5
- 238000013097 stability assessment Methods 0.000 description 16
- 238000010586 diagram Methods 0.000 description 10
- 230000008878 coupling Effects 0.000 description 6
- 238000010168 coupling process Methods 0.000 description 6
- 238000005859 coupling reaction Methods 0.000 description 6
- 230000003993 interaction Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000006698 induction Effects 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 238000013016 damping Methods 0.000 description 2
- 230000001965 increasing effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012897 Levenberg–Marquardt algorithm Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000035899 viability Effects 0.000 description 1
Abstract
The invention provides a wind power plant stability area and stability margin online evaluation method and an evaluation terminal, which belong to the technical field of wind power generation, and are used for constructing an impedance model of a wind power generator and a wind power plant and performing stability evaluation based on a generalized Nyquist criterion; establishing a digital twin system for stability evaluation, and estimating impedance; defining a multi-parameter stable region and a region boundary according to a generalized Nyquist criterion, defining a minimum characteristic track as an index of a plurality of parameter functions to evaluate relative stability, and analyzing physical limits of the plurality of parameters based on the main oscillation frequency of the wind power plant as a function of the plurality of parameters; and analyzing and evaluating the stability performance of the physical wind power plant by using a support vector regression expression with a kernel function. The invention can realize real-time division of the stable region and enhancement of the stability margin, and test the effectiveness of the stability index passing through the digital twin system in a numerical simulation and experimental mode.
Description
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to an online assessment method and an assessment terminal for a wind power plant stability region and stability margin.
Background
In order to achieve stable operation of wind power generation, analysis of relevant parameters of the wind power generation process is required, such as impedance-based analysis has effectively evaluated the stability of small signals. While the stability performance of a wind power system depends on various parameters such as control parameters, circuit parameters and operating points.
In the prior art, the influence of control and circuit parameters on the stability performance of small signals was studied. Doubly fed induction generators connected to weak grids may lose stability due to the relatively large gain of the phase locked loop oscillation and current control loop. A higher level of series compensation in the transmission line will reduce the damping of the subsynchronous resonance. The control and circuit parameters described above are typically not adjustable during actual power system operation. In actual operation, the operating points are adjustable, such as active power, reactive power, and wind speed. The stability assessment should be repeated every time these multiple parameters of the operating point change, which can lead to heavy computational burden and difficulty in real-time application. Thus, the above-described methods are typically used for off-line analysis.
In the prior art, the stability performance varies with the change of the operating point over a wide range. To address stability issues at different operating points, an effective small signal stability assessment index is needed. The stable region boundaries at different operating points can be evaluated by a graphical estimation method. The graph estimation method includes a contra-discussion criterion and a Middlebrook criterion. The stable regions of these graphical estimates are typically too conservative. Excessive stability margins lead to a lack of economic viability. To accurately describe the stability boundary, a point-wise traversal method may be applied. The stable region boundary of the wind farm series compensation value is determined by a point-by-point calculation method. The calculation of the point-by-point traversal is complex and difficult to apply online. Moreover, the computational complexity of the traversal method increases exponentially with increasing total number of parameters, taking into account the stability boundaries in the high-dimensional parameter space. The method brings great inconvenience to stability analysis of the wind power plant, can not effectively analyze the stability area and stability margin of the wind power plant, and brings uncertainty to operation of the wind power plant.
Disclosure of Invention
The invention provides an online assessment method for a wind power plant stability region and stability margin, which can realize online assessment of the wind power plant stability region and stability margin, and can meet the requirement of online assessment by a digital twin system.
The method comprises the following steps:
step one: constructing an impedance model of the wind driven generator and an impedance model of the wind power plant as an analysis function of the operation working point;
step two: calculating the impedance of an operating working point based on the digital twin system;
step three: according to the generalized Nyquist criterion, evaluating the stability under different operation working points, and constructing a stability margin data set;
step four: constructing a stable domain boundary based on a support vector regression method;
step five: and according to the stability evaluation result, evaluating the stability area and the stability margin of the wind power plant on line.
The invention also provides an evaluation terminal which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the on-line evaluation method of the multi-parameter stability area and the stability margin of the wind power plant when executing the program.
From the above technical scheme, the invention has the following advantages:
The on-line assessment method for the multi-parameter stability region and the stability margin of the wind farm can assess the stability region and the margin which are a plurality of parameters (such as active power, reactive power and wind speed) based on a regression analysis model.
According to the stability evaluation method, the impedance data of the wind power plant are utilized, and the generalized Nyquist criterion is analyzed and reconstructed by using a support vector regression method based on the impedance data of different working points. The minimum characteristic track is defined as a single parameter index of relative stability, and a stable region boundary is established. The dominant oscillation frequencies of the stable and unstable regions are also formulated analytically. Analytical solutions produce a well-defined mapping between stability performance and operating point. The stability of the analysis and evaluation process is enhanced, and the real-time stability margin of the wind power plant can be improved by optimizing the operation working point of the wind power plant by means of the explicit mapping realized in the digital twin system.
The invention also provides impedance data of the wind power plant and effective interaction between the physical system and the virtual system by using the DT system, thereby providing opportunities for online application. The verification of the invention is respectively carried out on a single wind driven generator and a wind farm. Through numerical simulation and experiments, it is proved that the stable region can be accurately and effectively observed, and the stability margin is improved by adjusting the operation working point. The method and the device effectively solve the problems that the detailed parameters of the wind power plant WPP cannot be obtained and the on-line assessment of the wind power plant stability area and stability margin cannot be realized in the prior art.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for online assessment of a wind farm stability region and stability margin;
FIG. 2 is an exemplary diagram of a wind farm arrangement;
figure 3 is a schematic diagram of a WPP split into source load subsystems at the coupling point;
FIG. 4 is a diagram of an example of a digital twinning system applied to wind farm stability determination;
FIG. 5 is a schematic diagram of a positive sequence impedance network of a wind farm;
FIG. 6 is a diagram illustrating a minimum feature trajectory as a single parameter stability indicator;
FIG. 7 is a graph of stable region and minimum signature as a function of active power PWT, reactive power QWT, and wind speed Vw for a single DIFG;
FIG. 8 is a graph of dominant oscillation frequency of the analysis functions of active power PWT, reactive power QWT, and wind speed Vw for a single DIFG;
FIG. 9 is a graph of stable region and minimum feature trajectory of the analytical function of wind speed Vw and active power PWT;
FIG. 10 is a schematic diagram showing the analysis function of the main oscillation frequency as Vw, PWT;
FIG. 11 is a graph of stable region and minimum feature trajectories of analytical functions of active power PWT and reactive power QWT;
FIG. 12 is a graph showing dominant oscillation frequency as an analytical function of PWT, QWT;
figure 13 is a schematic diagram of the stability domain and minimum feature trajectories of the WPP active power function;
figure 14 is a dominant oscillation frequency diagram of WPP;
FIG. 15 is a schematic diagram of stability verification of a single DFIG system at a stability zone boundary;
FIG. 16 is a graph of time domain simulation results for different active power schedules for a single DFIG system;
figure 17 is a schematic diagram of stability performance verification of a stability region boundary in WPP;
fig. 18 is a measurement schematic of nyquist plot based on detailed impedance model in single DFIG system time domain simulation.
