CN105790308A - Solar photovoltaic power generation system grid-connected operation control scheme - Google Patents

Abstract

The invention provides a solar photovoltaic power generation system grid-connected operation control scheme. The scheme comprises the following steps: a solar photovoltaic power generation experimental system is firstly built, wherein the solar photovoltaic power generation experimental system comprises four groups of solar photovoltaic power generation devices provided with DC/DC converters, and a common load, and the four groups of solar photovoltaic power generation devices together provide electric energy for the load; then, according to a physics principle, a mathematical model for the solar photovoltaic power generation experimental system is built, wherein the mathematical model belongs to an interconnection system with four nonlinear subsystems; and finally, based on the mathematical model, a distributed sampling event trigger controller is designed, and a simulation test platform for the system is given. According to the grid-connected operation control scheme provided by the invention, safe and stable micro power grid operation can be ensured, and communication data between power generation units are significantly reduced.

Description

The control program that a kind of solar photovoltaic generation system is incorporated into the power networks

Technical field

The control program that the present invention is incorporated into the power networks about a kind of solar photovoltaic generation system.

Background technology

Along with the aggravation of environmental pollution, people start to strengthen the construction of new forms of energy micro power network.Contrast AC network, it is high that direct current network has electric energy efficiency of transmission, and the control removing frequency from docks the characteristics such as easy with DC load, is widely studied.But, for there is the direct-current grid of multiple generation of electricity by new energy unit and there is the direct-current grid group that multiple direct-current grid forms, single electricity generation system stablize the stable operation being to ensure whole micro-capacitance sensor or micro-capacitance sensor group.Additionally, DC/DC current transformer is widely used in direct-current grid, it has the non-linear of inherence.These situations make the control of whole direct-current grid or micro-capacitance sensor group becomes more difficult.

Summary of the invention

In view of the foregoing, it is necessary to the control program that a kind of solar photovoltaic generation system is incorporated into the power networks is provided, it is possible to effectively solve the problems referred to above.

The present invention provides the control program that a kind of solar photovoltaic generation system is incorporated into the power networks, and comprises the steps:

First solar energy power generating experimental system is built, wherein, described solar energy power generating experimental system includes four groups of photovoltaic generation units and DC/DC current transformer and public load, and described common load is provided electric energy by described four groups of solar energy photovoltaic generators；

Secondly according to physics principle, setting up the mathematical model of described solar energy power generating experimental system, this mathematical model belongs to an interacted system with four nonlinearities systems；

It is finally based on described mathematical model, devises distributing sample event trigger controller, and give the simulation test platform of system.

The control program that solar photovoltaic power generation grid-connecting provided by the invention runs both can guarantee that micro-capacitance sensor safe and stable operation, and the communication data between each electricity generation system is substantially reduced.

Accompanying drawing explanation

Fig. 1 is that solar photovoltaic generation system is incorporated into the power networks the enforcement block diagram of control program.

Fig. 2 is that solar photovoltaic generation system is incorporated into the power networks the structural representation of control program.

Fig. 3 is photovoltaic power generation grid-connecting running experiment platform.

Detailed description of the invention

Refer to Fig. 1, the control program that a kind of solar photovoltaic generation system of the invention process is incorporated into the power networks, comprise the steps:

S1, refer to Fig. 2, builds solar photovoltaic generation system and is incorporated into the power networks system 100, wherein, the described photovoltaic generating system system 100 that is incorporated into the power networks includes four groups of solar energy power generating unit (10,20,30,40) and DC/CD current transformer (11,21,, and a common load 50 31,41), described four groups of solar energy power generating unit pass through transmission line (13,23,33,43) jointly common load 50 is powered；

S2, according to physics principle, establishes the mathematical model that described solar photovoltaic generation system is incorporated into the power networks, and this mathematical model belongs to an interacted system with four nonlinearities systems；

S3, based on described mathematical model, sets up the design of distributing sample event trigger mode fuzzy controllers.

In step sl, after described four groups of solar energy power generating unit (10,20,30,40) generating, the DC voltage with identical electric pressure is regulated and controled into by DC/DC current transformer (11,21,31,41)；Then, jointly common load 50 is powered respectively through transmission line (13,23,33,43).

