CN110460250B - Direct power control method for three-phase PWM rectifier - Google Patents
Direct power control method for three-phase PWM rectifier Download PDFInfo
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- H02M7/02—Conversion of ac power input into dc power output without possibility of reversal
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- H02M7/2173—Conversion of ac power input into dc power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only in a biphase or polyphase circuit arrangement
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Abstract
The invention discloses a direct power control method of a three-phase PWM rectifier, which comprises the steps of calculating the instantaneous active power and the instantaneous reactive power of the three-phase PWM rectifier according to the three-phase current and the three-phase voltage of the network side of the three-phase PWM rectifier, constructing an average state space model of the three-phase PWM rectifier, introducing decoupling control signals into the average state space model of the three-phase PWM rectifier, considering uncertainty items in a system, designing a neural network adaptive Backstepping controller of reactive power and a neural network adaptive Backstepping controller of direct-current output voltage, inputting relevant parameters into the neural network adaptive Backstepping controller of reactive power and the neural network adaptive Backstepping controller of direct-current output voltage to obtain corresponding output control rates, carrying out coupling transformation on the output control rates, and obtaining switch control signals of the three-phase PWM rectifier according to an SVPWM model; the control method provided by the invention has the advantages of small calculated amount, simple parameter adjustment and good robustness.
Description
Technical Field
The invention relates to a direct power control method of a three-phase PWM rectifier, belonging to the technical field of power electronics.
Background
The three-phase PWM rectifier is widely applied to the traditional industry and the emerging industry, such as intelligent microgrid. Generally, in wind power generation, a PWM rectifier performs AC/DC power conversion while performing generator speed regulation. Another important field of application is as an interface circuit between an electric vehicle and the power grid, where the electric vehicle as an energy storage device can absorb power from the power grid and can also feed power to the power grid using V2G technology. In addition, the PWM rectifier is also widely used in a static var compensator (SVG), an active filter (APF), a Unified Power Flow Controller (UPFC), and the like.
Control of PWM rectifiers is classified into two broad categories, one of which is based on voltage-oriented control (VOC) in which the ac side current is decomposed into active and reactive components in a d-q coordinate system, and the q-axis current is usually set to zero to realize unity power factor control, however, the control performance of VOC is affected because the current controllers (hysteresis, Proportional Integral (PI), and Proportional Resonance (PR)) are sensitive to parameter changes and external disturbances. Due to magnetic saturation and the existence of a switching device, the dynamic process of the three-phase PWM rectifier has nonlinear and unexpected external disturbance. In recent years, sliding mode control has been widely used in highly non-linear and non-deterministic power electronic circuits, however, intermediate variables of the controller may cause system oscillation, especially when the system switching frequency is limited. The other is direct power control based on instantaneous active and reactive power theory, and the active power and the reactive power are directly used as control variables without current loops, so that the control system has better dynamic performance. The method includes that a table look-up method selects the switching state of the next period according to a power predicted value and a switching table, model prediction control selects a voltage vector according to a minimum evaluation function so as to determine the switching state, the system is simple in structure and quick in dynamic response, but output power fluctuation is large, switching frequency is not fixed, and besides, direct power control based on the table look-up method also needs quick and accurate power estimation. The direct power control based on the S/SVPWM is superior in the aspects of switching frequency fixation and control precision, and most of the existing direct power controllers adopt PI control and have the same defects as VOC.
The Backstepping control (BSC) technology is concerned by the nonlinear iterative design, and the overall lyapunov function composed of the lyapunov functions in each iterative step ensures the stability of the whole system and each iterative step. The existing Backstepping controller design does not consider the influence of system uncertainty items, and when system parameter changes and external disturbance occur, the control performance of the system cannot be guaranteed.
Disclosure of Invention
The present invention is directed to a method for controlling direct power of a three-phase PWM rectifier, so as to solve one of the above drawbacks or shortcomings in the prior art.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention provides a direct power control method of a three-phase PWM rectifier, which comprises the following steps:
calculating instantaneous active power and instantaneous reactive power of the three-phase PWM rectifier according to three-phase current and three-phase voltage of the three-phase PWM rectifier network side, and constructing an average state space model of the three-phase PWM rectifier;
introducing a decoupling control signal into an average state space model of a three-phase PWM rectifier, considering uncertainty items in a system, and designing a neural network adaptive Backstepping controller of reactive power and a neural network adaptive Backstepping controller of direct-current output voltage; the uncertainty term in the system includes uncertainty terms in the dc output voltage model and in the reactive power model.
Will output the DC voltage VOSquare of (d) and square of given value of DC output voltage
Error and active power input to DC output voltage of the neural network adaptive Backstepping controller obtains output control rate upcon(ii) a The reactive power Q and the given value Q of the reactive power*The neural network self-adaptive Backstepping controller for the error input reactive power obtains the output control rate uqcon；
Will be paired with upconAnd uqconPerforming coupling transformation to obtain d-axis component u of coupling result in voltage space vectorcondAnd q-axis component uconqAnd obtaining a switch control signal of a switch in the three-phase PWM rectifier as the input of the SVPWM modulation strategy.
The uncertainty items of the system comprise filter parameter change, power grid fluctuation and load change in the system;
further, a neural network observer is used to estimate the value of the uncertainty term in the system on-line.
The expression of the uncertainty term is:
wherein, Delta Ls，△C，△rsAnd Δ RlAre respectively Ls，C，rsAnd RlThe amount of change in (c);
Usis the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side; c is a filter capacitor at the direct current output end of the three-phase PWM rectifier; u. ofpIs a decoupled control signal; w is apFor uncertainty term, w, in the DC output voltage modelqIs an uncertainty term in the reactive power model; rlIs a load;
wPand wqIs given as | wp(t)|<ρp，|wq(t)|<ρqWherein | is takenOperation of absolute value, ppAnd ρqGiven a normal number.
Further, the method for calculating the instantaneous active power and the instantaneous reactive power of the three-phase PWM rectifier comprises the following steps:
performing equal-power coordinate transformation from a three-phase stationary coordinate system to a two-phase rotating coordinate system on three-phase voltage and three-phase current on the network side of the three-phase PWM rectifier by using an equal-power clark transformation method and an equal-power park transformation method to obtain a d-axis component u of a voltage value under the two-phase rotating coordinate systemsdQ-axis component u of voltage valuesqD-axis component i of the current valuesdQ-axis component i of the current valuesq；
The calculation formula of the instantaneous active power and the instantaneous reactive power of the three-phase PWM rectifier is as follows:
P＝usdisd+usqisq
Q＝usqisd-usdisq
wherein, P is the instantaneous active power of the three-phase PWM rectifier, and Q is the instantaneous reactive power of the three-phase PWM rectifier.
Further, the average state space model of the three-phase PWM rectifier is:
wherein, VOIs the DC output voltage of a three-phase PWM rectifiersdThe d-axis component of the voltage value under the two-phase rotating coordinate system is obtained after the equal power conversion from the three-phase stationary coordinate system to the two-phase rotating coordinate system is carried out on the three-phase voltage at the network side of the three-phase PWM rectifier,p is the instantaneous active power of the three-phase PWM rectifier, Q is the instantaneous reactive power of the three-phase PWM rectifier, DdAnd DqThe components of duty ratio on d axis and q axis respectively, C is the filter capacitor of DC output end of three-phase PWM rectifier, UsIs the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side;
Rlis the load equivalent resistance.
Further, the method for designing the neural network adaptive Backstepping controller of the reactive power comprises the following steps:
constructing a dynamic equation of a reactive power Q model of the three-phase PWM rectifier considering system uncertainty:
wherein,
Usis the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side; u. ofqIs a decoupled control signal; w is aqIs an uncertainty term in the reactive power model;
and designing a neutral network self-adaptive Backstepping controller of the reactive power according to a reactive power model dynamic equation of the three-phase PWM rectifier considering the uncertainty.
