CN110797857A - Switching fuzzy model prediction control method for uncertain direct-current micro-grid - Google Patents
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Abstract
A switching fuzzy model prediction control method for an uncertain direct current microgrid is characterized by firstly, giving a nonlinear direct current microgrid system with a constant power load. And obtaining parameters by obtaining a T-S fuzzy model of the direct current micro-grid. And then discretizing the T-S fuzzy model of the direct-current microgrid. And then designing a fuzzy state observer and an output feedback model predictive controller of the direct-current micro-grid. The gains of the observer and the fuzzy controller can be calculated through a numerical LMI solver, and the gains do not need to be calculated manually. And assigning the gains of the controller and the observer obtained by solving to a loop to realize the uncertain direct current micro-grid output feedback fuzzy model prediction control of the constant power supply load.
Description
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a switching type fuzzy model prediction control method for a direct current microgrid of an uncertain direct current microgrid.
Background
With the increasing energy and environmental problems and the gradual depletion of fossil energy sources such as coal and petroleum, solar energy, wind energy and the like are receiving more and more attention as important components of new energy and renewable energy. Meanwhile, the continuous development of distributed energy in the power grid forms a new power supply network, namely a microgrid. Due to the advantages of high reliability, economy and the like, the micro-grid is widely concerned. The microgrid is a technology for organically combining and connecting a renewable energy power generation device, a load, an energy storage device, a control device and the like into a power grid. With the development of a power distribution system, a direct-current micro-grid has more advantages than an alternating-current micro-grid, and if the existing alternating-current power supply is changed into direct-current power supply, the power consumption can be reduced by about 20% by changing the composition of a power supply system, so that the aims of energy conservation, optimization and low carbon are fulfilled.
At present, the efforts for the control and stabilization of dc microgrid based on constant power loads are numerous, but almost all existing research efforts assume that the controller can operate without any error or model accuracy, and that the values of the dc microgrid parameters are completely known. In practice, however, the parameter uncertainty of a microgrid with a constant power load, inaccuracies and modeling errors in the Energy Storage System (ESS) model, and controller errors due to computational delay and computational approximation. Therefore, the controller for which the system is designed must be able to accommodate or absorb the negative effects of system uncertainty and modeling errors.
Disclosure of Invention
The method provided by the invention provides an uncertain direct current micro-grid output feedback fuzzy model prediction control method for realizing constant power supply load. First, a nonlinear direct current microgrid system with a constant power load is given. And obtaining parameters by obtaining a T-S fuzzy model of the direct current micro-grid. And then discretizing the T-S fuzzy model of the direct-current microgrid. And then designing a fuzzy state observer and an output feedback model predictive controller of the direct-current micro-grid. The gains of the observer and the fuzzy controller can be calculated through a numerical LMI solver, and the gains do not need to be calculated manually. And assigning the gains of the controller and the observer obtained by solving to a loop to realize the uncertain direct current micro-grid output feedback fuzzy model prediction control of the constant power supply load.
A switching fuzzy model prediction control method for an uncertain direct current microgrid comprises the following steps:
step 1, establishing a dynamic model of a direct current micro-grid system;
step 2, constructing a corresponding TS fuzzy model according to the model of the direct-current micro-grid;
step 3, discretizing a TS fuzzy model of the direct-current micro-grid system;
step 4, designing a switching type fuzzy model prediction controller of the uncertain direct current micro-grid;
step 5, providing solving conditions of the gain of the switching type fuzzy model prediction controller;
and 6, using the switching type fuzzy model prediction controller for the on-line control of the uncertain direct current microgrid.
