CN105958493A - Grid harmonic current signal tracking control method - Google Patents

Abstract

The invention provides a grid harmonic current signal tracking control method. For the influence on a system by a control delay, with an implanted hybrid active power filter IHAPF as a research object, a harmonic current signal tracking control method based on delay compensation is proposed. The method is mainly formed by an improved smith predictor and a neural network PI control, wherein the improved smith predictor allows the time delay process of the system to be switched to the outside from the inside of a controlled closed-loop, and thus the influence on the system by the control delay is reduced.

Description

Mains by harmonics current signal tracking and controlling method

Technical field

The present invention relates to a kind of mains by harmonics current signal tracking and controlling method.

Background technology

Along with power electronic equipment is widely used in power distribution network, the harmonic pollution problems in enterprise power distribution network is the tightest Weight, causes massive losses to business economic every year.And each harmonic can only be filtered by passive filter, and easily and electrical network Produce resonance, can not meet the requirement to harmonics restraint.Active filter is also a kind of important means of suppression harmonic wave, and it is former Reason is to search out harmonic current the target compensated from needing, then is generated that one equal in magnitude with this harmonic wave and pole by the equipment of compensation The anti-phase compensation electric current of property is cancelled out each other with harmonic current.Active filter can carry out dynamic compensation to each harmonic, and rings The cycle of answering is short, thus is used widely in mains by harmonics filters.But active filter there is also, and capacity is little, structure complicated with And use cost crosses the shortcomings such as height, seldom individually use in power distribution network for this.Therefore, active electric power filter is used under normal circumstances Ripple device and passive power filter be combined with each other composition mixed active electric power filter, thus realize the purpose of harmonic filtration.

Electric current accurate tracking is controlled to directly affects wave filter overall performance by Active Power Filter-APF, and evaluates system The two indices of system performance is system response time and stable state compensation precision, proposes for improving the numerous scholars of performance of filter Numerous methods.The active power filtering equipment digitized controller of many employings at present, it is achieved the most flexibly, but there is time delay phenomenon, right Performance of filter causes and has a strong impact on, special due to its structure for hybrid active filter, and its impact is compared by time delay Even more serious in single active filter.Smith Prediction Control, along with the development of computer, has become as solution industry One of effective ways that time delay is delayed, simth pre-estimation has a change fast response time to setting value, and it is excellent that tracking accuracy is high etc. Point.But, the mathematical model of controlled device is required higher by Smith predictor, and this point ratio in engineer applied is relatively difficult to achieve, The most traditional Smith predictor is limited by parameter cannot make system tend towards stability.

Summary of the invention

Technical problem underlying to be solved by this invention is to provide a kind of mains by harmonics current signal tracking and controlling method, main To be compensated smith prediction device by π to form with PI controller parameter being optimized by PSO-BP neutral net.π compensates smith Prediction device makes during system delay from the closed loop internal conversion controlled to outside, thus reduces the shadow controlling delay on system Ring.

In order to solve above-mentioned technical problem, the invention provides a kind of mains by harmonics current signal tracking and controlling method, Including following sequential steps:

1) by harmonic current i in electrical network_hThrough 1/G₀It is changed into harmonic voltage signal U_h；Described G₀Input for inverter Electric current i_cWith output voltage U_cBetween transmission function；

2) with described harmonic voltage signal U_hFor controlling target, by described harmonic voltage signal U_hImproved Smith is pre- Estimate device and inverter；The Smith predictor of described improvement includes that the PI being optimized parameter by PSO-BP neutral net controls Device and a π compensate Smith predictor；And meet following relational expression:

$\left\{\begin{array}{c}{u}_e=u_h-u_c\\ (u_e-d)G_c^{*}\left(s\right)=u\\ {\mathrm{uG}}_p^{*}\left(s\right)(1-e^{-\mathrm{\π}s})=d\\ {\mathrm{ue}}^{(\mathrm{\γ}-\mathrm{\π})s}{G}_p^{*}\left(s\right)e^{-\mathrm{\γ}s}=u_c\end{array}\right.$