Further, the method for designing the neural network adaptive Backstepping controller of the direct-current output voltage comprises the following steps:
constructing a dynamic equation of direct current output voltage fluctuation P of the three-phase PWM rectifier considering system uncertainty:
wherein,
Usis the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side; c is a filter capacitor at the direct current output end of the three-phase PWM rectifier; u. ofpIs a decoupled control signal; w is apIs an uncertain item in the direct current output voltage model; rlIs a load;
and designing a neural network self-adaptive Backstepping controller of the direct-current output voltage according to an output direct-current voltage dynamic equation of the three-phase PWM rectifier considering the uncertainty.
Uncertainty term w in DC output voltage modelpEstimated value of
The expression of (a) is:
wherein, WpWeight matrix between output layer and hidden layer of neural network observer in neural network adaptive Backstepping controller for DC output voltage, OpThe output of a neural network observer in a neural network self-adaptive Backstepping controller for outputting voltage for direct current;
uncertainty term w in reactive power modelqEstimated value of
The expression of (a) is:
wherein, WqWeight matrix between output layer and hidden layer of neural network observer in neural network adaptive Backstepping controller for reactive power, OqNeural network observation in neural network adaptive Backstepping controller for reactive powerThe output of the detector.
The invention has the beneficial effects that:
(1) an uncertain item is introduced into an average state model of the three-phase PWM rectifier, so that the robustness of the system to parameter change and external interference is improved;
(2) designing a neural network self-adaptive Backstepping controller of reactive power and a neural network self-adaptive Backstepping controller of direct-current output voltage; eliminating possible oscillation phenomena of intermediate variables of the control system;
(3) and a neural network observer is introduced to estimate the value of the uncertain item on line, so that the robustness of the system is further improved.
Drawings
Fig. 1 is a main circuit structure diagram of a three-phase PMW rectifier provided in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of a three-phase PWM rectifier direct power control system according to an embodiment of the present invention;
FIG. 3 is a diagram of a neural network architecture provided in accordance with an embodiment of the present invention;
FIG. 4 is a control block diagram of the adaptive Backstepping neural network controlling the DC output voltage Vo provided in accordance with an embodiment of the present invention;
fig. 5 is a control block diagram of controlling the reactive power Q by the adaptive Backstepping neural network according to the embodiment of the present invention;
FIG. 6 is a waveform of an output DC voltage when a load is suddenly applied according to an embodiment of the present invention of an adaptive Backstepping neural network control and a PR (proportional resonance) control;
FIG. 7 illustrates the variation of the grid side A-phase current THD when the grid side filter inductance varies within + -10% of the nominal value for adaptive Backstepping neural network control and PR (proportional resonance) control provided in accordance with an embodiment of the present invention;
FIG. 8 is a diagram illustrating active and reactive power waveforms when an adaptive Backstepping neural network is used to control an abrupt load according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a harmonic analysis result of a net side a-direction current when a net side a-phase voltage drops by 5% under the control of the adaptive Backstepping neural network according to the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Referring to the main circuit structure of the three-phase PWM rectifier shown in FIG. 1, the inductor L represents the net side filter with equivalent resistance rL，T1～T6Denotes six switching devices, D1～D6Is a diode connected with six switching devices in inverse parallel, C is a filter capacitor at a direct current output end, RLIs a load; i is the DC output current, iCFor the current, i, on the filter capacitor at the DC outputOIs a load RLThe current in the capacitor.
