CN108683214A - Wind-electricity integration system self-adaption damping control method based on multiple convex polytope - Google Patents
Wind-electricity integration system self-adaption damping control method based on multiple convex polytope Download PDFInfo
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- H02J3/386—
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The invention discloses a kind of wind-electricity integration system self-adaption damping control methods based on multiple convex polytope for belonging to electrical engineering technical field.The method designs Multi-objective Robust controller first against single convex polytope；Secondly, categorised decision tree is built and trained using a large amount of off-line datas, and establishes regression tree, the affiliated convex polytope region of identification current point of operation using online wide-area data；Finally, according to on-line decision mechanism, the adaptive corresponding robust controller of switching convex polytope in real time.For the present invention when a wide range of random fluctuation occurs for wind power output, the random drift behavior for capableing of effectively tracing system operating point carries out effective damping to carry out self-adaptive damping control to system oscillation.
Description
Technical field
The invention belongs to electrical engineering technical field more particularly to a kind of wind-electricity integration systems based on multiple convex polytope
Self-adaptive damping control method.
Background technology
In recent years, wind-power electricity generation has been greatly developed in China, and installed capacity is growing day by day, and China is accumulative within 2011
Installed capacity 62GW, ranks the first in the world, it is contemplated that arrives the year two thousand twenty, installed capacity of wind-driven power will at least up to 1.5 hundred million kW.Wind-power electricity generation
The features such as possessed intermittent and stochastic volatility so that the access of large-scale wind power field has stability of power system certain
Influence.At the same time, China's power grid is in the rapid development period of big regional power grid interconnection, and low-frequency oscillation, which becomes, to be influenced greatly
Power System Interconnection restricts the key factor of electrical grid transmission ability.The intermittence and stochastic volatility of wind-powered electricity generation make the resistance of grid-connected system
Damping characteristics are more intricate, and new challenge is proposed to conventional damper control.
Electric system is a huge strong nonlinearity, and the dynamical system of Non-Self-Governing is simple to rely on the adaptive of model
It should control and be difficult to realize.The rapid development of synchronous phasor measurement unit provides strong work for the global viewable of electric system
Tool so that the method for " on-line identification+self adaptive control " is provided with certain exploitativeness.Decision tree approaches discrete as one kind
The method of functional value is a kind of typical sorting technique, and this method generates readable rule and decision tree using inductive algorithm, so
New data is analyzed using decision afterwards.Wherein classification regression tree (Classification and Regression
Tree, CART) it is not only suitable for classification problem, and it is suitable for regression problem, it is assessed in power system voltage stabilization, transient stability
Prediction, load prediction etc. have been applied.Optimal Control Problem is brought for systematic uncertainty, it is main at present to use
Norm-bounded condition describes, such as H2Control theory, H∞Control theory etc..Using the H2 norms of system as the optimum control of performance indicator
Theory, the dynamic and steady-state behaviour that can have been obtained, but its robust stability is slightly worse；In comparison, H∞Control can obtain
Good robust stability, but its dynamic and steady-state adjustment performance are slightly worse, by by H2Control and H∞Control is combined to obtain H2/H∞
Mixing control, then can make whole system that can obtain excellent regulation performance and keep robust stability.However it is directed to norm
The controller of bounded design makes system operating point that a wide range of become occur just for single operating point, the strong stochastic volatility of wind-powered electricity generation
Change, different operating points are carried out control design case by convex polytope, can take into account the robustness of different operating points.So
And there are still following both sides problems at present：1) calculation amount of more cell space optimal controls is counted out by operation and is limited, and is transporting
Row count out it is increased in the case of, be easy to cannot get feasible solution.In practical power systems, operating point considerable number, it is difficult to
All operating conditions being likely to occur are considered using a convex polytope；2) electric system unintentional nonlinearity can not protect
The linear combination of card controller can show good performance.
Invention content
In view of the above-mentioned problems, the present invention proposes a kind of wind-electricity integration system self-adaption damping based on multiple convex polytope
Control method, which is characterized in that include the following steps：
The multiple target H of the single convex polytope of step 1, structure∞/H2Robust controller considers all operation works of electric system
Condition, by the multiple target H of multiple single convex polytopes∞/H2Robust controller covers the whole service space of electric system；
Step 2 establishes classification regression tree, categorised decision tree is built first with a large amount of off-line datas, by big
Amount off-line data is trained, and is formed classifying rules, is recycled online wide-area data to establish regression tree, utilize real time data
Regressing calculation is carried out, and identifies the affiliated convex polytope region of current point of operation, forms online decision-making mechanism；
Step 3, foundation on-line decision mechanism, establish the self-adaptation control method of multiple convex polytope, adaptive to throw in real time
The corresponding robust controller of convex polytope is cut, adaptive switching is realized between each controller, to eliminate the resistance of wind-electricity integration system
Buddhist nun is vibrated.