Further, the step 1 specifically includes the following steps:
the overall architecture of the direct-current micro-grid system comprises a plurality of constant power loads CPL, a direct-current power supply and an energy storage system ESS;
to derive the overall dynamics of a dc microgrid with multiple constant power loads, first, for studying the properties of the dc power supply and a single constant power load, the dynamic equations of the jth constant power load system and the dc link in series with the RLC filter are as follows:
wherein PjIs the constant power of the jth CPL for the equilibrium point [ iL0,jvC0,j]Constant power PjThe following constraints must be satisfied:
ensuring that the Jacobian matrix of equation (1) is Hurwitz and has a negative real root to ensure that there are actual points of operability;
the voltage source connected to the ESS via the RLC filter is then investigated, the ESS being supplied by the current source iesThen the state space is represented as follows:
by the coordinate transformation method, the formula (1) and the formula (3) are transformed as follows:
further, the state space expression for a given plurality of constant power loads is as follows:
where j ═ Q +1 denotes the state of the dc source filter, x, · Q }, and s ═ Q +1 denotes the state of the dc source filterj＝[iL,jvC,j]T and xs＝[iL,svC,s]TRespectively showing the states of a jth constant power load filter and a direct current source filter;
the source subsystem may be represented as follows:
wherein
Suppose that the current point coordinates are transformed and let the current i of the energy storage systemesAnd changing the control input into a control input, rewriting the whole direct current micro-grid system, wherein the balance point of the direct current micro-grid system is at the origin, and the form is as follows:
wherein 
h＝[h1,...,hQ]T，
Further, in step 2, the TS fuzzy model needs to obtain a local linear system and a fuzzy membership function, and the TS fuzzy model equivalent to the nonlinear system is systematically calculated by a sector nonlinear method, so that each nonlinear item of the original system enters two linear sectors, and all groups of the two sectors are aggregated to derive a membership function and a local matrix of the TS fuzzy;
for simplicity, several constant power load micro grids are converted into an equivalent constant power load micro grid; for a given areaCalculate the sector
And
satisfy the requirement of
By computing inequalities for changing sectors
Convert it to min-max notEquation of
wherein ：
based on the sector non-linear approach, consider the following:
obtaining membership function M by solving equation (10)1 and M2：
Substituting the formula (10) into the formula (8) to obtain a TS fuzzy model equivalent to the system:
wherein 
Further, in step 3, discretizing the TS fuzzy model, where the step length is T, to obtain a discretized TS fuzzy model:
where φ (kT) is a non-linear term and d (kT) is a perturbation term.
Further, in the step 4, the switching fuzzy model predictive controller of the system is designed, and at any time k, a number n always exists, wherein n belongs to the group1, so that μn≥μi，i∈{1,...,L}：
wherein ,
μn≥μii ∈ { 1., L } and i ≠ n.
Further, in the step 5, the solving condition of the gain of the switching type fuzzy model predictive controller includes two solving sub-conditions at the same time:
solving sub-condition (1): for the TS fuzzy system given above, if there is a positive definite matrix XiAnd
where i, n is in the { 1.,..,. L }, and the matrix Lj，
Z，Λ-1＝diag[ρ1ρ2… ρq]-1, wherein ρ1,ρ2,…,ρq> 0, positive definite scalar lambda belongs to R(0,1)So that the following matrix inequality holds:
wherein 
ZssRepresents the s diagonal element of the matrix Z, i, j, l ∈ Z[1,L]For a given set Ω ═ { x | xTPμx is less than or equal to ξ }, wherein
The fuzzy system is a robust positive definite invariance set;
solving sub-condition (2): for the given TS ambiguity system described above, if equations (14), (15), (16) and the following inequalities are satisfied:
wherein Then omegatIs a terminal constraint set;
the terminal cost function of the system is as follows:
wherein ξ is
To minimize
The following optimization problem was introduced:
min ξ,subjected to V(x(k))≤ξ (19)
and is
Further, in step 6, the algorithm of model predictive control is as follows:
step 6-1, obtaining the state x (k) of the system;
and 6-2, solving the following optimization problem:
satisfying the formulae (14), (15), (16), (17), (20), followed by the next step.
Step 6-3, calculating control input
wherein And moving the time from k to k +1 and returning to the step 6-1.
By adopting the technical scheme, the invention has the following advantages: the control margin of the output feedback fuzzy model predictive controller design is improved by providing a real-time switching control idea, and the method is applied to a direct current micro-grid system to deal with the parameter uncertainty of the direct current micro-grid with constant power load, the uncertainty in an energy storage system model and the controller error caused by calculation delay, quantization effect and approximation. It should be noted that the proposed fuzzy model predictive controller and observer of the dc microgrid can fuse more system information due to having more complex structural forms, and the gain thereof can obtain a feasible control design solution with significantly reduced conservatism using the developed LMI design method, which is beneficial to the online implementation of the fuzzy control scheme. The negative influences caused by factors such as uncertainty, immeasurable control parameters and modeling errors of the direct-current micro-grid system can be adapted and eliminated, and stable operation of the direct-current micro-grid of the uncertain direct-current micro-grid system is further ensured.
Drawings
Fig. 1 is a schematic diagram of a dc microgrid model according to an embodiment of the present invention.
Fig. 2 shows a simplified version of a dc microgrid model with Q loads according to an embodiment of the present invention.