Referring to fig. 2, the neural network adaptive Backstepping three-phase PWM rectifier direct power control system provided in this embodiment includes two independent adaptive Backstepping direct current output voltage models and reactive power models, and the control system is executed on the DSP of the microcontroller TMS320F 28335. The embodiment provides a direct power control method of a three-phase PWM rectifier, which is a direct power control method of the three-phase PWM rectifier and comprises the following steps:
step 1: constructing an average state space model of the three-phase PWM rectifier:
step 1.1: collecting three-phase current and three-phase voltage at the network side of the three-phase PWM rectifier;
detection of three-phase sinusoidal voltage u on network side by Hall voltage sensor and current sensorsa、usb、uscThree-phase sinusoidal current i on network sidesa、isb、iscHall voltage sensor detecting DC output voltage VO。
Step 1.2: carrying out equal-power conversion from a three-phase static coordinate system to a two-phase rotating coordinate system on three-phase voltage and three-phase current;
step 1.2.1: obtaining values of three-phase sinusoidal voltage and current on the network side under a two-phase static coordinate system by utilizing equal-power clark transformationusα、usβAnd isα、isβ；
Step 1.2.2: obtaining a d-axis component u of a voltage value under a two-phase rotating coordinate system by using the equal power park transformationsdQ-axis component u of voltage valuesqD-axis component i of the current valuesdQ-axis component i of the current valuesq；
Wherein ω is 2 pi f, where f is the grid frequency; and t is the system execution time.
Step 1.3: calculating instantaneous active power and reactive power of the three-phase PWM rectifier;
the calculation formula of the instantaneous active power P and the reactive power Q of the three-phase PWM rectifier is as follows:
P＝usdisd+usqisq (3a)
Q＝usqisd-usdisq (3b)
step 1.4: establishing an instantaneous power dynamic model of the decoupled three-phase PWM rectifier and a dynamic equation of direct-current output voltage;
step 1.4.1: establishing an average state space model represented by instantaneous power of the three-phase PWM rectifier;
based on voltage orientation, considering rectifier input power and output power conservation, the average state space model of the three-phase PWM rectifier instantaneous power representation can be expressed as:
wherein D isdAnd DqC is the filter capacitance of the DC output for the component of the duty ratio on the d-q axis, RLIs a load, rLThe equivalent resistance of the network side filter inductor L;
in the formula (4b) and the formula (4c),
wherein U issIs the effective value of the network phase voltage.
Step 2: designing a neural network self-adaptive Backstepping controller of reactive power and a neural network self-adaptive Backstepping controller of direct-current output voltage:
step 2.1: establishing an instantaneous power dynamic model of the decoupled three-phase PWM rectifier and a dynamic equation of direct-current output voltage;
designing a decoupled control signal upAnd uq：
up＝(usd-LsωQ-DdVO)/Ls (5a)
uq＝(LsωP+DqVO)/Ls (5b)
Substituting the formula 5a into the formula 4b, substituting the formula 5b into the formula 4c to obtain an expression of the instantaneous power dynamic model of the decoupled three-phase PWM rectifier:
taking derivatives on both sides of the equal sign of the formula (4a), and substituting the formula (6a), a dynamic equation of the direct current output voltage model can be obtained:
and a decoupling control signal is introduced, so that the independent design of the direct-current output voltage and the reactive power is realized, and the design and the parameter debugging process of the controller are simplified.
Step 2.2: establishing a dynamic equation of an actual three-phase PWM rectifier considering uncertainty items;
considering the existence of uncertainty items such as circuit parameters in the system, the dynamic equation of the three-phase PWM rectifier considering the uncertainty items can be expressed as:
wherein an uncertainty term w in the DC output voltage model is assumedpAnd uncertainty term w in the reactive power modelqIs bounded and can be represented as:
wherein Δ Ls，△C，△rsAnd Δ RlAre respectively Ls，C，rsAnd RlThe amount of change in (c). Uncertainty term w in DC output voltage modelPAnd uncertainty term w in the reactive power modelqIs given as | wp(t)|<ρp， |wq(t)|<ρqWhere | is absolute value operation, ρpAnd ρqGiven a normal number.