In the step 1, the multiple target H of single convex polytope is built∞/H2The method of robust controller is:
1) convex polytope model is established, each vertex of model represents the typical operating condition of electric system, described convex more
Cell space model is：
In formula, S { S1,S2,…,SnIndicate the convex cell space being made of multiple vertex, aiFor weight coefficient, SiIt is pushed up for i-th
Point；
2) each vertex of convex polytope model is linearized, obtains multiple target H∞/H2The state of robust control is empty
Between equation, and each operating condition state space equation；
The multiple target H∞/H2The state space equation of robust control is：
In formula, x is system state variables, and u is input quantity, and ζ is the disturbance in system, z∞Indicate H∞Index, z2Indicate H2Refer to
Number, A is systematic observation matrix, B1For the corresponding gain matrixs of disturbance ζ, B2Input matrix in order to control, C1For with H∞Performance indicator phase
The state matrix of pass, C2For with H2The relevant state matrix of performance indicator, D11For with H∞The relevant disturbance input square of performance indicator
Battle array, D12For with H∞The relevant control input matrix of performance indicator, D21For with H2The relevant disturbance input matrix of performance indicator, D22
For with H2The relevant control input matrix of performance indicator；
Each the state space equation of operating condition is：
In formula, k indicates k-th of vertex of convex polytope model；
3) controller u=K is designed according to formula (3)x, closed-loop system is formed, wherein K is feedback oscillator, the closed loop system
System meets following constraints：
A) closed loop transfer function, Tζz∞No more than defined maximum figure γ∞；
B) it is closed transmission function Tζz2No more than specified maximum figure γ2；
C) closed-loop pole of closed-loop system is located in the regions D of setting.
Each intermediate node of the classification regression tree indicates bifurcated node, to the data of each intermediate node into
Row two is classified, and each metric data is finally made to terminate at a terminal note according to top-down principle, and each terminal note indicates
One equalization point, the i.e. operating condition of system.
In the step 2, the detailed process for establishing classification regression tree is as follows：
(1) equalization point measurement of the selection with ornamental and controllability, calculates active power, and calculation formula is as follows：
In formula, δiAnd UiIt is the phase angle and voltage magnitude on i-th line road, δ respectivelykAnd UkThe respectively phase angle of kth circuit
And voltage magnitude；
(2) categorised decision tree is built, the equalization point measurement of multidimensional is converted by one-dimensional optimal surpass using FLDSD technologies
Plan range vector, the optimal hyperlane distance vector are that the measurement of multidimensional is mapped to hyperspace as operating point to sit
Mark, is configured to an optimal hyperlane for dividing different operating points to greatest extent, and calculate each operating point to optimal hyperlane
Distance current point of operation is classified finally according to calculated distance symbol, each operating point is to optimal super flat
Identity distance from calculation formula be：
In formula, d is distance of the operating point to optimal hyperlane, and h is current point of operation, and α and β expressions two are different classes of,
∑αAnd ∑βThe covariance that respectively α and β is measured, μαAnd μβThe means of measurement α and β are indicated respectively；
(3) regression tree is built, by new operating point h ' to each optimal hyperlane apart from variable d ' input decision trees,
Some terminal node that decision tree is reached according to bifurcated rule from top to bottom, determines current point of operation in parameter space with this
In position.
The self-adaptation control method of the multiple convex polytope is：
Parameter space is established first with decision tree and measures the mapping relations in space, by the change for measuring space measurement value
Change the variation of reflection parameter space：When electric system is interfered, the current point of operation in parameter space will deviate from equalization point,
After electric system is disturbed, current point of operation is restored to equalization point, or stablizes in other equalization points；According to current operation
The disturbed track of point judges the equalization point domain of attraction that system will enter, and is trained to decision tree using the disturbed track,
Operation of Electric Systems operating mode is obtained in the position of parameter space, the controller that input matches with current point of operation at this time, in wind
Electricity occurs to carry out effective damping to low frequency oscillations in the case of a wide range of random fluctuation.