Detailed Description
The wind power plant stability area and stability margin online evaluation method provided by the invention can acquire and process the data related to the wind power plant based on the artificial intelligence technology. The wind farm stability area and stability margin online evaluation method utilizes a digital computer or a machine controlled by the digital computer to simulate, extend and expand human intelligence, sense environment, acquire knowledge and acquire a theory, a method, a technology and an application device of an optimal result by using the knowledge.
Of course, the wind farm online evaluation method also has a machine learning function, wherein the machine learning and the deep learning in the method generally comprise artificial neural network, confidence network, reinforcement learning, migration learning, induction learning, teaching learning and other technologies. And (3) an impedance model of the wind generating set and the wind farm is manufactured and used as an analysis function of the operation working point. By measuring the working point, the impedance of the wind power generator is established in the evaluation terminal and the impedance of the wind power plant is established. Then, based on the generalized Nyquist criterion, an effective index of the stable region and the dominant oscillation frequency is established as an analytical function of a plurality of variable parameters. And evaluating the influence of the wind power plant parameters on the stability performance by using support vector regression with a kernel function. The method further effectively solves the problem that the detailed parameters of the wind power plant WPP cannot be obtained in the prior art, and the on-line assessment of the wind power plant stability area and stability margin cannot be realized.
The wind farm of the present invention may also be described as WPP (wind power plant) and the wind generator as WTG. The english abbreviations mentioned above are expressed in the same meaning as the corresponding chinese language.
For the present invention, a Digital Twin (DT) system is used to provide technical support for online assessment of stability areas and margins. The DT system may provide, among other things, comprehensive field measurements of operational data. Impedance data for stability analysis may be established in the DT system. The stability assessment proposed by the present invention also makes use of impedance data in the DT system and provides an efficient interaction between the virtual system and the WPP. By means of this interaction, the policy provided by the DT virtual system may enhance the stability of WPP.
In the present invention, the stable region and oscillation frequency of WPP are also evaluated using impedance data of different operating points. The data driven stability assessment was performed on a Digital Twin (DT) system. In order to provide an effective small signal stability evaluation index, a Support Vector Regression (SVR) method is adopted to analyze and reconstruct a generalized Nyquist criterion based on an impedance model. And defining the minimum characteristic track as an analysis index of a stability margin of the small signal stability by analyzing the reconstruction criterion, and establishing a stability area boundary. The small signal stability of the WPP can be observed in real time through on-site measurement of active power, reactive power and wind speed.
The wind farm stability area and stability margin online assessment method can be applied to one or more assessment terminals, wherein the assessment terminals are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware comprises, but is not limited to, microprocessors, application-specific integrated circuits (SpecificIntegratedCircuit, ASIC), programmable gate arrays (Field-ProgrammableGate Array, FPGA), digital processors (DigitalSignalProcessor, DSP), embedded devices and the like.
The evaluation terminal may be any electronic product that can interact with a user in a human-computer manner, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PersonalDigitalAssistant, PDA), an interactive web TV (InternetProtocolTelevision, IPTV), etc. The network in which the evaluation terminal is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VirtualPrivateNetwork, VPN), and the like.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for online evaluation of a stability region and a stability margin of a wind turbine farm according to an embodiment is shown, where the method includes:
s101: constructing an impedance model of the wind driven generator and an impedance model of the wind power plant as an analysis function of the operation working point;
For the present embodiment, the configuration of the wind farm WPP is shown in fig. 2. Consisting of several branches. Each branch has several wind turbines WTG. The wind turbine WTG has a corresponding impedance modeling. On the basis of the impedance model, the generalized Nyquist criterion is used to evaluate the stability of the small signal.
According to an embodiment of the present application, modeling is performed for a wind turbine WTG impedance, wherein a small signal model of the WTG may be expressed in the form of a state space as
Wherein the method comprises the steps ofIs a +.>Matrix (S)>Is the number of state variables, matrix->Is->Matrix->Is-> Is an input matrix, and->i is the output matrix, WTG is calculated as follows:
where s is the Laplace operator and I is the identity matrix.
It is to be noted that, it is assumed that the output fatvs of the formula (1) is included in the state variable. Impedance model Zline of the line is thus formed
Where Rline and Rline are the resistance and inductance of the line and ωs is the angular speed of the ac power system.
In FIG. 3, the splitting of WPP into source load subsystems at coupling Point (PCC) is shown, and by knowing the impedance model of wind turbines and lines, the admittance model Y of WPP can be obtained WPP 。
Sequence admittance Y of wind farm WPP Is given by
Wherein the superscripts p and n represent positive and negative sequences.
For the positive sequence component of fp, there will be a coupled negative sequence component at fp-2fs, the sequence componentand/>Voltage of the parallel point>and/>Is the current injected into the wind farm as shown in fig. 2.
Wherein,is the grid voltage rotating the dq axis, but +.>Is the voltage at the PCC point.
Equation (6) is considered equivalent to the closed loop transfer function of a multi-variable system. Can be obtained by applying Generalized Nyquist Criterion (GNC) to the back-to-back matrixTo study small signal stability.
For this embodiment, stability evaluation is also performed on the system based on the generalized Nyquist criterion.
Specifically, kirchhoff's voltage law is applied to the source load subsystem in fig. 2. Grid-tied wind farm WPP may be described as
Wherein,the method comprises the steps of carrying out a first treatment on the surface of the The back ratio matrix->The eigenvalues of (2) are calculated as follows
Obtaining
The eigenvalue is obtained by the following method
According to the generalized Nyquist criterion, the eigenvalue λ 1,2 The trajectory relative to the critical point (-1, j 0) can be used to evaluate the stability of the small signal.