Using a neural network observer to estimate the value of the uncertainty in the system on-line, referring to fig. 3, the neural network includes three parts, i.e., an input layer, a hidden layer and an output layer, and the input layer and the hidden excitation function both select a sigmiod function.
The input signal of the i layer of the input layer is neti＝xiOutput of input layer OiComprises the following steps:
Oi＝f(neti)＝[1+exp(-neti)]-1 (10)
wherein f is the excitation function, i is 1,2 … Ri。
Weighted addition of the outputs of the input layers as the input net of the hidden layer j layerj＝∑WjiOi，j＝1,2…RjThe output of the hidden layer is:
Oj＝f(netj)＝[1+exp(-netj)]-1 (11)
weighted addition of hidden layer outputs as input net to output layer k layerk＝∑WkjOkAnd k is 1, and the output layer directly outputs.
Uncertainty term w in DC output voltage modelpThe expression of the estimated value of (a) is:
in the formula WpWeight matrix between output layer and hidden layer of neural network observer in neural network adaptive Backstepping controller for DC output voltage, OpFor outputting electricity as direct currentAnd (3) outputting of a neural network observer in the neural network self-adaptive Backstepping controller.
Uncertainty term w in reactive power modelqThe estimated value of (a) is expressed as:
in the formula WqWeight matrix between output layer and hidden layer of neural network observer in neural network adaptive Backstepping controller for reactive power, OqAnd (3) the output of a neural network observer in the Backstepping controller is self-adapted to the neural network with reactive power.
In order to improve the online learning capability of the neural network, weights are trained online by using a gradient descent method, a direct-current output voltage neural network observer is taken as an example (the reactive power neural network observer is the same in method), and a minimum error is constructed
Wp *When the error is 0, the weight value between the output layer and the hidden layer in the neural network,
is EPAn estimated value of.
In the formula wpjiAnd wpkjThe weighted values between the hidden layer and the input layer and between the output layer and the hidden layer in the direct current output voltage neural network estimator are respectively, n is the nth sampling period, and eta represents the learning rate.
Step 2.3: designing a DC output voltage VONeural network ofThe output control law of the self-adaptive Backstepping controller is upcon；
Designing the DC output voltage V according to the DC output voltage dynamic equation of the three-phase PWM rectifier considering the uncertainty term as shown in the formula (8a)OThe neural network self-adaptive Backstepping controller;
DC output voltage VONeural network self-adaptive Backstepping controller output control law upconThe expression is as follows:
in the formula of alpha1Error of DC output voltage as stable function in Backstepping controller recursion process
Virtual control error
kv、ksIs a normal number, and is,
is wpThe estimated value of (a) is,
and
the adaptive law expression of (1) is:
in the formula of gammapAnd betapIs a normal number.
Taking the Lyapunov function as:
the derivative of the lyapunov function is:
if the control rate of the neural network self-adaptive Backstepping controller of the direct current output voltage is (17) - (19), then
Namely, it is
The error (e) is known as a negative definite function according to the Lyapunov's theorem and the Barbalt's theoremvAnd es) Will gradually become zero, WpWill progress over Wp *，
Will gradually become zero, thereby can guarantee the stability of direct current output voltage control.
Step 2.4: designing a neural network self-adaptive Backstepping controller of reactive power Q, wherein the output control law of the controller is uqcon；
Designing a neural network adaptive Backstepping controller of the reactive power Q according to a reactive power model dynamic equation of the three-phase PWM rectifier considering the uncertainty term as shown in a formula (8 b);
neural network self-adaptive Backstepping controller output control law u of reactive power QqconThe expression is as follows:
in the formula kqIs a normal number, a reactive power error eq＝Q-Q*，Q*Is a given value of the reactive power,
is wqTo construct a minimum error
Is EqAn estimated value of.
in the formula of gammaqAnd betaqIs a normal number.
Taking the Lyapunov function as:
the derivative of the lyapunov function is:
if the control rate of the neural network adaptive Backstepping controller of the reactive power Q is (20) - (22), then
Namely, it is
As a negative definite function, according to LyapunovThe error e can be known by the Fufford's theorem and the Barbalat's theoremqWill gradually become zero, WqWill progress over Wq *，
Will asymptotically become zero, whereby the stability of the reactive power control can be guaranteed.
Based on the Lyapunov function and the Barbalt's theorem, the convergence of control variables is guaranteed, the system is stable, the sine of the network side current is guaranteed while the zero static difference adjustment of the direct-current output voltage is realized, the distortion rate of the network side current is reduced, and the zero static difference direct adjustment of active power and reactive power can be realized.
And step 3: acquiring switching control signals of six switches in the three-phase PWM rectifier:
step 3.1: will output the DC voltage VOSquare of (d) and square of given value of DC output voltage
The error and the active power are input to the neural network self-adaptive Backstepping controller of the direct current output voltage to obtain the output control rate upcon(ii) a The reactive power Q and the given value Q of the reactive power*The neural network self-adaptive Backstepping controller for the error input reactive power obtains the output control rate uqcon；
Step 3.2: for u is pairedpconAnd uqconCoupling change is carried out to obtain a d-axis component u of a coupling result in a voltage space vectorcondAnd q-axis component uconqThe expression of (a) is:
ucond＝usd-LsωQ-Lsup (24a)
uconq＝LsωP-Lsuq (24b)
step 3.3: will ucondAnd uconqAs input of SVPWM modulation strategy, generating six-way PWM signal to control switching control signals of six switching devices, so that DC output voltage VOTracking set point
Reactive power Q tracking given value Q*。
And an SVPWM (space vector pulse width modulation) strategy is adopted, the switching frequency is equal to the sampling frequency, and the switching frequency is fixed, so that the design of the filter parameters of the three-phase PWM rectifier is facilitated.
The effect of the method provided by the embodiment of the invention is tested and analyzed:
referring to fig. 6, when the load is suddenly added for 1s and the load is suddenly removed for 1.5 s, the direct power control method of the three-phase PWM rectifier provided by the present invention compares the output dc voltage waveform with the PR control method, and the comparison simulation experiment result can obtain: according to the control method provided by the invention, during loading and unloading, the direct-current voltage is not only slightly overshot, but also the stabilization time is much faster than that of a PR control method;
fig. 7 is a comparison graph of the THD value of the net side a-phase current in the three-phase PWM rectifier direct power control method and the PR control method when the net side filter inductance is changed within ± 10% of the rated value 5mH, and the comparison result can be obtained: when the filter inductance of the network side is changed from 4.5mH to 6.5mH, the THD value of the A-phase current of the network side under the PR control method is changed by 17.5 percent, while under the control of the method of the invention, the THD value of the A-phase current of the network side is changed by 3 percent, and simultaneously, under the control method of the invention, the THD value of the A-phase current of the network side is lower than the PR control method 52.47 percent;
fig. 8 is a waveform diagram of active power and reactive power under a direct power control method of a three-phase PWM rectifier according to the present invention when a load is suddenly added for 1s and a load is suddenly removed for 1.5 s, and the active power has a fast response speed and no overshoot when the load is added or removed; and when loading and unloading, the reactive power response is not influenced.
Fig. 9 shows the harmonic analysis of the grid current when the grid a-phase voltage drops by 5% and is asymmetric to B, C, and the simulation results show that: within the allowable fluctuation range of the power grid voltage, the control method can still ensure that the current harmonic wave on the grid side is lower than 2 percent.