The beneficial effects of the present invention are：
The present invention is capable of the random drift of effective tracing system operating point when a wide range of random fluctuation occurs for wind power output
Behavior carries out effective damping to carry out self-adaptive damping control to system oscillation.
Description of the drawings
Attached drawing 1 is a kind of wind-electricity integration system self-adaption damping control method flow chart based on multiple convex polytope；
Attached drawing 2 is parameter space and measurement space reflection relation schematic diagram；
Attached drawing 3 is that 16 generators and 68 busbares test system in embodiment 1；
Attached drawing 4 is the decision tree obtained by training；
Attached drawing 5 is the cross validation estimation of wrong classification rate；
Attached drawing 6 is the post-class processing based on multiple convex polyhedron；
Attached drawing 7 is to put into the generator of different controllers with respect to power-angle curve in wind power fluctuation；
Specific implementation mode
The present invention is described in detail with reference to the accompanying drawings and examples.
Attached drawing 1 is a kind of wind-electricity integration system self-adaption damping control method flow chart based on multiple convex polytope, such as
Shown in Fig. 1, described method includes following steps：
The multiple target H of the single convex polytope of step 1, structure∞/H2Robust controller, it is contemplated that all operations of electric system
Operating mode, by the multiple target H of multiple single convex polytopes∞/H2Robust controller covers the whole service space of electric system；
Step 2 establishes classification regression tree, categorised decision tree is built first with a large amount of off-line datas, by big
Amount off-line data is trained, and is formed classifying rules, is recycled online wide-area data to establish regression tree, utilize real time data
Regressing calculation is carried out, and identifies the affiliated convex polytope region of current point of operation, forms online decision-making mechanism；
Step 3, foundation on-line decision mechanism, establish the self-adaptation control method of multiple convex polytope, adaptive to throw in real time
The corresponding robust controller of convex polytope is cut, adaptive switching is realized between each controller, to eliminate the resistance of wind-electricity integration system
Buddhist nun is vibrated.
Specifically, in the step 1, in order to solve to be difficult to be utilized a single convex polytope in view of system is possible to out
The problem of existing operating condition, needs multiple single convex polytopes covering whole service space.Wherein, single convex polytope is built
Multiple target H∞/H2The method of robust controller is:
1) convex polytope model is established, each vertex of convex polytope model represents the typical operating condition of electric system,
Robust control based on convex polytope can not only make the operating condition that each vertex represents stablize, while it is also possible that position
Operating condition inside this convex polytope is stablized, its available sharp vertex of any point in convex polytope model is retouched
It states, the convex polytope model is：
In formula, S { S1,S2,...,SnIndicate the convex cell space being made of multiple vertex, aiFor weight coefficient, SiIt is pushed up for i-th
Point；
2) each vertex of convex polytope model is linearized, obtains multiple target H∞/H2The state of robust control is empty
Between equation, and each operating condition state space equation；
The multiple target H∞/H2The state space equation of robust control is：
In formula, x is system state variables, and u is input quantity, and ζ is the disturbance in system, z∞Indicate H∞Index, z2Indicate H2Refer to
Number, A is systematic observation matrix, B1For the corresponding gain matrixs of disturbance ζ, B2Input matrix in order to control, C1For with H∞Performance indicator phase
The state matrix of pass, C2For with H2The relevant state matrix of performance indicator, D11For with H∞The relevant disturbance input square of performance indicator
Battle array, D12For with H∞The relevant control input matrix of performance indicator, D21For with H2The relevant disturbance input matrix of performance indicator, D22
For with H2The relevant control input matrix of performance indicator；
Each the state space equation of operating condition is：
In formula, k indicates k-th of vertex of convex polytope model；
3) controller u=K is designed according to formula (3)x, closed-loop system is formed, wherein K is feedback oscillator, the closed loop system
System meets following constraints：
A) closed loop transfer function, Tζz∞No more than defined maximum figure γ∞；
B) it is closed transmission function Tζz2No more than specified maximum figure γ2；
C) closed-loop pole of closed-loop system is located in the regions D of setting.
Specifically, in the step 2, post-class processing (Classification and Regression Tree,
CART) be a kind of learning art of nonparametric decision tree, can be suitable for electric system, CART by a large amount of off-line datas into
Row training, forms classifying rules, then carries out regressing calculation using real time data, forms online decision-making mechanism.It is described to classify back
Return each intermediate node of decision tree to indicate bifurcated node, two classification are carried out to the data of each intermediate node, are finally made every
A metric data terminates at a terminal note according to top-down principle, and each terminal note indicates an equalization point, i.e. system
Operating condition.
Due in system can measurement it is more, be typically chosen the measurement of the ornamental and controllability that can characterize system
As characteristic quantity.Studies have shown that the active power in interconnection contains the relevant information of system model.Therefore the present invention in real time from
The generator rotor angle that WAMS is obtained calculates the active power of interconnection, and calculation formula is as follows：
In formula, δiAnd UiIt is the phase angle and voltage magnitude on i-th line road, δ respectivelykAnd UkThe respectively phase angle of kth circuit
And voltage magnitude.
Since measurement is one group of multi-C vector, and the data of decision tree are one-dimensional vectors, therefore use FLDSD technologies will
The measurement of multidimensional is mapped to the coordinate in hyperspace as operating point.First, it finds different operating points maximum as far as possible
The optimal hyperlane of limit segmentation calculates the distance that each operating condition arrives this hyperplane, to by the conversion of the measurement of multidimensional
It is last according to distance vector d is calculated for one-dimensional distance vector, classify to current point of operation, each operating point is to most
The calculation formula of the distance of excellent hyperplane is：
In formula, d is distance of the operating point to optimal hyperlane, and h is current point of operation, and α and β expressions two are different classes of,
∑αAnd ∑βThe covariance that respectively α and β is measured, μαAnd μβThe means of measurement α and β are indicated respectively.
Then, regression tree is built, by new operating point h ' to each optimal hyperlane apart from variable d ' input decisions
Tree reaches some terminal node of decision tree according to bifurcated rule from top to bottom, determines current point of operation in parameter with this
Position in space.
Above-mentioned classification regression tree is extended.Assuming that there are k operating condition, each operating condition has n survey
Magnitude, sample frequency rHZ.Above-mentioned two-dimensional space is expanded to more high-dimensional, it is empty that raw data set is classified as k son
Between, include the measurement data for belonging to an operating condition per sub-spaces.Thus the hyperplane of optimal allocation k operating conditions can be with
It is expressed as π12, π13..., πij... π(k-1), k.Wherein, πijSubscript indicate by corresponding hyperplane optimal allocation ithWith
jthOperating condition.Using FLD technologies, by mijWhat is indicated makes i and j detach the normal vector of maximized hyperplane by following formula table
Show：
The unit normal vector m that formula (7) is provided by vectorijGenerated projection can calculate h and hyperplane it
Between the distance between point h.
Raw data set in more higher dimensional space is replaced by the distance of the hyperplane of one-dimension array.Utilize known data point
It can rapidly make decision at a distance from the hyperplane calculated before.According to the convexity attribute of polyhedral model, once one
The stability on polyhedron vertex is guaranteed, and will expand to entire polyhedron set；For method proposed by the invention, meaning
Taste once current point of operation is identified as polyhedron, then corresponding controller will oscillation-damped.
Specifically, in the step 3, parameter space is established by decision tree and measures the mapping relations in space, using obtaining
The multi-input multi-output system measured value obtained learns operating condition in the position of parameter space, to realize oneself between controller
Switching is adapted to, detailed process is as follows：
Parameter space is established first with decision tree and measures the mapping relations in space, and mapping relations are as shown in Fig. 2, figure
In left-half be parameter space, right half part be measure space, wherein P1, P2And P3Parameter in expression parameter space becomes
Amount, y1And y2Indicate the measurement point after measurement, dot expression equalization point, box expression system are disturbed.It measures in space
Operating point variation is the operating point in parameter space caused by the variation that the service condition of variation occurs in parameter space
Variation leads to the change for measuring operating point position in space.Therefore, reflect parameter space by measuring the variation of space measurement value
Variation：When electric system is interfered, the current point of operation in parameter space will deviate from equalization point, when electric system by
After disturbance, current point of operation is restored to equalization point, or stablizes in other equalization points；Sentenced according to the disturbed track of current point of operation
The equalization point domain of attraction that disconnected system will enter, and decision tree is trained using the disturbed track, obtain electric system fortune
Row operating mode is put into use in the position of parameter space, at this time corresponding controller, to eliminate the damped oscillation of terminal node.
