ANALYSIS OF FUZZY INFERENCE SYSTEM BASED INTERLINE POWER FLOW CONTROLLER FOR POWER SYSTEM WITH WIND ENERGY CONVERSION SYSTEM DURING FAULTED CONDITIONS

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International Journal of Advanced Research in Engineering and Technology (IJARET)
Volume 11, Issue 4, April 2020, pp. 595-602, Article ID: IJARET_11_04_059
Available online at https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=4
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
DOI: https://doi.org/10.17605/OSF.IO/TCDB5
© IAEME Publication Scopus Indexed
ANALYSIS OF FUZZY INFERENCE SYSTEM
BASED INTERLINE POWER FLOW
CONTROLLER FOR POWER SYSTEM WITH
WIND ENERGY CONVERSION SYSTEM
DURING FAULTED CONDITIONS
E. Kalaiyarasi
Research Scholar, Annamalai University, Tamil Nadu, India
Dr. A.S. Kannan
Associate Professor, Annamalai University, Tamil Nadu, India
ABSTRACT
For series compensation, an interline power flow controller (IPFC) is a converter-
based FACTS controller for AC transmission networks that may regulate power flow
across many lines in the same corridor. A DC-link connects a series of voltage source
converters in the architecture of IPFC. Using a shared DC-link, real and reactive power
may be transferred between the voltage source converters. An IPFC system with two
voltage source converters is employed in this work to control the impedances of two
parallel transmission lines having similar characteristics. In this study, the fuzzy
inference system is proposed as the controller for the control circuits of both master
and slave converters of the IPFC. To show the system behavior of the IPFC, the model
is developed in MATLAB/Simulink, and the simulation studies are carried out during
faulted conditions. The results are compared with PI controller-based IPFC and
without IPFC
Key words: Fuzzy Logic Controller, Doubly-Fed Induction Generator, Interline
Power Flow Controller, Wind Energy.
Cite this Article: E. Kalaiyarasi and A.S. Kannan, Analysis of Fuzzy Inference System
Based Interline Power Flow Controller for Power System with Wind Energy Conversion
System During Faulted Conditions, International Journal of Advanced Research in
Engineering and Technology, 11(4), 2020, pp. 595-602.
https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=4
1. INTRODUCTION
Environmental concerns, regulations, and consequences, as well as the restricted and
uncompetitive cost nature of fossil fuel-based energy supply, have created a huge opportunity
for renewables to become mainstream energy sources for power generation. [1]. Wind energy

Analysis of Fuzzy Inference System Based Interline Power Flow Controller for Power System
with Wind Energy Conversion System During Faulted Conditions
is a promising choice for capturing natural energy among the many renewable energy sources.
Fixed-speed wind turbines (FSWTs), semi-variable-speed wind turbines (SVSWTs), and
variable-speed wind turbines (VSWTs) are the three types of wind turbines (VSWT) [2].
A doubly-fed induction generator (DFIG) is the dominating method in VSWTs because of
its significant advantages, such as running at about one-third of sync speed through a partial
scale converter [1], [3]. The stand-alone and grid-connected DFIGs have been used widely and
aggressively in the electrical utility system. The power system has faced deconstructive
consequences due to the anticipated high penetration rate of DFIG-based power plants,
including system inertia and interaction among DFIG converters. Consequently, the associated
power system's stability measures have witnessed considerable deviations, including frequency
stability, transient stability, dynamic stability, and voltage stability. Due to the aforementioned
stability requirements, wind energy utilization has been teetering on gigantic dimensions. Low-
frequency oscillations have impacted operational stability, and DFIG cannot be broadly
dispersed over the connected power system.
The major difficulty and concern related to decreasing system inertia via DFIG have been
solved because of recent improvements in WT technology. A suitable bed has been developed
for this generator to engage in frequency control and enter the power system efficiently and
safely. In order to produce synthetic inertia, energy must be conserved in the system's posterior
power electronic connections, such as batteries, spinning masses in wind turbine blades, and
other power electronic-based compensators. A flexible alternating current transmission system
(FACTS) device is a power electronic-based compensator [4].
Voltage source converters (VSC) or current source converters (CSC)-based FACTS may be
used to adjust the steady-state and dynamic/transient performance of the power system. Static
var compensator (SVC) and thyristor-controlled series capacitor (TCSC), both thyristor-based
FACTS controllers, employ switched capacitors and reactors to generate/absorb reactive power.
Converter-based FACTS controllers benefit from not relying on AC capacitors and reactors.
Controlling the power system's active and reactive power flows independently is another benefit
of converter-based FACTS controllers [5]. Series-linked converter-based FACTS controllers
include the static synchronous series compensator (SSSC), unified power flow controller
(UPFC), and interline power flow controller (IPFC). An SSSC is a series compensator that
improves system stability by operating in capacitive and inductive modes [6]. One DC-link
between the static synchronous compensator (STATCOM) and the SSSC is shared by both
devices in a UPFC. The IPFC comprises two or more SSSCs that share a common DC-link.
Each SSSC comprises a VSC connected to the transmission line through a coupling transformer
and injects a voltage into the line that may be controlled in magnitude and phase angle. IPFCs
manage the reactive power of each line independently, while active power is transmitted across
compensated lines through a DC-link. An IPFC may also be used to balance active and reactive
power across transmission lines and move power from overloaded to underloaded lines [7].
Compared to other kinds of FACTS controllers, there are comparatively few research
articles concerning IPFC setups since its inception in 1998 [8]. In [7]–[9], the detail of the
IPFC's control architecture and steady-state performance are presented. The steady-state
operation of the New York power authority's Marcy 345 kV substation's 200 MVA convertible
static compensator (CSC) is reported in [10], [11]. The CSC can manage voltage, boost power
transmission, and improve the power system's dynamic performance. Depending on the circuit
configurations, it has two VSCs and may act as a STATCOM, SSSC, UPFC, or IPFC.
This paper presents a MATLAB/Simulink-based model of the IPFC based on [8], [10]. This
article discusses a fuzzy controller that excels in two areas: simplicity and the absence of the
need for complex IPFC computation. In section 2, the model of DFIG and IPFC is presented.

E. Kalaiyarasi and A.S. Kannan
Section 3 studies the IPFC control scheme using fuzzy controller for two identical transmission
lines.
The IPFC's DC-link capacitor voltages are balanced by the fuzzy controller, which regulates
the transmission line impedance (R and X). The simulation findings are used to evaluate the
power system's capacity to regulate transmission line impedance using fuzzy controller-based
IPFC. At the conclusion of this research, simulation results confirm the suggested control
method's superior performance. To evaluate and validate the proposed controller's dynamic
performance, a 4-bus power system was subjected to severe faults. In a nutshell, the simulation
results for the four-bus power system proved first the constructive contribution of DFIG-based
WECS and subsequently the dynamic stability augmentation provided by fuzzy controller-
based IPFC.
2. METHODOLOGY
As previously indicated, a high level of DFIG penetration without virtual inertia control might
substantially influence power system stability. For DFIG, an inertia control approach was used,
which uses active power regulation to maintain system frequency by absorbing or providing
kinetic energy. It was then used in a 4-bus power system to test dynamic stability in the face of
significant load disturbances. The IPFC was then linked to the 4-bus power system to test its
damping capabilities in the face of dynamic instability.
2.1. Transient Response of DFIG
In order to protect the frequency control margin, FSWTs have limited themselves from giving
the maximum harvestable power. In the present state of WT control technology, kinetic energy
has been conserved in the mechanical system of WTs, which VSWT can perform well. The
DFIG-based VSWT successfully captured kinetic energy to reinforce the basic frequency
control by operating in a wide range of wind speeds. The robust sliding mode control (RSC)
has been configured to receive an auxiliary power regulation signal from the inertia control
approach. During load disruptions, an additional active power source has been introduced to
the DFIG-based VSWT and the long-established power plants to allow rapid adjustment of its
active power and thereby contribute to the frequency stabilization mechanism.
Frequency dip
Synchronous instability
Transient trajectory
Frequency rise
P/kW
ω/rad.s-1
f0 - Δf3
f0 - Δf1
f0 - Δf2
f0 + Δf1 f0 + Δf2
Figure 1 Characteristics of DFIG during the disturbance
Fig. 1 depicts the characteristics of DFIG under the disturbance conditions. As can be seen,
DFIG allowed for acceleration during the frequency increase, capturing the active power
necessary for power network frequency stability. The rotor speed at the new equilibrium point

has beyond the permissible range due to the quick rise in frequency, at which point the pitch
angle system control will intervene to limit the rotor speed and prevent DFIG from
overspeeding. Additionally, during the frequency decrease, DFIG decelerated and discharged
the kinetic energy held in the spinning mass to help in power network frequency stability.
During a prolonged frequency decrease, DFIG may experience synchronous instability. For
instance, when the frequency is reduced to f0 – ᐃf3, DFIG does not have a stable equilibrium
point. Following that, the DFIG rotor speed and active power have been maintained, preventing
the rotor speed and active power from dropping to the point of DFIG shutdown.
2.2. Structure and Behavior of IPFC
The N VSCs make up the IPFC, which are utilized to correct for the N lines. These converters
are connected by a common DC line and exchange active power [12]. The algebraic sum of the
powers transferred via converters and lines should obviously equal zero. Otherwise, the DC-
link voltage (a capacitor) will be changed, impacting the output voltages of the converters.
In practice, if an N-line transmission system has IPFC, N-1 lines are major lines, while the
other N lines are called auxiliary lines. The auxiliary line supplies the active power for the N-1
primary lines. This aspect should be considered when designing the auxiliary line converter's
control system. The IPFC's controlled lines are considered to be two similar lines for the
purpose of ease in researching its behavior, as shown in Fig. 2.
Figure 2 Structure of IPFC
Fig. 3 depicts the IPFC's analogous circuit. In Fig. 3, the DC relationship between the two
converters used to exchange active power is shown by a bi-directional flash. According to Fig.
2, the following relationships may be used to determine the active and reactive power flow in
both lines.
𝑃𝑖𝑟 =
𝑉𝑟
|𝑍|
(𝑉𝑖𝑝𝑞sin⁡(
𝛿
2
+ 𝜃𝑖𝑝𝑞 − 𝜑) + 𝑉1sin⁡(𝜑 −
𝛿
2
)) (1)
𝑄𝑖𝑟 =
𝑉𝑟
|𝑍|
(𝑉𝑖𝑝𝑞cos⁡ (
𝛿
2
+ 𝜃𝑖𝑝𝑞 − 𝜑) + 𝑉1cos⁡(𝜑 −
𝛿
2
)) (2)

where i is the IPFC connected line index, V1 = Vr – Vs, φ = cos-1
(R/√(R2
+ (Lω)2
)), and θipq
is the phase difference between Vipq and V1.
V2pq
V1pq
Z1
Z2
P1r Qi1
P2r Q2r
i1
i2
Vs
V1r
V2r
Figure 3. IPFC - Equivalent circuit
Fig. 4 shows the vector diagram of the voltages and currents on two IPFC-controlled lines.
As can be seen, Vr is considered as the reference vector. The circle shown in Fig. 4 determines
the limits of the output voltages of the two converters. The radius of this circle equals the
maximum amplitude of the two converters' output voltages, which is a function of the common
DC connection voltage. The lines in the circle which are parallel with vector V1 (Vr - Vs) are
called voltage compensation lines. Whenever the tip of one of the converters' output voltage
vectors stays on a voltage compensation line, the converter's active power exchange with the
associated line remains constant.
An IPFC controller connected in two lines is shown in Fig. 4 as a vector voltage and current
diagram. The reference vector, Vr, is clearly visible. The output voltages of the two converters
are limited by the circle seen in Fig. 4. The maximum amplitude of the two converters' output
voltages, which is dependent on the common DC connection voltage, is represented by the
circle's radius. Voltage compensation lines are those lines in the circle that are parallel to vector
V1 (Vr - Vs). Voltage compensation lines are used to ensure that the output voltage vectors of
the converters always have a point on one of the compensation lines.
Figure 4 Vector diagram - voltages and currents of the IPFC and connected lines
The voltage compensation line, which goes through the circle's center and has the same
direction as V1, is the locus of the output voltages that do not interchange energy with

transmission lines. In this research, the voltage compensation line is referred to as an exchange-
free line. The two converters work separately as two SSSCs when the output voltage vectors'
tips are on an exchange-free line.
The output voltages that cause active power to be injected into the transmission line are
represented by the voltage compensation lines on the right side of the exchange-free line, while
the output voltages that cause active power to be absorbed by the transmission line are
represented by the voltage compensation lines on the left side of the exchange-free line. Thus,
the tip of the output voltage vectors of the two converters should be on the two voltage
compensation lines that are on opposite sides of the exchange-free line and are the same
distance from the circle's center in order for the algebraic sum of the exchanging power of the
two converters with the lines to be zero. This limitation only limits the voltages of the two
converters. It is now feasible to regulate the active and reactive power flows on the two
transmission lines by modifying the tip of the output voltage vectors on the two voltage
compensation lines, compensating the two transmission lines indicated in Fig. 2. Naturally,
compensation is restricted owing to the limits mentioned above. The highest amount of
variation possible of Pr and Qr is shown in Fig. 5 when the tip of the voltage vector of one of
the two converters travels on its corresponding voltage compensation line.
Figure 5 The compensation area of IPFC when its operating point is moving on the voltage
compensation line 2
3. CONTROL OF IPFC USING FUZZY LOGIC CONTROLLER
3.1. Fuzzy Expert Systems
Fuzzy logic refers to a logical framework that represents learning and reasoning in a loose or
fuzzy manner in order to reason in unclear circumstances [13]–[15]. When a scientific design
of a method does not exist or does exist but is extremely bothersome, making it hard to encode
and excessively complicated, making it impossible to be evaluated rapidly enough for
continuous operation, fuzzy logic is often appropriate. Unlike traditional logic frameworks, it
focuses on developing inaccuracy ways of logical reasoning, which play an important role in
the human ability to infer an approximated solution to a problem based on incorrect,
insufficient, or incomplete knowledge. Human experts' learning determines the precision of
fuzzy logic control (FLC). As a result, it is just as good as the rules' quality.
This study uses a mamdani-type fuzzy inference system (FIS) with two input sources (error
and change in error), ten fuzzy rules, and one output to represent and regulate the IPFC. The
suggested fuzzy expert system employs a max-min arrangement and the centroid of area
technique for defuzzification. The technique uses two fuzzy inputs for fuzzification to control

the IPFC. The first fuzzy input is an error (E) with five membership functions, which is
projected as error negative large (ENL), error negative small (ENS), error zero (EZ), error
positive small (EPS), and error positive large (EPL). Fig. 6 shows the membership function plot
of the fuzzy input magnitude. The change in error (CE), the second fuzzy input, has three
membership functions and is projected as a change in error negative (CEN), change in error
zero (CEZ), and change in error positive (CEP). Fig. 7 shows the membership function plot of
the fuzzy input change in error.
Figure 6 Error membership functions
The fuzzy output of the proposed FIS is the control signal (C) which has seven membership
functions and is projected as control negative large (CNL), control negative small (CNS),
control zero (CZ), control positive small (CPS), and control Error positive large (CPL). The
membership function plot of the fuzzy output is shown in Fig. 8. The training approach
estimates the error, change in error, and control signal's membership functions. The set of the
fuzzy rule is given in Table 1.
Table 1 Fuzzy Rules
1. If (Error is ENL) and (Change in Error is CEN) then (Control Signal is CPL)
2. If (Error is ENL) and (Change in Error is CEZ) then (Control Signal is CPL)
3. If (Error is ENL) and (Change in Error is CEP) then (Control Signal is CPS)
4. If (Error is ENS) and (Change in Error is CEN) then (Control Signal is CPL)
5. If (Error is ENS) and (Change in Error is CEZ) then (Control Signal is CPS)
6. If (Error is ENS) and (Change in Error is CEP) then (Control Signal is CPS)
7. If (Error is EZ) and (Change in Error is CEN) then (Control Signal is CPS)
8. If (Error is EZ) and (Change in Error is CEZ) then (Control Signal is CZ)
9. If (Error is EZ) and (Change in Error is CEP) then (Control Signal is CZ)
10. If (Error is EPS) and (Change in Error is CEN) then (Control Signal is CZ)
11. If (Error is EPS) and (Change in Error is CEZ) then (Control Signal is CNS)
12. If (Error is EPS) and (Change in Error is CEP) then (Control Signal is CNS)
13. If (Error is EPL) and (Change in Error is CEN) then (Control Signal is CNS)
14. If (Error is EPL) and (Change in Error is CEZ) then (Control Signal is CNL)
15. If (Error is EPL) and (Change in Error is CEP) then (Control Signal is CNL).

Figure 7 Change in error membership functions
Figure 8 Output membership functions
3.2. IPFC with FLC
The IPFC is intended to keep the two transmission lines' impedance constant. There are two
converter systems in the IPFC system such as (i) the control of Line 1 reactance and common
DC-link voltage of the VSC are provided by an integrated slave converter system, and (ii) the
control of both resistive and inductive impedance of Line 2 by a master converter system. As a
result, each VSC may be controlled separately. Fig. 9 illustrates the importance of properly
balancing the DC voltages on the DC-link capacitors. Over-voltages on the switching devices
might be damaging if the capacitors are charged unevenly.

Figure 9 IPFC slave converter system controller
Alternate solutions include (a) changing the PWM pattern, (b) utilizing an extra charge-
balancing leg, or (c) employing independent DC sources. The neutral point's voltage must be
adjusted in order to maintain an equal DC voltage. Here, the VSC's DC-link capacitors are
balanced using the zero sequence current i0, as shown in [8]. Fig. 9 depicts the slave IPFC
system's control diagram. For each of the three control loops, there are three components: (a)
for controlling the injected reactance, and (b) for controlling DC-link voltage, (c) for balancing
the voltages on the DC side capacitors. Using Block 1, 3-phase voltages (vinj_a, vinj_b, vinj_c
are transformed into the 0 coordinates α−β−0, and Vinj_α is obtained as given in (3).
[
𝑣𝑖𝑛𝑗 − 𝛼
𝑣𝑖𝑛𝑗 − 𝛽
𝑣𝑖𝑛𝑗 − 0
] = √
2
3
[
1
−1
2
−1
2
0
√3
2
−√3
2
1
√2
1
√2
−1
√2 ]
[
𝑣𝑖𝑛𝑗−𝑎
𝑣𝑖𝑛𝑗−𝑏
𝑣𝑖𝑛𝑗−𝑐
] (3)

Figure 10 IPFC master converter system with fuzzy controller
The reactance controller is used to control the injected voltage amplitude. The suggested
fuzzy controller amplifies the mistake by comparing the injected reactance Xinj to a reference
reactance value Xref. Add the resultant to the d-component of the preferred reference
waveform, which modifies the PWM controller's reference signals. The suggested fuzzy
controller regulates the active power exchange by adjusting the phase angle ϕ of the injected
voltage in response to an inaccuracy in the DC-link voltage. It is possible to modify the injected
voltage amplitude by altering the converter's DC/AC gain, which maintains the constant voltage
of the DC-link. A slave control system must also provide the master system with adequate active
power when compensating for VSC losses.
As a result, the slave system works as a kind of energy storage for the master system (ESS).
Fig. 10 depicts the master IPFC system's overall control structure. The DC voltage controller
and the balancing controller are the two primary variations between this block diagram and the
IPFC slave system block diagram. The DC voltage and balancing controller are no longer
required since the slave system regulates the DC-link voltage. Here two control loops are
needed to govern the reactance and resistance of the Line 2 synchronous reference frame in
order for the d- and q-components to be properly controlled. A reactance controller is used to
govern the injected reactance. The injected reactance (Xinj-1) is compared to a reference value
(Xref) to test the suggested fuzzy controller. Vd' is generated by taking the resultant and adding
it to the required reference waveform.
4. RESULTS AND DISCUSSIONS
To determine whether the suggested fuzzy controller can maintain and restore test system
stability while also working with IPFC, two different failure scenarios are simulated and
explored (in all cases, the IPFC is equipped with the fuzzy controller).

(1) Case 1: 10 cycles single phase to ground fault at bus 4
(2) Case 2: 10 cycles three phases symmetrical fault at bus 4.
4.1. Single Phase to Ground Fault
The 20 cycles of single phase to ground fault are simulated at bus 4 of the 4-bus test system by
grounding phase A, i.e., by closing the phase A leg of the circuit breaker towards the ground
for a duration of 2.5 s to 2.833 s. The entire simulation is carried out for a duration of 10 s.
Fig. 11 shows the comparison of real power flow at bus 4 of the 4-bus test system with
fuzzy controller-based IPFC, PI controller-based IPFC, and without IPFC during the single
phase to ground fault. In the case of the 4-bus test system with fuzzy controller-based IPFC, the
real power flow at faulted bus remains virtually constant, apart from some transients that last
for about 8.2 s when the single phase to ground fault occurs.
Figure 11 Comparison waveform under single phase to ground fault
Table 2 shows the comparison of voltage at bus 4 of the 4-bus test system with fuzzy
controller-based IPFC, PI controller-based IPFC, and without IPFC during the single phase to
ground fault. In the case of the 4-bus test system with fuzzy controller-based IPFC, the bus
voltage at faulted bus remains virtually constant, apart from some transients that last for about
8.2 s, when the single phase to ground fault occurs.
4.2. Three Phase to Ground Fault
The 20 cycles of three phase to ground fault is simulated at bus 4 of the 4-bus test system by
grounding phase A, i.e., by closing the phase A leg of the circuit breaker towards the ground
for a duration of 2.5 s to 2.833 s. The entire simulation is carried out for a duration of 10 s.
Fig. 12 shows the comparison of real power flow at bus 4 of the 4-bus test system with
fuzzy controller-based IPFC, PI controller-based IPFC, and without IPFC during the three
phase to ground fault. In the case of the 4-bus test system with fuzzy controller-based IPFC, the
real power flow at faulted bus remains virtually constant, apart from some transients that last
for about 9.3 s when the three phase to ground fault occurs.
Table 2 shows the comparison of voltage at bus 4 of the 4-bus test system with fuzzy
controller-based IPFC, PI controller-based IPFC, and without IPFC during the three phase to
ground fault. In the case of the 4-bus test system with fuzzy controller-based IPFC, the bus
voltage at faulted bus remains virtually constant, apart from some transients that last for about

9.3 s, when the three phase to ground fault occurs. Table 2 also shows the performance
comparison of voltage at the faulted bus during single phase to ground fault and three phase to
ground fault scenarios.
Table 2 Comparison result
IPFC device
Three phase to ground fault Single phase to ground fault
Overshoot Undershoot
Settled
time
Overshoot Undershoot
Settled
time
(%) (%) (s) (%) (%) (s)
Without IPFC 113 53.8 90.2 49.78
> 10 > 10
PI controller 89.3 60.94 16.5 14.27
9.5 8.87
Fuzzy controller 67.2 69.7 8.9 10.74
9.3 8.5
Figure 12 Comparison waveform under three phase to ground fault
5. CONCLUSIONS
In this work, the analysis of fuzzy inference system-based interline power flow controller for
power system with wind energy conversion system during faulted conditions has been
presented. According to the results of an IPFC system with two parallel lines, the transmission
system can more easily regulate active/reactive power under three single phase to ground fault
and three phase to ground fault scenarios. The system's response to different faulty
circumstances at the transmission system's receiving end is shown and studied. When it comes
to compensating for both transmission line resistance and reactance, IPFC simulation results
show that it is capable of doing so.

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ANALYSIS OF FUZZY INFERENCE SYSTEM BASED INTERLINE POWER FLOW CONTROLLER FOR POWER SYSTEM WITH WIND ENERGY CONVERSION SYSTEM DURING FAULTED CONDITIONS

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