S102: calculating the impedance of an operating working point based on the digital twin system;
in one exemplary embodiment, the stability zone assessment depends on stability performance under different operating conditions. A large number of evaluation results are required to determine the stability boundary. Furthermore, the evaluation result in the unstable case is hardly available in the actual operating system. Machine learning methods are applied in this case, by training using a limited number of evaluation results, the stability performance of the operating point in the wind range can be predicted. With the development of DT technology, the system is a platform for data-driven stability assessment of WPP. And together with the machine learning algorithm, stability assessment is achieved by processing impedance data in the range of the operating point wind in the frequency domain.
According to an embodiment of the present application, a digital twin system for stability assessment is established, and in combination with fig. 4, the structure of the digital twin system for WPP stability assessment is established.
The DT system is composed of a physical system, an impedance system, a service system and DT data. The physical system refers to the actual WPP. It is assumed that a measurement device is equipped to collect operational data of the physical system.
In this embodiment, the impedance system is established by observing the operation data of the physical system. The stability area of the service system was analyzed based on the WPP impedance system. Furthermore, a stability enhancement policy is implemented in the service system.
Through interactions between the service system and the physical system, the stability of the physical WPP may be enhanced through decisions in the service system. The DT data system interacts with the physical system, the impedance system, and the service system. The operating point, impedance data and stability indicators are stored in the DT data system.
According to the embodiment of the application, the detailed working principle of the DT system is as follows. First, the active power P of the jth wind power generator is measured j Reactive power Q j And wind speed V w As a plurality of parameters for operating an operating point in a physical system. The multiple parameters of these operating points are provided to the DT data and impedance system.
And secondly, using the actual measurement operation data to establish an impedance system of the DT system. Impedance Z of jth table WTG WTj The structure is as follows:
impedance model of wind turbine generator and line is built to impedance Z of WPP WPP Is a kind of medium.
Then obtain the back ratio matrix in (6). And then apply the generalized Nyquist criterion toData of relative stability and oscillation frequency are obtained and stored in the DT data system. Using a data-driven machine learning method, analytical expressions of the mapping between the stability index and the active power, reactive power and wind speed were developed. Finally, a method is implemented in a service systemStability enhancement strategies.
For this embodiment, the wind farm impedance is also estimated. In particular, network node analysis is applied to establish the impedance of the WPP in fig. 2. WPP consists of k branches. Each branch has n WTGs. For the sequence impedance, the positive sequence impedance at fp and the negative sequence impedance at fp-2fs are coupled, where fs is the fundamental frequency of the ac grid. The positive sequence impedance network at frequency fp is shown in fig. 5. For the application of network node analysis, the grid connection point of the wind farm is defined as node 0.
Nodes inside the wind farm are denoted by 1 to 2kn+1. Each WTG is represented as a parallel connection of an admittance and a controlled current source. For example, in a first WTG of a first branch of a positive-sequence impedance network And negative sequence voltage->The relation of (2) is thatWhere WT11 represents the first table fan of the first leg.
，k=1，n=1。
Is the coupling admittance between the positive and negative sequence impedance of the WTG. Collecting transformer impedance from->And (3) representing. The impedance of the collecting line is->And (3) representing.
It should be noted that the current generated by the negative sequence voltage on the positive and negative sequence coupling admittance is used to describe the influence of the negative sequence voltage in the positive sequence circuit, and a controlled current source is used in the embodiment。
Based on the node voltage equation of the positive sequence impedance and the negative sequence impedance shown in fig. 5, the following is given:
wherein,and->Is a node admittance matrix of positive and negative sequence impedance networks. />And->Is the small signal node voltage. Item->And->Is the small signal voltage at the grid connection point of the wind farm. Item->And->Is an admittance vector that represents the admittance of directly connecting node 0 with other nodes. The positive and negative sequence impedance networks are coupled by a controlled current source, the controlled current source being given by (13)
Wherein,and->Is the coupling admittance between the positive and negative sequence impedance networks.
The voltage and current at the grid connection point of the WPP, centered on the branches at node 0 and node 2kn+1, is given by
Wherein Y pp-MT and Y nn-MT are the main transformer admittances of positive and negative sequence. Inserting (12) into (11), and taking further consideration (13), obtaining the WPP admittance in (4) as
Wherein,、/>、/>and->Consists of WTG impedance, line impedance and transformer impedance.
S103: according to the generalized Nyquist criterion, evaluating the stability under different operation working points, and constructing a stability margin data set;
s104: constructing a stable domain boundary based on a support vector regression method;
s105: and according to the stability evaluation result, evaluating the stability area and the stability margin of the wind power plant on line.
In this embodiment, the impedance model and generalized nyquist criterion provide a good basis for small signal stability analysis of WPP. Current research focuses on systems with constant parameters or single variable parameters. However, various characteristics of wind turbines and WPP may affect stability performance in practical applications. The present embodiment proposes an effective index for stability assessment as a function of a plurality of parameters.
First, a multiparameter stable region and boundary are defined according to the generalized nyquist criterion. A minimum feature trajectory is proposed as an indicator of a number of parameters to evaluate relative stability. Second, the main oscillation frequency is derived as a plurality of parameters. The main oscillation frequency may be derived based on equation (20). Third, the physical limits of the various parameters are analyzed, providing further limitations to the stability area in practical applications.
In this embodiment, both the circuit and control parameters of the WPP affect the dynamic response of the system under small signal disturbance, thereby generating a high-order frequency domain impedance model. Thus, impedance modelAnd return ratio->Is a function of a plurality of parameters. The multiparameter space is defined as a set.
The plurality of parameters includes parameters of the grid and parameters of the WPP. Network impedance available resistorInductance->And capacitance->To represent.
Defining a three-dimensional parameter space of the power grid impedance as. The parameter space of the WPP impedance is characterized by a high dimension and is defined by WPP. The parameters of WPP impedance include circuit parameters, control parameters, and operating points. The multiparameter space of grid-bound WPP is defined by +.>Set definition and formulation into
In this embodiment, a variable parameter space having n operational parameters may be defined byDefinition, wherein->With->And->。
Illustratively, operating point parameters may be selected [9, 27, 28 ] that may significantly affect the small signal stability performance]. Taking a wind turbine generator set as an example, the parameter space selected in the formula (16)Can be designated as:
wherein,and->Is the output power of WTG, and +.>Is wind speed. The specific mapping between the elements of equation (17) yields different modes of operation.