Wherein, U_eFor harmonic voltage signal U_hWith inverter output voltage U_cDifference；It is respectively PSO- Inverter transmission function after transmission function, the identification of the PI controller of BP Neural Network Optimization；D is amount of transition；U is PI control Device output voltage signal；

Thus the output voltage U of inverter can be obtained_cWith harmonic voltage signal U_hTransmission function between the two is:

$\frac{u_c}{u_h}=\frac{G_c^{*}\left(s\right)G_p^{*}\left(s\right)e^{-\mathrm{\π}s}}{1+G_c^{*}\left(s\right)G_p^{*}\left(s\right)};$

The described algorithm flow by the PI controller of PSO-BP Neural Network Optimization is as follows:

1) according to the carrying out practically state of the Active Power Filter-APF of pouring-in mixing, in conjunction with neutral net input, output Sample set, sets up the forecast model of neutral net, and connection weights all of between neuron become real number vector with threshold coding Represent the individual particles in population；

2) initial position of particle, speed, inertia coeffeicent w, Studying factors c are initialized₁、c₂With c '₁、c′₂, it is stipulated that maximum Iterations；

3) according to input, output sample, the forwards algorithms of BP network is utilized

Δ u (t)=k_p(u_e(t)-u_e(t-1))+k_iu_e(t)

And particle cluster algorithm optimizing error function

$J\left(t\right)=\frac{{\left(u_e\right)}^2}{2}=\frac{{\[-u_h-u_c\]}^2}{2}$

Calculate each particle fitness function value, and using the desired positions of each particle as its history optimum position, Start iteration；

Wherein, parameter k in the most corresponding PI controller of output node_p、k_i；

4) 4 iterative formulas of PSO-BP algorithm are utilized

Δw_ij(t)=(w-1) (w_ij(t)-w_ij(t-1))+r′₁c′₁(w_ij(b)-w_ij(t))+r′₂c₂₂(w_ij(B)-w_ij (t))

Δw_li(t)=(w-1) (w_li(t)-w_li(t-1))+r₁c₁(w_li(b)-w_li(t))+r₂c₂(w_li(B)-w_li(t))

$\begin{array}{c}{w}_{li}^{\left(3\right)}(t+1)=w_{li}^{\left(3\right)}\left(t\right)+{\mathrm{\β\δ}}_l^{\left(3\right)}{x}_j^{\left(1\right)}(t+1)+(w-1)\left(w_{li}^{\left(3\right)}\right(t)-w_{li}^{\left(3\right)}(t-1\left)\right)\\ +r_1{c}_1\left(w_{li}\right(b)-w_{li}(t\left)\right)+r_2{c}_2\left(w_{li}\right(B)-w_{li}(t\left)\right)\end{array}$

$\begin{array}{c}{w}_{ij}^{\left(2\right)}(t+1)=w_{ij}^{\left(2\right)}\left(t\right)+{\mathrm{\β\δ}}_i^{\left(2\right)}{x}_j^{\left(1\right)}(t+1)+(w-1)\left(w_{ij}^{\left(2\right)}\right(t)-w_{ij}^{\left(2\right)}(t-1\left)\right)\\ +r_1^{\′}{c}_1^{\′}\left(w_{ij}\right(b)-w_{ij}(t\left)\right)+r_2^{\′}{c}_2^{\′}\left(w_{ij}\right(B)-w_{ij}(t\left)\right)\end{array}$

In formula, w is inertia coeffeicent, r₁、r₂With r '₁、r′₂For the random number of 0-1, b is that particle itself is found the most at present The node of excellent solution, is referred to as individual extreme point, and B is the node of the optimal solution that whole population is found at present, referred to as global extremum point；

Sgn (x) is sign function, and β is Learning Step；

${\mathrm{\δ}}_i^{\left(2\right)}\left(2\right)=f^{\′}\left({\mathrm{net}}_i^{\left(2\right)}\right(t\left)\right)\underset{l=1}{\overset{2}{\Σ}}{\mathrm{\δ}}_l^{\left(3\right)}{w}_{li}^{\left(3\right)}\left(t\right);$

Speed and position to particle are updated, and search out particle optimum position.

