CN102594244A - Joint control method of primary frequency modulation for doubly-fed wind power generation set - Google Patents

Abstract

The invention discloses a joint control method of a primary frequency modulation for a doubly-fed wind power generation set, which comprises the steps of performing the on-line optimal design on the PD (proportional derivative) controller parameter by using continuous Hopfield neural network so as to build a neutral controller with relatively strong self-adaptability and realize the joint control of the rotor kinetic energy and backup power; using the minimum frequency change as the target function so that the network weight corresponds to the variable of the system state, and using the output of the neuron as the parameter of the PD controller; obtaining the change rule of the parameter by combining the target function expression and the energy function expression, and further seeking for the stable output according to the rule. By using the PSCAD (power system computer aided design)/EMTDC (electromagnetic transient in DC system) simulation platform, the method of the invention simulates and researches the neural joint control strategy in details, and compares with the traditional frequency control strategy; and the result shows that the Hopfield neural joint control has a better primary frequency modulation control effect.

Description

Double-fed fan motor unit primary frequency modulation combination control method

Technical field

The present invention relates to double-fed fan motor unit primary frequency modulation control method, the control raising power system frequency stability in order to the research and utilization wind-driven generator belongs to technical field of wind power generation.

Background technology

In recent years, the development and use of regenerative resource more and more receive the extensive attention of countries in the world, and wind energy with its nonstaining property and recyclability, becomes a kind of rising green energy resource as a kind of new forms of energy of sustainable development.Wind energy is compared with tidal energy, solar energy equal energy source, and its utilance is the highest, has the competitiveness that can compare with the conventional power generation usage mode.Therefore the wind power generation industry of China has obtained development rapidly in recent years, and the wind-powered electricity generation installed capacity improves constantly.

In wind generating technology; Compare with traditional constant-speed and constant-frequency wind generator system based on the wind generator system of variable speed constant frequency doubly-fed induction machine (DFIG) and to have remarkable advantages, therefore become the mainstream model of wind-power market gradually based on common asynchronous generator.Because the double-fed fan motor unit control system makes the decoupling zero of decoupling zero, rotating speed and the system frequency of its mechanical output and system's electromagnetic power; Wind energy conversion system rotor mechanical part can't change system frequency make response fast and effectively, so its rotation function does not almost have the contribution of system inertia.A large amount of double-fed fan motor units inserts electrical network and substitutes part conventional power generation usage unit, and the inertia of whole system will inevitably be affected and reduce relatively.The inertia of known system is relevant with the rate of change that frequency reduces, and when electrical network generation serious frequency reduction accident, inertia is low more, and system frequency reduces soon more, so the frequency of system will more difficult control.

Nowadays, increasing Utilities Electric Co. has proposed strict wind farm grid-connected technological guide rule, and the FREQUENCY CONTROL ability is that wherein important techniques one of requires.Therefore, require wind power generation to have the ability of participating in electrical network one secondary frequencies and become an important and urgent task as conventional power generation usage factory.

Research to double-fed fan motor unit frequency control system both at home and abroad mainly comprises three kinds of methods: 1) rotor kinetic energy control.Contain a large amount of rotation functions in the rotor of double-fed fan motor unit,, can be implemented in and reduce rotating speed when frequency descends, thereby the kinetic energy that discharges in the blade provides frequency to support through increasing additional FREQUENCY CONTROL link control rotor torque reference value.2) non-firm power control.Need have certain reserve capacity when being similar to the operation of synchronous generator unit, in order to guarantee the margin of power of speed-changing draught fan, blower fan has to operate at the working point that is not that maximal wind-energy is followed the trail of.Generally can or regulate the power speed curves, make blower fan unloading operation, make the active power reference value be lower than the best power curve, thereby guarantee the primary frequency modulation reserve capacity through the control propeller pitch angle.When system frequency significantly reduces, thereby reduce propeller pitch angle or move to increase meritorious output on the optimal power speed curves, participate in primary frequency modulation.3) jointly control.Consider the control of rotor kinetic energy and non-firm power simultaneously.

Traditional control model; Comprise that rotor kinetic energy control, non-firm power control and common jointly controls; All need set up a system mathematic model effectively, and for DFIG wind-powered electricity generation unit, because the uncertainty of wind speed and the complexity of power electronics model; Model trends towards non-linear and time variation, and the Mathematical Modeling of setting up a cover detailed complete is very difficult; On the other hand, the Control Parameter of traditional control method generally artificially confirms to have very big subjectivity according to experience, and the variation of Control Parameter is bigger to system's control characteristic influence, so gained Control Parameter robustness is relatively poor.

