Microgrid frequency control using the virtual inertia and ANFIS-based Controller

(1)

International Journal of Industrial Electronics, Control and Optimization .

Vol. 2, No. 2, pp. 145-154, April (2019)

Microgrid frequency control using the virtual inertia and

ANFIS-based Controller

Akbar Karimipouya

1

, Shahram karimi

†2

and Hamdi Abdi

3

†,1,2,3

Department of Electrical Engineering, Razi university, Kermanshah, Iran

The main challenge in associate islanded Micro grid (MG) is the frequency stability due to the inherent low-inertia feature of distributed energy resources. That is why, energy storage devices, are utilized in MGs as the promising sources for grid short-term frequency regulation. Though energy storage devices, improve the dynamic operation of the load-frequency control (LFC) system, these devices increase system costs. Moreover, the modification or uncertainty of the system parameters will significantly degrade the performance of the conventional LFC system. This article proposes the execution of rotating-mass-based virtual inertia in Double-Fed Induction Generator (DFIG) to support the first frequency control associated an adaptive Neuro-Fuzzy inference System (ANFIS) controller, as secondary frequency control. The simulation conclusions illustrate that the suggested control scheme ameliorate the dynamic operation of the LFC system and also the studied islanded MG remains stable, despite severe load variation and parametric uncertainties.

Article Info

Keywords:

Microgrid, Frequency Control,

DFIG, Virtual Inertia, Fuzzy PID Controller, ANFIS.

Article History:

Received 2018-08-05

Accepted 2018-12-09

I.

I

NTRODUCTION

In recent decades, the use of renewable energy has enhanced, the main cause of which is environmental pollution and economic aspects [1]. Wind energy has become one in all the most necessary renewable energies and the largest view in terms of new technologies and economical. Due to the variable speed of wind, for maximum efficiency at different speeds, the turbine with variable speed is required

[2,3].Therefore, the wind turbine equipped with Double-Fed

Induction Generators (DFIGs) are widely utilized in Wind

Energy Conversion System (WECS) [4]. There

is generally no inertia response between the DFIG rotor speed and the frequency of the system. There is additionally no primary frequency regulation and only the maximum power point tracking (MPPT) method is used in DFIG power control system [5]. Therefore, with high wind power penetration, inevitably there are major challenges in frequency regulation. In addition, the main challenge in an islanded Micro grid (MG) is the frequency stability due to the

inherent low-inertia feature of Distributed Energy Resources (DERs). That is why, energy storage equipment, because of their rapid response time and ability to charge and discharge efficiently, are used in MGs as the promising sources for grid short-term frequency regulation [6]. Although energy

storage instruments, like batteries, flywheels, and

super-capacitors, improve the dynamic operation of the Load-Frequency control (LFC) system, these devices, along with their respective inverter, increase system costs.

Wind turbines participation in frequency adjustment is the idea to make better the dynamic frequency respond characteristics of power systems. In [7-15], by implementing virtual inertia in DFIG control system, the dynamic behavior of the MG is improved. In [7, 8], the primary frequency control strategy, virtual inertia and blade angle control are used in DFIG to reduce the frequency variation. In [9, 10], the super-capacitor, as the source of inertia, has been used. In [11], using double-layer capacitor energy storage, dynamic stability is improved. These methods also, due to the use of additional equipment, is costly. In [12-15], the inertia of the wind turbine rotating mass is used to implement virtual inertia in DFIG control system. Although implementation of

† _{Corresponding Author}_{: [email protected]}

*

Electrical Engineering Department, Razi University, Kermanshah, Iran A

(2)

International Journal of Industrial Electronics, Control and Optimization .

virtual inertia with rotating masses does not require additional equipment and costs, its implementation is subject to some limitations. In this method, the virtual inertia appertain to the wind speed, and therefore, its value is completely variable and stochastic. Another limitation to the use of rotating-mass-based virtual inertia is a thermal constraint. At high wind speeds, frequency deviation leads to the additional power generation, due to the virtual inertia control loop, and consequently, the temperature of the DFIG rises, which may be more than allowed [13]. Moreover, in this paper, it is shown that the change or uncertainty of the system parameters, such as droop factor, can considerably degrade the performance of the LFC system.

Contrariwise, in the conventional LFC systems, the PI or PID controllers are usually used as secondary frequency control. Although these controllers have such advantages as simplicity and easy implementation, they are mostly used for systems with simple and constant configuration. Therefore, because the structure and configuration of micro grids are constantly changing and there are many uncertainties, conventional controllers with constant parameters cannot be used. [15]. Also, Most of the linearization methods used to control the frequency increase the system's order, which makes the micro grid more complicated [16].

Fuzzy logic and neural controllers also have been developed in many control applications to intricate models [17]. The fuzzy is based on the rules "if and then", which we get these rules from the system description. Since we do not have much information about the behavior of most systems, conclusion from these types of systems is difficult and neural controllers need a lot of time for training. Combined methods are among the methods developed to control complex systems in recent years. One of these combined algorithms is Adaptive Neuro-Fuzzy Inference System (ANFIS). An ANFIS benefits from both fuzzy and neural control systems. In this paper, instead of the conventional PID controller, an ANFIS-based controller is proposed to use as secondary frequency control while the rotating-mass-based virtual inertia is implemented to support the primary frequency control.

So far, several intelligent controllers have been explored as complementary frequency control of micro grids. In [6], frequency control has been improved using the fuzzy-PSO method with the amount of electric power generated by wind turbines. In [18], frequency control has been improved using

the HPSO algorithmdecentralized. In [19] using fuzzy logic,

control for the micro grid frequency is done with high

penetration of wind turbines. In [20], frequency control is

performed using an on-line method that detects frequency variations using PSO and fuzzy algorithms. But until now, research on the use of ANFIS-based controller as complementary frequency control of the micro grid has not been reported when virtual inertia is involved in the LFC system.

The rest of this study is segmented as follows: The second part describes the model of the micro grid studied. In Section 3, the implementation of rotating-mass-based virtual inertia in

DFIG are explained. Fuzzy PID and ANFIS-based controllers’ design are addressed in part 4. Results and simulations of different controllers and their comparison are presented in

part 5. Eventually, the general conclusionis summarized in

part 6.

II.

THE

STUDIED

MG

STRUCTURE

Figure 1 shows an islanded micro grid containing various DGs. This system includes wind turbine, solar cell, diesel generator, fuel cell, battery and flywheel storage devices. The parameters of the studied MG are also presented in Table 1.

In the studied MG, as shown in Fig. 1, diesel generator consists of the primary and complementary frequency control loop Also, a complementary frequency control loop; control the fuel cell output power. Droop control is used for the primary frequency control and the storage devices are used for supporting the primary frequency control. Different controllers are designed for the secondary frequency control loops. These controllers are the classical PID controller, PID fuzzy and proposed ANFIS-based controller.

TABLE I. MICRO GRID PARAMETERS

TABLE II. NOMINAL POWER OF DG UNITS AND LOAD

Load (kW) Rated Power (kW)

430

100 WTG

30 PV panel

70 FC

160 DEG

45 FESS

45 BESS

Value Parameter

0.08

( )

0.015 D(Pu/Hz)

0.4

( )

0.1667 2H(Pu s)

0.004

/( )

0.1

( )

0.04

( )

0.1

( )

3 R(Hz/Pu)

0.26

(3)

International Journal of Industrial Electronics, Control and Optimization .

PV model

WTG model

Σ Σ

Σ

P

I

D

/F

u

z

y

P

I

D

/

A

N

F

I

S

C

o

n

tr

o

l

le

r

1

1+T SIN. /

1 1+T SI C.

1 1+T SFC.

1

1+T SIN. /

1 1+T SI C.

1 1+T St. 1

1+T Sg.

1 2 .

D+H S

1 1+TFESS.S

1 1+TBESS.S m

P

∆

Load

P

∆

f

∆

1

R

inverter

FC system

DEG system

MG system

FESS

BESS

-Fig.1. schematic block diagram of the studied islanded MG.

III.

I

MPLEMENTING VIRTUAL INERTIA

In a DFIG, stator directly and rotor via two converters connected to the power system. It produces the highest wind power using a converter that is located on the rotor's side and it adjusts the stator voltage. DC voltage can be adjusted using the network side converter. Wind turbines such as synchronous generators have high inertia, which this energy is stored as kinetic in its axis. However, this energy cannot participate in frequency adjusting due to the decoupled mechanical and electrical power controllers.

The maximum power that can be obtained from a wind turbine, Pm, opt can be expressed

(1) 3

,

m opt opt m

P =K

ω

Where ωm is DFIG rotational speed and Kopt is fixed

when the angle of the turbine blade is constant.

The input mechanical and the output electrical powers (Pm, Pe) are related to each other as the following

(2)

− =

In this equation, J is the moment of inertia of the rotating mass connected to the rotor.

In synchronous generators, in order to control the grid frequency, a speed governor controls the input mechanical power. As a result, the generator output power depends on the input mechanical power and a derivative term that introduces the rotating mass inertia. In a DFIG, where Pm is undispatchable, the output electrical power is controlled directly. Therefore, the grid frequency variation and the rotating mass inertia do not affect the amount of generator output power. Thus, the DFIG inertia does not participate in frequency tuning.

If equation (2) is written in the per unit form, it is as follows:

(3)

2 m

m e m

d P P H

dt

− =

ω

In this equation,

H

is the DFIG inertia constant and Pe is

equal to Pm, opt. After linearization around the basis rotational

speed (3) can be represented as:

(4)

− = 2

Where,

(5)

2 0 3

e opt m m

P K ω ω

∆ = ∆

To implement virtual inertia, the output electrical power control is changed in such a way that, moreover to tracking the maximum power point, it also responds to system frequency variations. Therefore, the above equation can be modified as follows:

(6)

2

0 0

3 ( )

e opt m m m

P K ω ω Hν s ω ω

∆ = ∆ − ∆

In the above equation, the frequency of the grid is represented by ω and Hv(s) is the transfer function of virtual inertia, which represented as follows [22]:

(7)

1 2

( )

( 1)( 1)

M s

H s

s s

ν ν

τ τ

=

+ +

In the above transfer function, is the virtual inertial

constant, and

τ

1 and

τ

2 are the virtual inertia time

constants. At low frequencies, Hv(s) behaves similarly to a

derivative.

By replacing (6) in (4) we can write:

(8)

= 3!"# $ − ( )

+ 2

Using the above equation is obtained according to

the equation (9).

(9)

= 1

2 + 3!"# $

∆

+ ( )

2 + 3!"# $

By replacing (9) in (6) we will have:

= )*+,-./0

$1 2)*+,-./0

∆ − $113( )4/0 $1 2)*+,-./0

(10)

Based on (10), can be changed as a function of ∆

and . Therefore, the grid frequency variation and the

virtual inertia affect the amount of DFIG output power. By

setting

and consequently

Hv(s) to zero, wind

(4)

International Journal of Industrial Electronics, Control and Optimization

equipped with figure, the transf the first and second part

Fig.2

with virtual inertia.

IV.

controllers loop. The

of the proportional, integral and derivative conventional PI and PID controllers, value,

for different operation conditions and in presence of disturbances and uncertainties.

pa

that happen in the grid, the

One of the features of Fuzzy PID and controller

ANFIS

MG that is separated from the MG. diagram of the

PID

Fuzzy PID Controller

In this study, the MG

International Journal of Industrial Electronics, Control and Optimization

= ,"# −

Figure 2 shows equipped with DFIG figure, the transf the first and second part

PV model Σ P I D /F u z z y P I D / A N F I S C o n tr o ll e r 1 1+T SFC.

1

R

inverter

-Fig.2. the schematic block diagram of the studied islanded MG

with virtual inertia.

IV. FUZZY PID AN

In conventional power systems, conventional PI and PID controllers are often used

loop. The operation of these controllers pertain of the proportional, integral and derivative conventional PI and PID controllers, value, is not guaranteed

for different operation conditions and in presence of disturbances and uncertainties.

parameters can automatically that happen in the grid, the

One of the features of Fuzzy PID and controllers is the automatic adjustment of parameters.

The aim of this

ANFIS-based controller for complementary control loop in a MG that is separated from the MG.

diagram of the secondary frequency control ID and ANFIS

Fuzzy PID Controller

A. Design of Fuzzy PID Controller

Fuzzy rules have been created based on human knowledge. In this study, the fuzzy

the MG frequency error, and the other is der

International Journal of Industrial Electronics, Control and Optimization

64

6

shows adding of virtual inertia to the wind turbine DFIG in the s

figure, the transfer functions G1

the first and second part of the equation

2( )

G s

1( )

G s

1

1+T SIN. /

1 1+TI C.S

1

1+T SIN. /

1 1+TI C.S

1 1+T St. 1

1+T Sg. m P ∆ inverter FC system DEG system WTG model with virtual inerisa

. the schematic block diagram of the studied islanded MG with virtual inertia.

FUZZY PID AN ANFIS

In conventional power systems, conventional PI and PID often used in the secondary

operation of these controllers pertain of the proportional, integral and derivative conventional PI and PID controllers,

is not guaranteed to achieve the desired performance for different operation conditions and in presence of disturbances and uncertainties.

s can automatically tune

that happen in the grid, the desired performance achievable One of the features of Fuzzy PID and

is the automatic adjustment of parameters. this part is to design a Fuz

based controller for complementary control loop in a MG that is separated from the MG.

secondary frequency control and ANFIS-based controller

Fuzzy PID Controller is illustrated in Fig. 4.

Design of Fuzzy PID Controller

Fuzzy rules have been created based on human knowledge. the fuzzy block has

frequency error, and the other is der

International Journal of Industrial Electronics, Control and Optimization

of virtual inertia to the wind turbine studied islanded MG

1(s) and G2(s) are respectively of the equation (10).

Σ

Σ Σ

1T .S

1T S.

Load P ∆ FESS

-. the schematic block diagram of the studied islanded MG

ANFIS-BASED CONTROLLER SESIGN

In conventional power systems, conventional PI and PID in the secondary frequency control operation of these controllers pertain

of the proportional, integral and derivative parameters. conventional PI and PID controllers, with fix parameters’

to achieve the desired performance for different operation conditions and in presence of disturbances and uncertainties. While the PID controller tune matching to the variations desired performance achievable One of the features of Fuzzy PID and

is the automatic adjustment of parameters.

part is to design a Fuzzy PID and an based controller for complementary control loop in a MG that is separated from the MG. Figure 3 shows the block

secondary frequency control using the based controller. The overall

is illustrated in Fig. 4.

Design of Fuzzy PID Controller

Fuzzy rules have been created based on human knowledge. block has two inputs, one of which is frequency error, and the other is der

International Journal of Industrial Electronics, Control and Optimization

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of virtual inertia to the wind turbine tudied islanded MG. In this are respectively

1 2 .

D+H S

1 1+TFESS.S

1 1+TBESS.S

ω ∆ MG system

FESS

BESS

. the schematic block diagram of the studied islanded MG

BASED CONTROLLER

In conventional power systems, conventional PI and PID frequency control operation of these controllers pertains on the values parameters. Using with fix parameters’ to achieve the desired performance for different operation conditions and in presence of While the PID controller matching to the variations desired performance achievable.

ANFIS-based is the automatic adjustment of parameters.

zy PID and an based controller for complementary control loop in a igure 3 shows the block using the fuzzy The overall structure of

Fuzzy rules have been created based on human knowledge. two inputs, one of which is frequency error, and the other is derived from the

International Journal of Industrial Electronics, Control and Optimization

of virtual inertia to the wind turbine In this are respectively

. the schematic block diagram of the studied islanded MG

BASED CONTROLLER

In conventional power systems, conventional PI and PID frequency control on the values g with fix parameters’ to achieve the desired performance for different operation conditions and in presence of While the PID controller matching to the variations . based

zy PID and an based controller for complementary control loop in a igure 3 shows the block fuzzy structure of

Fuzzy rules have been created based on human knowledge. two inputs, one of which is ived from the

MG frequency error. The law that has been wri block is 49 laws;

range of membership func between

-Fig.3. Block diagram

1

e

Fig. 4. The overall structure of Fuzzy PID Controller

CE E NB NM NS Z PS PM PB

International Journal of Industrial Electronics, Control and Optimization

frequency error. The law that has been wri block is 49 laws; these rules

range of membership func

-3 and +3 as shown in Figure 5

. Block diagram of the MG frequency control

10

du/dt

Derivative 3.4285 3.4285

. The overall structure of Fuzzy PID Controller

TABLE III

NM NB NS NB Z NB PS NB PM NB PB NM Z NS PS Z

International Journal of Industrial Electronics, Control and Optimization .

© 201

frequency error. The law that has been wri these rules are fully written

range of membership functions for inputs and outputs shown in Figure 5

of the MG frequency control

-K--K E CE Fuzzy Inference System

. The overall structure of Fuzzy PID Controller

III.TABLE OF FUZZY RULES

Z NS NB NB NM NB NS NM Z NS PS Z PM PS PB PM (a)

frequency error. The law that has been written for this fully written in table 3 tions for inputs and outputs shown in Figure 5.

of the MG frequency control

1/s K- -K-+ 1/S Limited Integrator Integrator PI Control Feed Forward PD Control

. The overall structure of Fuzzy PID Controller ABLE OF FUZZY RULES

PM PS NS NM Z NS PS Z PM PS PB PM PB PB PB PB

IECO 148

tten for this in table 3. The tions for inputs and outputs is

(5)

International Journal of Industrial Electronics, Control and Optimization

Fig.5

Fig. 5

block, b) Second input of the fuzzy block (Error derivative) c) output of fuzzy block

the fuzzy controller that

proposed be computed.

is to attain the highest control proficie the neural network,

controllers to the

Considering different scenarios such as load variation and parametric uncertainties, the output of each two controllers have been

finally have been used to teach the proposed ANFIS controller.

fuzzy system has 49 rules that amounts t

inputs (x

an error derivative

rules, if and then the fuzzy, which are in the Takagi Sugeno

first

described below.

International Journal of Industrial Electronics, Control and Optimization

Fig.5 Continued

Fig. 5. Membership functions: a) The error input of the fuzzy

block, b) Second input of the fuzzy block (Error derivative) c) output of fuzzy block

B. Design of ANFIS

The behaviour of the neuro the fuzzy controller

that the neural system has been added. To design the proposed ANFIS

be computed. Since the purpose of mode is to attain the highest control proficie the neural network,

controllers to the

Considering different scenarios such as load variation and parametric uncertainties, the output of each two controllers

have been recorded separately.

finally have been used to teach the proposed ANFIS controller.

The neural network used in this article has five layers fuzzy system has 49 rules that

amounts to two.

inputs (x, y), one of which is an error an error derivative

rules, if and then the fuzzy, which are in the Takagi

Sugenotype, are

first-order Sugeno described below.

International Journal of Industrial Electronics, Control and Optimization

Continued

(b)

(c)

Membership functions: a) The error input of the fuzzy block, b) Second input of the fuzzy block (Error derivative) c) output of fuzzy block.

Design of ANFIS-based controller

ur of the

neuro-the fuzzy controller and neuro-the only difference

he neural system has been added. To design the ANFIS-based controller, the training data must first

Since the purpose of mode is to attain the highest control proficie

the neural network, first, we apply classical and fuzzy controllers to the studied MG as

recorded separately.

finally have been used to teach the proposed ANFIS

The neural network used in this article has five layers fuzzy system has 49 rules that the

o two. It is supposed y), one of which is an error an error derivative (y = de) and has an output

rules, if and then the fuzzy, which are in the Takagi type, are the foundation

Sugeno system, it produces two rules that are described below.

International Journal of Industrial Electronics, Control and Optimization

(b)

c)

Membership functions: a) The error input of the fuzzy block, b) Second input of the fuzzy block (Error derivative) c)

based controller

-fuzzy controller

the only difference between them is he neural system has been added. To design the controller, the training data must first Since the purpose of modelling this controller is to attain the highest control proficiency, for

first, we apply classical and fuzzy studied MG as complement

recorded separately. These recorded data are finally have been used to teach the proposed ANFIS

The neural network used in this article has five layers the ANFIS system reduced this

that the fuzzy system has two y), one of which is an error (x = e), and the other is

and has an output z =f (x, y) rules, if and then the fuzzy, which are in the Takagi

the foundationof the fuzzy rules

system, it produces two rules that are

International Journal of Industrial Electronics, Control and Optimization

based controller

fuzzy controller is similar to between them is he neural system has been added. To design the controller, the training data must first lling this controller ncy, for the training of first, we apply classical and fuzzy complementary control. Considering different scenarios such as load variation and parametric uncertainties, the output of each two controllers

These recorded data are finally have been used to teach the proposed ANFIS-based

The neural network used in this article has five layers.The

ANFIS system reduced this fuzzy system has two , and the other is

z =f (x, y).The

rules, if and then the fuzzy, which are in the Takagi and

of the fuzzy rules that for a system, it produces two rules that are

International Journal of Industrial Electronics, Control and Optimization

similar to between them is he neural system has been added. To design the controller, the training data must first lling this controller the training of first, we apply classical and fuzzy . Considering different scenarios such as load variation and parametric uncertainties, the output of each two controllers These recorded data are based

The ANFIS system reduced this fuzzy system has two , and the other is The and that for a system, it produces two rules that are

Rule 1: if x is

Rule 2: if x is

Where

Where,

The structure of designed ANFIS is As shown in Fig.6, the output of ANFIS by,

Weights of the

preceding layer outputs and the output of the two rules outputs [2

The neural network structure is composed connected together with the notations

from layer

ANFIS model can be Layer 1:

function defined B i , 1 L A i , 1 L µ = µ =

Where e and

assigned fuzzy function to the node. Layer 1 is consisting of the input variables (membership functions). Membership functions can be defined

so on. For e function parameters a

Layer 2: computes node is the defined by,

Layer 3: i-th node

1 1 1 1

f =Pe+R e∆ +s

2 2 2 2

f P e R e s

e= −

e ∆ =

w f w f

f = =w f +w f

( )

1 i

A e

µ =

2,i i A B

L =W = e ∆e

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Rule 1: if x is 78and y is

Rule 2: if x is 7$and y is

Where

Where, 9 :is the reference

structure of designed ANFIS is As shown in Fig.6, the output of ANFIS

Weights of the w1and

preceding layer outputs and the output of the two rules outputs [2

The neural network structure is composed connected together with the notations

rom layer j for the five layer ANFIS model can be Layer 1: In a layer, the i

defined by ) e ( B ) e ( A 2 i i ∆ −

Where e and ∆e are input to a node and

assigned fuzzy function to the node. Layer 1 is consisting of the input variables (membership functions). Membership functions can be defined

so on. For example, A

function parameters as follows:

Layer 2: each node in computes the firing power node is the result of whole

by,

Layer 3: every node in node computes the respect

1 1 1 1

f =Pe+R e∆ +s

2 2 2 2

f =P e+R e s∆ +

ref r

e=

ω

−

ω

( ref r) d

e

dt

ω −ω

∆ =

1 1 2 2

1 2 w f w f

f w f w f

w w + = = + + 2 1 1 i b i i e c a _ ₋ _    +        ( ) ( ) i i

i i A B

L =W =µ e µ ∆e

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© 201

and y is ;8then:

and y is ;$then:

eference angular structure of designed ANFIS is depicture As shown in Fig.6, the output of ANFIS

and w2 are obtained as the product of the

preceding layer outputs and the output f

of the two rules outputs [23, 24].

The neural network structure is composed

connected together with the notations L

layers.

ANFIS model can be defined as below

In a layer, the i-th node corresponds to the node

e are input to a node and

assigned fuzzy function to the node. Layer 1 is consisting of the input variables (membership functions). Membership functions can be defined as triangular form or Gaussian, and

Ai can be membership by

s follows:

node in which layer is a

power wi of a rule. The output of

whole the entry

node in which layer is a the respect of the i

2 2 2 2

f =P e+R e s∆ +

1 ₁ 2 ₂ f = =w f +w f

( ) ( )

L W e e

angular speed.

depictured in Figure 6. As shown in Fig.6, the output of ANFIS can be represented

(1

are obtained as the product of the

f is a weighted average

The neural network structure is composed of five

Lj,ifor the output node i

below.

th node corresponds to the node

e are input to a node and Ai and

assigned fuzzy function to the node. Layer 1 is consisting of the input variables (membership functions). Membership triangular form or Gaussian, and can be membership by Gaussian

layer is a constant node of a rule. The output of

entry signals to it and is

layer is a constant node. Each i-th rule’s firing

IECO 149

(12)

(13)

(14)

(15)

d in Figure 6. represented

(16)

are obtained as the product of the is a weighted average

five layers for the output node i

th node corresponds to the node

(17)

Bi-2are assigned fuzzy function to the node. Layer 1 is consisting of the input variables (membership functions). Membership triangular form or Gaussian, and Gaussian

(18)

node that of a rule. The output of every signals to it and is

(19)

(6)

International Journal of Industrial Electronics, Control and Optimization

to the

output from the defined

node function

resultant parameter collection.

computes the total output as the aggregate of whole entry signals, i.e.

the ANFIS reduced this value to 2. designed

Figure

Fig. 6

Fig. 7

L W

to the aggregate output from the defined by,

Layer 4: In a layer, the node function defined

Where <=is the output of Layer 3 and {

resultant parameter collection. Layer 5: This layer

computes the total output as the aggregate of whole entry signals, i.e.

The base fuzzy system has 49 rules (Figure the ANFIS reduced this value to 2.

designed ANFIS Figure 8. e e ∆ 1 A 2 A 2 B 1 B Layer1

Fig. 6. The structure of the designed ANFIS controller

Fig. 7. Fuzzy rules

3, 1 2 i i i W L W W W = = +

4,i i i i i i i

L =W∗f =W .(P e+R∆ +e s )

5,i i i i

L =

∑

W .f =

International Journal of Industrial Electronics, Control and Optimization

of firing powers

output from the i-th node is the normalized firing

In a layer, the i defined by,

=_{is the output of Layer 3 and {}

resultant parameter collection. Layer 5: This layer contains just

computes the total output as the aggregate of whole entry

The base fuzzy system has 49 rules (Figure the ANFIS reduced this value to 2.

ANFIS-based controller in MATLAB illustrated i

Π Π Ν Ν 1 W 2 W Layer2 Layer3

. The structure of the designed ANFIS controller

. Fuzzy rules-based system for studied MG

1 2

W W

4,i i i i i i i

L =W∗f =W .(P e+R∆ +e s )

i i i 5,i i i i W .f

L W .f

W = =

∑

International Journal of Industrial Electronics, Control and Optimization

powers of whole node is the normalized firing

i = 1, 2

i-th node corresponds to the

i=1,2

is the output of Layer 3 and {

contains just one constant node that computes the total output as the aggregate of whole entry

The base fuzzy system has 49 rules (Figure

the ANFIS reduced this value to 2. The structure of based controller in MATLAB illustrated i

e∆e

e ∆ e Ν Ν 11 W .f 2 W .f 2 W 1 W Layer3 Layer4

based system for studied MG

4,i i i i i i i

L =W∗f =W .(P e+R∆ +e s )

International Journal of Industrial Electronics, Control and Optimization

the rules. The node is the normalized firing power

i = 1, 2 (20)

th node corresponds to the

i=1,2 (21)

, > , } is the

one constant node that computes the total output as the aggregate of whole entry

(22)

The base fuzzy system has 49 rules (Figure 7), which for The structure of the based controller in MATLAB illustrated in

∑ f 1 W .f 2 2 W .f Layer5

International Journal of Industrial Electronics, Control and Optimization

the rules. The power

th node corresponds to the

)

} is the

one constant node that computes the total output as the aggregate of whole entry

), which for the n

Fig. 8. The Structure of designed ANFIS

Matlab

In this part, the effect of the MG short

addition, the performance of conventional PID, fuzzy PID and ANFIS

considering with and without the virtual inertia Furthermore,

investigated in the attendance of different disturbances and uncertainties

conventional PID controller parameters have the same value. Table 4 shows these parameters, which

Ziegler-Nichols method

A.

In this

occurred at t = 1 s on the short

frequency control is performed by a convention figure illustrates

the MG dynamic response and reduces the short frequency variation. Moreover,

increasing aberration

zero regardless of the

The same step load variation compare

ANFIS-based controllers for frequency regulation. Figure 10 depicts the simulation results considering

This figure controllers

compared with classical PID controller. In addition, the settling time, overshoot and

the ANFIS controller.

International Journal of Industrial Electronics, Control and Optimization

The Structure of designed ANFIS

V.

SIMULATION RESULTS

In this part, the effect of the

short-period frequency regulation

addition, the performance of conventional PID, fuzzy PID and ANFIS-based controllers for frequency regulation considering with and without the virtual inertia

Furthermore, robustness and performance of controllers are gated in the attendance of different disturbances and uncertainties parametric.

conventional PID controller parameters have the same value. Table 4 shows these parameters, which

Nichols method.

Scenario 1: Step load

In this scenario, %10 step load occurred at t = 1 s. Fig.

on the short-period frequency regulation when secondary frequency control is performed by a convention

figure illustrates that implementing virtual inertia improves the MG dynamic response and reduces the short frequency variation. Moreover,

increasing leads to less short

aberration. In addition, zero regardless of the deal

The same step load variation

compare the performance and efficacy of fuzzy PID and based controllers for frequency regulation. Figure 10 epicts the simulation results considering

This figure demonstrates

controllers ameliorate the dynamic response

compared with classical PID controller. In addition, the settling time, overshoot and

ANFIS-based controller compared controller.

International Journal of Industrial Electronics, Control and Optimization .

© 201

The Structure of designed ANFIS

SIMULATION RESULTS

In this part, the effect of the virtual inertia frequency regulation

addition, the performance of conventional PID, fuzzy PID based controllers for frequency regulation considering with and without the virtual inertia

robustness and performance of controllers are gated in the attendance of different disturbances and parametric. In all test scenarios, the conventional PID controller parameters have the same value. Table 4 shows these parameters, which

.

tep load variation

, %10 step load variation

. 9 display the effect of virtual inertia period frequency regulation when secondary frequency control is performed by a convention

that implementing virtual inertia improves the MG dynamic response and reduces the short frequency variation. Moreover, as one can see in figure 9,

leads to less short . In addition, the steady-state

deal of .

The same step load variation is used to evaluate and the performance and efficacy of fuzzy PID and based controllers for frequency regulation. Figure 10 epicts the simulation results considering

demonstrates that fuzzy PID and ANFIS ameliorate the dynamic response

compared with classical PID controller. In addition, the settling time, overshoot and undershoot are reduced by using

based controller compared

The Structure of designed ANFIS-based controller in

SIMULATION RESULTS

virtual inertia on the studied frequency regulation investigated. In addition, the performance of conventional PID, fuzzy PID based controllers for frequency regulation considering with and without the virtual inertia is compared.

robustness and performance of controllers are gated in the attendance of different disturbances and In all test scenarios, the conventional PID controller parameters have the same value. Table 4 shows these parameters, which are obtained u

variation

variation (∆PL = 0.1 pu) is

9 display the effect of virtual inertia period frequency regulation when secondary frequency control is performed by a conventional PID.

that implementing virtual inertia improves the MG dynamic response and reduces the short

as one can see in figure 9, leads to less short-period frequency

state frequency error is

is used to evaluate and the performance and efficacy of fuzzy PID and based controllers for frequency regulation. Figure 10

epicts the simulation results considering equal to 10.

that fuzzy PID and ANFIS ameliorate the dynamic response of the LFC compared with classical PID controller. In addition, the ershoot are reduced by using based controller compared with the fuzzy

IECO 150

based controller in

on the studied investigated. In addition, the performance of conventional PID, fuzzy PID based controllers for frequency regulation is compared. robustness and performance of controllers are gated in the attendance of different disturbances and In all test scenarios, the conventional PID controller parameters have the same value. are obtained using the

= 0.1 pu) is 9 display the effect of virtual inertia period frequency regulation when secondary al PID. This that implementing virtual inertia improves the MG dynamic response and reduces the short-term as one can see in figure 9, frequency frequency error is

(7)

International Journal of Industrial Electronics, Control and Optimization .

B. Scenario 2: Step load variation with parametric uncertainties

In this case, first, each parameter is varied separately, and then the effect of simultaneously changes in the system parameters and modelling errors are considered as a worst case to evaluate the controllers’ robustness. Fig. 11 to Fig. 14 show the effect of parameters change on the frequency regulation when a conventional PID performs the secondary frequency control. Fig. 11 illustrates the MG frequency

response considering Tg andTt variations when is equal

to 3. As one can see the augmentation of the governor and turbine time constant degrade the MG dynamic response. The effect of 50% variation in H and D are presented in Fig. 12

and 13 for equal to 3 and 0.5, respectively. These figures

show that the reduction of load sensitivity coefficient

degrades noticeably the MG dynamic response when is

equal to 0.5. In addition, due to the small amount of the parameter H, its variations have little effect on the MG frequency's behavior. Figure 14 depicts the effect of 50%

variation in R, TFESS and TBESS on the frequency regulation.

This figure shows that the reduction of R decreases the MG

stability and increasing of R leads to more frequency

deviation. Furthermore, due to the small amount of the

parameters TFESS and TBESS, their variations have little effect

on the frequency regulation.

Table 5 shows the intended system parameters variations and modelling errors as a worst case. The simulation results are presented in Fig. 15 and Fig. 16 for this case. As Fig. 15 shown the studied MG become unstable when the controller is conventional PID. Zoomed results are illustrated in Fig. 16. As one can see, in this case, with the Fuzzy-PID controller the studied MG has poor stability. Instead, the proposed control strategy (ANFIS-based controller with implemented virtual inertia) regulates properly the MG frequency and its performance is not much affected by the parameters changes.

TABLE IV. PID CONTROLLER PARAMETERS

P

K

I

K

D

K

2.3 15.19 0.2

Fig. 9. Impact of virtual inertia on the short-period frequency

regulation.

Fig. 10. Performance of different controllers on the short-period

frequency adjustment in the attendance of virtual inertia.

Fig. 11. MG frequency response considering and variations.

Fig. 12. MG frequency response considering H and D variations

for ?= 3

0 1 2 3 4 5 6 7 8 9 10

time (s) 49.75

49.8 49.85 49.9 49.95 50 50.05 50.1 50.15

frequency ,Hz

frequency

without virtual inertia(Mv=0) with virtual inertia(Mv=1) with virtual inertia(Mv=5) with virtual inertia(Mv=10)

0 1 2 3 4 5 6 7 8 9 10

time (s) 49.75

49.8 49.85 49.9 49.95 50 50.05 50.1 50.15

frequency ,Hz

frequency

without virtual inertia

with virtual inertia & Mv=10 & PID controller PID fuzzy controller & Mv=10 anfis controller & Mv=10

0 1 2 3 4 5 6 7 8 9 10

time (s) 49.97

49.98 49.99 50 50.01 50.02

frequency ,Hz

frequency

without parameter change & Mv=3 Tg=+50%

Tg=-50% Tt=+50 Tt=-50

0 1 2 3 4 5 6 7 8 9 10

time (s)

49.97 49.98 49.99 50 50.01 50.02

frequency ,Hz

frequency

without parameter change & Mv=3 H=+50%

(8)

International Journal of Industrial Electronics, Control and Optimization .

Fig. 13. MG frequency response considering H and D variations

for ?= 0.5

Fig. 14. MG frequency response considering R, TFESS and

TBESS variations for ?= 3.

Fig. 15. MG frequency response considering the system

parameters variations and modelling errors shown in table 5.

Fig. 16. Zoomed MG frequency response.

C. Scenario 3: Step load variation with parametric uncertainties without energy storage equipment

In this scenario, the conditions are the same as those in the previous scenario. Nevertheless, in this scenario; energy storage equipment has been removed from the system under study. As can be seen in Figure 17 removing energy storage equipment leads to performance degradation of the conventional PID and Fuzzy PID controllers, where both controllers become unstable. However, using the suggested control scheme, the system frequency is effectively regulated even without energy storage equipment. Furthermore, Fig. 18, which compares the effectiveness of Fuzzy PID and ANFIS-based controllers in this scenario, depicts that the system frequency response remains approximately unchanged with and without energy storage equipment. Fig. 19 illustrates

the performance of the ANFIS-based controller versus Mν

under such conditions. This figure shows that the performance of the ANFIS-based controller is acceptable

even with smallM_ν =2, but M_ν =1leads to MG frequency

instability.

VI.

CONCLUSION

In this paper, to regulate the frequency of studied islanded MG a control scheme is proposed. The proposed control strategy consists of an ANFIS-based controller as secondary frequency control and a rotating-mass-based virtual inertia, implemented in DFIG control system, as an inherent energy storage system. The simulation conclusions demonstrate that using the suggested control strategy the frequency of studied MG can effectively regulate despite removing energy storage equipment, severe system parameters changes and modelling error. three different controllers are implemented in order to achieve good frequency regulation/tracking regardless of the presence of disturbances and parameter variations. In practice,

simple PI&PID controllers are commonly used that providea

poor performance in the presence of serious disturbances and The fuzzy controller is heavily dependent on membership functions. eventually the results reveal that the ANFIS-based controller outperforms other controllers in all aspects such as overshoot, undershoot, steady state error, rise time and settling time.

TABLE V. SYSTEM PARAMETERS CHANGES AND MODELLING

ERRORS

Value Parameter

+50% ( )

-50% D

+50% ( )

-50% H(s)

+50% ( )

-50% R

0 1 2 3 4 5 6 7 8 9 10

time (s) 49.94

49.96 49.98 50 50.02 50.04 50.06

frequency ,Hz

frequency

without parameter change & Mv=0.5 H=+50%

H=-50% D=+50 D=-50

0 5 10 15

time (s) 49.97

49.98 49.99 50 50.01 50.02

frequency ,Hz

frequency

without parameter change & Mv=3 R=-50%

R=+50% Tfess=-50% Tbess=+50%

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

time (s) 49.7

49.8 49.9 50 50.1 50.2 50.3

frequency ,Hz

frequency without parameter change & PID controller with parameter change & PID controller with parameter change & PID fuzzy controller without parameter change & PID fuzzy controller without parameter change & anfis controller with parameter change & anfis controller

Mv=3

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

time (s) 49.97

49.98 49.99 50 50.01 50.02 50.03 50.04 50.05 50.06

frequency ,Hz

frequency without parameter change & PID controller with parameter change & PID controller with parameter change & PID fuzzy controller without parameter change & PID fuzzy controller without parameter change & anfis controller with parameter change & anfis controller

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International Journal of Industrial Electronics, Control and Optimization .

Fig. 17. MG frequency response without energy storage

equipment and considering the parameters changes.

Fig. 18. Zoomed MG frequency response without energy storage

equipment and considering the parameters changes.

Fig. 19. Performance of ANFIS-based controller versus

?without energy storage equipment and considering the

parameters changes.

R

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0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

time (s) 49.4

49.6 49.8 50 50.2 50.4 50.6

frequency ,Hz

frequency conventional PID controller with Energy Storage conventional PID controller without Energy Storage fuzzy PID controller without Energy Storage fuzzy PID controller with Energy Storage anfis controller without Energy Storage anfis controller with Energy Storage

Mv=3

1 2 3 4 5 6 7 8

time (s) 49.96

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frequency ,Hz

frequency fuzzy PID controller without Energy Storage fuzzy PID controller with Energy Storage anfis controller with Energy Storage anfis controller without Energy Storage

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0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

time (s) 49.992

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frequency ,Hz

frequency

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electrical engineering main research interest is Intelligent controllers. Authority (KWPA).

in 2008. He is currently an Assistant Professor in Department of Electrical Engineering, Razi University, Kermanshah, Iran. His research interests

control, active power filters, power quality, FACTS Devices and fault tolerant converters.

Electrical Engineering, Razi University, Kermanshah, Iran. His research interests include power system op

and planning, smart grids, demand response,

system, energy hub, load forecasting, and design of electrical and control systems for industrial plant.

International Journal of Industrial Electronics, Control and Optimization

Akbar

Khuzestan, Iran. He received from Jundishapur University of Iran, in 2011, M.

systems

2013. He is currently electrical engineering at Razi

main research interest is Micro grid frequency control, Intelligent controllers. He work

Authority (KWPA).

Shahram Karimi

Iran, in 1972. He received the B.S. degree from University of Tabriz, Tabriz, Iran, in 1995, the M.S. degree from Sharif University of Technology, Tehran, Iran, in 1997, and the Ph.D. degree

the Université Henri Poincaré, Nancy, France, in 2008. He is currently an Assistant Professor in Department of Electrical Engineering, Razi University, Kermanshah, Iran. His research interests include

active power filters, power quality, FACTS Devices and fault tolerant converters.

Hamdi Abdi

1973. He received his B.Sc. degree from Tabriz University, Tabriz, Iran in 1995; M.Sc., and Ph.D. degrees from Tarbiat Modares

Tehran, Iran, in 1999, and 2006, respectively, all in Electrical Engineering. Currently, he is an associate professor in the Department of Electrical Engineering, Razi University, Kermanshah, Iran. His research interests include power system op

International Journal of Industrial Electronics, Control and Optimization .

© 201

Akbar karimipouya was born in Andimeshk

Khuzestan, Iran. He received Jundishapur University of in 2011, M.Sc. degree in electrical

s from the Kurdistan University He is currently

Razi University,

Micro grid frequency control,

e works in Khuzestan Water and Power

Shahram Karimi was born in Kermanshah,

Iran, in 1972. He received the B.S. degree from University of Tabriz, Tabriz, Iran, in 1995, the M.S. degree from Sharif University of Technology, Tehran, Iran, in 1997, and the Ph.D. degree all in electrical engineering from niversité Henri Poincaré, Nancy, France, in 2008. He is currently an Assistant Professor in Department of Electrical Engineering, Razi University, Kermanshah, Iran. His include Microgrid voltage and frequency active power filters, power quality, FACTS Devices and

Hamdi Abdi was born in Paveh, Iran, in July

1973. He received his B.Sc. degree from Tabriz University, Tabriz, Iran in 1995; M.Sc., and Ph.D. degrees from Tarbiat Modares

Tehran, Iran, in 1999, and 2006, respectively, all in Electrical Engineering. Currently, he is an associate professor in the Department of Electrical Engineering, Razi University, Kermanshah, Iran. His research interests include power system op

was born in Andimeshk Khuzestan, Iran. He received the B.Sc

Jundishapur University of Technology, degree in electrical from the Kurdistan University He is currently pursuing his Ph

, Kermanshah, Iran Micro grid frequency control, drives and

in Khuzestan Water and Power

was born in Kermanshah, Iran, in 1972. He received the B.S. degree from University of Tabriz, Tabriz, Iran, in 1995, the M.S. degree from Sharif University of Technology, Tehran, Iran, in 1997, and the in electrical engineering from niversité Henri Poincaré, Nancy, France, in 2008. He is currently an Assistant Professor in Department of Electrical Engineering, Razi University, Kermanshah, Iran. His Microgrid voltage and frequency active power filters, power quality, FACTS Devices and

was born in Paveh, Iran, in July 1973. He received his B.Sc. degree from Tabriz University, Tabriz, Iran in 1995; M.Sc., and Ph.D. degrees from Tarbiat Modares University, Tehran, Iran, in 1999, and 2006, respectively, all in Electrical Engineering. Currently, he is an associate professor in the Department of Electrical Engineering, Razi University, Kermanshah, Iran. His research interests include power system optimization, operation and planning, smart grids, demand response,multi carrier energy system, energy hub, load forecasting, and design of electrical and IECO 154

was born in Andimeshk, c. degree Technology, degree in electrical power from the Kurdistan University, Iran in Ph.D. in ran. He’s drives and in Khuzestan Water and Power

was born in Kermanshah, Iran, in 1972. He received the B.S. degree from University of Tabriz, Tabriz, Iran, in 1995, the M.S. degree from Sharif University of Technology, Tehran, Iran, in 1997, and the in electrical engineering from niversité Henri Poincaré, Nancy, France, in 2008. He is currently an Assistant Professor in Department of Electrical Engineering, Razi University, Kermanshah, Iran. His Microgrid voltage and frequency active power filters, power quality, FACTS Devices and