According to the direct power control method for the three-phase PWM rectifier provided by the embodiment of the invention, the reactive power and the active power of the three-phase PWM rectifier are calculated according to the instantaneous power theoryPower, on-line estimation of system uncertainty value by neural network, DC output voltage, output control law u by neural network self-adaptive Backstepping controllerpOutput control law u of reactive power neural network self-adaptive Backstepping controllerqTo u, to upAnd uqCoupling transformation is carried out to obtain a d-axis component u of a voltage space vectorcondAnd q-axis component uconqSending the two voltage space vectors into an SVPWM module to generate a PWM control signal to realize VOAnd zero static difference adjustment of Q, the scheme provided by the embodiment of the invention has stronger robustness on system parameters and external disturbance.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A method for direct power control of a three-phase PWM rectifier, said method comprising the steps of:
calculating instantaneous active power and instantaneous reactive power of the three-phase PWM rectifier according to three-phase current and three-phase voltage of the three-phase PWM rectifier network side, and constructing an average state space model of the three-phase PWM rectifier;
introducing a decoupling control signal into an average state space model of a three-phase PWM rectifier, considering uncertainty items in a system, and designing a neural network adaptive Backstepping controller of reactive power and a neural network adaptive Backstepping controller of direct-current output voltage; the uncertainty items in the system comprise uncertainty items in a direct current output voltage model and uncertainty items in a reactive power model;
will output the DC voltage VOSquare of (d) and square of given value of DC output voltage
The error and active power of the controller are input to the neural network adaptive Backstepping controller of the DC output voltageTake the output control rate upcon(ii) a The reactive power Q and the given value Q of the reactive power*The neural network self-adaptive Backstepping controller for the error input reactive power obtains the output control rate uqcon；
Will be paired with upconAnd uqconD-axis component u of coupling result obtained by coupling transformation in voltage space vectorcondAnd q-axis component uconqThe switching control signal of a switch in the three-phase PWM rectifier is obtained as the input of an SVPWM (space vector pulse width modulation) strategy;
output control law u of neural network self-adaptive Backstepping controller of reactive powerqconComprises the following steps:
in the formula, kqIs a normal number, and is,
rLis the equivalent resistance, U, of the network-side filter inductor LsIs the effective value of the phase voltage of the power grid, Q is the reactive power, eqFor reactive power error, Q*Is a given value of the reactive power,
is wqAn estimated value of (w)qFor the uncertainty term in the reactive power model,
is the minimum error EqAn estimated value of;
output control law u of neural network self-adaptive Backstepping controller of direct-current output voltagepconComprises the following steps:
in the formulaC is the filter capacitor of the DC output terminal, RlTo load equivalent resistance, α1For the stability function in the recursion process of Backstepping controllers, evError of the DC output voltage, esTo virtually control the error, kv、ksIs a normal number, and is,
p is the active power, and P is the active power,
is wpAn estimated value of (w)PFor the uncertainty term in the dc output voltage model,
is the minimum error EqAn estimated value of.
2. The method of direct power control of a three-phase PWM rectifier according to claim 1, wherein the method of calculating instantaneous active power and instantaneous reactive power of a three-phase PWM rectifier comprises the steps of:
performing equal-power coordinate transformation from a three-phase stationary coordinate system to a two-phase rotating coordinate system on three-phase voltage and three-phase current on the network side of the three-phase PWM rectifier by using an equal-power clark transformation method and an equal-power park transformation method to obtain a d-axis component u of a voltage value under the two-phase rotating coordinate systemsdQ-axis component u of voltage valuesqD-axis component i of the current valuesdQ-axis component i of the current valuesq；
The calculation formula of the instantaneous active power and the instantaneous reactive power of the three-phase PWM rectifier is as follows:
P＝usdisd+usqisq
Q＝usqisd-usdisq
wherein, P is the instantaneous active power of the three-phase PWM rectifier, and Q is the instantaneous reactive power of the three-phase PWM rectifier.
3. The method of claim 1, wherein the three-phase PWM rectifier has an average state space model of:
wherein, VOIs the DC output voltage of a three-phase PWM rectifiersdCarrying out equal-power conversion from a three-phase stationary coordinate system to a two-phase rotating coordinate system on three-phase voltage on the network side of the three-phase PWM rectifier to obtain a D-axis component of a voltage value under the two-phase rotating coordinate system, wherein P is instantaneous active power of the three-phase PWM rectifier, Q is instantaneous reactive power of the three-phase PWM rectifier, and D isdAnd DqThe components of duty ratio on d axis and q axis respectively, C is the filter capacitor of DC output end of three-phase PWM rectifier, UsIs the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side;
Rlis the load equivalent resistance.
4. The direct power control method of the three-phase PWM rectifier according to claim 1, wherein the method for designing the neural network adaptive Backstepping controller of reactive power comprises the following steps:
constructing a dynamic equation of a reactive power Q model of the three-phase PWM rectifier considering system uncertainty:
wherein,
Usis the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side; u. ofqIs a decoupled control signal; w is aqIs an uncertainty term in the reactive power model;
and designing a neutral network self-adaptive Backstepping controller of the reactive power according to a reactive power model dynamic equation of the three-phase PWM rectifier considering the uncertainty.
5. The direct power control method of the three-phase PWM rectifier according to claim 1, wherein the method for designing the neural network adaptive Backstepping controller of the DC output voltage comprises the following steps:
constructing a dynamic equation of a direct current output voltage model of the three-phase PWM rectifier considering system uncertainty:
wherein,
Usis the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side; c is a filter capacitor at the direct current output end of the three-phase PWM rectifier; u. ofpIs a decoupled control signal; w is apIs an uncertain item in the direct current output voltage model; rlIs a load equivalent resistance; vOThe DC output voltage of the three-phase PWM rectifier;
and designing a neural network self-adaptive Backstepping controller of the direct-current output voltage according to the direct-current output voltage dynamic equation of the three-phase PWM rectifier considering the uncertainty.
6. The method of claim 1, wherein the system uncertainty term is expressed as:
wherein, Δ Ls，ΔC，ΔrsAnd Δ RlAre respectively Ls，C，rsAnd RlThe amount of change in (c);
Usis the equivalent value of the network phase voltage rLThe equivalent resistance of the filter inductor L at the network side; c is a filter capacitor at the direct current output end of the three-phase PWM rectifier; u. ofpIs a decoupled control signal; w is apFor an uncertainty term in the DC output voltage model, wqIs an uncertainty term in the reactive power model; rlIs a load equivalent resistance; vOThe DC output voltage of the three-phase PWM rectifier;
wPand wqIs given as | wp(t)|＜ρp，|wq(t)|＜ρqWhere | is absolute value operation, ρpAnd ρqGiven a normal number.
7. The method of claim 1, further comprising obtaining the uncertainty w in the model of the dc output voltage using a neural network observerpEstimated value of and reactive powerAn estimate of an uncertainty term in the rate model.
8. The three-phase PWM rectifier direct power control method of claim 7,
uncertainty term w in DC output voltage modelpEstimated value of
The expression of (a) is:
wherein, WpWeight matrix between output layer and hidden layer of neural network observer in neural network adaptive Backstepping controller for DC output voltage, OpThe output of a neural network observer in a neural network self-adaptive Backstepping controller for outputting voltage for direct current;
uncertainty term w in reactive power modelqEstimated value of
The expression of (a) is:
wherein, WqWeight matrix between output layer and hidden layer of neural network observer in neural network adaptive Backstepping controller for reactive power, OqAnd (3) the output of a neural network observer in the Backstepping controller is self-adapted to the neural network with reactive power.
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Application publication date: 20191115 Assignee: HUAI'AN HUATAI ELECTRONIC TECHNOLOGY Co.,Ltd. Assignor: HUAIYIN INSTITUTE OF TECHNOLOGY Contract record no.: X2021980015115 Denomination of invention: A direct power control method for three-phase PWM rectifier Granted publication date: 20210316 License type: Common License Record date: 20211222 |