For example, active powerAnd wind speed->By->In connection, the system operates in a maximum power tracking mode, where k opt Is an optimization factor for maximum power tracking. Active power is set toWhen the system is operating in the load shedding mode.
It should be noted that the rotor speed is not included in the parameter space, since the rotor speed is given the active powerAnd wind speed->And (3) determining. Multiple parameters of the operating point +.>And->Is easy to measure from the physical system.
The present example also uses the stability zone effective index for stability evaluation. Here, the generalized nyquist criterion is combined to provide valuable information about absolute stability.
To achieve an effective indicator of small signal stability assessment, stable regions and region boundaries are derived, providing an effective absolute stability indicator. The return ratio is according to formula (6)The nyquist locus of equation (9) can be used to evaluate small signal stability in the s-domain.
For an open loop stabilization system, if the nyquist locus does not surround the critical point (-1, j 0), the closed loop system is stable. The scope of stability analysis is limited to open loop stabilization systems, as most industrial systems are open loop stabilized.
In the critical steady state, the characteristic trace of the yield passes through the critical point (-1, j 0). In the case of a stable operation, the control device,all variable parameter sets in (1) form a feasible domain with +.>And (3) representing. Feasible region->The relation to the variable parameter space is +.>. We will->Defined as feasible region->Is defined by the boundary of (a). The boundaries of the stable region are described as
Embodiments of the present invention extend the idea of the generalized Nyquist criterion and may define a stability margin based on determinant. For multiple-input multiple-output (MIMO) systems, the gain margin and phase margin related to the stability of a single gain in the input channel cannot correctly capture the uncertainty in the off-diagonal entries of the transfer function matrix. The idea of generalized nyquist criterion is expanded and a stability margin based on determinant can be defined. For multiple-input multiple-output (MIMO) systems, the gain margin and phase margin related to the stability of a single gain in the input channel cannot correctly capture the uncertainty in the off-diagonal entries of the transfer function matrix. To facilitate the relative stability assessment, a single parameter index is helpful.
And the invention also disclosesThe vector margin is referred to as a single parameter margin, which eliminates ambiguity in evaluating stability due to the combination of gain margin and phase margin. According to the SISO vector margin concept, the minimum characteristic track is developed as a single parameter stability index of the MIMO system. The minimum feature trajectory is defined as the return ratio And (-1, j 0), as shown in FIG. 6, the minimum feature trajectory is formulated as a variable parameter +.>Function of->
Wherein,is complex frequency, +.>Is a set of selected variable parameters. Item->Is the return ratioIs a characteristic value of (a).
The embodiment of the invention also utilizes the effective main oscillation frequency index for stability evaluation. The WPP herein may oscillate in stable and unstable regions. The oscillations are convergent in the stable region and divergent in the unstable region. The oscillation frequency is determined from the intersection of the nyquist locus and the unit circle. The main oscillation provides valuable information about the oscillation mode that affects the system significantly. According to the main oscillation frequency
The distance between the intersection with the critical point (-1, j 0) determines that the minimum distance corresponds to the case of the primary resonance. The oscillation frequency of the main resonance is determined by a variable parameter +.>Function of->The definition is as follows:
dominant oscillation frequencyAnd operating point->The mapping between IS implemented in an Impedance System (IS) of a digital twin system as in fig. 4.
In the present embodiment, a feasible region is set as a plurality of variable parametersIs limited by physical limits in practical systems. Variable parameter +. >Is determined based on the thermal, mechanical and control limits of the system. According to aerodynamic characteristics of the wind driven generator, active power is limited by the following limit
In the method, in the process of the invention,for air density->For the power factor>To optimize tip speed ratio; the optimization coefficients can be defined as。
Active power considering Doubly Fed Induction Generator (DFIG) systemsFlows through the generator and the converter according to the slip s. In steady state operation with neglected losses, the output active power of the generator stator is +.>While the output active power of the rotor is +.>. Reactive power supplied by the stator +.>The thermal limits of the converter and the machine are considered:
where Ls is the stator inductance, lm is the magnetizing inductance of the machine, us is the stator voltage, I rmax Is the maximum rotor current.
The stable region refers to a region where the wind farm and the wind turbine stably operate, and may be understood as a plane, and the stable boundary is a line dividing the stable and unstable regions, and is a boundary delineating the stable region. The minimum characteristic track refers to a track of which the Nyquist curve corresponding to each condition is closest to a critical point (-1, j 0) in a multi-parameter space (running point change), and can be used as a single-parameter stability margin index for a multi-input multi-output system, and is a method for describing the stability margin index.
For the embodiment of the invention, the stability index is compared with the back-to-back matrix in equation (6)The return ratio is known to be Gao Jiebiao, and the mapping of the selected variable parameters and indices is implicit and complex. Analytical expressions are the first choice for achieving efficient evaluation. In this way, regression-based approximation methods are utilized to provide analytical expressions for small signal stability indicators. The analytical expression here is an analytical expression that developed a mapping between the stability index and the active power, reactive power and wind speed. Among these, the present embodiment is equipped with Support Vector Regression (SVR) of kernel functions.
In this embodiment, a plurality of parameters are selected according to the stability analysis requirements. The selected plurality of parameters may be small signal stability indicators and may depend on the sampled data of the selected variable parameter and the corresponding nyquist locus.
Assume thatIs the i-th sample point of the selected variable parameter, which is the input of the stability index. At->In total, M sampling points are considered. The output of the stability index is composed of->Definition, can be designated as minimum feature track +.>Or the main oscillation frequency +.>Is the i-th sample of (c).
The impedance data of this embodiment is obtained in the DT data of the digital twin system in fig. 4. According to the return ratio in formula (9) Characteristic value of +.>The minimum feature trajectory in equation (19) is obtainedAnd dominant oscillation frequency in equation (20)>Finally, the ith sample data of the smallest feature trace is obtained +.>. The sampled data is stored in DT data of a digital twin system.
For the support vector regression-based analytical expression of the present embodiment, the support vector regression is used to estimate the analytical index, which is generally defined as. The computational complexity of SVR is independent of the dimension of the input space. In addition, SVR has good generalization ability and higher prediction accuracy. Support vector regression uses a "tube" around the true regression function. Such a tube with a certain distance contains most of the sampling points. Points not included in the tube pass through xi and xi' i [33]Defined relaxation variables are described. Relaxation variable->And->The definition is as follows. If sampling point +.>Is positioned at->-above the tube->. If adoptSample spot->Is positioned at->Below the tube, then . For points outside the epsilon-tube, the value of the relaxation variable depends on the loss function. The relaxation variable of the point within the epsilon-tube is zero.