When inspection meets the error requirements that termination condition, current location or maximum iteration time reach predetermined, then stop repeatedly Generation, the final weights of output nerve network and threshold value, i.e. parameter k of PI controller_p、k_i；

Inverter transmission function after identificationExpression formula is as follows:

${G_p}^{*}\left(s\right)=\frac{k_{inv}}{T_{inv}s+1}{e}^{-\mathrm{\γ}s}$

The transmission function of the PI controller of described PSO-BP Neural Network OptimizationExpression formula is as follows:

${G_c}^{*}\left(s\right)=k_p(1+\frac{1}{T_{i}s})$

Wherein, k_pFor controller gain, T_iFor controller time of integration.

Thus obtain:

$s^2+\frac{k_p{k}_{inv}+1}{T_{inv}}s+\frac{k_p{k}_{inv}}{T_i{T}_{inv}}=0$

The optimal culminating paint equation of second order with ITAE as criterion is

$s^2+2w_n\mathrm{\ξ}s+w_n^2=0$

Wherein, w_nFor the frequency of undamped oscillation, ξ is damping ratio；Selected w_nEngineering method be according to required closed loop T transit time of response_r, have:

$w_n=\frac{1+1.5\mathrm{\ξ}+{\mathrm{\ξ}}^2}{t_r}$

The mathematic(al) representation between parameter in the transmission function of inverter and PI controller can be obtained, for:

$k_{inv}=\frac{1}{k_p}(\frac{2w_n\mathrm{\ξ}}{2w_n\mathrm{\ξ}-w_n^2{T}_i}-1)$

$T_{inv}=\frac{1}{2w_n\mathrm{\ξ}-w_n^2{T}_i}.$

Compared to prior art, technical scheme possesses following beneficial effect:

A kind of based on compensation of delay the mains by harmonics current signal tracking and controlling method that the present invention provides, is mainly mended by π Repay smith prediction device to form with PI controller parameter being optimized by PSO-BP neutral net.π compensates smith prediction device Make during system delay from the closed loop internal conversion controlled to outside, thus reduce the impact controlling delay on system.Pass through PSO-BP algorithm is optimized process to PI controller parameter.By ITAE criterion set up smith prediction device and PI control parameter it Between mathematic(al) representation, thus the method for relation and Neural Network Optimization obtains the optimized parameter of two kinds of controllers.Finally to this The method that literary composition proposes has carried out simulating, verifying, and simulation result shows that context of methods has more preferable dynamic response compared with traditional method Characteristic and higher stable state compensation precision.

Accompanying drawing explanation

Fig. 1 is the structural representation of IHAPF；

Fig. 2 is the harmonic wave one phase equivalent circuit of IHAPF；

Fig. 3 is traditional harmonic current control method；

Fig. 4 is the harmonic voltage control method after improving；

The tracing control block diagram of Fig. 5 preferred embodiment of the present invention；

Fig. 6 is the compensation simulation waveform figure under conventional PI control algorithm；

Fig. 7 is the compensation simulation waveform figure of the preferred embodiment of the present invention；

Fig. 8 is provided without the current waveform figure of inventive algorithm；

Fig. 9 is the current waveform figure that have employed inventive algorithm.

Detailed description of the invention

Below in conjunction with drawings and Examples, the present invention is described in further detail.

IHAPF structure is as it is shown in figure 1, mainly by reactive-load compensation capacitor, first-harmonic resonance branch road, voltage source inverter, no Controlled rectification circuits etc. form.

The harmonic wave one phase equivalent circuit of IHAPF as shown in Figure 2, load is counted as harmonic current source i_h, u_c、i_cIt is respectively The output voltage of inverter and input current, i_sFor harmonic current in electrical network.

In order to the harmonic current in Fig. 2 is filtered, can be by the output electric current i in inverter_cControl is:

i_c=-i_h (1)

Then have

i_s=0 (2)

Traditional current control method is as it is shown on figure 3, wherein G₀For i_cWith u_cBetween transmission function, controller use pass The PI control method of system is controlled.

By Fig. 2, transmit function G₀It is represented by:

$G_0=\frac{i_c}{u_c}=({\mathrm{sL}}_0+\frac{(\frac{1}{{\mathrm{sC}}_F}+{\mathrm{sL}}_s)(\frac{1}{{\mathrm{sC}}_1}+{\mathrm{sL}}_1)}{\frac{1}{{\mathrm{sC}}_F}+{\mathrm{sL}}_s+\frac{1}{{\mathrm{sC}}_1}+{\mathrm{sL}}_1})---\left(3\right)$

Convolution (1), (2), (3) can obtain:

$u_h=\frac{i_h}{G_0}$

u_c=-u_h (4)

Wherein u_hHarmonic current i for load_hThrough transmission function 1/G₀The voltage signal harmonic voltage letter of output Number.

Due to transmission function G₀Exponent number is higher, and in POLE PLACEMENT USING, ratio is relatively difficult to achieve.Therefore herein in conjunction with (4) formula by harmonic wave Voltage signal is as controlling target, and have impact on current follow-up control, then in view of there is time delay phenomenon in IHAPF system Actual voltage signal tracing control block diagram is as shown in Figure 4.

In Fig. 4, u_c、-u_hBetween transmission function be:

$\frac{u_c}{-u_h}=\frac{G_c\left(s\right)G_p\left(s\right)e^{-\mathrm{\γ}s}}{1+G_c\left(s\right)G_p\left(s\right)e^{-\mathrm{\γ}s}}---\left(5\right)$

In formula, γ is the control time delay of IHAPF system, G_cS () is transmission function, G_pS () is the transmission of voltage source inverter Function.Be can be seen that by formula (5) and contain time delay item in equation, the stability of system can be made by this time delay item with control performance Become impact.

Therefore, the present embodiment can produce impact for time delay to the control of IHAPF system, it is proposed that a kind of based on improvement Smith predictor current compensation scheme.Control block diagram is as it is shown in figure 5, the Smith predictor improved is mainly neural by PSO-BP PI controller and a π that parameter is optimized by network compensate Smith predictor composition.

Following relational expression can be obtained by Fig. 5:

$\left\{\begin{array}{c}{u}_e=u_h-u_c\\ (u_e-d)G_c^{*}\left(s\right)=u\\ {\mathrm{uG}}_p^{*}\left(s\right)(1-e^{-\mathrm{\π}s})=d\\ {\mathrm{ue}}^{(\mathrm{\γ}-\mathrm{\π})s}{G}_p^{*}\left(s\right)e^{-\mathrm{\γ}s}=u_c\end{array}\right.---\left(6\right)$

Comprehensively (6) formula, can simplify:

$\left\{\begin{array}{c}\[u_h-u_c-{\mathrm{uG}}_p^{*}\left(s\right)(1-e^{-\mathrm{\π}s})\]G_c^{*}\left(s\right)=u\\ u=u_c{e}^{\mathrm{\π}s}{\left(G_p^{*}\right(s\left)\right)}^{-1}\end{array}\right.---\left(7\right)$

Can be obtained fom the above equation:

$u_h{G}_c^{*}\left(s\right)=u_c{e}^{\mathrm{\π}s}{\left(G_p^{*}\right(s\left)\right)}^{-1}+u_c{e}^{\mathrm{\π}s}{G}_c^{*}\left(s\right)---\left(8\right)$

Thus the output voltage u of inverter can be obtained_cWith reference voltage signal u_hTransmission function between the two is:

$\frac{u_c}{u_h}=\frac{G_c^{*}\left(s\right)G_p^{*}\left(s\right)e^{-\mathrm{\π}s}}{1+G_c^{*}\left(s\right)G_p^{*}\left(s\right)}---\left(9\right)$

As can be seen from the above equation, in the characteristic equation of closed loop system, neither comprise e^-γsThe most do not comprise e^-πs, illustrate that this is System can effectively eliminate the harmful effect that delay on system causes.And have e at above formula molecular moiety^-πs, show u_cCompare u_hDelayed π, thus, opposite polarity equal in magnitude with the voltage signal of harmonic wave, serve the control effect of formula (4).

PSO-BP neutral net essence is through improving the population function of search of particle cluster algorithm to BP neutral net Weights configure with threshold value so that it is reach optimum.

