CN104052059A - Active power filter control method based on fuzzy neural network PID - Google Patents
Active power filter control method based on fuzzy neural network PID Download PDFInfo
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- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/20—Active power filtering [APF]
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Abstract
The invention discloses an active power filter (APF) control method based on fuzzy neural network PID. The method comprises the steps that a non-linear dynamic model of an APF inverter is established according to power electronics; a PIC controller of a current control loop is designed according to the established non-linear dynamic model; a fuzzy neural network compensating controller is designed and used for improving the dynamic response of the current control loop of the inverter; a PI controller of a voltage control loop of the inverter is designed, and a reference current value which needs to be tracked by the output current of the inverter is solved. According to the method, PID control and fuzzy neural network control are combined, the design process is simple, engineering realization is easy, and the response performance of the output current of the inverter is further improved.
Description
Technical field
The present invention relates to a kind of Control Method of Active Power Filter based on Fuzzy Neural PID, belong to field of power system control.
Background technology
Along with the diversity of topological structure of electric and circuit load increases, harmonic pollution is more and more serious and arouse widespread concern on the impact of network system.Harmonic pollution is not only the obstruction to Development of Power Electronic Technology, harmonic wave also may cause local series resonance or harmonic wave in parallel occur in electric power system, cause burning of equipment thereby amplify harmonic content, harmonic wave also can produce serious interference to communication system and electronic equipment etc.As can be seen here, harmonic pollution makes electric power system depart from desirable, normal electric process and behavior, the decline of the quality of power supply has not only affected power system security and has powered reliably, also can bring various harm to electric power system, personal safety and economic benefit are had to comparatively serious impact simultaneously.The harm that harmonic wave brings has caused increasing concern, no matter be from electric power system normally work, the angle of device security stable operation, or the angle of collateral security power system security, reliable, economical operation, the improvement that Harmonious Waves in Power Systems is polluted has become urgent problem.
Active Power Filter-APF (APF) is acknowledged as the strongest instrument of harmonic wave control, but APF Waveform Control technology is difficult point and the focus of PWM inverter area research always, various control theories are all application to some extent on inverter, and common control program has PID control, fuzzy control, dead beat control, repeats control, STATE FEEDBACK CONTROL etc.They have solved some control problem in field separately, but have equally various corresponding limitation.And because PWM inverter is non-linear and probabilistic in essence, the current Waveform Control technology of carrying does not have the fine robustness of system and dynamic response and the steady-state response of following the tracks of waveform taken into account.
Summary of the invention
The object of the invention is: a kind of Control Method of Active Power Filter based on Fuzzy Neural PID is provided, and scientific and effective is administered the actual pressing problem of this puzzlement electrical network quality of power supply of mains by harmonics.
In order to solve the problems of the technologies described above, the technical solution adopted in the present invention is that this control method comprises the following steps:
Step 1, according to power electronics, sets up the non-linear dynamic model of the inverter of APF;
Step 2, the non-linear dynamic model of setting up according to step 1, the PID controller of design current control loop;
Step 3, design fuzzy neural network compensating controller, in order to improve the dynamic response of inverter current control loop;
Step 4, adopts PI algorithm, designs the voltage circuit controller of inverter, calculates the reference current value of the required tracking of inverter output current.
Wherein, in step 1, the non-linear dynamic model of inverter is:
Wherein,
,
for a, b, c three-phase current is converted to d, the electric current under q coordinate;
,
for the index of modulation under rotating coordinate system;
,
for line voltage under rotating coordinate system;
,
for inductance and the equivalent resistance of inverter AC;
,
represent respectively electric capacity and the voltage thereof of inverter direct-flow side,
for power frequency,
the expression time; Above-mentioned non-linear dynamic model is expressed as:
Wherein,
,
,
,
,
represent transposition,
,
.
Wherein, in step 2, the design of the PID controller of current control loop is as follows:
, wherein:
,
for controller parameter,
,
for reference current value.
Wherein, in step 3, the design of the fuzzy neural network compensating controller of current control loop is as follows:
Set
,
for the input of fuzzy neural network, the output of compensating controller
for the output of fuzzy neural network, the fuzzy rule of designed fuzzy neural network is defined as follows:
Wherein
with
for the corresponding fuzzy set of input of fuzzy neural network,
for the zero-order function of Then part, wherein
,
for the number of membership function, the mapping relations of the input and output of fuzzy neural network are write as follows:
the 1st layer:definition membership function is as follows:
Wherein
with
be respectively mean value and the standard deviation of membership function, these parameters are adjustable in fuzzy neural network;
the 2nd layer:node function definition is as follows:
the 3rd layer:the normalization of each node is defined as foloows:
the 4th layer:the output of the each node in this layer is write as follows:
the 5th layer:the output of whole fuzzy neural network is write as follows:
So in conjunction with PID and fuzzy compensation controller, obtain inverter current control loop controller and be output as:
Utilize sliding formwork theoretical definition sliding-mode surface as follows:
The parameter of design fuzzy compensation controller regulates rule as follows:
Wherein: Sgn is switch function,
for learning rate,
be by
the weight matrix forming,
, and
be defined as:
。
Wherein, adopt PI algorithm in step 4, the voltage circuit controller of design inverter is as follows:
In formula:
,
for PI controller gain,
for the Voltage Reference track of setting,
for the output voltage of inverter,
for the current reference value calculating,
,
for the initial and termination time.
The invention has the beneficial effects as follows: 1, the present invention has carried out hypothesis and caused the physical system that set up model can not complete reaction reality in the time that step 1 is set up model, be that the present invention is in the non-linear dynamic model situation of unknown inverter, make inverter current control loop there is the characteristic of good control performance and strong robustness and follow current reference locus fast and accurately, further improve and control effect and control precision; 2, the present invention proposes the method that PID and fuzzy neural network combine, design process is simple, is easy to Project Realization, and further improves dynamic and steady-error coefficient performance; 3, the Fuzzy Neural PID control method proposing in the present invention does not need the non-linear dynamic model of known inverter, and method realizes simple, needs the parameter of adjusting less.
Brief description of the drawings
Fig. 1 is flow chart of the present invention.
Fig. 2 structure of fuzzy neural network block diagram.
Fig. 3 is that the control of the inventive method realizes schematic diagram.
Fig. 4 is grid side three-phase voltage oscillogram.
Fig. 5 is the grid side current waveform before filtering under nonlinear load effect.
Fig. 6 is for adopting the filtered grid side current waveform of this law invention APF.
Embodiment
Below in conjunction with Figure of description, the invention will be further described; Following examples are only for technical scheme of the present invention is more clearly described, and can not limit the scope of the invention with this.
As shown in Figure 1, be the flow chart of the Control Method of Active Power Filter based on Fuzzy Neural PID, comprise the following steps:
Step 1, according to power electronics, sets up the non-linear dynamic model of the inverter of APF;
The differential equation of Active Power Filter-APF (APF) is as follows:
Wherein,
,
for inductance and the equivalent resistance of inverter AC; The circuit flowing through therebetween
,
,
the offset current of injection electrical network producing for inverter;
,
represent respectively electric capacity and the voltage thereof of inverter direct-flow side;
,
,
for a, b, the index of modulation under c coordinate;
,
,
for line voltage;
Taking space vector of voltage as d direction of principal axis, vertical direction is q direction of principal axis with it, sets up two phase coordinate systems, obtains a, b, and c is to d, and q transform is as follows:
Wherein
for power frequency,
be expressed as the time;
Three-phase voltage type pulse-width modulation (PWM) inverter model under d-q synchronous rotating frame is:
Wherein,
,
for a, b, c three-phase current is converted to d, the electric current under q coordinate;
,
for the index of modulation under rotating coordinate system;
,
for line voltage under rotating coordinate system; Above-mentioned inverter non-linear dynamic model is expressed as form:
Wherein,
,
,
,
,
represent transposition,
,
;
Step 2, the PID controller of design current control loop is as follows:
, wherein:
,
for controller parameter,
,
for reference current value;
Step 3, adopts fuzzy neural network compensating controller to improve the dynamic response performance of inverter current control loop; The fuzzy neural network adopting adopts 5 layers of structure, and its structure as shown in Figure 2;
Set being input as of fuzzy neural network
,
, the output of compensating controller
for the output of fuzzy neural network, the fuzzy rule of designed fuzzy neural network is defined as follows:
Wherein
with
for the corresponding fuzzy set of input of fuzzy neural network,
for the zero-order function of Then part, wherein
,
for the number of membership function, the mapping relations of the input and output of fuzzy neural network are write as follows:
the 1st layer:definition membership function is as follows:
Wherein
with
be respectively mean value and the standard deviation of membership function, these parameters are adjustable in fuzzy neural network;
the 2nd layer:node function definition is as follows:
the 3rd layer:the normalization of each node is defined as foloows:
the 4th layer:the output of the each node in this layer is write as follows:
the 5th layer:the output of whole fuzzy neural network is write as follows:
So in conjunction with PID and fuzzy compensation controller, obtain inverter current control loop controller and be output as:
Utilize sliding formwork theoretical definition sliding-mode surface as follows:
The parameter of design fuzzy compensation controller regulates rule as follows:
Wherein: Sgn is switch function,
for learning rate,
be by
the weight matrix forming,
, and
be defined as:
Here adopt fuzzy neural network controller to control and implement compensation PID, its structural principle as shown in Figure 3; In inverter current control, fuzzy neural network controller realizes control system dynamic and steady-state error inhibition to the tracking of original current-order;
Step 5, adopts PI algorithm, designs the voltage circuit controller of inverter, calculates the reference current value of the required tracking of inverter output current; The voltage circuit controller of design inverter is as follows:
In formula:
,
for PI controller gain,
for the Voltage Reference track of setting,
for the output voltage of inverter,
for the current reference value calculating,
,
for the initial and termination time.
Embodiment: the number of choosing membership function
,
; Taking APF as object, under Matlab/Simulink environment, it is carried out to simulating, verifying, emulation platform adopts controlled thyristor, and sample frequency is
, maximum switching frequency is
, circuit parameter is:
,
; System parameters is: power supply phase voltage
, the set-point of capacitance voltage
; Fig. 4 is grid side three-phase voltage oscillogram; Fig. 5 is the grid side current waveform before filtering under nonlinear load effect; Fig. 6 is for adopting the filtered grid side current waveform of the inventive method APF; From Fig. 5-6, under institute's extracting method, the output current of APF suppresses harmonic current preferably, has obtained good compensation effect.
In sum, the present invention, in the non-linear dynamic model situation of unknown inverter, makes inverter current control loop have the characteristic of good control performance and strong robustness and follow current reference locus fast and accurately; The Fuzzy Neural PID control method proposing in the present invention has advantages of that design process is simple, be easy to Project Realization, and further improves dynamic and steady-error coefficient performance; The fuzzy Neural Network Control Method proposing in the present invention does not need the non-linear dynamic model of known inverter, and method realizes simple, needs the parameter of adjusting less, and the combination of PID control and two kinds of methods of Fuzzy Neural-network Control, forms and have complementary advantages.
More than show and described general principle of the present invention, principal character and advantage; The technical staff of the industry should understand, the present invention is not restricted to the described embodiments, that in above-described embodiment and specification, describes just illustrates principle of the present invention, without departing from the spirit and scope of the present invention, the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention; The claimed scope of the present invention is defined by appending claims and equivalent thereof.
Claims (5)
1. the Control Method of Active Power Filter based on Fuzzy Neural PID, is characterized in that it comprises the following steps:
Step 1, according to power electronics, sets up the non-linear dynamic model of the inverter of APF;
Step 2, the non-linear dynamic model of setting up according to step 1, the PID controller of design current control loop;
Step 3, design fuzzy neural network compensating controller, in order to improve the dynamic response of inverter current control loop;
Step 4, adopts PI algorithm, designs the voltage circuit controller of inverter, calculates the reference current value of the required tracking of inverter output current.
2. the Control Method of Active Power Filter based on Fuzzy Neural PID according to claim 1, is characterized in that the non-linear dynamic model of inverter in step 1 is:
Wherein,
,
for a, b, c three-phase current is converted to d, the electric current under q coordinate;
,
for the index of modulation under rotating coordinate system;
,
for line voltage under rotating coordinate system;
,
for inductance and the equivalent resistance of inverter AC;
,
represent respectively electric capacity and the voltage thereof of inverter direct-flow side,
for power frequency,
the expression time; Above-mentioned non-linear dynamic model is expressed as follows:
Wherein,
,
,
,
,
represent transposition,
,
.
3. the Control Method of Active Power Filter based on Fuzzy Neural PID according to claim 2, is characterized in that the PID controller design of current control loop in step 2 is as follows:
wherein:
,
for controller parameter,
,
for reference current value.
4. the Control Method of Active Power Filter based on Fuzzy Neural PID according to claim 1, is characterized in that the fuzzy neural network compensating controller design of current control loop in step 3 is as follows:
Set
,
for the input of fuzzy neural network, the output of compensating controller
for the output of fuzzy neural network; The fuzzy rule of designed fuzzy neural network is defined as follows:
Wherein
with
for the corresponding fuzzy set of input of fuzzy neural network,
for the zero-order function of Then part, wherein
,
for the number of membership function, the mapping relations of the input and output of fuzzy neural network are write as follows:
the 1st layer:definition membership function is as follows:
Wherein
with
be respectively mean value and the standard deviation of membership function, these parameters are adjustable in fuzzy neural network;
the 2nd layer:node function definition is as follows:
the 3rd layer:the normalization of each node is defined as foloows:
the 4th layer:the output of the each node in this layer is write as follows:
the 5th layer:the output of whole fuzzy neural network is write as follows:
So in conjunction with PID and fuzzy compensation controller, obtain inverter current control loop controller and be output as:
Utilize sliding formwork theoretical definition sliding-mode surface as follows:
The parameter of design fuzzy compensation controller regulates rule as follows:
Wherein: Sgn is switch function,
for learning rate,
be by
the weight matrix forming,
, and
be defined as:
。
5. the Control Method of Active Power Filter based on Fuzzy Neural PID according to claim 1, is characterized in that adopting PI algorithm in step 4, and the voltage circuit controller of design inverter is as follows:
In formula:
,
for the PI controller gain of voltage control loop,
for the Voltage Reference track of setting,
for the output voltage of inverter,
for the current reference value calculating,
,
for the initial and termination time.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104730921A (en) * | 2015-01-13 | 2015-06-24 | 河海大学常州校区 | Method for controlling fuzzy neural network of active power filter based on terminal sliding mode |
| CN106169754A (en) * | 2016-09-21 | 2016-11-30 | 河海大学常州校区 | Active Power Filter-APF neural network dynamic PID total-sliding-mode control method |
| CN106786647A (en) * | 2016-12-27 | 2017-05-31 | 三峡大学 | A kind of three-phase four-wire system parallel connection non-linear composite control method of APF two close cycles |
| CN108649817A (en) * | 2018-06-15 | 2018-10-12 | 湖北德普电气股份有限公司 | A kind of self-adaptive initial pulse design method based on Technics of Power Electronic Conversion device |
| CN109103885A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF metacognition fuzzy Neural Network Control Method |
| CN109103884A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF back stepping control method based on metacognition fuzzy neural network |
| CN109586293A (en) * | 2017-09-29 | 2019-04-05 | 西华大学 | A kind of active filter |
| CN110442016A (en) * | 2019-08-15 | 2019-11-12 | 四川轻化工大学 | A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network |
| CN112103960A (en) * | 2020-09-12 | 2020-12-18 | 河海大学常州校区 | Active power filter fractional order sliding mode control method and system based on neural network and storage medium |
| CN108762088B (en) * | 2018-06-20 | 2021-04-09 | 山东科技大学 | Sliding mode control method for hysteresis nonlinear servo motor system |
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| CN104730921B (en) * | 2015-01-13 | 2017-05-10 | 河海大学常州校区 | Method for controlling fuzzy neural network of active power filter based on terminal sliding mode |
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| CN106169754A (en) * | 2016-09-21 | 2016-11-30 | 河海大学常州校区 | Active Power Filter-APF neural network dynamic PID total-sliding-mode control method |
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| CN106786647A (en) * | 2016-12-27 | 2017-05-31 | 三峡大学 | A kind of three-phase four-wire system parallel connection non-linear composite control method of APF two close cycles |
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| CN108649817A (en) * | 2018-06-15 | 2018-10-12 | 湖北德普电气股份有限公司 | A kind of self-adaptive initial pulse design method based on Technics of Power Electronic Conversion device |
| CN108762088B (en) * | 2018-06-20 | 2021-04-09 | 山东科技大学 | Sliding mode control method for hysteresis nonlinear servo motor system |
| CN109103884A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF back stepping control method based on metacognition fuzzy neural network |
| CN109103885A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF metacognition fuzzy Neural Network Control Method |
| CN110442016A (en) * | 2019-08-15 | 2019-11-12 | 四川轻化工大学 | A kind of intelligent Anticorrosive Power device and its control method based on fuzzy neural network |
| CN112103960A (en) * | 2020-09-12 | 2020-12-18 | 河海大学常州校区 | Active power filter fractional order sliding mode control method and system based on neural network and storage medium |
| CN112103960B (en) * | 2020-09-12 | 2022-07-19 | 河海大学常州校区 | Active power filter fractional order sliding mode control method and system based on neural network and storage medium |
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Application publication date: 20140917 |