Embodiment 1
The present embodiment uses a New England's New York interconnected electric power system model for having 16 generators and 68 buses
Verify the validity and feasibility of this method.Three wind power plants are connected on respectively at busbar 41,42 and 52.The line chart of the system is such as
Shown in Fig. 3, in order to verify the performance of self adaptive control proposed by the present invention, a TCSC, a SVC and one are added in system
A ESD.All generators have been respectively mounted 1 type DC excitation systems of IEEE.The present embodiment is divided using mixing load model
Analysis, utilizes the optimal location of dimensional measurement (GMC) identification controller of controllability.The most suitable installation sites of TCSC are circuits
41-42, SVC and ESD are respectively busbar 19 and busbar 22 in node.
Classify to the formation of the classification tree under different situations, first with interconnection 1-2,8-9,41-42 and 50-51
In active power as four of appropriate controller measurements of matching.In order to meet test request, the difference according to different location
Wind power output power, it is as shown in table 1 to establish 12 typical operating points, and applies different disturbances for the operation of each typical condition,
Each emulation 100 times, to form the learning training sample set of decision tree.
12 typical operating points that table 1 is established according to the different wind power output powers of different location
| Operating point | 1 output power of wind power plant (MW) | 2 output power of wind power plant (MW) | 3 output power of wind power plant (MW) |
| 1 | 100 | 100 | 100 |
| 2 | 100 | 100 | 200 |
| 3 | 100 | 200 | 100 |
| 4 | 100 | 200 | 200 |
| 5 | 200 | 100 | 100 |
| 6 | 200 | 100 | 200 |
| 7 | 200 | 200 | 100 |
| 8 | 200 | 200 | 200 |
| 9 | 300 | 100 | 100 |
| 10 | 300 | 100 | 200 |
| 11 | 300 | 200 | 100 |
| 12 | 300 | 200 | 200 |
The decision tree obtained by training is as shown in figure 4, it has 12 terminal nodes of corresponding 12 different operating points.
It can be divided into three groups, i.e. three convex polytopes according to node 2,6 and 7.Wherein convex polytope 1 includes 1,2,3,4 He of operating condition
5, convex polytope 2 includes operating mode 6,7 and 9, and convex polytope 3 includes operating mode 8,10,11 and 12.Using H∞/H2Robust control, this reality
It is that three convex polytopes devise three controllers to apply example, performance of controller when applied to its respective polyhedron such as table 2,
3, shown in 4：
Frequency and damping ratio under 1 back zone domain model of controller is added in table 2 in convex polytope 1
Frequency and damping ratio under 2 back zone domain model of controller is added in table 3 in convex polytope 2
Frequency and damping ratio under 3 back zone domain model of controller is added in table 4 in polyhedron 3
Best decision tree, the present embodiment is selected to analyze the son of one group of minimum cost complexity using multiple convex polytope
Tree, attached drawing 5 are the cross validation estimation of wrong classification rate, as shown in figure 5, determining the big of tree according to classification intensive reading and complexity
Small, the tree with 5 terminal nodes is suitable known to analysis, and the wrong classification rate of the subtree is 8.9%, mistake classification rate
It is higher the reason is that because there is convex polytope overlapping region, this region that partly overlaps can ensure the flat of controller handoff procedure
Steady transition.
Attached drawing 6 is the post-class processing based on multiple convex polyhedron, and there are five terminal nodes for the decision tree.Certain operating point arrives
The distance of hyperplane uses d1 respectively, and d2, d3 are indicated, are returned by CART to determine the convex polytope where current point of operation
Region, and the controller that switching matches with operating point.
The validity and superiority of this paper institutes extracting method are verified by time-domain-simulation.Electric system initial operating state is table
The output power of operating point 1 in 1, i.e., three wind power plants is 100MW, and Operation of Electric Systems is in the region of convex polytope 1 at this time.
When t=3s, the output power of wind power plant 3 becomes 300MW, at this time the corresponding operating point 9 of system, table 5 be disturbance after in 1 second
CART output datas, as can be seen from Table 5, the identification result of CART is mostly 2, shows the system operation after disturbance convex
The region of more cell spaces 2.Operating point 9, which is located at 2 region of convex polytope, to be also indicated that the classification of convex polytope in Fig. 4.From the defeated of CART
Go out data can be seen that wind power variation after, system still operates in the region of convex polytope 2, therefore, changes throwing at this time
The controller 2 that convex polytope 2 matches, control effect is as shown in Figure 7.Wherein, dotted line is to utilize static controller (controller 1)
Obtained generator with respect to power-angle curve, solid line for carried using the present invention adaptive controller (level controller 1 before disturbance,
Switching controller 2 is changed by the Adaptive Identification of CART after disturbance) obtained opposite power-angle curve, by comparing this two songs
Line, it can be seen that the adaptive controller based on CART can effectively suppression system vibrate, and static controller is fluctuated in wind-powered electricity generation
In the case of cannot effectively inhibit to vibrate.Simulation result shows that the auto-adaptive control scheme based on multiple convex polytope can have
Effect tracking operation states of electric power system can be one between different controllers due to the overlapping region between multiple convex polytope
Determine to realize steady switching in degree, avoids the impact brought to system due to switch controller.
After the variation of 5 Power Output for Wind Power Field of table, the output valve in CART mono- second
| Sample | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Convex polytope serial number | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 2 |
| Sample | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| Convex polytope serial number | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 |
| Sample | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Convex polytope serial number | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
This embodiment is merely preferred embodiments of the present invention, but scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, the change or replacement that can be readily occurred in,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims
Subject to.
Claims (5)
1. a kind of wind-electricity integration system self-adaption damping control method based on multiple convex polytope, which is characterized in that including with
Lower step：
The multiple target H of the single convex polytope of step 1, structure∞/H2Robust controller considers all operating conditions of electric system, will
The multiple target H of multiple list convex polytopes∞/H2Robust controller covers the whole service space of electric system；
Step 2, establish classification regression tree, first with a large amount of off-line datas build categorised decision tree, by largely from
Line number is formed classifying rules, recycles online wide-area data to establish regression tree, carried out using real time data according to being trained
Regressing calculation, and identify the affiliated convex polytope region of current point of operation, form online decision-making mechanism；
Step 3, foundation on-line decision mechanism, establish the self-adaptation control method of multiple convex polytope, and adaptive switching in real time is convex
The corresponding robust controller of more cell spaces realizes that adaptive switching, the damping to eliminate wind-electricity integration system are shaken between each controller
It swings.
2. a kind of wind-electricity integration system self-adaption damping control side based on multiple convex polytope according to claim 1
Method, which is characterized in that in the step 1, build the multiple target H of single convex polytope∞/H2The method of robust controller is:
1) convex polytope model is established, each vertex of model represents the typical operating condition of electric system, the convex polytope
Model is：
In formula, S { S1,S2,...,SnIndicate the convex cell space being made of multiple vertex, aiFor weight coefficient, SiFor i-th of vertex；
2) each vertex of convex polytope model is linearized, obtains multiple target H∞/H2The state space side of robust control
Journey, and each state space equation of operating condition；
The multiple target H∞/H2The state space equation of robust control is：
In formula, x is system state variables, and u is input quantity, and ζ is the disturbance in system, z∞Indicate H∞Index, z2Indicate H2Index, A
For systematic observation matrix, B1For the corresponding gain matrixs of disturbance ζ, B2Input matrix in order to control, C1For with H∞Performance indicator is relevant
State matrix, C2For with H2The relevant state matrix of performance indicator, D11For with H∞The relevant disturbance input matrix of performance indicator, D12
For with H∞The relevant control input matrix of performance indicator, D21For with H2The relevant disturbance input matrix of performance indicator, D22For with H2
The relevant control input matrix of performance indicator；
Each the state space equation of operating condition is：
In formula, k indicates k-th of vertex of convex polytope model；
3) controller u=Kx is designed according to formula (3), forms closed-loop system, wherein K is feedback oscillator, and the closed-loop system is full
It is enough lower constraints：
A) closed loop transfer function, Tζz∞No more than defined maximum figure γ∞；
B) it is closed transmission function Tζz2No more than specified maximum figure γ2；
C) closed-loop pole of closed-loop system is located in the regions D of setting.
3. a kind of wind-electricity integration system self-adaption damping control side based on multiple convex polytope according to claim 1
Method, which is characterized in that each intermediate node of the classification regression tree indicates bifurcated node, to each intermediate node
Data carry out two classification, and each metric data is finally made to terminate at a terminal note according to top-down principle, each section eventually
Point indicates an equalization point, the i.e. operating condition of system.