This algorithm has adaptive learning, Serial Distribution Processing and the feature such as stronger robustness and fault-tolerance, and has There are more preferable convergence rate and generalization ability, prevent it to be absorbed in local optimum, optimize PI controller parameter than traditional method Having and preferably control effect, therefore, herein according to the carrying out practically state of IHAPF, the input layer selecting neutral net is 3 Node, hidden layer is 5 nodes, and output layer is 2 nodes.

The input layer input of network is:

$x_j^{\left(1\right)}\left(t\right)=x_j,(j=1,2,3)---\left(10\right)$

3 nodes of input layer correspond respectively to the instruction harmonic voltage u in IHAPE system_h, inverter actual output voltage u_cAnd difference u between the two_e, x⁽¹⁾T () is input layer sample set, t is the frequency of training of network, also referred to as learning gain, under It literary composition is also this implication.

The input of network hidden layer and being output as

${\mathrm{net}}_i^{\left(2\right)}\left(t\right)=\underset{j=1}{\overset{3}{\Σ}}{w}_{ij}^{\left(2\right)}{x}_j,O_i^{\left(2\right)}\left(t\right)=f\left({\mathrm{net}}_i^{\left(2\right)}\right(t\left)\right),(i=1,2,\mathrm{...},5)---\left(11\right)$

WhereinFor the connection weights of input layer to hidden layer, net⁽²⁾T () is that hidden layer inputs sample set, O⁽²⁾(t) be Hidden layer output sample set, f (x) is excitation function, and the Sigmoid function of employing Symmetrical is:

$f\left(x\right)=\frac{e^x-e^{-x}}{e^x+e^{-x}}---\left(12\right)$

In like manner, the input of network output layer and being output as:

${\mathrm{net}}_l^{\left(3\right)}\left(t\right)=\underset{i=1}{\overset{5}{\Σ}}{w}_{li}^{\left(3\right)}{O}_i^{\left(2\right)}\left(t\right),O_l^{\left(3\right)}\left(t\right)=g\left({\mathrm{net}}_l^{\left(3\right)}\right(t\left)\right),(l=1,2)---\left(13\right)$

$O_1^{\left(3\right)}\left(t\right)=k_p,O_2^{\left(3\right)}\left(t\right)=k_i---\left(14\right)$

WhereinFor the connection weights of hidden layer to output layer, net⁽³⁾T () is that output layer inputs sample set, O⁽³⁾(t) be Output exports sample set layer by layer, and g (x) is excitation function, and the Sigmoid function of employing non-negative is:

$g\left(x\right)=\frac{e^x}{e^x+e^{-x}}---\left(15\right)$

Parameter k in formula, in the most corresponding PI controller of output node_p、k_i；

The control of PI controller is output as

Δ u (t)=k_p(u_e(t)-u_e(t-1))+k_iu_e(t) (16)

Wherein u_e=-u_h-u_c。

Particle cluster algorithm optimizing error function is

$J\left(t\right)=\frac{{\left(u_e\right)}^2}{2}=\frac{{\[-u_h-u_c\]}^2}{2}---\left(17\right)$

Herein on the basis of traditional BP algorithm, introduce particle cluster algorithm and network weight adjustment is improved, Make PI controller parameter k eventually_p、k_iDetermination optimized.Input layer is to hidden layer and hidden layer to the network weight of output layer The correction of value is respectively as follows:

Δw_ij(t)=(w-1) (w_ij(t)-w_ij(t-1))+r′₁c′₁(w_ij(b)-w_ij(t))+r′₂c′₂(w_ij(B)-w_ij (t)) (18)

Δw_li(t)=(w-1) (w_li(t)-w_li(t-1))+r₁c₁(w_li(b)-w_li(t))+r₂c₂(w_li(B)-w_li(t)) (19)

In formula, w is inertia coeffeicent, c₁、c₂With c '₁、c′₂For group cognition coefficient, also referred to as Studying factors, r₁、r₂And r ′₁、r′₂For the random number of 0-1, b is the node of the optimal solution that particle itself is found at present, is referred to as individual extreme point, and B is whole The node of the optimal solution that population is found at present, referred to as global extremum point.