With reference to the sampled datSup>A in section V-A, the mapping of the inputs and outputs of the stability index is non-linear. One common approach is to map data to a high-dimensional space, where the data is linearly separable. Is provided with Is a mapping from the variable parameter space X to the Gao Weixi erbet space H. The linear regression function in space H is described as. For linear insensitivity loss, the initial optimization problem is to findAnd->
The flatness of the regularized C balance function [ mu ] is offset by a tolerance of greater than ¦ beta.
Original Lagrangian quantityIs formed by non-negative lagrangian multipliers α, α, β and β'. By applying an original Lagrangian quantity relative to multiplicationPartial derivative of the device, the dual problem of the original Lagrangian quantity is formulated as +.>
Wherein the method comprises the steps of
The lagrangian multipliers a and a' can be derived from the sequence least optimization. Then, the coefficients in ω are as follows
The estimated analytical expression is as follows
For the kernel function in this embodiment, it supports vector regression. This allows the kernel function to avoid high-dimensional Hilbert spaceMiddle and inner volume->。
Such kernel functions result in an effective solution for (27): kernel function When the core satisfies the Mercer condition [34 ]]When a kernel function is designed By using the original variable parameter +.>To calculate the parameter space +.>Is a product of the inner product of (a). Such kernel functions result in an effective solution for (27):
original variable parameter spaceCan be used as a polynomial core:
Wherein,scale factor +.>The method comprises the steps of carrying out a first treatment on the surface of the b is a constant term. And d is the order of the polynomial core. On the one hand, a larger d helps to improve the accuracy of the regression. On the other hand, the polynomial core is not suitable for the case where d is too large. If the order d is too large, a larger number of parameters should be determined. Notably, the laplace radial basis function may also be used as a generic kernel function, which is applied mainly in the absence of a priori knowledge.
Analytical solution equation (28) and equation (29) can be instantiated for the sampled data for minimum feature site evaluationIs a regression of (2). The minimum signature trajectory is then expressed as a polynomial of the plurality of parameters in the set, which polynomial relates the active power, the reactive power and the wind speed. Also, regression is applied to the sampled dataAn analytical expression of the dominant oscillation frequency is given. The analytical expression of the minimum signature trajectory and dominant oscillation frequency as a function of active power, reactive power and wind speed is implemented in the service system of the DT system in fig. 4. With these analytical solutions, the stability performance of the physical WPP can be effectively evaluated.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In order to verify the effect of the online evaluation method of the embodiment, the method combines the combination method with a single DFIG system to execute wind farm stability evaluation. The specific method comprises the following steps: as shown in fig. 7 and 8, first, the Doubly Fed Induction Generator (DFIG) was subjected to regional stability evaluation. Second, the proposed method is applied to WPP. Although the focus of the example is on a DFIG system, the proposed method can also be applied to other renewable power generation, such as permanent magnet synchronous generators and photovoltaic systems.
The DFIG system is taken as a research object, and the stability of the DFIG system is evaluated by using the proposed effective analysis index. Control of the DFIG system regulates the active and reactive power. Rotor speed varies with active power according to the aerodynamic characteristics of the turbine. The parameters of the DFIG system are shown in table II, while the parameters of the fan and gearbox are shown in table III of the appendix. Grid impedance is represented by rs=0.065, and ls=0.02 mH are connected in series resulting in a short circuit ratio scr=1.76.
From the analytical solution formula (28) and the formula (29), the analytical expression of the minimum feature trajectory is obtained as the active powerReactive power->And wind speed->Third order polynomial of (2). The minimum feature trajectory is marked with a gradient color in fig. 7. Then, referring to equation (18), the feasible region fig. 7 is instantiated. The feasible region takes into account the physical constraints in equations (21) through (23). For a given test system, the stability performance has a lower sensitivity to reactive power than to active power and wind speed.
Reference (20) and its analytical solutions (28) and (29) obtain PWT, reactive power as active powerAnd wind speedThe main oscillation frequency fosc of the function of (2). The oscillation frequency is marked with a gradient color, fig. 8. For a given test system, the oscillation frequency is within the subsynchronous range. For a network with a synchronous frequency fs, there is +.>Is provided for the coupling oscillation frequency of the optical fiber. In the above analysis, FIGS. 7 and 8 illustrate +.>、/>And->Stability performance over a wide range.
Stability analysis is performed in three dimensions. By application of、/>And->The specific relation between the two is analyzed in two-dimensional space. Thus, it is determined thatSmall signal stability performance in a particular mode of operation.
1) Stability performance analysis for variable active power and wind speed:
the invention analyzes the influence of active power PWT and wind speed Vw on stability performance. Reactive power is set to zero, i.eBecause WPP typically does not provide reactive power. The feasible region is shown in fig. 9 with reference to the stable region boundaries in (18) and the physical limits in (21) to (23). Such a region corresponds to a portion of the region in fig. 7 where reactive power is zero. In the feasible region, the minimum feature trajectory described in (19) is marked with gradient color. When wind speeds are low, DFIG systems connected to weak grids may become unstable in situations where active power is relatively large. / >
The dominant oscillation frequency of the DFIG system in (20) is described analytically using the proposed regression-based method. The analytical expression thus obtained is shown in fig. 10. For the system under investigation, a relatively low oscillation frequency is achieved. Compared with the modal analysis result, the method provided by the invention avoids the calculation of a high-order state matrix and a differential equation. An accurate and effective analysis expression is provided, and the influence of the variable operation working point on the oscillation frequency can be deeply known.
To verify efficiency and accuracy, SVR and Direct Polynomial Regression (DPR) were performed on 141 samples. DPR employs the Levenberg-Marquardt algorithm. The algorithm is applied to a personal computer with a CPU of i5-11400 and a RAM of 32 GB in Matlab. The performance comparisons of Support Vector Regression (SVR) and Direct Polynomial Regression (DPR) in the stability zone assessment are given in table I.
The polynomial of the mapping function is third order. Using the estimation result in (28)And (19)Root Mean Square Error (RMSE) between to evaluate where m=141 is the number of samples. SVR has proven to be much faster than direct polynomial regression, while achieving similar approximation accuracy. Therefore, the proposed SVR-based stability assessment method can be applied online in a Digital Twin (DT) system. DT-based stability assessment can then be established in real-time applications.
TABLE 1
Accuracy of regression:
where m=141 is the number of samples. SVR has proven to be much faster than direct polynomial regression, while achieving similar approximation accuracy. Therefore, the proposed SVR-based stability assessment method can be applied online in a Digital Twin (DT) system. DT-based stability assessment can then be established in real-time applications.
2) Stability performance analysis at variable active and reactive power;
in the case of this power regulation, it is assumed that the wind speed is constant at 12 m/s. According to fig. 6, the DFIG system may be stable or unstable at wind speeds with different output powers. Referring to the physical limits in (18) and (21) to (23), FIG. 11 gives V W Stable region of DFIG =12 m/s. In the stable region, the minimum feature trajectory is marked with a gradient color. For a given test system, the DFIG system is more stable with either a greater input reactive power or a lesser output active power. Stability performance is sensitive to active power and less sensitive to reactive power. This is because the active power varies with the rotor speed according to the aerodynamics of the turbine. Corresponding to a relatively large rotor speed at a relatively large active power, when the wind speed is constant . The DFIG system operates in a supersynchronous mode with a relatively large rotor speed. In the supersynchronous mode, the slip is negative. As can be seen from the stator terminals, the negative slip results in a negative rotor equivalent resistance. When the total resistance of the DFIG system becomes negative, negative damping may cause small signal instability. According to (20), asAnd->The dominant oscillation frequency of the analytical function of (c) is shown in figure 12. The oscillation frequency shows that oscillations in the subsynchronous range are sensitive to active power and less sensitive to reactive power.
A. Stability performance of wind farm
The regional assessment of small signal stability can be applied to WPP in fig. 1. Assume that WPP has three branches, and each branch is composed of ten DFIG systems. Each DFIG system was rated at 2.5MW and the parameters are shown in tables II and III. The wind speed of the WPP was set to 12 m/s and the reactive power was set to zero. The stable regions of active powers Pbr1, pbr2 and Pbr3 of the three branches in WPP were analyzed.
The stability zone is shown in fig. 13. The smallest feature trajectory is marked with a gradient color, and a larger smallest feature trajectory corresponds to a more stable case. WPP is less stable for a given test system with a relatively large output active power. The dominant oscillation frequency is shown in fig. 14 and 15. The oscillation frequencies of the stable and unstable regions are explained as follows.
In the embodiment of the invention, in order to verify the proposed regression-based stability evaluation index, a generalized Nyquist criterion and a time domain simulation are performed on MATLAB/Simulink and an experimental platform. First, the stability area of a single DFIG system and WPP system was verified. Second, a strategy is presented to improve small signal stability by scheduling the operating points.
A. Verification of stability area and oscillation frequency
As shown in fig. 7, a feasible region of a single DFIG system is given as、/>And->Is a function of (2). Focusing on the border of the feasible region, the system is critically stable,/->=0.56 p.u.，/>=0 p.u. ，/>=12 m/s. The nyquist locus of the DFIG system at this operating point is shown in fig. 15 based on the impedance model measured by frequency sweep in the time domain simulation. The DIFG system has proven to be in a critical steady state due to the nyquist locus crossing the critical point (-1, j 0). The dominant oscillation frequency is shown as 40.4 Hz, which is consistent with the analytical estimation in the figure. Therefore, the proposed analytical expression of the stability region boundary and the main oscillation frequency proves to be highly accurate. As shown in fig. 2, the stable region and minimum feature trajectory of the WPP are shown in fig. 13. The system has critical stability, and the active power of the three branches is 0.7 p.u., namely 17.5 MW. The wind speed of WPP was set to 12m/s.
The nyquist locus of WPP at this operating point is shown in fig. 16. Multi-machine systems have proven to be critically stable. The dominant oscillation frequency is shown as 41.5 Hz, which is consistent with the analytical estimation of fig. 14. The proposed stability assessment analysis index is effective for multi-machine systems.
B. Enhancing stability by adjusting operating points
The proposed stable region may guide the scheduling of the running operating point. This strategy is implemented in the Service System (SS) of the digital twin system in fig. 4. Through interaction between the service system and the Physical System (PS), the stability of the WPP can be improved by adjusting the power.
1) Case study of single DFIG system: referring to fig. 7, for a single wind turbine, one set of operating points within the feasible region is pwt=0.6 p.u., qwt=0p.u., and vw=12 m/s. Such a running operating point is critical because the scene is close to the feasible region boundary. The system is in steady state from start to 2 seconds. The short-circuit ratio changes from 1.76 to 1.7 at 2 seconds, resulting in a weaker grid. The system becomes unstable as shown in fig. 15, with a divergent oscillation of 40 Hz. To enhance the stability of the small signal, the active power is reduced to pwt=0.52 p.u. At 5s, as the active power decreases, the operating point is repositioned to a feasible region and the system becomes stable. In time domain simulation, the generalized Nyquist criterion is applied to a measured impedance model of the DFIG system, so that the stability of the small signal can be verified. =0.6 p.u. and ∈ ->Nyquist curves =0.52 p.u. are marked in fig. 18. The system of pwt=0.6 p.u. is unstable because the nyquist locus surrounds the critical point (-1, j 0). The intersection of the nyquist locus with the unit circle gives an oscillation frequency of 39.97 Hz. The oscillation frequency is close to 40Hz, the time domain result. The oscillation frequency can also be obtained from the proposed analytical index. The estimated analysis result is 40.5Hz, which is close to 40Hz of the time domain result. />The system of (2) proved to be stable because the nyquist locus did not enclose the critical point (-1, j 0), as shown in the enlarged curve of fig. 18. In summary, time domain simulations and generalized nyquist criteria verify that the stability of the DFIG system is improved by scheduling the operating points of the system.
2) Wind farm case study: the case study of WPP is performed on a hardware platform of WPP digital twin system. Referring to the structure of the DT system shown in fig. 3, the Physical System (PS) is implemented in an RT Lab OP 5700 real-time simulator. Data of the operating point is transmitted to the edge computing device via the udp protocol. The transmitted data includes the power and wind speed of the WTG. The edge computing device is equipped with a CPU racing N3350.WTG impedance is established in an edge computing device. The edge computing device transmits the WTG impedance and the polynomial coefficients of the operating point to the server via the MQTT protocol. The server is configured with an Intel Xeon Gold 6230 processor. Within the server, the Impedance System (IS) of the WPP of fig. 3 IS established. Based on WPP impedance, the construction of the stable region and dominant oscillation frequency in (18) and (20) was performed. The proposed stability index is sent to the wind farm controller. According to the measured working point and the proposed stability index, the real-time stability of the system can be judged. When the system is approaching instability, the wind farm controller will send a modified operating point to the RT laboratory. Therefore, the stability performance of the wind power plant can be enhanced in real time.
The stability enhancement by active power adjustment was verified in the experimental platform. WPP was performed in RT laboratory OP5700 real-time simulator. The DT system of WPP is built in the server and the edge computing device. The stability index was demonstrated in the proctorial society of DT systems as shown in the upper left corner of the figure. The operating point is issued by the wind farm controller. WPP operates from 0 s to 9 s in state 1 with active power for the three legs of 18MW, 14MW and 10 MW, respectively, with a total active power of 42 MW. WPP needs to increase the power to 54 MW at 9 s, the increased power being distributed equally to three legs. Thus, in state 2, the active power in the three branches is 22MW, 18MW and 14MW, respectively. Such operating points are outside the pre-stability region of fig. 7. To improve stability, active power was redistributed to 14MW, 20MW and 20MW at 11 s. WPP at such an operating point is known to be stable. Thus, the system is unstable in state 2 where the oscillation frequency is 41 Hz. By redistributing active power, the system is proven to be stable in state 3.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An online evaluation method for a multi-parameter stability region and a stability margin of a wind farm is characterized by comprising the following steps:
step one: constructing an impedance model of the wind driven generator and an impedance model of the wind power plant as an analysis function of the operation working point;
step two: calculating the impedance of an operating working point based on the digital twin system;
step three: according to the generalized Nyquist criterion, evaluating the stability under different operation working points, and constructing a stability margin data set;
step four: constructing a stable domain boundary based on a support vector regression method;
step five: and according to the stability evaluation result, evaluating the stability area and the stability margin of the wind power plant on line.
2. The method for online assessment of a multi-parameter stability region and stability margin of a wind farm according to claim 1, wherein step one further comprises: defining a small signal model of the wind driven generator in a state space form, wherein the small signal model of the wind driven generator is expressed as,
wherein,is a +.>Matrix (S)>Is the number of state variables, matrix->Is thatThe method comprises the steps of carrying out a first treatment on the surface of the Matrix->Is-> Is an input matrix,/->i is the output matrix;
the impedance model of the wind turbine is calculated based on:
where s is the Laplacian and I is the identity matrix;
an impedance model Zline of the line is defined based on the following formula:
wherein R is line And L line Is the resistance and inductance of the line, ω s Is the angular velocity of the ac power system.
3. The method for online assessment of a multi-parameter stability region and stability margin of a wind farm according to claim 2, wherein step one further comprises:
applying kirchhoff voltage law to a source load subsystem, the grid-connected wind farm is described as:
wherein,the gyrostatic matrix is calculated by the following formula>Is used for the characteristic value of the (c),
the eigenvalues are calculated by the following formula,
based on characteristic valuesStability of the small signal is evaluated with respect to the trajectory of the critical point (-1, j 0).
4. The method for online assessment of a multi-parameter stability region and stability margin of a wind farm according to claim 1, wherein the digital twin system in step two comprises: physical system, impedance system, service system and DT data;
defining a physical system as a wind power plant, and acquiring operation data of the physical system based on measurement equipment;
establishing an impedance system through observing operation data of a physical system;
based on the impedance system, a stability area of the service system is obtained and a stability enhancement policy is executed in the service system.
5. The method for online assessment of a multi-parameter stability region and stability margin of a wind farm according to claim 4, wherein step two further comprises:
measuring active power P of jth wind power generator j Reactive power Q j And wind speed V w As a multiparameter of measurement points in a physical system;
constructing an impedance system of the digital twin system by utilizing multiple parameters of the measuring points;
impedance Z of jth table WTG WTj The structure is as follows:
impedance Z of the j-th wind driven generator WTj Impedance Z built into wind farm WPP In (3), a back ratio matrix in the formula (6) is obtained；
Applying generalized Nyquist criterion toObtaining data of relative stability and oscillation frequency, and storing the data in a digital twin system The method comprises the steps of (1) unifying;
by using a data-driven machine learning method, an analytical expression of mapping between the stability index and the active power, reactive power and wind speed is developed, and a stability enhancement strategy is implemented in the service system.
6. The method for online assessment of a multi-parameter stability region and stability margin of a wind farm according to claim 4, wherein step two further establishes impedance of the wind farm by network node analysis;
the wind farm comprises: k branches, each branch is provided with n wind power generators WTG;
for the sequence impedance, the positive sequence impedance at frequency fp and the negative sequence impedance at fp-2fs are coupled; fp is the disturbance harmonic frequency injected in the small signal model, fs is the fundamental frequency, fs is 50hz;
wherein, the grid connection point of the wind power plant is defined as a node 0; nodes inside the wind power plant WPP are denoted by 1 to 2kn+1; each wind generator is represented as a parallel connection of admittance and controlled current source.
7. The method for online assessment of a multi-parameter stability region and stability margin of a wind farm according to claim 1 or 2, step three further comprising: selecting a plurality of parameters for stability analysis;
the plurality of parameters includes parameters of the grid and parameters of the WPP; resistor for electric network impedance Inductance->And capacitance->To represent;
grid impedance including resistorInductance->And capacitance->；
Defining grid impedance as；
WPP impedance is defined asThe parameters include: circuit parameters, control parameters and operating points;
the multi-parameter set is defined as:
。
8. the method for online assessment of a multi-parameter stability region and stability margin for a wind farm of claim 7, step three further comprising:
in a critical steady state, the characteristic track of the yield rate passes through a critical point (-1, j 0);
in the state of being in a stable state,all variable parameter sets in (a) form the feasible domain +.>；
Feasible regionThe relation to the variable parameter space is +.>；
Will beDefined as feasible region->Is described as the boundary of the stable region
According to the SISO vector margin concept, the minimum characteristic track is defined as a single parameter stability index of the multiple-input multiple-output system;
the minimum feature trajectory is defined as the return ratioAnd (-1, j 0), the minimum feature track being formulated as a variable parameter +.>Function of->
Wherein,is complex frequency, +.>Is a set of selected variable parameters, item +.>Is the return ratio->Is a characteristic value of (2);
according to the main oscillation frequency
Determining the minimum distance between the two points of intersection with the critical point (-1, j 0) to obtain a main resonance state; wherein the oscillation frequency of the main resonance is determined by a variable parameter +. >Function of->Is defined as
Dominant oscillation frequencyAnd operating point->The mapping between the two is realized in an impedance system of a digital twin system;
according to aerodynamic characteristics of the wind driven generator, active power is limited by the following limit
In the method, in the process of the invention,for air density->For the power factor>To optimize the leavesTip speed ratio; the optimization coefficient is defined as。
The output active power of the wind driven generator stator isThe output active power of the rotor is；
Reactive power provided by statorThe thermal limits of the converter and the machine are considered to be expressed by the following formula:
where Ls is the stator inductance, lm is the magnetizing inductance of the machine, us is the stator voltage, I rmax Is the maximum rotor current.
