CN112054724A - Excitation generator controller based on fuzzy control and control method thereof - Google Patents
Excitation generator controller based on fuzzy control and control method thereof Download PDFInfo
- Publication number
- CN112054724A CN112054724A CN202011026428.5A CN202011026428A CN112054724A CN 112054724 A CN112054724 A CN 112054724A CN 202011026428 A CN202011026428 A CN 202011026428A CN 112054724 A CN112054724 A CN 112054724A
- Authority
- CN
- China
- Prior art keywords
- excitation
- deviation
- signal
- control
- delta
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005284 excitation Effects 0.000 title claims abstract description 157
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000008569 process Effects 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims description 25
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 claims description 6
- 238000004088 simulation Methods 0.000 description 8
- 238000012546 transfer Methods 0.000 description 6
- 230000005347 demagnetization Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000002401 inhibitory effect Effects 0.000 description 5
- 230000009467 reduction Effects 0.000 description 4
- 230000001105 regulatory effect Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000033228 biological regulation Effects 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 230000005389 magnetism Effects 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004148 unit process Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
- H02P9/10—Control effected upon generator excitation circuit to reduce harmful effects of overloads or transients, e.g. sudden application of load, sudden removal of load, sudden change of load
- H02P9/105—Control effected upon generator excitation circuit to reduce harmful effects of overloads or transients, e.g. sudden application of load, sudden removal of load, sudden change of load for increasing the stability
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0275—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
Abstract
The invention discloses an excitation generator controller based on fuzzy control, which comprises a steady-state excitation controller and a dynamic excitation controller, wherein the input end of the steady-state excitation controller inputs the rotating speed of a generator rotor and load current, the steady-state excitation controller outputs a PWM1 signal through a PID control algorithm, the dynamic magnetic controller processes the input rotating speed of the generator rotor and the load current through a fuzzy control algorithm and then outputs a delta PWM1, and the PWM1 signal and the delta PWM1 signal are superposed to control a power tube in an excitation power module. The invention has the advantages that: the dynamic excitation controller of the excitation generator is designed based on fuzzy control, so that the fluctuation of load current and rotating speed can be effectively inhibited, and the high-precision stable control of output voltage is realized.
Description
Technical Field
The invention relates to the field of excitation generator control, in particular to an excitation generator controller based on fuzzy control and a control method thereof.
Background
The generator can be divided into a permanent magnet generator and an excitation generator, the excitation magnetic field of the permanent magnet generator is generated by a permanent magnet which is a magnetic source and a magnetic circuit component, and the excitation magnetic field of the excitation generator provides excitation current for an external excitation system, so that the excitation generator is widely applied in consideration of the problems of uncontrollable excitation magnetic field, irreversible demagnetization, high manufacturing cost and the like of the permanent magnet generator.
The traditional excitation control system is a mechanical control system and mainly comprises mechanical components such as a motor, a sliding rheostat and a travel switch. After the rotating speed is reduced or the load is increased, the motor drives the sliding rheostat to move to carry out magnetism increasing operation, and finally the voltage is stabilized at a set voltage; after the rotating speed is increased or the load is reduced, the motor drives the slide rheostat to move to perform the demagnetization operation, and finally, the motor is stabilized at the set voltage. The mechanical excitation control system can realize functions only by means of combined matching of mechanical components such as a motor, a sliding rheostat and a travel switch, and has the problems of slow regulation of set voltage, low precision, poor stability and the like.
With the rapid development of power electronic devices and control theory, the electronic excitation control system gradually replaces the traditional mechanical excitation control system and mainly comprises electronic components such as an excitation power module, an excitation controller and the like,
the excitation power module provides excitation current for the excitation rotor to serve as an excitation power supply part of the excitation control system; the excitation controller controls the size of the excitation power supply according to the input signal and a set criterion, and further realizes the control of the rotor magnetic field. After the rotating speed is reduced or the current load is increased, the excitation controller increases the frequency of the trigger pulse, so that the excitation current of the excitation power module is increased, and the magnetism increasing operation is realized; after the rotating speed is increased or the current load is reduced, the excitation controller reduces the frequency of the trigger pulse, so that the excitation current of the excitation power module is reduced, and the demagnetization operation is realized. The electronic excitation control system is completed by combining and matching electronic components such as an excitation power module, an excitation controller and the like, the excitation controller regulates and controls the output voltage of the excitation power module and the excitation generator in real time according to the fed-back rotating speed and load current, and therefore corresponding control criteria and control algorithms need to be designed, accurate and stable control of an excitation magnetic field is achieved, and high-precision voltage stabilization output of the excitation generator is achieved.
The excitation regulator of the traditional excitation generator adopts a PID control algorithm, the PID control algorithm has obvious effect of inhibiting steady-state constant disturbance, and has poor inhibiting capability on dynamic disturbance changing along with time, so that the dynamic rotating speed and load disturbance of the excitation generator influence the stability of output voltage, therefore, the influence of the dynamic disturbance on the output voltage needs to be compensated through the control algorithm, and the high-precision stable control of the output voltage is further realized.
In consideration of the complexity of an excitation generator control system, the invention adopts fuzzy control to design an excitation generator excitation controller, compensates the dynamic rotating speed and the load disturbance of the excitation generator and realizes the high-precision stable control of the output voltage of the excitation generator.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an excitation generator controller based on fuzzy control, which can realize compensation control on dynamic rotor servo and load disturbance, thereby realizing high-precision stable control on the output voltage of an excitation generator.
In order to achieve the purpose, the invention adopts the technical scheme that: an excitation generator controller based on fuzzy control comprises a steady-state excitation controller and a dynamic excitation controller, wherein the input end of the steady-state excitation controller inputs the rotating speed and the load current of a generator rotor, the steady-state excitation controller outputs a PWM1 signal through a PID control algorithm, the dynamic magnetic controller processes the input rotating speed and the load current of the generator rotor through a fuzzy control algorithm and then outputs a delta PWM1, and the PWM1 signal and the delta PWM1 signal are superposed to control a power tube in an excitation power module.
The dynamic excitation controller comprises a deviation processing unit, a fuzzy control algorithm unit, a DSP control panel and a driving module, wherein the deviation processing unit is used for respectively carrying out deviation processing on dynamic rotating speed and load current, processed deviation data are sent to the fuzzy control algorithm unit, the deviation data are processed by the fuzzy control algorithm unit to obtain a pulse modulation signal delta PWM signal, and the delta PWM is sent to the driving module to convert a digital signal delta PWM into an analog control signal delta PWM1 for driving a power module.
The fuzzy control algorithm unit comprises a fuzzification processing unit, a fuzzy inference rule establishing unit and a de-fuzzification processing unit, wherein the fuzzification processing unit fuzzifies the deviation current, the deviation rotating speed and the deviation PWM signal and generates a fuzzy inference table according to a rule set by the fuzzy inference rule unit, and the de-fuzzification stall unit is used for outputting a corresponding delta PWM signal by combining the fuzzy inference table with the fuzzy inference rule.
A control method of an excitation generator controller based on fuzzy control comprises the following steps:
a steady state excitation control signal generation step: the steady-state excitation controller outputs a PWM1 control signal based on PID control through the input load current and the rotating speed value;
a dynamic excitation control signal generation step: the dynamic magnetic controller processes the input rotor speed and the load current of the generator through a fuzzy control algorithm and outputs delta PWM 1;
a signal output step: and superposing the PWM1 signal and the delta PWM1 signal to be used as an output PWM signal, wherein the PWM signal is used for controlling a power tube in the excitation power module.
The dynamic excitation control signal generating step includes:
(1) carrying out deviation processing on the load current and the rotating speed of the excitation generator;
(2) fuzzification is carried out on deviation amount, a deviation fuzzy inference rule is established, and defuzzification output delta PWM is obtained;
(3) the DSP control board outputs a delta PWM output driving signal according to the deblurring;
(4) the driving module outputs a delta PWM1 signal according to the driving signal.
In the step (2), the fuzzification processing step comprises:
and respectively carrying out interval division on the current deviation value, the rotating speed deviation value and the Pulse Width Modulation (PWM) signal deviation value, taking the same number of endpoint values for each deviation value, and carrying out interval division on the deviation values, wherein the endpoint values are numerical values in corresponding areas.
In the step (3), a deviation fuzzy inference rule and a deblurring output delta PWM are established, the end point values corresponding to each deviation amount are arranged from small to large, then a fuzzy inference table corresponding to the end point values of the current deviation amount, the end point values of the rotating speed deviation amount and the end point values of the deviation amount of the pulse width modulation PWM signal is formed, and the corresponding output delta PWM numerical value is obtained through the fuzzy inference table according to the real-time current deviation and the rotating speed deviation.
The DSP control board converts the analog delta PWM signal into a digital signal and converts the digital signal into an analog signal delta PWM1 which can drive a power tube in the excitation power module through a driving signal.
The invention has the advantages that: the dynamic excitation controller of the excitation generator is designed based on fuzzy control, so that the fluctuation of load current and rotating speed can be effectively inhibited, and the high-precision stable control of output voltage is realized; the stable-state excitation controller can restrain the disturbance of the stable-state rotating speed and the load current, and the dynamic excitation controller restrains the disturbance of the dynamic rotating speed and the load current, so that the high-precision voltage-stabilizing output of the excitation generator is finally realized, the output of the generator is accurately and dynamically controlled by a control signal, and the stability and reliability of the output voltage are ensured.
Drawings
The contents of the expressions in the various figures of the present specification and the labels in the figures are briefly described as follows:
fig. 1 is a schematic structural diagram of a control system corresponding to an excitation generator controller according to the present invention.
FIG. 2 is a schematic block diagram of a dynamic excitation controller of the present invention;
FIG. 3 is a simulation block diagram of an excitation generator control system according to the present invention;
fig. 4 is a schematic diagram showing the comparison between the stability of the output voltage waveform of the excitation generator controller of the present invention and the stability of the output voltage waveform of the prior art.
Detailed Description
The following description of preferred embodiments of the invention will be made in further detail with reference to the accompanying drawings.
The excitation regulator of the traditional excitation generator adopts a PID control algorithm, the PID control algorithm has obvious effect of inhibiting steady-state constant disturbance, and has poor inhibiting capability on dynamic disturbance changing along with time, so that the dynamic rotating speed and load disturbance of the excitation generator influence the stability of output voltage, therefore, the influence of the dynamic disturbance on the output voltage needs to be compensated through the control algorithm, and the high-precision stable control of the output voltage is further realized. The excitation generator control system is based on the design, and comprises a stable excitation controller adjusted by a PID algorithm and a dynamic excitation controller realized by a fuzzy algorithm, so that the accurate output control of the excitation generator is realized.
The embodiment discloses an excitation generator controller based on fuzzy control, and the excitation controller comprises a steady-state excitation controller and a dynamic excitation controller. The steady state excitation controller adopts the traditional PID control design, the PWM1 is regulated and controlled according to the steady state value of the rotating speed and the load current, the dynamic excitation controller adopts the advanced fuzzy control design, the Delta PWM1 is regulated and controlled according to the dynamic value of the rotating speed and the load current, the two are regulated and controlled together (the two are regulated and controlled together, namely PWM signals are superposed, the real-time PWM1 and the Delta PWM1 are obtained according to the load current and the rotating speed detected in real time, and the superposition is carried out to obtain the final regulation and control PWM.) the MOSFET on-off of the excitation power module and the magnetic field intensity of the rotor of the excitation generator, so as to regulate and. The steady-state excitation controller can restrain the disturbance of the steady-state rotating speed and the load current, and the dynamic excitation controller restrains the disturbance of the dynamic rotating speed and the load current, so that the high-precision voltage-stabilizing output of the excitation generator is finally realized. The excitation controller of the invention has the advantages that: the influence of dynamic rotating speed and load current on the stability of the output voltage of the excitation generator can be effectively compensated, and high-precision voltage stabilization output is finally realized.
As shown in fig. 1, an excitation generator controller based on fuzzy control includes a steady-state excitation controller and a dynamic excitation controller, the input end of the steady-state excitation controller inputs the rotation speed of the generator rotor and the load current, the steady-state excitation controller outputs a PWM1 signal through a PID control algorithm, the dynamic magnetic controller processes the input rotation speed of the generator rotor and the load current through the fuzzy control algorithm and outputs a Δ PWM1, a PWM1 signal, and a Δ PWM1 signal, which are superposed to control the power tube in the excitation power module.
The dynamic excitation controller comprises a deviation processing unit, a fuzzy control algorithm unit, a DSP control panel and a driving module, wherein the deviation processing unit is used for respectively carrying out deviation processing on dynamic rotating speed and load current, processed deviation data are sent to the fuzzy control algorithm unit, the fuzzy control algorithm unit processes the deviation data to obtain a pulse modulation signal delta PWM signal, and the delta PWM is sent to the driving module to convert a digital signal delta PWM into an analog control signal delta PWM1 for driving the power module.
The fuzzy control algorithm unit comprises a fuzzification processing unit, a fuzzy inference rule establishing unit and a de-fuzzification processing unit, wherein the fuzzification processing unit fuzzifies the deviation current, the deviation rotating speed and the deviation PWM signal and generates a fuzzy inference table according to a rule set by the fuzzy inference rule unit, and the de-fuzzification stall unit is used for outputting a corresponding delta PWM signal by combining the fuzzy inference table with the fuzzy inference rule.
A fuzzy control-based excitation generator controller control method comprises the following steps:
a steady state excitation control signal generation step: the steady-state excitation controller outputs a PWM1 control signal based on PID control through the input load current and the rotating speed value;
a dynamic excitation control signal generation step: the dynamic magnetic controller processes the input rotor speed and the load current of the generator through a fuzzy control algorithm and outputs delta PWM 1;
a signal output step: and superposing the PWM1 signal and the delta PWM1 signal to be used as an output PWM signal, wherein the PWM signal is used for controlling a power tube in the excitation power module.
The functional block diagram of the dynamic excitation controller is shown in fig. 2, and the dynamic excitation controller is designed by the following steps:
(1) carrying out deviation processing on the load current and the rotating speed of the excitation generator;
(2) fuzzification is carried out on deviation amount, a deviation fuzzy inference rule is established, and defuzzification output delta PWM is obtained;
(3) the DSP control board outputs a delta PWM output driving signal according to the deblurring;
(4) the driving module outputs a delta PWM1 signal according to the driving signal.
The method comprises the following specific steps:
s1: carrying out deviation processing on the load current and the rotating speed of the excitation generator, and taking the deviation of the load current I as follows: dI/dt; taking the deviation of the rotating speed n as: dn/dt.
S2: the dI/dt and dn/dt are fuzzified. And respectively carrying out interval division on the current deviation value, the rotating speed deviation value and the Pulse Width Modulation (PWM) signal deviation value, taking the same number of endpoint values for each deviation value, and carrying out interval division on the deviation values, wherein the endpoint values are values in the corresponding intervals.
Setting the deviation current of the current load current I and the last load current I as delta I, presetting a deviation current range, dividing the deviation current range, and dividing a plurality of deviation current intervals, wherein if the deviation current range is set to be deviation-3- +3, the-3 is less than-3 mA, and the +3 is greater than the last load current 3mA (specifically mA is a current unit, and the deviation range is determined according to actual conditions). When (-3- +3) is divided into 6 intervals, the intervals are respectively: -3 to-2, -2 to-1, -1 to 0, 0 to 1, 1 to 2, 2 to 3, and-3, -2, -1, 0, 1, 2, 3 are represented by NB, NM, NS, ZO, PS, PM, PB, respectively. The corresponding endpoint values are NB, NM, NS, ZO, PS, PM, PB. This results in a plurality of intervals of deviation.
Let the deviation between the current rotation speed n and the last rotation speed n be Δ n, and the deviation rotation speed setting range is deviation (-300 to +300), that is, greater than or less than 300 revolutions/min, and divide this interval into 6 intervals, which are: -300 to-200, -200 to-100, -100 to 0, 0 to 100, 100 to 200, 200 to 300, and-300, -200, -100, 0, 100, 200, 300 are represented by NB, NM, NS, ZO, PS, PM, PB, respectively.
Let the deviation current of the present PWM and the last PWM be Δ PWM, and its deviation (-30% to + 30%) is divided into 6 intervals, which are: -30% to-20%, 20% to-10%, 10% to 0, 0 to 10%, 10% to 20%, 20% to 30%, and-30%, -20%, -10%, 0, 10%, 20%, 30% are represented by NB, NM, NS, ZO, PS, PM, PB, respectively.
S3: establishing a deviation fuzzy inference rule and a deblurring output delta PWM; establishing a deviation fuzzy inference rule and a fuzzy output delta PWM, arranging the end point values corresponding to each deviation amount from small to large, then forming a fuzzy inference table corresponding to the end point values of the current deviation amount and the rotation speed deviation amount and the end point value of the deviation amount of the pulse width modulation PWM signal, and obtaining a corresponding output delta PWM numerical value through the fuzzy inference table according to the real-time current deviation and the rotation speed deviation.
Setting intervals of the obtained current deviation, the obtained rotating speed deviation and the obtained PWM deviation, calibrating the relation of the current deviation, the rotating speed deviation and the PWM deviation according to an actual experiment, correspondingly adjusting PWM in different intervals of the current deviation and the rotating speed deviation, finding interval points corresponding to the corresponding PWM, and then forming a corresponding table, so that no matter how the current deviation and the rotating speed deviate, a corresponding PWM signal can correspond, the PWM signal can be controlled to reduce the deviation, and the following fuzzy inference table is arranged according to the load current I, the rotating speed n and the result of PWM fuzzification processing: a
Description of fuzzy inference table:
1) the load current I deviation is NB (-3), the rotating speed n deviation is ZO (0), the load current is reduced, the rotating speed of the engine is unchanged, and in order to maintain the stability of the output voltage, the rotor of the engine needs to be greatly magnetized to compensate for the reduction of the load current, so that the output Delta PWM of the Delta PWM fuzzy controller is PB.
2) The load current I deviation is NB (-3), the rotating speed n deviation is NM (-200), the load current is reduced, the rotating speed of the engine is reduced, and in order to maintain the stability of the output voltage, the rotor of the engine needs to be greatly magnetized to compensate the reduction of the load current and the reduction of the rotating speed, so that the output Delta PWM of the Delta PWM fuzzy controller is PB.
3) The load current I deviation is PB (+3), the rotating speed n deviation is NM (-200), the load current is increased, the rotating speed of the engine is reduced, in order to maintain the stability of the output voltage, on one hand, small-amplitude demagnetization of the rotor of the engine is needed to compensate for the increase of the load current, and on the other hand, large-amplitude demagnetization of the rotor of the engine is needed to compensate for the reduction of the rotating speed, so that the delta PWM fuzzy controller outputs the delta PWM as ZO.
4) The load current I deviation is ZO (0), the rotating speed n deviation is ZO (0), the load current is unchanged, and the rotating speed of the engine is unchanged, so that the output Delta PWM of the Delta PWM fuzzy controller is ZO.
5) Other fuzzy reasoning similar principles are not described in detail.
S4: the DSP control board outputs a driving signal according to a deblurring rule, wherein the DSP control board is designed by adopting a DSP2812 chip and converts the analog delta PWM into a digital signal.
S5: the MOSFET driving module outputs delta PWM1 according to the driving signal, and the driving chip converts the digital signal into an analog signal delta PWM1 by adopting ir2110 s.
In steps s4, s5, the DSP control board converts the analog Δ PWM signal into a digital signal, and converts it into an analog signal Δ PWM1 that can drive the power tube in the excitation power module by the driving signal.
The feasibility of a dynamic excitation controller of an excitation generator designed based on fuzzy control is verified through simulation, the dynamic excitation controller is verified to dynamically adjust the load current I and the rotating speed n, and the simulation verification steps are as follows:
(1) as shown in fig. 3, a simulation control system of the excitation generator is built, and the building steps of the simulation model are as follows:
and S1, determining the transfer function of the subsystem according to the excitation generator function relation.
The transfer function of the rotating speed voltage conversion module is as follows: k/(1+ T × S), where the proportionality constant K is taken as: 1, the first order constant T is taken as: 100.
the excitation power module transfer function is: k1/(1+ T1 × S), where the proportionality constant K1 is taken as: 5, the first order constant T1 is taken as: 1.
the transfer function of the excitation generator is: k2/(1+ T2 × S), where the proportionality constant K2 is taken as: 1, the first order constant T2 is taken as: 0.05.
the voltage measurement module transfer function is: k3/(1+ T3 × S), where the proportionality constant K3 is taken as: 1, the first order constant T2 is taken as: 0.5.
the external load transfer function is: kf, where the proportionality constant Kf is taken as: 1.
s2, designing a steady-state excitation controller (PID control) according to the excitation generator control system, wherein the P parameter is as follows: 0.58, I parameters are: 0.39, parameters D are: 0.22.
s3: and (3) integrating a dynamic excitation controller of the excitation generator based on fuzzy control design into a simulation control system.
(2) Under the steady-state working condition of the output voltage, firstly giving a fluctuation signal of +/-50 RPM of the rotating speed, then giving a fluctuation signal of 5A of the load current, and comparing the stability of the output voltage of the traditional excitation controller and the output voltage of the fuzzy excitation controller.
The output voltage simulation waveform is shown in fig. 4, 0-1.5min is the starting working stage of the excitation generator, and the output voltage is stabilized at the set voltage of 28V, at this time, the steady-state excitation controller (PID control) can accurately control the output voltage; 1.5-4.8min is a fluctuation stage of the rotating speed at +/-50 RPM, the traditional excitation controller (PID control) is difficult to make up for the influence of the rotating speed fluctuation on the stability of the output voltage, and the dynamic excitation controller can restrain the rotating speed fluctuation, so that high-precision stable voltage is output; 4.8-8.2min is the fluctuation stage of the load current 5A, and the dynamic excitation controller has obvious effect of inhibiting the fluctuation of the load current.
The simulation results show that the dynamic excitation controller of the excitation generator designed based on the fuzzy control can effectively restrain the fluctuation of the load current and the rotating speed, and realize the high-precision stable control of the output voltage.
It is clear that the specific implementation of the invention is not restricted to the above-described embodiments, but that various insubstantial modifications of the inventive process concept and technical solutions are within the scope of protection of the invention.
Claims (8)
1. A field generator controller based on fuzzy control is characterized in that: the excitation power control system comprises a steady-state excitation controller and a dynamic excitation controller, wherein the input end of the steady-state excitation controller inputs the rotating speed and the load current of a generator rotor, the steady-state excitation controller outputs a PWM1 signal through a PID control algorithm, the dynamic magnetic controller processes the input rotating speed and the load current of the generator rotor through a fuzzy control algorithm and then outputs a delta PWM1, and the PWM1 signal and the delta PWM1 signal are superposed to control a power tube in an excitation power module.
2. A fuzzy control based exciter generator controller according to claim 1, characterised in that: the dynamic excitation controller comprises a deviation processing unit, a fuzzy control algorithm unit, a DSP control panel and a driving module, wherein the deviation processing unit is used for respectively carrying out deviation processing on dynamic rotating speed and load current, processed deviation data are sent to the fuzzy control algorithm unit, the deviation data are processed by the fuzzy control algorithm unit to obtain a pulse modulation signal delta PWM signal, and the delta PWM is sent to the driving module to convert a digital signal delta PWM into an analog control signal delta PWM1 for driving a power module.
3. A fuzzy control based exciter generator controller according to claim 2, characterised in that: the fuzzy control algorithm unit comprises a fuzzification processing unit, a fuzzy inference rule establishing unit and a de-fuzzification processing unit, wherein the fuzzification processing unit fuzzifies the deviation current, the deviation rotating speed and the deviation PWM signal and generates a fuzzy inference table according to a rule set by the fuzzy inference rule unit, and the de-fuzzification stall unit is used for outputting a corresponding delta PWM signal by combining the fuzzy inference table with the fuzzy inference rule.
4. A fuzzy control based control method of an excitation generator controller according to any one of claims 1 to 3, characterized in that: the method comprises the following steps:
a steady state excitation control signal generation step: the steady-state excitation controller outputs a PWM1 control signal based on PID control through the input load current and the rotating speed value;
a dynamic excitation control signal generation step: the dynamic magnetic controller processes the input rotor speed and the load current of the generator through a fuzzy control algorithm and outputs delta PWM 1;
a signal output step: and superposing the PWM1 signal and the delta PWM1 signal to be used as an output PWM signal, wherein the PWM signal is used for controlling a power tube in the excitation power module.
5. The fuzzy control-based control method of the excitation generator controller according to claim 4, wherein: the dynamic excitation control signal generating step includes:
(1) carrying out deviation processing on the load current and the rotating speed of the excitation generator;
(2) fuzzification is carried out on deviation amount, a deviation fuzzy inference rule is established, and defuzzification output delta PWM is obtained;
(3) the DSP control board outputs a delta PWM output driving signal according to the deblurring;
(4) the driving module outputs a delta PWM1 signal according to the driving signal.
6. The fuzzy control-based control method of the excitation generator controller according to claim 5, wherein: in the step (2), the fuzzification processing step comprises:
and respectively carrying out interval division on the current deviation value, the rotating speed deviation value and the Pulse Width Modulation (PWM) signal deviation value, taking the same number of endpoint values for each deviation value, and carrying out interval division on the deviation values, wherein the endpoint values are values in the corresponding intervals.
7. The fuzzy control-based control method of the excitation generator controller according to claim 5 or 6, wherein: in the step (3), a deviation fuzzy inference rule and a deblurring output delta PWM are established, the end point values corresponding to each deviation amount are arranged from small to large, then a fuzzy inference table corresponding to the end point values of the current deviation amount, the end point values of the rotating speed deviation amount and the end point values of the deviation amount of the pulse width modulation PWM signal is formed, and the corresponding output delta PWM numerical value is obtained through the fuzzy inference table according to the real-time current deviation and the rotating speed deviation.
8. The fuzzy control-based control method of the excitation generator controller according to claim 5 or 6, wherein: the DSP control board converts the analog delta PWM signal into a digital signal and converts the digital signal into an analog signal delta PWM1 which can drive a power tube in the excitation power module through a driving signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011026428.5A CN112054724A (en) | 2020-09-25 | 2020-09-25 | Excitation generator controller based on fuzzy control and control method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011026428.5A CN112054724A (en) | 2020-09-25 | 2020-09-25 | Excitation generator controller based on fuzzy control and control method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112054724A true CN112054724A (en) | 2020-12-08 |
Family
ID=73604861
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011026428.5A Pending CN112054724A (en) | 2020-09-25 | 2020-09-25 | Excitation generator controller based on fuzzy control and control method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112054724A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0533987A (en) * | 1991-07-31 | 1993-02-09 | Hitachi Ltd | Controlling method for air conditioner |
| JPH0678600A (en) * | 1992-08-27 | 1994-03-18 | Central Res Inst Of Electric Power Ind | Excitation control system for generator |
| US5508924A (en) * | 1992-03-19 | 1996-04-16 | Kabushikikaisha Equos Research | Driving force controller for an electric vehicle with electric motors provided for all driving wheels individually |
| CN202111599U (en) * | 2010-11-08 | 2012-01-11 | 华北电力大学 | AC excitation control system for paratactic structure hybrid excitation synchronous generator |
| CN102664580A (en) * | 2012-05-16 | 2012-09-12 | 重庆交通大学 | Mixed smart control method of power system multi-stabilizer |
| CN107276474A (en) * | 2017-06-29 | 2017-10-20 | 贵州电网有限责任公司电力调度控制中心 | A kind of synchronous generator exciting control method based on fuzzy sliding mode |
| CN108494302A (en) * | 2018-03-23 | 2018-09-04 | 南京航空航天大学 | Aerogenerator pressure regulation method based on fuzzy PI hybrid control |
-
2020
- 2020-09-25 CN CN202011026428.5A patent/CN112054724A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0533987A (en) * | 1991-07-31 | 1993-02-09 | Hitachi Ltd | Controlling method for air conditioner |
| US5508924A (en) * | 1992-03-19 | 1996-04-16 | Kabushikikaisha Equos Research | Driving force controller for an electric vehicle with electric motors provided for all driving wheels individually |
| JPH0678600A (en) * | 1992-08-27 | 1994-03-18 | Central Res Inst Of Electric Power Ind | Excitation control system for generator |
| CN202111599U (en) * | 2010-11-08 | 2012-01-11 | 华北电力大学 | AC excitation control system for paratactic structure hybrid excitation synchronous generator |
| CN102664580A (en) * | 2012-05-16 | 2012-09-12 | 重庆交通大学 | Mixed smart control method of power system multi-stabilizer |
| CN107276474A (en) * | 2017-06-29 | 2017-10-20 | 贵州电网有限责任公司电力调度控制中心 | A kind of synchronous generator exciting control method based on fuzzy sliding mode |
| CN108494302A (en) * | 2018-03-23 | 2018-09-04 | 南京航空航天大学 | Aerogenerator pressure regulation method based on fuzzy PI hybrid control |
Non-Patent Citations (1)
| Title |
|---|
| 于会群;高扬;张浩;彭道刚;: "并网双馈异步风电机组模糊自适应控制", 电子测量与仪器学报, vol. 30, no. 05, pages 676 - 683 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cheng et al. | Robust composite nonlinear feedback control with application to a servo positioning system | |
| Ren et al. | Nonlinear feedback control of chaos in permanent magnet synchronous motor | |
| Mahmud et al. | Control BLDC motor speed using PID controller | |
| Wang et al. | An improved sliding mode control using disturbance torque observer for permanent magnet synchronous motor | |
| Divandari et al. | Speed control of switched reluctance motor via fuzzy fast terminal sliding-mode control | |
| Kiruthika et al. | Mathematical modelling and speed control of a sensored brushless DC motor using intelligent controller | |
| Zuo et al. | Simultaneous identification of multiple mechanical parameters in a servo drive system using only one speed | |
| Wang et al. | Bounded integral controller with limited control power for nonlinear multiple-input multiple-output systems | |
| Roy et al. | Robust adaptive backstepping sliding mode controller for a DC-DC buck converter fed DC motor | |
| CN112054724A (en) | Excitation generator controller based on fuzzy control and control method thereof | |
| Mohamed et al. | Design of Speed Control System of DC motor Based on PID tuning with fuzzy define weighting point | |
| Ghafari et al. | Design and real-time experimental implementation of gain scheduling PID fuzzy controller for hybrid stepper motor in micro-step operation | |
| He et al. | Speed control study of brushless DC motor based on fuzzy optimization PID | |
| Bennassar et al. | Fuzzy logic based adaptation mechanism for adaptive luenberger observer sensorless direct torque control of induction motor | |
| Normanyo et al. | MATLAB implementation of position control of a hybrid stepper motor using fuzzy logic controller | |
| Geng et al. | An improved electronic line shafting control for multimotor drive system based on sliding mode observer | |
| CN114268263A (en) | Double-closed-loop fuzzy control method for motor driver | |
| CN114374346A (en) | High-performance control method for permanent magnet synchronous motor | |
| Mahmud et al. | Advanced Adaptive PID Controller for BLDC Motor | |
| RU2521617C2 (en) | Dynamic torque control method for engine flywheel | |
| Gokyildirim | A Novel Chaotic Attractor With a Line and Unstable Equilibria: Dynamics, Circuit Design, and Microcontroller-Based Sliding Mode Control Un Nouvel Attracteur Chaotique Avec Une Ligne d’équilibres Et Un équilibre Instable: Dynamique, Conception De Circuit Et contrôle De Mode Coulissant basé Sur Un microcontrôleur | |
| Brandstetter et al. | Application of fuzzy logic in control of switched reluctance motor | |
| Koondhar et al. | PI and fuzzy logic controller based comparative analysis of separately excited DC motor | |
| Bahi et al. | Fuzzy logic control for vector control six phase induction machines | |
| Mallesham et al. | Improvement in dynamic response of electrical machines with PID and fuzzy logic based controllers |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |