CN109449949B - Improved fuzzy self-adaptive PI (proportional integral) control method of static var generator - Google Patents

Improved fuzzy self-adaptive PI (proportional integral) control method of static var generator Download PDF

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CN109449949B
CN109449949B CN201811405826.0A CN201811405826A CN109449949B CN 109449949 B CN109449949 B CN 109449949B CN 201811405826 A CN201811405826 A CN 201811405826A CN 109449949 B CN109449949 B CN 109449949B
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CN109449949A (en
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吕广强
刘潇逸
许峰
林毅
许文敏
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Nanjing University of Science and Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
    • H02J3/1835—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators with stepless control
    • H02J3/1842—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators with stepless control wherein at least one reactive element is actively controlled by a bridge converter, e.g. active filters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/10—Flexible AC transmission systems [FACTS]

Abstract

The invention discloses an improved fuzzy self-adaptive PI control method of a static var generator, which comprises the following steps: simplifying a structure model of the static var generator, and establishing a mathematical model; designing an inner ring double decoupling control strategy; dividing the error input quantity and the change rate of the PI controller and the adjustment value of the PI parameter according to the maximum change range, uniformly mapping the error input quantity and the change rate to a standard domain, and converting the accurate value on the domain to a corresponding fuzzy subset; defining fuzzy rules, wherein the change of the parameters follows the fuzzy rules; and processing the obtained fuzzy value of the correction parameter, performing defuzzification by adopting a gravity center method to obtain an accurate value, and adjusting the PI parameter by adopting an accumulation form. The invention improves the compensation accuracy and enhances the disturbance resistance of the system.

Description

Improved fuzzy self-adaptive PI (proportional integral) control method of static var generator
Technical Field
The invention relates to the technical field of reactive power compensation, in particular to an improved fuzzy self-adaptive PI control method for a static var generator.
Background
At present, the development of the power industry is faster and faster, and the requirements of various industries on the stable operation of a power grid and the quality of electric energy are higher and higher, so that higher requirements are provided for the compensation of system useless current. The static var generator has the characteristics of good low-voltage characteristic, high response speed, flexible control and the like, and is widely applied to reactive compensation of a power system. However, the dynamic change of the load also puts higher requirements on the compensation accuracy of the static var generator, and particularly on the user side, the dynamic change of the load is more frequent, so the design of the control strategy needs to be adaptive.
The PI control is a linear control, and has a certain limitation on the control of a nonlinear system. And the three-phase system is not always in a steady-state operation state, and the algorithm design shows that the PI parameter of the inner loop decoupling controller is related to the R, L parameter of the system, and the change of the load side parameter can also affect the PI control. If the load side parameter changes dynamically in the operation process, the set PI parameter is not applicable any more, and the problems of reactive power and insufficient negative sequence current compensation exist.
Disclosure of Invention
The invention aims to provide an improved fuzzy self-adaptive PI control method of a static var generator, which overcomes the defects of the existing compensation strategy and improves the compensation accuracy of the static var generator applied to a dynamic load system.
The technical scheme for realizing the purpose of the invention is as follows: an improved fuzzy self-adaptive PI control method of a static var generator comprises the following steps:
step 1, simplifying a structure model of a static var generator, and establishing a mathematical model;
step 2, designing an inner-ring double-decoupling control strategy;
step 3, dividing the error input quantity and the change rate of the PI controller and the adjustment value of the PI parameter according to the maximum change range, uniformly mapping the error input quantity and the change rate to a standard domain, and converting the accurate value on the domain to a corresponding fuzzy subset;
step 4, defining a fuzzy rule, wherein the change of the parameters follows the fuzzy rule;
and 5, processing the obtained fuzzy value of the correction parameter, obtaining an accurate value by defuzzification by adopting a gravity center method, and adjusting the PI parameter by adopting an accumulation form.
Compared with the prior art, the invention has the beneficial effects that: (1) the method realizes the self-adaptive adjustment of the PI parameters based on the fuzzy control, has low requirement on the accuracy of the model, and is suitable for a dynamic load system; (2) the PI parameter adjustment mode is improved, an accumulation mode is adopted, and the self-adaptive adjustment range is wider.
Drawings
FIG. 1 is a control flow chart of the present invention.
Fig. 2 is a main circuit structure diagram of the static var generator of the present invention.
FIG. 3 is a positive and negative sequence synchronous decoupling control block diagram adopted by the present invention.
Fig. 4 is a block diagram of an adaptive controller designed by the present invention.
FIG. 5 is a diagram of a PI parameter adjustment mode according to the present invention.
Fig. 6(a) -6 (c) are graphs of the a-phase compensation current tracking obtained by simulation of the present invention.
Fig. 7(a) to 7(c) are graphs of a-phase reactive power obtained by simulation of the present invention.
Detailed Description
For the condition of dynamic change of the load of the power system, the traditional control strategy has insufficient regulation capacity, and the adaptive control strategy suitable for the dynamic load system is designed. On the basis of inner loop decoupling control, fuzzy control is introduced to carry out self-adaptive adjustment on PI parameters so as to improve the compensation accuracy of the static var generator and reduce dynamic compensation errors. The traditional fuzzy PI control is adjusted up and down by taking a PI initial parameter as a center, the adjusting range is limited, and aiming at the problem, the adjustment of the PI parameter is added in an accumulation mode, so that the adjusting range is enlarged. Simulation analysis shows that compared with the traditional fuzzy PI control, the compensation accuracy is further improved, and the disturbance resistance of the system is enhanced.
Firstly, the structure of the SVG connected in parallel with the power grid is simplified, and the structure diagram of the main circuit of the SVG can be obtained as shown in FIG. 1. Wherein esa、esb、escFor three-phase voltage values of the grid, ea、eb、ecFor the output of the voltage value, i, at the AC side of the SVGa、ib、icFor the output current of the SVG AC side, R, L is the equivalent value of the series resistance inductance of the SVG AC side, UdThe voltage of the capacitor at the direct current side of the SVG is obtained.
An improved fuzzy self-adaptive PI control method of a static var generator comprises the following steps:
step one, simplifying a structure model of the static var generator, establishing a mathematical model and designing a positive sequence compensation control link.
Referring to fig. 2, a three-phase loop equation is listed:
Figure BDA0001877313790000031
Figure BDA0001877313790000032
Figure BDA0001877313790000033
to satisfy the stability of PI control, analysis is typically performed under a dq coordinate system, converting the above equation to a dq rotation coordinate system:
Figure BDA0001877313790000034
it is possible to obtain:
Figure BDA0001877313790000035
by converting the above equation into PI control, it is possible to obtain:
Figure BDA0001877313790000036
kp、kirespectively, PI parameter value, omega angular frequency, idrefIs the active reference current.
Step two, designing an inner ring double decoupling control strategy according to the step one, as shown in fig. 3;
designing a control strategy based on a dq coordinate system, and adopting inner loop decoupling control; load current iLabcExtraction of the reactive current component i by dq conversionLqI.e. reactive reference current iqrefObtaining an active reference current i through a voltage outer loopdrefAnd subtracting the actual compensation current of the static var generator from the reference current to obtain an error signal e, and adjusting by using a PI (proportional integral) controller to obtain a corresponding reference voltage.
Thirdly, inputting the error input quantity e of the PI controller and the change rate delta e thereof and the adjustment value delta k of the PI parameterp、ΔkiDividing according to the maximum variation range, uniformly mapping to a standard domain of discourse, and then converting the precise value of the domain of discourse into a corresponding fuzzy subset.
First, the relationship between the data to be processed, i.e. the input variable λ of the controller and its rate of change Δ λ, and the change Δ k of the PI parameter, is definedp、ΔkiThe relationship (2) of (c). And carrying out interval division according to the maximum variation range of the four data, uniformly mapping the four data onto a standard domain of discourse, converting accurate values on the domain of discourse into corresponding fuzzy subsets, and replacing the accurate values with language variable values capable of representing size relations.
Fuzzy sets { NB, NM, NS, ZO, PB, PM, PS } are defined in terms of fuzzy partitions, where the letter N indicates that there is a negative directional deviation of the data from a standard value, the letter P indicates a negative directional deviation, ZO indicates a 0 deviation, and B, M, S indicates that the deviation values are large, medium, and small, respectively. The membership function adopts a triangular membership function with simple calculation.
And step four, summarizing the existing theoretical knowledge and the actual debugging experience into a fuzzy rule in the form of' IF () THEN (), wherein the parameter change needs to follow the fuzzy rule.
First, the relationship between the data to be processed, i.e. the input e of the controller and its rate of change Δ e, and the change Δ k of the PI parameter is definedp、ΔkiThe relationship (2) of (c). And carrying out interval division according to the maximum variation range of the four data, uniformly mapping the four data onto a standard domain of discourse, converting accurate values on the domain of discourse into corresponding fuzzy subsets, and replacing the accurate values with language variable values capable of representing size relations.
Fuzzy sets { NB, NM, NS, ZO, PB, PM, PS } are defined in terms of fuzzy partitions, where the letter N indicates that there is a negative directional deviation of the data from a standard value, the letter P indicates a negative directional deviation, ZO indicates a 0 deviation, and B, M, S indicates that the deviation values are large, medium, and small, respectively. The membership function adopts a triangular membership function with simple calculation.
The key of fuzzy reasoning is to summarize a set of rules for parameter tuning, and summarize the existing theoretical knowledge and the experience of actual debugging into fuzzy rules in the form of IF () THEN (), such as IF (e is NB) and (Δ e is NB) THEN (Δ k)pis PB)(Δkiis NB). The deviation is controlled within the allowable range by these rules. To prevent large errors, the following rules are followed during debugging:
(1) when e is large, the error is biased in the positive direction, and Δ k should be setpTake a small value, and Δ kiTaking a larger value, and setting the larger value and the larger value to be 0 if necessary;
(2) when e is medium, the error is not very large, Δ kpShould be increased appropriately,. DELTA.kiProperly reduced;
(3) when e is small, the compensation error direction is negative, delta kpShould continue to increase, take a larger value, Δ kiContinuously reducing, and taking a smaller value;
(4) considering the influence of the input target change rate Δ e, the larger Δ kpThe smaller, Δ kiThe larger.
Step five, the obtained correction parameter delta kp、ΔkiThe fuzzy value is processed, a gravity center method is adopted to defuzzify to obtain an accurate value, and an accumulation form is adopted for the adjustment of the PI parameter.
The idea is to take the membership function of the fuzzy subset obtained by the user and a standard value corresponding to the gravity center of a graph formed by the surrounding of a horizontal axis as an accurate result, and the formula is obtained as follows:
Figure BDA0001877313790000051
in the formula (7), η is the membership function, kp、kiRespectively PI parameter values. By reasonable derivation of the above steps, the fuzzy controller can be designed as shown in FIG. 4. After obtaining the precise adjustment value of the PI parameter, the method of accumulation adjustment is adopted to obtain the accurate k required by peoplep、kiThe values, the adjustment mode are shown in fig. 5. At an increment Δ kpRear settingA time delay link, thus forming the accumulation of the adjustment quantity, and the adjustment of each time is k at the last momentp、kiPerformed on the parameter values. And repeatedly accumulating and adjusting until the required requirements of the system are met. At the same time, if a new k is receivedp、kiAnd (4) resetting the accumulated amount and readjusting. Thus, the adjustment range can be reduced, and the adjustment precision can be improved.
The following provides a detailed description of embodiments of the invention.
Examples
Establishing a simulation model on MATLAB/SIMULINK, and designing parameters: the three-phase voltage is set to 380V, and the frequency is 50 Hz. The capacitance of the direct current side of the SVG is 5.64mF, the equivalent inductance of the alternating current side is 3mH, and the equivalent resistance is 0.2 omega. Setting the variation range of the input quantity e of the controller to be-10, 10]The range of the change rate delta e is [ -20,20]，ΔKpThe adjusting range is [ -8,8 [)]，ΔKiThe adjusting range is [ -15,15 [)]Setting the fuzzy factors of the error e and the change rate thereof as k10.6 and k21.2, the PI parameter adjustment Δ K is setpAnd Δ KiRespectively is k34/3 and k42.5, uniformly mapping variable variation ranges to standard discourse field [ -6,6 [ -6 [ ]]。
Setting 3 resistance load, load 1: the resistance is 1 omega, the inductance is 5mh, and the input time is 0 s; and (3) loading 2: the resistance is 0.8 omega, the inductance is 4mh, and the input time is 0.4 s; and (3) loading: the resistance is 1.2 omega, the inductance is 6mh, and the input time is 0.7 s.
The input time of the static var generator is 0.1s, and simulation is carried out according to the set dynamic load. Fig. 6(a), 6(b), and 6(c) show a phase a compensation current tracking curve, which includes a phase a compensation command value, a phase a compensation current of SVG, and a compensation error.
Fig. 7(a) shows a simulation result without adaptive control, after 0.1s, SVG effectively compensates reactive current, after 0.4s, a second group of loads is switched, the compensation error increases, the error reaches about 12A, after 0.7s, a load 3 is switched, the error increases again, and the error reaches about 17A.
Fig. 7(b) shows the simulation result of the PI parameter adjustment based on the initial value, and the adjustment amount is not accumulated. For the dynamic change of the load, the compensation error is reduced, after 0.4s, the error is reduced from 12A to 7A, after 0.7s, the error is reduced from 17A to 12A, but the error is slightly larger.
Fig. 7(c) is a simulation result of the adaptive controller using PI parameter accumulation adjustment, and it is obvious that an error curve is almost a straight line, after the load 2 is switched for 0.4s, the error is reduced to 3A through adaptive adjustment, and after the load 3 is switched for 0.7s, the error is reduced to 4A, which is further reduced compared with the error in fig. 7 (b).
Compared with the traditional control, the fuzzy rule set in the method, namely the adjustment mode of the PI parameter, further improves the accuracy of the dynamic compensation of the static var generator.

Claims (2)

1. An improved fuzzy self-adaptive PI control method for a static var generator is characterized by comprising the following steps:
step 1, simplifying a structure model of a static var generator, and establishing a mathematical model; the method specifically comprises the following steps:
the three-phase loop equation is listed:
Figure FDA0003440819180000011
Figure FDA0003440819180000012
Figure FDA0003440819180000013
wherein esa、esb、escFor three-phase voltage values of the grid, ea、eb、ecFor the output of the voltage value, i, at the AC side of the SVGa、ib、icOutputting current for an alternating current side of the SVG, wherein R, L is an equivalent value of a series resistor and an inductor on the alternating current side of the SVG; converting the above equation to dq rotation coordinatesComprises the following steps:
Figure FDA0003440819180000014
it is possible to obtain:
Figure FDA0003440819180000015
by converting the above equation into PI control, it is possible to obtain:
Figure FDA0003440819180000016
kp、kirespectively, PI parameter value, omega angular frequency, idrefIs an active reference current;
step 2, designing a control strategy based on a dq coordinate system, and adopting inner loop decoupling control;
step 3, inputting the input quantity e of the controller, the change rate delta e of the input quantity and the change value delta k of the PI parameterp、ΔkiInterval division is carried out according to the maximum variation range, the interval division is mapped to a standard discourse domain in a unified mode, and then accurate values on the discourse domain are converted into corresponding fuzzy subsets; the method specifically comprises the following steps:
interval division is carried out according to the maximum variation range of the four data, the four data are uniformly mapped to a standard domain of discourse, then accurate values on the domain of discourse are converted into corresponding fuzzy subsets, and language variable values capable of representing size relations are used for replacing the accurate values; defining a fuzzy set { NB, NM, NS, ZO, PB, PM, PS } according to fuzzy division, wherein a letter N indicates that negative direction deviation exists between data and a standard value, a letter P indicates the negative direction deviation, ZO indicates 0 deviation, B, M, S indicates that a deviation value is large, medium and small respectively, and a membership function adopts a triangular membership function;
step 4, defining a fuzzy rule, wherein the change of the parameters follows the fuzzy rule;
and 5, processing the obtained fuzzy value of the correction parameter, obtaining an accurate value by defuzzification by adopting a gravity center method, and adjusting the PI parameter by adopting an accumulation form.
2. The improved fuzzy adaptive PI control method for SVG as claimed in claim 1, wherein step 4 summarizes the existing theoretical knowledge and actual debugging experience as fuzzy rules in the form of "IF () THEN ()", and the parameter changes are subject to the fuzzy rules, which are used to control the deviation within the allowable range.
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