Fig. 3 is a flowchart illustrating an uncertain dc microgrid output feedback fuzzy model predictive control method with a constant power supply load according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
A switching fuzzy model prediction control method for an uncertain direct current microgrid comprises the following steps:
step 1, establishing a dynamic model of a direct current micro-grid system;
step 2, constructing a corresponding TS fuzzy model according to the model of the direct-current micro-grid;
step 3, discretizing a TS fuzzy model of the direct-current micro-grid system;
step 4, designing a switching type fuzzy model prediction controller of the uncertain direct current micro-grid;
step 5, providing solving conditions of the gain of the switching type fuzzy model prediction controller;
and 6, using the switching type fuzzy model prediction controller for the on-line control of the uncertain direct current microgrid.
Step 1, in a given nonlinear direct current microgrid system with constant power loads, a jth constant power load connected to a direct current link is considered. The constant power supply load is modeled by a voltage controlled current source, the value of which depends non-linearly on the power and voltage of the constant power load. Furthermore, for simplicity, the dc link is modeled by a voltage source. The dynamic equation for the jth constant power load system and the dc link in series with the RLC filter is given by:
wherein PjIs the jth constant power supply load. For equilibrium point [ i ]L0,jVC0,j]Constant power PjThe following constraints must be satisfied:
then, consider a voltage source connected to the ESS via an RLC filter, the ESS being supplied by a current source iesModeling, the state space representation is as follows:
the states of the nonlinear systems (1) and (3) change in a new nonlinear dynamic manner with an origin balance point. The transformation is beneficial to stability analysis based on Lyapunov stability theory and controller synthesis of a nonlinear system. In other words, in order to perform stability analysis by the Lyapunov stability method, there must be a dynamic system with the equilibrium point at the origin. For this purpose, the dynamics (1) and (3) will be converted into equations (4) and (5), respectively, by taking into account the change in coordinates.
The entire microgrid has a plurality of constant power supply loads, an energy storage system and a dc source, connected by RLC filters as shown in figure 2, based on a single constant power load connected to the dc link and an energy storage system connected to the dc source. As is apparent from fig. 2, we can decouple the entire dc microgrid into a Q +1 subsystem. Where i represents current, v represents voltage, L represents inductance, C represents capacitance, r represents resistance, and P represents power.
State space representation of the filter:
for j ═ 1, 2., Q } and s ═ Q +1 represent the states of the dc power supply filter. In addition, xj＝[iL,jvC,j]T and xs＝[iL,svC,s]TAre the states of the jth constant power load filter and the DC source filter, respectively, and
the source subsystem may be described as:
matrix array
Again, assume a coordinate change with respect to the operating point and let the ESS current iesTo control the inputs, we can rewrite the overall dc microgrid with its balance point at the origin in the following form.
wherein And is
h＝[h1,…,hQ]TAnd is
The balance point of the system (10) is also the origin, as in equations (3) and (4). This means that the non-linear term h appearing in equation (10)jSatisfy hj(0) 0. As can be seen from (10), the entire microgrid system includes Q nonlinear terms (i.e., hj). To derive the T-S fuzzy model, a so-called fan-shaped non-linear method is employed, each non-linearity being accurately represented by an equivalent T-S model in a predefined local region. For the jth constant power load, a region is defined
wherein 
Andthus, an equivalent T-S fuzzy model for region R ∩ j
Andthe selection of (b) is completely arbitrary and is based on the range of variation of the state of the entire dc microgrid. However, in this context, these parameters are selected based on a given local stability analysis, wherein a system LMI method for calculating a maximum value of the local stability of the constant power load is given.
Step 2, obtaining a direct current micro-grid T-S fuzzy model to obtain Mi，Ai，Bes，BsThe TS fuzzy model is a non-linear fuzzy hybrid of a finite number of local linear state space representations of a non-linear dynamical system. By TS fuzzy modeling, a nonlinear system is represented by the IF-THEN rule, where the preconditions are still nonlinear. Therefore, the TS fuzzy model can accurately capture the behavior of the smooth nonlinear system. Due to their linear results part, TS fuzzy modeling provides a straightforward method to apply well-known linear control theory to non-linear systems without linearization. The combination of linear control theory and fuzzy concepts allows the use of simple linear controllers to ensure semi-global stability. This is a major advantage of using a TS-based controller compared to other conventional linear and non-linear control methods.