9. The method for online assessment of a multi-parameter stability region and stability margin of a wind farm according to claim 8, wherein step four further comprises:
is provided withIs the i-th sampling point of the selected variable parameter,>defining M sampling points;
the output of the stability index is composed ofDefinition (S)/(S)>For minimum feature track->Or the main oscillation frequencyIs the i-th sample output of (c).
10. An evaluation terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for on-line evaluation of a multi-parameter stability area and stability margin of a wind farm according to any of claims 1 to 9 when the program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311225675.1A CN117375018A (en) | 2023-09-21 | 2023-09-21 | Online assessment method and assessment terminal for wind farm stability region and stability margin |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311225675.1A CN117375018A (en) | 2023-09-21 | 2023-09-21 | Online assessment method and assessment terminal for wind farm stability region and stability margin |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117375018A true CN117375018A (en) | 2024-01-09 |
Family
ID=89401260
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311225675.1A Pending CN117375018A (en) | 2023-09-21 | 2023-09-21 | Online assessment method and assessment terminal for wind farm stability region and stability margin |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117375018A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110429648A (en) * | 2019-08-12 | 2019-11-08 | 南京理工大学 | Consider the small interference stability nargin probability evaluation method of failure of wind speed random fluctuation |
| CN110994668A (en) * | 2019-10-29 | 2020-04-10 | 国网甘肃省电力公司电力科学研究院 | Stability analysis method based on output impedance model of doubly-fed wind power plant grid-connected system |
| CN111009921A (en) * | 2019-12-19 | 2020-04-14 | 华北电力大学 | Double-fed fan grid-connected system oscillation analysis method based on Nyquist stability criterion |
| CN114002954A (en) * | 2021-10-28 | 2022-02-01 | 南京航空航天大学 | Wind power plant stability evaluation method and system based on active equipment node impedance |
| US20220397879A1 (en) * | 2020-12-18 | 2022-12-15 | State Grid Jiangsu Electric Power Co., Ltd. Economic Research Institute | Method for evaluating resonance stability of flexible direct current (dc) transmission system in offshore wind farm |
-
2023
- 2023-09-21 CN CN202311225675.1A patent/CN117375018A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110429648A (en) * | 2019-08-12 | 2019-11-08 | 南京理工大学 | Consider the small interference stability nargin probability evaluation method of failure of wind speed random fluctuation |
| CN110994668A (en) * | 2019-10-29 | 2020-04-10 | 国网甘肃省电力公司电力科学研究院 | Stability analysis method based on output impedance model of doubly-fed wind power plant grid-connected system |
| CN111009921A (en) * | 2019-12-19 | 2020-04-14 | 华北电力大学 | Double-fed fan grid-connected system oscillation analysis method based on Nyquist stability criterion |
| US20220397879A1 (en) * | 2020-12-18 | 2022-12-15 | State Grid Jiangsu Electric Power Co., Ltd. Economic Research Institute | Method for evaluating resonance stability of flexible direct current (dc) transmission system in offshore wind farm |
| CN114002954A (en) * | 2021-10-28 | 2022-02-01 | 南京航空航天大学 | Wind power plant stability evaluation method and system based on active equipment node impedance |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wen et al. | Real-time identification of power fluctuations based on LSTM recurrent neural network: A case study on Singapore power system | |
| Sessarego et al. | Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow | |
| Munteanu et al. | Energy-reliability optimization of wind energy conversion systems by sliding mode control | |
| CN110854884B (en) | Wind power collection area subsynchronous oscillation risk online assessment and early warning method | |
| Sheikh Ahmadi et al. | Improving MPPT performance in PV systems based on integrating the incremental conductance and particle swarm optimization methods | |
| Raach et al. | ℋ∞ controller design for closed-loop wake redirection | |
| CN110474323B (en) | Method for measuring inertia time constant of power system | |
| CN108223274A (en) | Large Scale Variable Pitch Wind Turbine System discrimination method based on optimization RBF neural | |
| Mishra et al. | Design of non-linear controller for ocean wave energy plant | |
| Muñoz et al. | Fuzzy PID control system analysis for a wind turbine maximum power point tracking using FAST and Matlab Simulink | |
| CN117375018A (en) | Online assessment method and assessment terminal for wind farm stability region and stability margin | |
| Chen et al. | Improved pitch control strategy for the robust operation of wind energy conversion system in the high wind speed condition | |
| CN114298478A (en) | Small disturbance stability identification method and system for wind power grid-connected system | |
| Pašić et al. | The application of artificial neural networks to pseudo measurement modeling in distribution networks | |
| Phan et al. | ANFIS Wind Speed Estimator-Based Output Feedback Near-Optimal MPPT Control for PMSG Wind Turbine | |
| Duraipandy et al. | Development of extreme learning machine for online voltage stability assessment incorporating wind energy conversion system | |
| Wang et al. | Online assessment of multi-parameter stability region and stability margin of wind power plants | |
| Yang et al. | Evaluation and adjustment based on broad learning system for small signal stability analysis of power system integrated with wind farms | |
| Silva-Saravia et al. | Effect of wind farm spatial correlation on oscillation damping in the WECC system | |
| Zhou et al. | Uncertainty analysis of dynamic thermal rating of overhead transmission line | |
| Prasertwong et al. | Conventional and fuzzy logic controllers at generator location for low frequency oscillation damping | |
| Kalyan et al. | Fruit Fly Optimization Technique Based Regulator for LFC of Conventional Power System with the Integration of Plugin Electric Vehicles | |
| Ye et al. | Gas Turbine Fault Diagnosis Based on IPSO-SVM | |
| CA | Intelligent control for the variable-speed variable-pitch wind energy system | |
| Kang et al. | Subsynchronous Oscillation Parameters Identification Based on Prony-Levenberg Marquardt |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |