.�. 2012 China International Conference on Electricity Distribution (CICED 2012) Shanghai, 5-6 Sep. 2012

Study on novel fuzzy controller and its application in PWM inverters

LIU Xiao-xian\ wu Wei-nong\ ZOU Lei2,DONG Ya-Ii 2

(I.Chongqing Power Information & Communication Branch Company, 401123,China;

2. Chongqing Runbridge Communication CO.,Ltd, 401122, China)

Abstract-With the development of power electronics,

PWM inverters become more and more important and gain

more and more wide applications. PWM inverters are

common parts of a lot of power electronic devices and their

controller design is very important for working performance.

In order to better adapt the nonlinearity and uncertainty of

power system, and absorb the advantages of conventional PI

controller and fuzzy controller and conquer their

disadvantages, this paper proposes a novel fuzzy self

adjustable PI controller. In this novel controller, the

parameters of PI controller are dynamically adjusted by

fuzzy self adjustable controller, so the high steady-state

precision and fast dynamic responding speed can all be

obtained at the same time. The novel controller designed in

this paper has simple structure and is very convenient to be

realized in digital control system, which improves the

practicability of the proposed controller. Finally, the

proposed controller is applied into the active power filter

device which is composed of PWM inverter. Simulation and

experiment results verify the effectiveness and superiority of

the proposed controller.

Keywords-Self adjustable, fuzzy control, PWM inverter,

active power filter

1 . INTRODUCTIONS

With the development of power electronics and digital

signal processing techniques, a lot of power devices appear to solve different power quality problems. These devices

all base on PWM inverters[l,2]. And the PWM inverters are also the main components of wind power and photovoltaic

devices. So the PWM inverters become a common study issue. For PWM inverters the control system is very important for the device performance, and it is the core of

the whole device. When designing the controller, the

steady-state and transient performance should be both considered, and its practicability in engineering

applications must be considered too.

In recent years, more and more attention has been paid to PWM inverter control strategies, and different

control theories have been applied into PWM inverter

controllers. The most common is PI controller with advantages of simple structure and strong engineering

practicability. But the fatal disadvantage of PI controller is that it depends on the precise mathematical model of system being controlled and cannot adapt power system

with nonlinearity and uncertainty; furthermore, the

parameters of PI controller are very difficult to be set properlyl305].

While fuzzy control theory can well conquer this

problem. The praiseworthy advantage of fuzzy controller is that it doesn't depend on the mathematical model of

system, and it can conquer nonlinearity effect and has

strong robustness to system being controlled [608]. But its

steady-state performance is always not satisfactory. With the development of fuzzy theory, the fuzzy controller gains

more and more applications in different fields. The existed fuzzy controller has some limits, such as fixed control

rules and complex hardware realization. Based on the contents mentioned above, regarding the

PWM inverters as study objects, this paper proposes a novel fuzzy self adjustable PI controller. In this novel controller, the parameters of PI controller are dynamically adjusted by fuzzy self adjustable controller. Because the fuzzy self adjustment is executed in the whole region, and

its adjustment process can well accord with people

thinking characteristics during decision-making process,

the high steady-state precision and fast dynamic responding speed can all be obtained at the same time. The

proposed control strategy can effectively improve object tracking characteristics and anti-interference and

robustness, and it has little calculation burden and is very convenient to be realized.

2. THE STRUCTURES AND OPERATING PRINCIPLES

OF PWM INVERTERS

According to DC source types, the inverters can be divided into voltage-source inverters (VSls) and current-source inverters (CSIs). The input side of VSI is

connected large capacity capacitors, and the inverters convert DC voltage into AC voltage; while the input side

of CSI is connected large capacity inductors, and the

inverters convert DC current into AC current. Recently, VSls become the emphases of PWM inverters with

advantages of simple structure, low loss and convenient

control system. This paper adopted voltage-source three-phase inverter as study object, as shown in Fig.l .

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2012 China International Conference on Electricity Distribution (CICED 2012) Shanghai, 5-6 Sep. 2012

A_,..-_....I

B __ ���� __ -+ ____ �

C--++,-Jn�-T---;---'

Reference Current

Fig.l The structure of three-phase VSI

From Fig.l, it can be seen that the whole device

functions properly by close-loop control of output

current of PWM inverter. First, the real output current is

detected, and it is compared to the specified reference

current to get the error information. Then the current

error signals are input to the controller to get responding

output voltage signals, and then through PWM

modulation segment, the final PWM signals to control

IGBTs are obtained. And through controlling the

turn-on and shut-off of TGBTs, the PWM inverters can

output proper voltages and currents with any frequency,

any amplitude and any phase.

3. DESIGN OF FUZZY SELF ADJUSTABLE PI CONTROLLER

3.1 Design of fuzzy self adjustable controller

In fuzzy control system, the system will be

influenced greatly by the performance of fuzzy controller, and the performance of fuzzy controller

depends on the fuzzy rules determination and adjustment. In conventional fuzzy controller, the fuzzy

control rules cannot change, so it is not satisfactory in

complex systems. So the designed fuzzy control system must have adjustable control rules.

Based on the study mentioned above, an adjustable

parameter is introduced to regulate the control rules, and

a fuzzy controller with adjustable factor in the whole

region is designed to adapt the system complexity and variety. The designed controller is as shown in Fig.2.

Fig.2 The structure scheme of fuzzy controller

Tn this fuzzy controller, the error and its variation

ratio are fuzzified to E* and ii*, and the fuzzy

variables are input to the controller, and by fuzzy

deducing logic, the fuzzy output U* is then obtained.

The fuzzy control rules with self adjustable factor in the

whole region are adopted as follows.

{u* =-(aE* +(l-a)£*)

a = � (as -ao) I E* I + ao (I)

There into, 0::;; ao < a, ::;; 1, and they are constants,

a E [ao ' as ] is self adj ustable factor. The

characteristics of the control rules are that the adjustable

factor a verifies linearly between ao and as with

the variation of IE* I , and it can adjust the weight of

E according to the magnitude of E. This adjustment

process well coincide the error control logic and optimization can be realized.

In order to realize convenient engineering

applications of fuzzy controller and improve the

practicability of the fuzzy controller, being different from conventional fuzzification and defuzzification methods, the

method of non-uniform quantization is used during the process of fuzzification and defuzzification. Dividing the

region into [-N, N], when the exact values of E and

£ are small, we divide them finely; conversely when they are large, we divide them coarsely. And probability statistic method has been adopted in ascertaining the dividing

sections. We can omit the process of ascertaining

membership functions because the dividing method itself is a process of ascertaining membership function. For

example, regarding N as 7 and the fuzzification of E is as shown in Fig.3.

In order to further improve the adaptability of system,

all variables are normalized in the whole control system.

-0.7 -0.5 : I

I

-0,1 0 0.1 I ({3 0.5: 0.7 0.9 I

Fig.3 The non-uniform quantization of error

For example, in Fig.3 when error signal E:?: 0.6 , it is

qualified as N=7; when error signal 0.3::::; E ::::; 0.6, it is

qualified as N=6; when error signal 0.2::::; E ::::; 0.3 , it is

qualified as N=5. And when the error becomes smaller and

smaller, the qualification process becomes more and more finely. It can be seen that the qualification process is not uniform. When the error is large, we divide them coarsely; conversely when they are small, we divide them fmely.

The fuzzy values U* can be calculated out by fuzzy

deduction, and then it is rounded into integer. Then the

exact control values U can be gained through

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· . -GOY .. � 2012 China International Conference on Electricity Distribution (C[CED 2012) Shanghai, 5-6 Sep. 2012

non-uniform defuzzifier. After considering the peak value

limit of the control variables the real system can be controlled.

3.2 Novel fuzzy self adjustable P[ controller

[n order to improve the robustness and anti-inference

ability of control system, this paper introduces fuzzy

control theory into PI controller, and a fuzzy controller

with self adjustable factor is proposed in this paper to

adjust the parameters of P[ controller. The P[ controller

and fuzzy controller are combined effectively, so they can

conquer the drawbacks of each other and obtain excellent

performance in both transient and steady state.

In conventional PI controller, the role of proportional

component is to reflect the error signal E of control

system proportionally. Once the error E is generated, the

controller actions quickly in order to decrease the error. If

the value of proportional control parameters Kp is always

very large, the system oscillations will happen that destroy

the system dynamic performance. Based on that mentioned

above, when error E is large, the Kp is set a large value

in order to improve system dynamic performance; while

when error is small, the Kp should be set a small value in

order to avoid overshoot and system oscillations. And at

the same time, the factor of variation ratio £ should be

considered. When E and i; remains same signs, that

indicates the output departures from steady state, so Kp should be enlarged; while reverse, Kp should be

decreased. The characteristic of the control rules shown in ( 1) is

that the factor a can regulate in the whole region. When

the error E is very big, it indicates that the system is far from balance state. So the controller should enlarge its control variables in order to make the system attain balance state as soon as possible. In this case, the error E plays a more important role in control variables. However, when the error E is very small, it indicates that the system is close to the balance state. [n order to make the

system steady and restrain the oscillations as soon as possible, the most important thing is to decrease the overshoot. Then the role of E in the control variables is

small while the role of E becomes large. The self-adjustable factor can regulate in the whole region

similar to thinking characteristics of human being in the process of controlling. And as a result it can realize

optimization.

The role of integrating component is mainly to eliminate steady state error and improve the steady precision. The integrating component integrates error

signal and will appear certain lagging function. [f the

function of integrating component is too strong, the system overshoot will happen, furthermore, system oscillation will

happen. [n conventional P[ controller, in order to avoid integrating saturation, the integrating loop is often

separated out. When the error is very large, the value of

Kj is set to zero in order to avoid overshoot, while when

the error is small, the value of Kj increases with the

decreasing of error in order to eliminate the steady state error and improve controlling precision.

The designed controller is as shown in FigA. The

combined controller is composed of PI controller and

fuzzy controller with self adjustable factor. The function of

fuzzy controller with self adjustable factor is mainly to adjust the controlling parameters of P[ controller

instantaneously. [n the proposed fuzzy controller, the fuzzy

values E* and i;* of error signal E and its incremental

variation E are regarded as inputs, through fuzzy deducing, the adjustment values of PI controller

parameters are output from the fuzzy controller based on

the pre-setting values in order to attain the best controlling performance.

Fig. 4 The scheme of novel controller

The adopted fuzzy controller is a two-dimension input and two-dimension output controller. [t calculates the

values of Mp and Mj according to different input E and £. And then the adjustment of PI controller parameters can be realized, as shown in (2). {Kp = K1,o + Mp

Kj =Kro+Mj (2)

There into, Kpo and Kro is the pre-setting values of

controller parameters.

The proposed fuzzy adjusting rules of Kp with self

adjustable factor are shown in follows. {f...Kp = (aE' +C1-a)£') aK? = � (as -ao)IE*I+ao

(3)

The proposed fuzzy adjusting rules of K] with self

adjustable factor are shown in follows.

Kj =0

f...Kj = -(aE* + (1-a)£*) Kj = Kro +f...Kj aKr =�(as -ao)IE'I+ao

N

(4)

The adjustment rules shown in (4) is based on the

adjustment principles mentioned above, that is when the

error is very large, the increment component is not adopted in order to avoid too larger overshoot and oscillations; while when the error is small to certain degree, the increment component is plunged into and its function

becomes stronger in order to improve steady state

precision.

The novel control system as shown in FigA is applied into controller part in Fig.2, and then the fuzzy self

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2012 China International Conference on Electricity Distribution (CICED 2012) Shanghai, 5-6 Sep, 2012

adjustable PI controller of PWM inverter is realized,

4, SIMULA nON AND EXPERIMENT RESULTS

PWM inverters gain more and more applications in

different power electronics compensating and controlling

devices, and they function differently in different devices,

The PWM inverter can function as active power filter (APF) as shown in Fig,S, We can see that the main

topology of APF is PWM inverter, and through effective controlling of output current, APF can well eliminate

harmonic current and compensate reactive power current. The compensating principles of APF are not detailed

in this paper, The whole control system of APF is as shown

in Fig,6, We can see that the whole control system is composed of two controllers, The outer-loop is DC-voltage

controller in order to maintain DC-link voltage, and the

inner-loop is current tracking controller which detects the

error signal of output current and generates switch acting signals to drive semiconductors, The designed novel

controller is applied into the inner-loop current controller of APF and the inner-loop controller directly determines

the APF operating performance, The outer-loop voltage controller adopts the common PI controller because this

loop is much simpler,

Fig,S The scheme of APF system

Fig,6 The scheme of APF control system

Simulation results are shown in Fig,7, Channel 1 is

the load current which also is the source current before compensation with APF, Channel 2 is the compensated

source current, and channel 3 is the compensating current output by APF. It can be seen that through fuzzy self

adjustable PI controller functioning properly, the APF can

well eliminate harmonic current, and it can track quickly when load currents vary, That the steady precision and transient tracking speed are both attained, The THD of

source current decreases from 27,68% to 4,56% with APF

application, The compensated source currents can meet the

national requirements of harmonic eliminating

�:: t. ,LI1"'. "/111"" ,/111-. ..

' j� ," IIh,

'/ilk ,

'1Ih , ' 1Ih1

�,: � 0.04 0..0.6 0.08 0.1 as) 0..'2 0..'4 0.16 0.19

Fig,7 Simulation results of novel controller applied into APF system

Experiment results are shown in Fig,8, The source current before compensation is distorted seriously like the

channell waveform in Fig,7, In Fig,8, channell is the compensated source currents, and channel 2 is the

compensating current output by APF. It can be seen that

the experiment results well coincide with simulation results, With the novel controller proposed in this paper, APF can function well, and the steady and transient

performance of system is both excellent. Fig,9 shows the reference output current and real

output current of APF. We can see that with the novel

controller, APF can well track reference current, and the tracking speed is quick, tracking error is small. And all those verify the excellent performance of APF and novel controller proposed in this paper,

Fig,S Compensated source current and compensating current output by APF

Fig,S Reference output current and real output current of APF

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· . -GOY .. � 2012 China International Conference on Electricity Distribution (CICED 2012)

5. CONCLUSIONS

With the development of power electronics, the

applications of PWM inverters become more and more

wide. And precise control becomes more important too. Based on the disadvantages of conventional PI controller

and fuzzy controller, a novel fuzzy self adjustable PI controller is proposed in this paper. This novel controller absorbs the merits of PI controller which has high accuracy in steady state and fuzzy controller which has strong

robustness and excellent self-adaptability in transient state.

The novel controller designed in this paper has simple structure and is very convenient to be realized in digital

control system, which improves the practicability of the proposed controller in engineering applications. Finally,

the proposed controller is applied into the active power filter device which is composed of PWM inverter. And

simulation and experiment results verify the effectiveness and superiority of the proposed novel controller.

REFERENCES

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[3] D.M. Brod, D.W Novotny. Current Control of V SI-PWM Inverters. IEEE Procs. of IAS.1984: 418-425.

[4] 1. Dixon, S. Tepper, 1. Moran. Practical Evaluation of Different Modulation Techniques for Current-Controlled V oltage Source

Inverters. lEE procs. Electr. Power App!. 1996, 143(4): 301-306. [5] Marian P. Kazmierkowski, Luigi Malesani. Current Control

Techniques for Three-Phase Voltage-Source PWM Converters: A Survey. IEEE Trans. on Industrial Electronics. 1998, 45(5): 691-703

[6] Xu Wanfang, Luo An, Wang Lina, etal. Development of Hybrid Active Power Filter Using Intelligent Controller[J]. Automation of Electric Power Systems, 2003, 27 (10): 49-52.

[7] S.K.Jain,P.Agrawal, etal. Fuzzy logic controlled shunt active power filter for power quality improvement[J]. IEE Proc.-Eletrc. Power Apply, 2002,149 (5):317-328.

[8] Huang Zhenyu, et al. Study on Fuzzy Modulation Control of UPFC. Power System and Automation, 2000 (25).

LlU Xiao-xian was born in Sichuan, China, in 1962. Currently, he works as a senior engineer with ChongQing Electric Power Corporation, China. His research interest include the automation and information of electric power system.

WU Wei-nong was born in Sichuan, China, in 1965. Currently, he works as a senior engineer with ChongQing Electric Power Corporation,

China. His research interest include the communication and information

of electric power system. ZOU Lei was born in Chongqing, China, in 1969. BE in

Engineering of HAERBIN ship engineering University of Electronic Technology,his main research direction is power electronic and communications technology.

DONG Ya-Ii was born in Hubei, China, in 1986. Master's degree, her main research direction is power electronic technology.

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