Traditional BP algorithm uses error back propagation to adjust connection weights, is modified according to gradient descent method, at this Convolution (18), (19) on the basis of algorithm, i.e. obtain PSO-BP modified weight algorithm:

$\begin{array}{c}{w}_{li}^{\left(3\right)}(t+1)=w_{li}^{\left(3\right)}\left(t\right)+{\mathrm{\β\δ}}_l^{\left(3\right)}{x}_j^{\left(1\right)}(t+1)+(w-1)\left(w_{li}^{\left(3\right)}\right(t)-w_{li}^{\left(3\right)}(t-1\left)\right)\\ +r_1{c}_1\left(w_{li}\right(b)-w_{li}(t\left)\right)+r_2{c}_2\left(w_{li}\right(B)-w_{li}(t\left)\right)\end{array}---\left(20\right)$

WhereinSgn (x) is sign function, and β is Learning Step.

$\begin{array}{c}{w}_{ij}^{\left(2\right)}(t+1)=w_{ij}^{\left(2\right)}\left(t\right)+{\mathrm{\β\δ}}_i^{\left(2\right)}{x}_j^{\left(1\right)}(t+1)+(w-1)\left(w_{ij}^{\left(2\right)}\right(t)-w_{ij}^{\left(2\right)}(t-1\left)\right)\\ +r_1^{\′}{c}_1^{\′}\left(w_{ij}\right(b)-w_{ij}(t\left)\right)+r_2^{\′}{c}_2^{\′}\left(w_{ij}\right(B)-w_{ij}(t\left)\right)\end{array}---\left(21\right)$

Whereinβ is Learning Step.

The algorithm flow of modified model PSO-BP Neural Network Optimization PI controller is as follows:

Carrying out practically state according to IHAPF, in conjunction with neutral net input, output sample set, sets up the pre-of neutral net Survey model, connection weights all of between neuron are become with threshold coding the individual particles in real number vector representation population.

Initialize the initial position of particle, speed, inertia coeffeicent w, Studying factors c₁、c₂With c '₁、c′₂, it is stipulated that maximum is repeatedly Generation number etc..

According to input, output sample, utilize forwards algorithms (16) and the particle cluster algorithm optimizing error function of BP network (17) calculate each particle fitness function value, and using the desired positions of each particle as its history optimum position, start Iteration.

Utilize 4 iterative formulas (18) of PSO-BP algorithm, (19), (20), (21) formula that speed and the position of particle are entered Row updates, and searches out particle optimum position.

When inspection meets the error requirements that termination condition, current location or maximum iteration time reach predetermined, then stop repeatedly Generation, the final weights of output nerve network and threshold value, i.e. parameter k of PI controller_p、k_i, otherwise go to 3 execution.

It is unknowable that π compensates smith prediction device parameter, utilizes ITAE criterion to set up π herein and compensates smith prediction device ginseng Relational expression between number and PI controller parameter, thus realize effective identification of parameter.

Expression formula is obtained after voltage source inverter is modeled:

$G_p\left(s\right)=\frac{k_{inv}}{T_{inv}s+1}---\left(22\right)$

In formula, k_invFor transmitting the process gain constant of function, T_invFor inertia constant.

Because of the time delay of IHAPF, the transmission function of controlled device is:

${G_p}^{*}\left(s\right)=\frac{k_{inv}}{T_{inv}s+1}{e}^{-\mathrm{\γ}s}---\left(23\right)$

Herein by improve PSO-BP neural net method optimization process PI controller parameter, obtain improved after PI Controller transfer function, expression formula is as follows:

${G_c}^{*}\left(s\right)=k_p(1+\frac{1}{T_{i}s})---\left(24\right)$

Wherein, k_pFor controller gain, T_iFor controller time of integration.

It is updated in formula (9) to obtain by formula (23), (24):

$s^2+\frac{k_p{k}_{inv}+1}{T_{inv}}s+\frac{k_p{k}_{inv}}{T_i{T}_{inv}}=0---\left(25\right)$

The optimal culminating paint equation of second order with ITAE as criterion is

$s^2+2w_n\mathrm{\ξ}s+w_n^2=0---\left(26\right)$

Wherein, w_nFor the frequency of undamped oscillation, ξ is damping ratio.Wherein select w_nEngineering method [11] be according to being wanted T transit time of the closed loop response asked_r, have:

$w_n=\frac{1+1.5\mathrm{\ξ}+{\mathrm{\ξ}}^2}{t_r}---\left(27\right)$

The number between parameter can be obtained in the transmission function of inverter and PI controller by contrast (25) and (26) Learn expression formula, for:

$k_{inv}=\frac{1}{k_p}(\frac{2w_n\mathrm{\ξ}}{2w_n\mathrm{\ξ}-w_n^2{T}_i}-1)---\left(28\right)$

$T_{inv}=\frac{1}{2w_n\mathrm{\ξ}-w_n^2{T}_i}---\left(29\right)$

The design parameter size of voltage source inverter can be obtained by formula (28), (29), thus realize smith prediction device The identification of model.

In order to verify the effectiveness of institute's extracting method herein, methods herein it is applied in IHAPF system and is imitated True analysis, and carried algorithm and traditional PI algorithm herein are carried out simulation comparison, simulation parameter is: supply voltage is AC380V/ 50HZ；

Equivalent inductance L_s=1mH；Inject electric capacity C_F=100 μ F；The inductance L of first-harmonic branch road₁=40mH, electric capacity C₁=249 μ F, quality factor q=50；Output inductor L₀=0.5mH, output filter capacitor C₀=24.1 μ F, equivalent resistance R₀=0.09 Ω.Parameter in PSO-BP algorithm is: weighter factor w=0.4, c=0.03, L=0.03, c₁=c₂=2, c '₁=c '₂= 1.4。

Current simulations waveform under distinct methods is used for load, as can be seen from the figure when 1s when Fig. 6-7 changes Load changes, and under traditional PI control method, electric current could slowly tend towards stability through 3.5 time cycles.And use Only need 1.5 time cycle current waveforms just can tend towards stability under methods herein.

In order to prove the effectiveness of carried algorithm herein further, carry out Related Experimental Study.Fig. 8-9 is for using herein Algorithm is to the current waveform figure before and after current compensation, and the waveform after administering as seen from the figure has had before comparing improvement and carries the most greatly Height, waveform is nearly close to sinusoidal wave form.

The above, the only present invention preferably detailed description of the invention, but protection scope of the present invention is not limited thereto, Any those familiar with the art in the technical scope that the invention discloses, the change that can readily occur in or replacement, All should contain within protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims It is as the criterion.

Claims (1)

1. a mains by harmonics current signal tracking and controlling method, it is characterised in that include following sequential steps:

1) by harmonic current i in electrical network_hThrough 1/G₀It is changed into harmonic voltage signal U_h；Described G₀Input current i for inverter_c With output voltage U_cBetween transmission function；

2) with described harmonic voltage signal U_hFor controlling target, by described harmonic voltage signal U_hImproved Smith predictor And inverter；The Smith predictor of described improvement include the PI controller that parameter is optimized by PSO-BP neutral net with One π compensates Smith predictor；And meet following relational expression:

$\left\{\begin{array}{c}{u}_e=u_h-u_c\\ (u_e-d)G_c^{*}\left(s\right)=u\\ {\mathrm{uG}}_p^{*}\left(s\right)(1-e^{-\mathrm{\π}s})=d\\ {\mathrm{ue}}^{(\mathrm{\γ}-\mathrm{\π})s}{G}_p^{*}\left(s\right)e^{-\mathrm{\γ}s}=u_c\end{array}\right.$

Wherein, U_eFor harmonic voltage signal U_hWith inverter output voltage U_cDifference；It is respectively PSO-BP god Inverter transmission function after transmission function, the identification of the PI controller of the network optimization；D is amount of transition；U is that PI controller is defeated Go out voltage signal；

Thus the output voltage U of inverter can be obtained_cWith harmonic voltage signal U_hTransmission function between the two is:

$\frac{u_c}{u_h}=\frac{G_c^{*}\left(s\right)G_p^{*}\left(s\right)e^{-\mathrm{\π}s}}{1+G_c^{*}\left(s\right)G_p^{*}\left(s\right)}.$