4. a kind of wind-electricity integration system self-adaption damping control side based on multiple convex polytope according to claim 1
Method, which is characterized in that in the step 2, the detailed process for establishing classification regression tree is as follows：
(1) equalization point measurement of the selection with ornamental and controllability, calculates active power, and calculation formula is as follows：
In formula, δiAnd UiIt is the phase angle and voltage magnitude on i-th line road, δ respectivelykAnd UkThe respectively phase angle and electricity of kth circuit
Pressure amplitude value；
(2) categorised decision tree is built, the equalization point measurement of multidimensional is converted by one-dimensional optimal hyperlane using FLDSD technologies
Distance vector, the optimal hyperlane distance vector are to be mapped to hyperspace coordinate using the measurement of multidimensional as operating point,
Be configured to an optimal hyperlane for dividing different operating points to greatest extent, and calculate each operating point to optimal hyperlane away from
From, finally according to calculated distance symbol, current point of operation is classified, each operating point to optimal hyperlane away from
From calculation formula be：
In formula, d is distance of the operating point to optimal hyperlane, and h is current point of operation, and α and β indicate two different classes of, ∑sαWith
∑βThe covariance that respectively α and β is measured, μαAnd μβThe means of measurement α and β are indicated respectively；
(3) regression tree is built, by new operating point h ' to each optimal hyperlane apart from variable d ' input decision trees, according to
Bifurcated rule from top to bottom reaches some terminal node of decision tree, determines current point of operation in parameter space with this
Position.
5. a kind of wind-electricity integration system self-adaption damping control side based on multiple convex polytope according to claim 1
Method, which is characterized in that the self-adaptation control method of the multiple convex polytope is：
Parameter space is established first with decision tree and measures the mapping relations in space, it is anti-by the variation for measuring space measurement value
Reflect the variation of parameter space：When electric system is interfered, the current point of operation in parameter space will deviate from equalization point, work as electricity
After Force system is disturbed, current point of operation is restored to equalization point, or stablizes in other equalization points；According to current point of operation
Disturbed track judges the equalization point domain of attraction that system will enter, and is trained to decision tree using the disturbed track, obtains
Operation of Electric Systems operating mode is sent out in the position of parameter space, the controller that input matches with current point of operation at this time in wind-powered electricity generation
Effective damping is carried out to low frequency oscillations in the case of raw a wide range of random fluctuation.
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| CN109450310A (en) * | 2018-11-30 | 2019-03-08 | 河海大学 | A kind of wind power generating set H of disturbance suppression∞Robust control method |
| CN109995080A (en) * | 2019-04-08 | 2019-07-09 | 华北电力大学 | A kind of robust control method inhibiting the grid-connected sub-synchronous oscillation in double-fed fan motor field |
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| CN110649604A (en) * | 2019-09-26 | 2020-01-03 | 华北电力大学 | Damping control method suitable for random fluctuation of new energy |
| CN111969624A (en) * | 2020-08-25 | 2020-11-20 | 华北电力大学 | Damping control method and system of wind power grid-connected system containing virtual synchronous generator |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109450310A (en) * | 2018-11-30 | 2019-03-08 | 河海大学 | A kind of wind power generating set H of disturbance suppression∞Robust control method |
| CN109995080A (en) * | 2019-04-08 | 2019-07-09 | 华北电力大学 | A kind of robust control method inhibiting the grid-connected sub-synchronous oscillation in double-fed fan motor field |
| CN110259647A (en) * | 2019-06-21 | 2019-09-20 | 江南大学 | A kind of Wind turbines slow change type method for diagnosing faults based on just more cell space filtering |
| CN110259647B (en) * | 2019-06-21 | 2020-05-22 | 江南大学 | Wind turbine generator slow-varying type fault diagnosis method based on positive multiunit filtering |
| CN110298124A (en) * | 2019-07-03 | 2019-10-01 | 江南大学 | A kind of industrial control system actuator method for parameter estimation based on filtering |
| CN110649604A (en) * | 2019-09-26 | 2020-01-03 | 华北电力大学 | Damping control method suitable for random fluctuation of new energy |
| CN111969624A (en) * | 2020-08-25 | 2020-11-20 | 华北电力大学 | Damping control method and system of wind power grid-connected system containing virtual synchronous generator |
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