Summary of the invention

Technical problem to be solved by this invention provides a kind of double-fed fan motor unit control method for frequency; Need not precise math model and get final product execution control function; And its Control Parameter is that the operating structure according to system calculates in real time, has strong adaptability and robustness.

For solving the problems of the technologies described above, the present invention provides a kind of double-fed fan motor unit primary frequency modulation combination control method, it is characterized in that, may further comprise the steps:

(1) in the double-fed blower fan control system, sets up rotor kinetic energy control module and non-firm power control module respectively; The input variable of rotor kinetic energy control module and non-firm power control module is frequency departure; The output variable of rotor kinetic energy control module is the index word of torque reference value, and the input variable of non-firm power control module is the index word of propeller pitch angle reference value;

(2) set up the Hopfield neural net; Confirm the controlling object system model; And it is minimum that target function is that electrical network occurrence frequency when skew system frequency changes, and the state variable that makes controlling object is corresponding to network weight, and with the parameter of neuronic output as the PD controller;

(3) target function expression formula and standard energy function expression are mapped, deriving obtains the inclined to one side value expression of connection weight matrix and network;

(4) connection weight matrix that obtains and network are imported in the dynamical equation of inclined to one side value matrix substitution Hopfield network, and the nonlinear characteristic of getting neuron output is symmetric form S nonlinear interaction function the Changing Pattern of the controlled device parameter of deriving.

The beneficial effect that the present invention reached: the double-fed fan motor unit primary frequency modulation combination control method based on the Hopfield neural net of invention; For target adopts the Hopfield neural net parameter of controller is carried out optimal design according to frequency decline is minimum, can more reasonably arrange the associating frequency modulation of control of rotor kinetic energy and non-firm power control.When the skew of system occurrence frequency, blower fan can be realized the rotation function of release stored in rotor blade, and increases meritorious exerting oneself and provide frequency to support, and the frequency that can further alleviate system descends.The Hopfield nerve network controller that adopts has been realized parameter adaptive function, is not subject to the influence that external environment changes, take place very much variation such as fast adaptive system parameter.

Description of drawings

Fig. 1 jointly controls sketch map for the Hopfield nerve;

Fig. 2 is four machines, two regional analogue systems;

Fig. 3 is neuron controller A and the neuron controller B sketch map among the PSCAD;

Fig. 4 is the Hopfield neural net sketch map among the neuron controller A;

Fig. 5 be under the various strategies of power disturbance the frequency modulation curve ratio than sketch map;

Fig. 6 be under the various strategies of wind speed disturbance the frequency modulation curve ratio than sketch map.

Embodiment

Below in conjunction with accompanying drawing the present invention is specifically described as follows:

(1) first of technical scheme: take all factors into consideration control of rotor kinetic energy and non-firm power control, in the double-fed blower fan control system, set up rotor kinetic energy control module and non-firm power control module (seeing accompanying drawing 1) respectively.The core of controlling Design is Hopfield neuron controller A and Hopfield neuron controller B, and rotor kinetic energy control module and non-firm power control module input variable are frequency deviation f, and output variable is respectively the index word Δ T of torque reference value _RefIndex word Δ β with the propeller pitch angle reference value _Ref, purpose is to optimize the frequency characteristics control that jointly controls.

(2) second portion of technical scheme: set up the Hopfield neural net; Confirm the controlling object system model, and make network weight known, corresponding to the state variable of controlling object; Neuronic output as the PD controller parameter, is confirmed that target function is that frequency change is minimum.

Because converters is controlled electrical power fast, can think does not have dynamically between whole torque reference value index word of being regulated of wind-powered electricity generation unit medium frequency controlling unit and propeller pitch angle reference value index word and the actual output:

$\mathrm{\&Delta;}{T}_{\mathrm{ref}}=-{\mathrm{<figure-callout\; id="1"\; label="K\; f"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">K</figure-callout>}}_f\mathrm{\&Delta;f}-K_{\mathrm{in}1}\frac{\mathrm{df}}{\mathrm{dt}}---\left(1\right)$

$\mathrm{\&Delta;}{\mathrm{\&beta;}}_{\mathrm{ref}}=-K_{f2}\mathrm{\&Delta;f}-K_{\mathrm{in}2}\frac{\mathrm{df}}{\mathrm{dt}}$

The object of controller A control can be thought $\left\{\begin{array}{c}\frac{\mathrm{Df}}{\mathrm{Dt}}=K_A\mathrm{\&Delta;}{T}_{\mathrm{Ref}}\\ y=f\end{array}\right.,$ The object of controller B control can be thought $\left\{\begin{array}{c}\frac{\mathrm{Df}}{\mathrm{Dt}}=K_B\mathrm{\&Delta;}{\mathrm{\&beta;}}_{\mathrm{Ref}}\\ y=f\end{array}\right.,$ Wherein the B parameter of the parameter A of neuron controller A and neuron controller B can obtain through simple rotor kinetic energy control is carried out simple identification with the operational factor of simple non-firm power control.Wherein f is a system frequency, and Δ f is a frequency departure, Δ T _RefBe the torque reference value index word, Δ β _RefBe propeller pitch angle reference value index word, K _F1, K _In1Be respectively proportional control factor and the differential adjustment factor of neuron controller A, K _F2, K _In2Be respectively proportional control factor and the differential adjustment factor of neuron controller B, y is the output of controller, K _ABe the parameter of the controlling object of controller A, K _BParameter for the controlling object of controller B.

System model y (t)=C [Ax (t)+Bu (t)] to the ordinary circumstance of controller A, B discusses, and the conclusion of deriving like this has generality, and model is following:

$\left\{\begin{array}{c}\frac{\mathrm{dx}\left(t\right)}{\mathrm{dt}}=\mathrm{Bu}\left(t\right)\\ y\left(t\right)=C[\mathrm{Ax}\left(t\right)+\frac{\mathrm{dx}\left(t\right)}{\mathrm{dt}}]\end{array}\right.---\left(2\right)$

In the formula, y (t) is system's output variable, and x (t) is a system state variables, and u (t) is system's input variable, and A, B, C are the undetermined coefficient of descriptive system model.

Controller adopts the PD controller, and it is output as:

$u\left(t\right)=k_{p}e\left(t\right)+k_d\frac{\mathrm{de}\left(t\right)}{\mathrm{dt}}---\left(3\right)$

In the formula, k _p, k _dBe controller proportional control factor and differential adjustment factor, e (t) is the control system error, and r (t) is a rated frequency, gets constant; Y (t) is current frequency), that is:

e(t)＝r(t)-y(t)(4)

The target function of control system is got:

$E=\frac{1}{2}{e}^2\left(t\right)---\left(5\right)$

Formula (2), (3), (4) substitution formula (5) launched:

$E\left(t\right)=\frac{1}{2}{e}^2\left(t\right)=\frac{1}{2}{\{r\left(t\right)-C[\mathrm{Ax}\left(t\right)+\mathrm{Bu}\left(t\right)\left]\right\}}^2$

$=\frac{1}{2}{\{r\left(t\right)-C[\mathrm{Ax}\left(t\right)+B(k_p\left(t\right)e\left(t\right)+k_d\left(t\right)\frac{\mathrm{de}\left(t\right)}{\mathrm{dt}})\left]\right\}}^2---\left(6\right)$

Because it is P, D parameter that the parameter of two controllers is arranged, getting Hopfield network output neuron number is 2, and promptly CHNN is made up of two neurons, and CHNN is output as at t constantly:

V(t)＝[V ₁(t)，V ₂(t)] ^T＝[k _p(t)，k _d(t)] ^T(7)

The third part of technical scheme: target function expression formula and standard energy function expression are mapped, and deriving obtains connection weight matrix and the inclined to one side value expression of network.

Make V ₁=k _p, V ₂=k _d, with E _N(t) launch:

$E_N\left(t\right)=-\frac{1}{2}[w_{11}\left(t\right)k_p\left(t\right)k_p\left(t\right)+w_{12}\left(t\right)k_p\left(t\right)k_d\left(t\right)+w_{21}\left(t\right)k_d\left(t\right)k_p\left(t\right)---\left(8\right)$

$+w_{22}\left(t\right)k_d\left(t\right)k_d\left(t\right)]-k_p\left(t\right)I_1\left(t\right)-k_d\left(t\right)I_2\left(t\right)$

V wherein ₁, V ₂Be respectively the output of neuron 1 and neuron 2, w _IjBe the weights that are connected of neuron i and neuron j, I ₁(t), I ₂(t) be respectively the threshold value of neuron 1 and neuron 2.

When the Hopfield network was in poised state, energy function was minimum, w ₁₂=w ₂₁, at this moment:

$\frac{\&PartialD;E_N}{\&PartialD;k_p}=\frac{\&PartialD;E}{\&PartialD;k_p}=0---\left(9\right)$

$\frac{{\&PartialD;E}_N}{\&PartialD;k_d}=\frac{\&PartialD;E}{\&PartialD;k_d}=0$

E wherein _NBe the standard energy function of network, E is a target function, and e is a system deviation, the x system mode, and r is constant input.

By $\frac{{\&PartialD;E}_N}{\&PartialD;k_p}=\frac{\&PartialD;E}{\&PartialD;k_p}=0$ :

$\frac{{\&PartialD;E}_N}{\&PartialD;k_p}=\frac{1}{2}({-2w}_{11}{k}_p-2w_{12}{k}_d)-{\mathrm{<figure-callout\; id="1"\; label="I"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">I</figure-callout>}}_1=0---\left(10\right)$

$\frac{\&PartialD;E}{\&PartialD;k_p}=\frac{1}{2}({2B}^2{C}^2{e}^2{k}_p+{2B}^{2}C{k}_{d}e\frac{\mathrm{de}}{\mathrm{dt}}+2{\mathrm{ABC}}^2\mathrm{xe}-2\mathrm{BCre})=0---\left(11\right)$

Get by top two formulas:

ω ₁₁＝-B ²C ²e ²， ${\mathrm{\&omega;}}_{12}={\mathrm{\&omega;}}_{21}=-2B^2{C}^{2}e\frac{\mathrm{de}}{\mathrm{dt}},---\left(12\right)$

I ₁＝-2ABC ²ex+2BCre

Same reason $\frac{{\&PartialD;E}_N}{\&PartialD;k_d}=\frac{\&PartialD;E}{\&PartialD;k_d}=0$ :

${\mathrm{\&omega;}}_{22}=-B^2{C}^2{\left(\frac{\mathrm{de}}{\mathrm{dt}}\right)}^2,$ ${\mathrm{\&omega;}}_{12}={\mathrm{\&omega;}}_{21}=-2B^2{C}^{2}e\frac{\mathrm{de}}{\mathrm{dt}},$ (13)

$I_2=-2{\mathrm{ABC}}^2\frac{\mathrm{de}}{\mathrm{dt}}x+2\mathrm{BCr}\frac{\mathrm{de}}{\mathrm{dt}}$

Inclined to one side value (threshold value) I that obtains connection weight matrix W and network through top derivation is following:

$W=-\left[\begin{array}{cc}{\left[\mathrm{CBe}\left(t\right)\right]}^2& 2B^2{C}^{2}e\left(t\right)\frac{\mathrm{de}\left(t\right)}{\mathrm{dt}}\\ 2B^2{C}^{2}e\left(t\right)\frac{\mathrm{de}\left(t\right)}{\mathrm{dt}}& {\left[\mathrm{CB}\frac{\mathrm{de}\left(t\right)}{\mathrm{dt}}\right]}^2\end{array}\right]---\left(14\right)$

$I={\left[\begin{array}{c}-2A{\mathrm{BC}}^{2}e\left(t\right)x\left(t\right)+2\mathrm{BCr}\left(t\right)e\left(t\right)\\ -2{\mathrm{ABC}}^2\frac{\mathrm{de}\left(t\right)}{\mathrm{dt}}x\left(t\right)+2\mathrm{BCr}\left(t\right)\frac{\mathrm{de}\left(t\right)}{\mathrm{dt}}\end{array}\right]}^T---\left(15\right)$

(3) the 4th part of technical scheme: will obtain connection weight matrix and network and import in the dynamical equation of inclined to one side value matrix substitution Hopfield network; And the nonlinear characteristic of getting neuron output is symmetric form S nonlinear interaction function, the Changing Pattern of the controlled device parameter of deriving.