The process of designing the linear controller is simple, but only the local stability near the working point can be ensured; also, the non-linear controller can ensure semi-global stability at the expense of a highly complex controller program. However, the design process of the controller based on TS is simple, and the stability of the closed loop is obtained by a fuzzy mathematical method based on a linear control theory. Furthermore, the smooth fuzzy membership functions of the fuzzy controller not only result in smooth transitions between local linear systems, but also ensure closed-loop stability between local models. This feature distinguishes TS-based fuzzy controllers from segment controllers, where stability between local regions is not guaranteed. Accordingly, a new TS-based fuzzy controller is proposed herein for dynamic stability of a dc microgrid with a constant power supply load.
Since the fuzzy controller is based on the TS fuzzy model, it is necessary to obtain a local linear system and fuzzy membership functions. In all proposed methods, the equivalent TS fuzzy model of the nonlinear system is systematically computed by deploying the so-called fan-shaped nonlinear method. In this method, each non-linear term of the original system goes into two linear sectors. All groups of two sectors are then aggregated to derive membership functions and local matrices for the TS ambiguity.
Without loss of generality, consider a microgrid having one constant power load and one source. A microgrid having a plurality of constant power loads may be converted into a microgrid having an equivalent constant power supply load. To compute the TS blur model, modeling of the model's non-linearity is required (10). There is only one non-linear term in the dynamics (i.e., h as defined in (12)). For a given area
Can calculate the sector
And
so that
Wherein the lower slope and the upper slope Umin and Umax。
By inequality of sectorConversion to min-max inequality
The slope can be simply calculated.
Based on a sector non-linear approach, consider
Solving (14) to obtain a membership function M1 and M2
Substituting (14) into (10), considering only the current, the equivalent T-S fuzzy model is
wherein ,
as can be seen from (16), the TS fuzzy model includes a nonlinear membership function Mi and a linear state space representation
Wherein i is 1, 2.
And 3, obtaining a T-S fuzzy model through discretization in discretization of the T-S fuzzy model of the obtained direct-current micro-grid, wherein an interference is added to an external factor of the direct-current micro-grid.
wherein
Where x (k) is the system state, u (k) is the control input, w (k) is the external interference, and y (k) is the system output.
And 4, in the design of the fuzzy state observer of the direct-current microgrid, the following state observer is used for estimating an uncertain state because the state is unknown.
wherein 
For the purpose of the estimation of the value,representing the observer gain. Defining an estimation error as
Thus obtaining a dynamic error.
wherein hj(ξ(k))＝max{h1(ξ(k)),…,hr(ξ(k))}。
From the above equation, the estimation error at sampling instant k is predicted as:
the quadratic function of the estimation error E (k)) is as follows:
E(e(k+i|k))＝e(k+i|k)TH0e(k+i|k)
if E (E (k + i | k)) satisfies the so-called QB condition, it is found that:
given H0Is greater than 0, and
as long as the following two equations are satisfied, (21) is randomly stable for any initial condition.
The matrix H can be obtained by the above matrix inequality0And observer gain
And 5, designing a controller which enables the system state to be stable step by step in the design of the direct current micro-grid output feedback fuzzy model prediction controller.
The enhanced closed loop system can be written as:
the enhanced state is defined as
A quadratic performance index function of
When both uncertainty and random variables exist, in order to derive the performance index upper bound, a quadratic lyapunov function is defined as:
to obtain an optimized stability condition, the following random puncturing condition is considered for the predicted state and input at the sampling time k.
ε (k) is the upper bound of
E{V(z(k|k))}≤ε(k) (29)
Therefore, minimizing the performance indicator function problem translates to minimizing ε. (28) And (29) satisfies the formulas (A) and (B).
minε
(28),(29) (30)
Solving for control input u and state gain
The following LMIs are satisfied:
if equation (31) is satisfied, the gain matrix prediction controller is:
and 6, assigning the gains of the controller and the observer obtained by solving to a loop to realize the uncertainty direct current micro-grid output feedback fuzzy model prediction control of the constant power supply load, and solving the gains of the observer by using the LMI through the expression
And controller gainSubstituting the formulas (20) and (24) to obtain the on-line control quantity and assigning the on-line control quantity to a control loop, thereby realizing the uncertain direct current micro-grid output feedback fuzzy model predictive control of the constant power load.
By the steps, negative influences caused by factors such as uncertainty, immeasurable control parameters and modeling errors of the direct current micro-grid system can be adapted and eliminated, and stable operation of the uncertain direct current micro-grid is further ensured. In summary, the uncertain dc micro-grid output feedback fuzzy model prediction control method with constant power supply load provided by the present invention has the following advantages: the control margin of the output feedback fuzzy model predictive controller design is improved by providing a real-time switching control idea, and the method is applied to a direct current micro-grid system to deal with the parameter uncertainty of the direct current micro-grid with constant power load, the uncertainty in an energy storage system model and the controller error caused by calculation delay, quantization effect and approximation. It should be noted that the proposed fuzzy model predictive controller and observer of the dc microgrid can fuse more system information due to having more complex structural forms, and the gain thereof can obtain a feasible control design solution with significantly reduced conservatism using the developed LMI design method, which is beneficial to the online implementation of the fuzzy control scheme. The negative influences caused by factors such as uncertainty, immeasurable control parameters and modeling errors of the direct-current micro-grid system can be adapted and eliminated, and stable operation of the direct-current micro-grid of the uncertain direct-current micro-grid system is further ensured.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.
Claims (7)
1. A switching fuzzy model prediction control method for an uncertain direct current microgrid is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a dynamic model of a direct current micro-grid system;
step 2, constructing a corresponding TS fuzzy model according to the model of the direct-current micro-grid;
step 3, discretizing a TS fuzzy model of the direct-current micro-grid system;
step 4, designing a switching type fuzzy model prediction controller of the uncertain direct current micro-grid;
step 5, providing solving conditions of the gain of the switching type fuzzy model prediction controller;
and 6, using the switching type fuzzy model prediction controller for the on-line control of the uncertain direct current microgrid.
2. The method for controlling the switching fuzzy model of the uncertain direct current microgrid according to claim 1, characterized in that: the step 1 specifically includes the following steps:
the overall architecture of the direct-current micro-grid system comprises a plurality of constant power loads CPL, a direct-current power supply and an energy storage system ESS;
to derive the overall dynamics of a dc microgrid with multiple constant power loads, first, for studying the properties of the dc power supply and a single constant power load, the dynamic equations of the jth constant power load system and the dc link in series with the RLC filter are as follows:
wherein PjIs the constant power of the jth CPL for the equilibrium point [ iL0,jvC0,j]Constant power PjThe following constraints must be satisfied:
ensuring that the Jacobian matrix of equation (1) is Hurwitz and has a negative real root to ensure that there are actual points of operability;
the voltage source connected to the ESS via the RLC filter is then investigated, the ESS being supplied by the current source iesThen the state space is represented as follows:
by the coordinate transformation method, the formula (1) and the formula (3) are transformed as follows:
further, the state space expression for a given plurality of constant power loads is as follows:
where j ═ Q +1 denotes the state of the dc source filter, x, · Q }, and s ═ Q +1 denotes the state of the dc source filterj＝[iL,jvC,j]T and xs＝[iL,svC,s]TRespectively showing the states of a jth constant power load filter and a direct current source filter;
the source subsystem may be represented as follows:
wherein 
Suppose that the current point coordinates are transformed and let the current i of the energy storage systemesAnd changing the control input into a control input, rewriting the whole direct current micro-grid system, wherein the balance point of the direct current micro-grid system is at the origin, and the form is as follows:
wherein 
3. The method for controlling the switching fuzzy model of the uncertain direct current microgrid according to claim 1, characterized in that: in the step 2, the TS fuzzy model needs to obtain a local linear system and a fuzzy membership function, and systematically calculates the TS fuzzy model equivalent to the nonlinear system by a sector nonlinear method, so that each nonlinear item of the original system enters two linear sectors, and all groups of the two sectors are aggregated to derive a membership function and a local matrix of the TS fuzzy;
for simplicity, several constant power load micro grids are converted into an equivalent constant power load micro grid; for a given area
Calculate the sectorAnd
satisfy the requirement of
By computing inequalities for changing sectors
Convert it into a min-max inequality
wherein ：
based on the sector non-linear approach, consider the following:
obtaining membership function M by solving equation (10)1 and M2：
Substituting the formula (10) into the formula (8) to obtain a TS fuzzy model equivalent to the system:
wherein 
4. The method for controlling the switching fuzzy model of the uncertain direct current microgrid according to claim 1, characterized in that: in the step 3, discretizing the TS fuzzy model with a step length of T to obtain a discretized TS fuzzy model:
where φ (kT) is a non-linear term and d (kT) is a perturbation term.
5. The method for controlling the switching fuzzy model of the uncertain direct current microgrid according to claim 1, characterized in that: in the step 4, a switching fuzzy model predictive controller of the system is designed, and at any time k, a number n always exists, wherein n is in an element of { 1.. multidot.n }, so that mu is enabled to be in an element of [ mu ], [ mu ]n≥μi，i∈{1,...,L}：
wherein ,
μn≥μii ∈ { 1., L } and i ≠ n.
6. The method for controlling the switching fuzzy model of the uncertain direct current microgrid according to claim 1, characterized in that: in the step 5, the solving conditions of the gain of the switching fuzzy model predictive controller simultaneously comprise two solving sub-conditions:
solving sub-condition (1): for the TS fuzzy system given above, if there is a positive definite matrix XiAnd
where i, n is in the { 1.,..,. L }, and the matrix Lj，Z，Λ-1＝diag[ρ1ρ2…ρq]-1, wherein ρ1,ρ2,…,ρq> 0, positive definite scalar lambda belongs to R(0,1)So that the following matrix inequality holds:
wherein ZssRepresents the s diagonal element of the matrix Z, i, j, l ∈ Z[1,L]For a given set Ω ═ { x | xTPμx is less than or equal to ξ }, wherein
The fuzzy system is a robust positive definite invariance set;
solving sub-condition (2): for the given TS ambiguity system described above, if equations (14), (15), (16) and the following inequalities are satisfied:
wherein 
Then omegatIs a terminal constraint set;
the terminal cost function of the system is as follows:
wherein ξ is
To minimizeThe following optimization problem was introduced:
min ξ,subjected to V(x(k))≤ξ (19)
and is
7. The method for controlling the switching fuzzy model of the uncertain direct current microgrid according to claim 1, characterized in that: in step 6, the algorithm of model predictive control is as follows:
step 6-1, obtaining the state x (k) of the system;
and 6-2, solving the following optimization problem:
satisfying the formulae (14), (15), (16), (17), (20), followed by the next step.
Step 6-3, calculating control input
wherein 
And moving the time from k to k +1 and returning to the step 6-1.
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| CN110994582A (en) * | 2019-11-19 | 2020-04-10 | 南京邮电大学 | Uncertain direct-current micro-grid output feedback fuzzy model prediction control method |
| CN111628525A (en) * | 2020-05-29 | 2020-09-04 | 辽宁工业大学 | Switching system-based micro-grid dual-mode stable control method |
| CN111725798A (en) * | 2020-07-24 | 2020-09-29 | 安徽工业大学 | Distributed economic dispatching prediction control method for direct-current micro-grid cluster |
| CN115309072A (en) * | 2022-08-01 | 2022-11-08 | 安徽工业大学 | T-S fuzzy-based large signal modeling method for grid-connected synchronous control system |
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| CN110277780A (en) * | 2019-07-18 | 2019-09-24 | 南京邮电大学 | Non-linear direct-current grid elasticity control method |
| CN110380403A (en) * | 2019-07-09 | 2019-10-25 | 闽江学院 | A kind of direct-current grid multi-mode transition control method based on network delay compensation |
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| CN109144164A (en) * | 2018-11-08 | 2019-01-04 | 南京邮电大学 | A kind of maximum power point Fuzzy Predictive Control method with uncertain photovoltaic system |
| CN110380403A (en) * | 2019-07-09 | 2019-10-25 | 闽江学院 | A kind of direct-current grid multi-mode transition control method based on network delay compensation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110994582A (en) * | 2019-11-19 | 2020-04-10 | 南京邮电大学 | Uncertain direct-current micro-grid output feedback fuzzy model prediction control method |
| CN111628525A (en) * | 2020-05-29 | 2020-09-04 | 辽宁工业大学 | Switching system-based micro-grid dual-mode stable control method |
| CN111628525B (en) * | 2020-05-29 | 2022-03-08 | 辽宁工业大学 | Switching system-based micro-grid dual-mode stable control method |
| CN111725798A (en) * | 2020-07-24 | 2020-09-29 | 安徽工业大学 | Distributed economic dispatching prediction control method for direct-current micro-grid cluster |
| CN115309072A (en) * | 2022-08-01 | 2022-11-08 | 安徽工业大学 | T-S fuzzy-based large signal modeling method for grid-connected synchronous control system |
| CN115309072B (en) * | 2022-08-01 | 2024-04-09 | 安徽工业大学 | Large-signal modeling method of grid-connected synchronous control system based on T-S fuzzy |
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