In step s3, the mathematical model such as formula (1) of power generation sub-system is often organized:

Further, the parameter of system is selectedAfter fuzzy former piece variable, then described solar energy power generating nonlinear system can carry out approximate expression by following T-S fuzzy model:

${\stackrel{\·}{x}}_i\left(t\right)=A_i\left({\mathrm{\μ}}_i\right)x_i\left(t\right)+B_i\left({\mathrm{\μ}}_i\right)u_i\left(t\right)+{\mathrm{\Σ}}_{j=1,j\≠i}^N{\stackrel{\‾}{A}}_{ij}\left({\mathrm{\μ}}_i\right)x_j\left(t\right).---\left(2\right)$

Wherein

In step s3, the distributing fuzzy event trigger controller that design has following form,

$u_i\left(t\right)=K_i\left({\widehat{\mathrm{\μ}}}_i\right){\hat{x}}_i(t_k^i-{\mathrm{\τ}}_i),t\∈\[t_k^i,t_{k+1}^i)---\left(4\right)$

WhereinIt is the time delay of signal,WithRepresent the fuzzy former piece variable of controller and the system state variables of controller use respectively, and there is following event activation pattern:

In step s3, the parameter of described distributing fuzzy event trigger controller is obtained by following method:

First described distributing fuzzy event trigger controller (6) is rewritten as:

$u_i\left(t\right)=K_i\left({\widehat{\mathrm{\μ}}}_i\right){\hat{x}}_i(t-{\mathrm{\η}}_i(t\left)\right),t\∈\[t_k^i,t_{k+1}^i)---\left(7\right)$

Wherein

In conjunction with formula (2) and (7), it is thus achieved that the Fuzzy control system of following closed loop:

${\stackrel{\·}{x}}_i\left(t\right)=A_i\left({\mathrm{\μ}}_i\right)x_i\left(t\right)+B_i\left({\mathrm{\μ}}_i\right)K_i\left({\widehat{\mathrm{\μ}}}_i\right){\hat{x}}_i(t-{\mathrm{\η}}_i(t\left)\right)+{\mathrm{\Σ}}_{j=1,j\≠i}^N{\stackrel{\‾}{A}}_{ij}\left({\mathrm{\μ}}_i\right)x_j\left(t\right).---\left(8\right)$

Additionally, definition

$e_i(t-{\mathrm{\η}}_i(t\left)\right)={\hat{x}}_i(t-{\mathrm{\η}}_i(t\left)\right)-x_i(t-{\mathrm{\η}}_i(t\left)\right)---\left(9\right)$

With

x_i(v)=x_i(t-η_i(t))-x_i(t-τ_i).(10)

Based on the description above, the Fuzzy control system (10) of closed loop can be rewritten as follows:

$\begin{array}{c}{\stackrel{\·}{x}}_i\left(t\right)=A_i\left({\mathrm{\μ}}_i\right)x_i\left(t\right)+B_i\left({\mathrm{\μ}}_i\right)K_i\left({\widehat{\mathrm{\μ}}}_i\right)\left(e_i\right(t-{\mathrm{\η}}_i\left(t\right))+x_i(t-{\mathrm{\τ}}_i)+x_i(v\left)\right)+\\ +{\mathrm{\Σ}}_{j=1,j\≠i}^N{\stackrel{\‾}{A}}_{ij}\left({\mathrm{\μ}}_i\right)x_j\left(t\right).\end{array}---\left(11\right)$

Then following LKF function is considered,

$V\left(t\right)={\mathrm{\Σ}}_{i=1}^N\[V_{i1}\left(t\right)+V_{i2}\left(t\right)\],t\∈\[t_k^i,t_{k+1}^i)---\left(12\right)$

Have

$V_{i1}\left(t\right)=x_i^T\left(t\right)P_i{x}_i\left(t\right)+{\∫}_{t-{\mathrm{\τ}}_i}^t{x}_i^T\left(a\right)Q_i{x}_i\left(a\right)da+{\mathrm{\τ}}_i{\∫}_{-{\mathrm{\τ}}_i}^0{\∫}_{t+\mathrm{\β}}^t{\stackrel{\·}{x}}_i^T\left(a\right)Z_i{\stackrel{\·}{x}}_i\left(a\right)dad\mathrm{\β},$

$\begin{array}{c}{V}_{i2}\left(t\right)={({\stackrel{\‾}{\mathrm{\η}}}_i-{\mathrm{\τ}}_i)}^2{\∫}_{t_k^i-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i^T\left(a\right)W_i{\stackrel{\·}{x}}_i\left(a\right)da\\ -\frac{{\mathrm{\π}}^2}{4}{\∫}_{t_k^i-{\mathrm{\τ}}_i}^{t-{\mathrm{\τ}}_i}{\[x_i\left(a\right)-x_i(t_k^i-{\mathrm{\τ}}_i)\]}^T{W}_i\[x_i\left(a\right)-x_i(t_k^i-{\mathrm{\τ}}_i)\]da,\end{array}$

Wherein P_i,Q_i,Z_i,W_iIt it is the matrix of symmetric positive definite.

After LKF function derivation, obtain:

$\begin{array}{c}{\stackrel{\·}{V}}_{i1}\left(t\right)=\\ 2x_i^T\left(t\right)P_i{\stackrel{\·}{x}}_i\left(t\right)+x_i^T\left(t\right)Q_i{x}_i\left(t\right)-x_i^T(t-{\mathrm{\τ}}_i)Q_i{x}_i(t-{\mathrm{\τ}}_i)+{\mathrm{\τ}}_i^2{\stackrel{\·}{x}}_i^T\left(t\right)Z_i{\stackrel{\·}{x}}_i\left(a\right)-\\ {\mathrm{\τ}}_i{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i^T\left(z\right)Z_i{\stackrel{\·}{x}}_i\left(a\right)da,---\left(13\right)\\ {\stackrel{\·}{V}}_{i2}\left(t\right)={({\stackrel{\‾}{\mathrm{\η}}}_i-{\mathrm{\τ}}_i)}^2{\stackrel{\·}{x}}_i^T\left(t\right)W_i{\stackrel{\·}{x}}_i\left(t\right)-\frac{{\mathrm{\π}}^2}{4}{x}_i^T\left(v\right)Q_i{x}_i\left(v\right).---\left(14\right)\end{array}$

By using Jensen inequality lemma, obtain:

$\begin{array}{c}-{\mathrm{\τ}}_i{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i^T\left(z\right)Z_i{\stackrel{\·}{x}}_i\left(t\right)da\≤-{\[{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i\left(a\right)da\]}^T{Z}_i\[{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i\left(a\right)da\]\\ =-{\left(x_i\right(t)-x_i(t-{\mathrm{\τ}}_i\left)\right)}^T{Z}_i\left(x_i\right(t)-x_i(t-{\mathrm{\τ}}_i\left)\right).\end{array}---\left(15\right)$

Definition matrixWith

${\mathrm{\χ}}_i\left(t\right)={\[{\stackrel{\·}{x}}_i^T\left(a\right)x_i^T\left(a\right)x_i^T(t-{\mathrm{\τ}}_i)x_i^T\left(v\right)e_i^T(t-{\mathrm{\η}}_i(t\left)\right)\]}^T,$

We obtain following inequality conversion,

Additionally, we have

$\begin{array}{c}\left|\right|e_i(t-{\mathrm{\η}}_i(t\left)\right)\left|\right|=\left|\right|{\hat{x}}_i(t-{\mathrm{\η}}_i(t\left)\right)-x_i(t-{\mathrm{\η}}_i(t\left)\right)\left|\right|\\ \≤{\∂}_i\left|\right|x_i(t-{\mathrm{\η}}_i(t\left)\right)\left|\right|\end{array}$

$={\∂}_i\left|\right|x_i(t-{\mathrm{\τ}}_i)+x_i\left(v\right)\left|\right|.---\left(17\right)$

Based on above description, Wo Menyou

WhereinΞ₍₁₎=[0I000]^T,

${\mathrm{\Θ}}_i=\left[\begin{array}{ccccc}{\mathrm{\Θ}}_i^{\left(1\right)}& P_i& 0& 0& 0\\ *& Q_i-Z_i& Z_i& 0& 0\\ *& *& -Q_i-Z_i+& {\∂}_i^2{U}_i& 0\\ *& *& *& -\frac{{\mathrm{\π}}^2}{4}{W}_i+{\∂}_i^2{U}_i& 0\\ *& *& *& *& -U_i\end{array}\right].$

By using Schur lemma, following inequality is set up and is implied that

Then, in order to inequality (19) converts to the situation of LMI, we define:

Formula below is solved, thus obtaining the parameter of distributing fuzzy event trigger controller by the LMI workbox of MATLAB:

WhereinIt is the matrix that positive definite is symmetrical,It is positive scalar, and

So the parameter of distributing fuzzy event trigger controller is calculated as follows:

$K_i^s={\stackrel{\‾}{K}}_i^s{G}_i^{-1},s\∈\{1,2,...,r_i\}.---\left(22\right)$

After step S3, refer to Fig. 3, can farther include to build the simulation test platform of DSPACE.

Note, above are only presently preferred embodiments of the present invention and institute's application technology principle.It will be appreciated by those skilled in the art that and the invention is not restricted to specific embodiment described here, various obvious change can be carried out for a person skilled in the art, readjust and substitute without departing from protection scope of the present invention.Therefore, although the present invention being described in further detail by above example, but the present invention is not limited only to above example, when without departing from present inventive concept, other Equivalent embodiments more can also be included, and the scope of the present invention is determined by appended right.

Claims (7)

1. the control program that a solar photovoltaic generation system is incorporated into the power networks, it is characterised in that comprise the steps:

Building solar energy power generating experimental system, wherein, described experimental system includes four groups of solar energy photovoltaic generators and DC/DC current transformer and a common load, and described four groups of solar energy photovoltaic generators provide electric energy for described load jointly；

According to physics principle, establishing the mathematical model of described solar energy power generating experimental system, this mathematical model belongs to an interacted system with four groups of nonlinearities systems；

Based on described mathematical model, the design of design distributing fuzzy event trigger controller.

2. control program as claimed in claim 1, it is characterised in that often organize the mathematical model such as formula (1) of solar photovoltaic generation system:

Wherein i={1,2,3,4}, for i-th subsystem, v_PV(i),v_0(i)Represent the voltage of solar photovoltaic generation system respectively, the electric current of inductance and electric capacity C_0(i)Voltage；R_0(i),R_L(i),R_line(i),R_loadIt is electric capacity C respectively_0(i), inductance L_(i), the impedance of transportion wire road and load；V_D(i)It is the forward voltage of current diode,It it is the measurement electric current of load.

3. control program as claimed in claim 1, it is characterised in that select the parameter of systemAfter fuzzy former piece variable, then described solar energy power generating nonlinear system can carry out approximate expression by following T-S fuzzy model:

${\stackrel{\·}{x}}_i\left(t\right)=A_i\left({\mathrm{\μ}}_i\right)x_i\left(t\right)+B_i\left({\mathrm{\μ}}_i\right)u_i\left(t\right)+{\mathrm{\Σ}}_{j=1,j\≠i}^N{\stackrel{\‾}{A}}_{ij}\left({\mathrm{\μ}}_i\right)x_j\left(t\right).---\left(2\right)$

Wherein

4. control program as claimed in claim 1, it is characterised in that described distributing fuzzy event trigger controller has following form,

$u_i\left(t\right)=K_i\left({\widehat{\mathrm{\μ}}}_i\right){\hat{x}}_i(t_k^i-{\mathrm{\τ}}_i),t\∈\[t_k^i,t_{k+1}^i)---\left(4\right)$

Whereinτ_iIt is the time delay of signal,WithRepresent the fuzzy former piece variable of controller and the system state variables of controller use respectively, and there is following event activation pattern:

5. control program as claimed in claim 1, it is characterised in that the parameter of described distributing fuzzy event trigger controller is obtained by following method:

First described distributing fuzzy event trigger controller (6) is rewritten as:

$u_i\left(t\right)=K_i\left({\widehat{\mathrm{\μ}}}_i\right){\hat{x}}_i(t-{\mathrm{\η}}_i(t\left)\right),t\∈\[t_k^i,t_{k+1}^i)---\left(7\right)$

Wherein

In conjunction with formula (2) and (7), it is thus achieved that the Fuzzy control system of following closed loop:

${\stackrel{\·}{x}}_i\left(t\right)=A_i\left({\mathrm{\μ}}_i\right)x_i\left(t\right)+B_i\left({\mathrm{\μ}}_i\right)K_i\left({\widehat{\mathrm{\μ}}}_i\right){\hat{x}}_i\left(t-{\mathrm{\η}}_i\left(t\right)\right)+{\mathrm{\Σ}}_{j=1,j\≠i}^N{\stackrel{\‾}{A}}_{ij}\left({\mathrm{\μ}}_i\right)x_j\left(t\right).---\left(8\right)$

Additionally, definition

$e_i(t-{\mathrm{\η}}_i(t\left)\right)={\hat{x}}_i(t-{\mathrm{\η}}_i(t\left)\right)-x_i(t-{\mathrm{\η}}_i(t\left)\right)---\left(9\right)$

With

x_i(v)=x_i(t-η_i(t))-x_i(t-τ_i).(10)

Based on the description above, the Fuzzy control system (10) of closed loop can be rewritten as follows:

$\begin{array}{c}{\stackrel{\·}{x}}_i\left(t\right)=A_i\left({\mathrm{\μ}}_i\right)x_i\left(t\right)+B_i\left({\mathrm{\μ}}_i\right)K_i\left({\widehat{\mathrm{\μ}}}_i\right)\left(e_i\left(t-{\mathrm{\η}}_i\left(t\right)\right)+x_i\left(t-{\mathrm{\τ}}_i\right)+x_i\left(v\right)\right)+\\ {\mathrm{\Σ}}_{j=1,j\≠i}^N{\stackrel{\‾}{A}}_{ij}\left({\mathrm{\μ}}_i\right)x_j\left(t\right).\end{array}---\left(11\right)$

Then following LKF function is considered,

$V\left(t\right)={\mathrm{\Σ}}_{i=1}^N\[V_{i1}\left(t\right)+V_{i2}\left(t\right)\],t\∈\[t_k^i,t_{k+1}^i)---\left(12\right)$

Have

$\begin{array}{c}{V}_{i1}\left(t\right)=x_i^T\left(t\right)P_i{x}_i\left(t\right)+{\∫}_{t-{\mathrm{\τ}}_i}^t{x}_i^T\left(a\right)Q_i{x}_i\left(a\right)da+{\mathrm{\τ}}_i{\∫}_{-{\mathrm{\τ}}_i}^0{\∫}_{t+\mathrm{\β}}^t{\stackrel{\·}{x}}_i^T\left(a\right)Z_i{\stackrel{\·}{x}}_i\left(a\right)dad\mathrm{\β},\\ V_{i2}\left(t\right)={\left({\stackrel{\‾}{\mathrm{\η}}}_i-{\mathrm{\τ}}_i\right)}^2{\∫}_{t_k^i-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i^T\left(a\right)W_i{\stackrel{\·}{x}}_i\left(a\right)da\\ -\frac{{\mathrm{\π}}^2}{4}{\∫}_{t_k^i-{\mathrm{\τ}}_i}^{t-{\mathrm{\τ}}_i}{\[x_i\left(a\right)-x_i\left(t_k^i-{\mathrm{\τ}}_i\right)\]}^T{W}_i\[x_i\left(a\right)-x_i\left(t_k^i-{\mathrm{\τ}}_i\right)\]da,\end{array}$

Wherein P_i,Q_i,Z_i,W_iIt it is the matrix of symmetric positive definite；

After LKF function derivation, obtain:

$\begin{array}{c}{\stackrel{\·}{V}}_{i1}\left(t\right)=\\ 2x_i^T\left(t\right)P_i{\stackrel{\·}{x}}_i\left(t\right)+x_i^T\left(t\right)Q_i{x}_i\left(t\right)-x_i^T\left(t-{\mathrm{\τ}}_i\right)Q_i{x}_i\left(t-{\mathrm{\τ}}_i\right)+{\mathrm{\τ}}_i^2{\stackrel{\·}{x}}_i^T\left(t\right)Z_i{\stackrel{\·}{x}}_i\left(a\right)-\\ {\mathrm{\τ}}_i{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i^T\left(a\right)Z_i{\stackrel{\·}{x}}_i\left(a\right)da,---\left(13\right)\\ {\stackrel{\·}{V}}_{i2}\left(t\right)={\left({\stackrel{\‾}{\mathrm{\η}}}_i-{\mathrm{\τ}}_i\right)}^2{\stackrel{\·}{x}}_i^T\left(t\right)W_i{\stackrel{\·}{x}}_i\left(t\right)-\frac{{\mathrm{\π}}^2}{4}{x}_i^T\left(v\right)Q_i{x}_i\left(v\right).---\left(14\right)\end{array}$

By using Jensen inequality lemma, obtain:

$\begin{array}{c}-{\mathrm{\τ}}_i{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i^T\left(a\right)Z_i{\stackrel{\·}{x}}_i\left(t\right)da\≤-{\[{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i\left(a\right)da\]}^T{Z}_i\[{\∫}_{t-{\mathrm{\τ}}_i}^t{\stackrel{\·}{x}}_i\left(a\right)da\]\\ =-{\left(x_i\left(t\right)-x_i\left(t-{\mathrm{\τ}}_i\right)\right)}^T{Z}_i\left(x_i\left(t\right)-x_i\left(t-{\mathrm{\τ}}_i\right)\right).\end{array}---\left(15\right)$

Definition matrix0 < M₀≤M_iWith

${\mathrm{\&chi;}}_i\left(t\right)={\&lsqb;{\stackrel{\&CenterDot;}{x}}_i^T\left(a\right)x_i^T\left(a\right)x_i^T(t-{\mathrm{\&tau;}}_i)x_i^T\left(v\right)e_i^T(t-{\mathrm{\&eta;}}_i(t\left)\right)\&rsqb;}^T,$

We obtain following inequality conversion,

Additionally, we have

$\begin{array}{c}\left|\right|e_i\left(t-{\mathrm{\&eta;}}_i\left(t\right)\right)\left|\right|=\left|\right|{\hat{x}}_i\left(t-{\mathrm{\&eta;}}_i\left(t\right)\right)-x_i\left(t-{\mathrm{\&eta;}}_i\left(t\right)\right)\left|\right|\\ \&le;\&part;\left|\right|x_i\left(t-{\mathrm{\&eta;}}_i\left(t\right)\right)\left|\right|\\ ={\&part;}_i\left|\right|x_i\left(t-{\mathrm{\&tau;}}_i\right)+x_i\left(v\right)\left|\right|.\end{array}---\left(17\right)$

Based on above description, Wo Menyou

WhereinΞ₍₁₎=[0I000]^T,

${\mathrm{\&Theta;}}_i=\left[\begin{array}{ccccc}{\mathrm{\&Theta;}}_i^{\left(1\right)}& P_i& 0& 0& 0\\ *& Q_i-Z_i& Z_i& 0& 0\\ *& *& -Q_i-Z_i+& {\&part;}_i^2{U}_i& 0\\ *& *& *& -\frac{{\mathrm{\&pi;}}^2}{4}{W}_i+{\&part;}_i^2{U}_i& 0\\ *& *& *& *& -U_i\end{array}\right].$

By using Schur lemma, following inequality is set up and is implied that

Then, in order to inequality (19) converts to the situation of LMI, we define:

6. method for designing as claimed in claim 5, control program, solves formula below by the LMI workbox of MATLAB, thus obtaining the parameter of distributing fuzzy event trigger controller:

WhereinIt is the matrix that positive definite is symmetrical,It is positive scalar, and

So the parameter of distributing fuzzy event trigger controller is calculated as follows:

$K_i^s={\stackrel{\&OverBar;}{K}}_i^s{G}_i^{-1},s\&Element;\{1,2,...,r_i\}.---\left(22\right).$

7. control program as claimed in claim 1, it is characterised in that include the simulation test platform building DSPACE.