The dynamical equation of standard Hopfield network is:

$\left\{\begin{array}{c}{C}_i\frac{{\mathrm{du}}_i}{\mathrm{dt}}=\underset{j}{\mathrm{\&Sigma;}}{w}_{\mathrm{ij}}{V}_j+I_i\\ V_i=f\left(u_i\right)\end{array}\right.---\left(16\right)$

Get C _i=1.0, W that is asked and I substitution following formula are got:

$\frac{{\mathrm{<figure-callout\; id="1"\; label="du"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">du</figure-callout>}}_1}{\mathrm{dt}}=w_{11}{\mathrm{<figure-callout\; id="1"\; label="V"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">V</figure-callout>}}_1+w_{12}{V}_2+{\mathrm{<figure-callout\; id="1"\; label="I"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">I</figure-callout>}}_1$

$=-B^2{C}^2{e}^{2}g\left(u_1\right)+2B^2{C}^{2}e\frac{\mathrm{de}}{\mathrm{dt}}g\left(u_2\right)-2{\mathrm{ABC}}^2\mathrm{ex}+2\mathrm{BCre}---\left(17\right)$

$\frac{{\mathrm{du}}_2}{\mathrm{dt}}=w_{21}{\mathrm{<figure-callout\; id="1"\; label="V"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">V</figure-callout>}}_1+w_{22}{V}_2+I_2$

$=2B^2{C}^{2}e\frac{\mathrm{de}}{\mathrm{dt}}g\left(u_1\right)-B^2{C}^2{\left(\frac{\mathrm{de}}{\mathrm{dt}}\right)}^{2}g\left(u_2\right)-2{\mathrm{ABC}}^2\frac{\mathrm{de}}{\mathrm{dt}}x+2\mathrm{BCr}\frac{\mathrm{de}}{\mathrm{dt}}$

W wherein _IjBe i neuron and j the neuronic weights that are connected, I _i, u _i, V _iBe respectively i neuronic inclined to one side value, quantity of state and output, f (u _i) be action function.

The nonlinear characteristic of getting neuron output is symmetric form S nonlinear interaction function (gain K):

$g\left(u_i\right)=K_i\frac{{1-e}^{-{\mathrm{\&beta;}}_i{u}_i}}{1+e^{-{\mathrm{\&beta;}}_i{u}_i}}=\frac{{2\mathrm{<figure-callout\; id="1"\; label="K\; i"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">K</figure-callout>}}_i}{1+e^{-{\mathrm{\&beta;}}_i{u}_i}}-K_i,i=\mathrm{1,2}---\left(18\right)$

G (u wherein _i) be action function, K _iBe gain, β _iBe parameter.

The actual of network is output as:

k _p＝g(u ₁)

(19)

k _d＝g(u ₂)

Because $1+e^{-{\mathrm{\&beta;}}_1{\mathrm{<figure-callout\; id="1"\; label="u"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">u</figure-callout>}}_1}=\frac{{2K}_1}{k_p+{\mathrm{<figure-callout\; id="1"\; label="K"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">\; <figure-callout\; id="1"\; label="K"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">K</figure-callout>\; </figure-callout>}}_1},$ $1+e^{-{\mathrm{\&beta;}}_2{u}_2}=\frac{{2K}_2}{k_d+K_2},$ Then have:

$\frac{{\mathrm{dk}}_p}{{\mathrm{du}}_1}=\frac{-2K_1{e}^{-{\mathrm{\&beta;}}_1{u}_1}\left({-\mathrm{\&beta;}}_1\right)}{{(1+e^{-{\mathrm{\&beta;}}_1{u}_1})}^2}=2{\mathrm{\&beta;}}_1{K}_1\frac{K_1-k_p}{k_p+K_1}\frac{{(k_p+K_1)}^2}{{\left({2K}_1\right)}^2}=\frac{{\mathrm{\&beta;}}_1({{\mathrm{<figure-callout\; id="1"\; label="K"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">K</figure-callout>}}_1}^2-{k_p}^2)}{2{\mathrm{<figure-callout\; id="1"\; label="K"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">K</figure-callout>}}_1}$

$\frac{{\mathrm{dk}}_p}{\mathrm{dt}}=\frac{{\mathrm{dk}}_{\mathrm{<figure-callout\; id="1"\; label="p\; d\; u"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">p</figure-callout>}}}{d{u}_{}}\frac{{\mathrm{du}}_1}{\mathrm{dt}}$

(20)

$=\frac{{\mathrm{\&beta;}}_1({{\mathrm{<figure-callout\; id="1"\; label="K"\; filenames="HDA0000136750190000013.png,HDA0000136750190000021.png"\; state="\{\{state\}\}">K</figure-callout>}}_1}^2-{k_p}^2)}{2K_1}(-B^2{C}^2{e}^{2}g\left(u_1\right)+2B^2{C}^{2}e\frac{\mathrm{de}}{\mathrm{dt}}g\left(u_2\right)-2{\mathrm{ABC}}^2\mathrm{ex}+2\mathrm{BCre})$

In like manner can get:

$\frac{{\mathrm{dk}}_d}{{\mathrm{du}}_2}=\frac{{\mathrm{\&beta;}}_2({K_2}^2-{k_d}^2)}{2K_2}---\left(21\right)$

$\frac{{\mathrm{dk}}_d}{\mathrm{dt}}=\frac{{\mathrm{dk}}_d}{d{u}_2}\frac{{\mathrm{du}}_2}{\mathrm{dt}}$

$=\frac{{\mathrm{\&beta;}}_2({K_2}^2-{k_d}^2)}{2K_2}({2B}^2{C}^{2}e\frac{\mathrm{de}}{\mathrm{dt}}g{\left(u_1\right)-B^2{C}^2\left(\frac{\mathrm{de}}{\mathrm{dt}}\right)}^{2}g\left(u_2\right)-2{\mathrm{ABC}}^2\frac{\mathrm{de}}{\mathrm{dt}}x+2\mathrm{BCr}\frac{\mathrm{de}}{\mathrm{dt}})---\left(22\right)$

Find the solution differential equation (20) and formula (22), the k after can being optimized _p, k _dThereby, realize adjusting of PD parameter.

For example adopt 4 machines, 2 regional models of KUNDUR, transform formation analogue system of the present invention control strategy in the literary composition is carried out simulating, verifying.As shown in Figure 2, analogue system comprises 4 of synchronous machines, and power output is respectively 120MW, 120MW, 124MW, 120MW; The wherein otherness of each typhoon group of motors has been ignored in the double-fed fan motor field, and with a double-fed blower fan equivalence, gross output is 80MW; System loading L1, L2 are respectively 206MW, 342MW.System is when 5s, and synchronous machine G2 analyzes system frequency situation of change this moment because the step-out fault is out of service, and perhaps disturbance appears in the wind speed of simulating area, and 12m/s becomes 10m/s by rated wind speed.In the PSCAD platform, build the analogue system model, respectively to rotor kinetic energy control, non-firm power control, jointly control, the Hopfield nerve jointly controls and carries out simulation analysis, system's frequency modulation characteristic difference under each control strategy of comparison.

In PSCAD, set up Hopfield neuron controller A and Hopfield neuron controller B module is as shown in Figure 3.Two inside modules all comprise one two neuronic Hopfield neural net; With neuron controller A is example; As shown in Figure 4, the state of system is imported weights and threshold value corresponding to network, neuronic output is as the parameter of controller; The solution procedure of nonlinear differential equation is to accomplish automatically, and wherein the sfunction module is a S type function module.

Relatively can find out through simulation result such as Fig. 5,6; Double-fed fan motor unit primary frequency modulation combination control method based on the Hopfield neural net proposed by the invention can be realized parameter adaptive function; Thereby the frequency modulation ratio that more can rationally arrange control of rotor kinetic energy and non-firm power control; That compares has adaptive advantage in jointly controlling of permanent parameter; Descend so can further alleviate the frequency of system, improve low-limit frequency and have better control effect improving double-fed fan motor unit primary frequency modulation characteristic.

Claims (1)

1. a double-fed fan motor unit primary frequency modulation combination control method is characterized in that, may further comprise the steps:

(1) in the double-fed blower fan control system, sets up rotor kinetic energy control module and non-firm power control module respectively; The input variable of rotor kinetic energy control module and non-firm power control module is frequency departure; The output variable of rotor kinetic energy control module is the index word of torque reference value, and the input variable of non-firm power control module is the index word of propeller pitch angle reference value;

(2) set up the Hopfield neural net; Confirm the controlling object system model; And it is minimum that target function is that electrical network occurrence frequency when skew system frequency changes, and the state variable that makes controlling object is corresponding to network weight, and with the parameter of neuronic output as the PD controller;

(3) target function expression formula and standard energy function expression are mapped, deriving obtains the inclined to one side value expression of connection weight matrix and network;

(4) connection weight matrix that obtains and network are imported in the dynamical equation of inclined to one side value matrix substitution Hopfield network, and the nonlinear characteristic of getting neuron output is symmetric form S nonlinear interaction function the Changing Pattern of the controlled device parameter of deriving.

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