IRJET- Fuzzy Control Scheme for Damping of Oscillations in Multi Machine Power System with UPFC

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 08 | Aug 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 881
Fuzzy Control Scheme for Damping of Oscillations in Multi Machine
Power System with UPFC
Aparna Kumari1, Anjana Tripathi2, Shashi Kala Kumari3
1 MTech Scholar, Department of Electrical Engineering, AISECT University, Bhopal, India
2 Associate Professor, Department of Electrical Engineering, AISECT University, Bhopal, India
3 Junior Electrical Engineer, SBPDCL, Patna
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Abstract Power stability is an important issue that is becoming increasingly important to power systems at all levels. We are
unable to achieve the stability of the system due to some factors. Low frequency oscillation’s is one of the major factors that
affect the transmission line capacity. Traditionally power system stabilizers (PSS) are being used to damp these inevitable
oscillations. In advanced technology FACTS devices such as unified power flow controllers (UPFC) are used to control the
power flow in transmission lines. They can also replace the PSS to damp the low frequency oscillations effectively through
direct control of voltage and power. In our model, single machine infinite bus power system with UPFC is considered. The
designed FUZZY based UPFC controllers adjusts four UPFC inputs by appropriately processing of input error signal and
provides an efficient damping. The results of the simulation show that the UPFC with FUZZY LOGIC controller is effectively
damping the LOW FREQUENCY OSCILLATIONS.
Key Words: FACTS Devices1, UPFC2, Power Oscillation Damping Controller3, Fuzzy Logic Controller4, Power
System Stability5.
1.INTRODUCTION
The demand for electric power is increasing day by day. Recent major black-outs across the globe caused by system
instability, even in very sophisticated and secure systems, illustrate the problems facing secure operation of power
systems. Earlier, stability was defined as the ability of a system to return to normal or stable operation after having been
subjected to some form of disturbance. This fundamentally refers to the ability of the system to remain in synchronism
[1]. However, modern power systems operate under complex interconnections, controls and extremely stressed
conditions. Further, with increased automation and use of electronic equipment, the quality of power has gained utmost
importance, shifting focus on to concepts of voltage stability, frequency stability, inter-area oscillations etc. thus increasing
the electric power demand. Nowadays FACTS devices can be used to control the power flow and enhance system stability,
which provides the needed correctionof the transmission system [2] [3].
FACTS (Flexible AC Transmission System) is defined as an alternating current transmission system incorporating
semiconductor-based power electronics and other static controllers in order to enhance power transfer capacity and
increase controllability of transmission. It increases the ability to accommodate changes in operating conditions
simultaneously, maintain steady state and transient stability margins.
FACTS devices have shown very promising results when used to improve the power system steady state performance [2].
In addition, because of the extremely fast control action associated with FACTS-device operations, they have been very
promising elements for utilization in power system damping enhancement. Unified Power Flow Controller (UPFC), being
one the member of the FACTS device thus emerged as one of the effective controllers for controlling and optimization of
the power flow in the electrical power transmission systems [3]. This device was formed due to the combination of the
two other FACTS devices, namely Static Synchronous Compensator (STATCOM) and the Static Synchronous Series
Compensator (SSSC). These are connected to each other by a common DC link, which is a typical a storage capacitor. The
all parameters of the power transmission line (impedance, voltage and phase angle) can be control simultaneously by
UPFC [4]. In addition, it can perform the control function of the transmission line real / reactive power flow, UPFC bus
voltage and the shunt-reactive- power flow control [5].
The control mechanism and the controller have an important effect on the performance of UPFC. A novel fuzzy inference
system described in matrix form was proposed and used to improve the dynamic control of real and reactive power [6].
Two fuzzy logic controllers based on Mamdani type fuzzy logic were used.
The fuzzy logic control technique has been an active research topic in automation and control theory since the work of
Mamdani proposed in 1974 based on the fuzzy sets theory of Zadeh proposed in 1965 to deal with the system control

problems which are not easy to be modelled [7]. The concept of FLC is to utilize the qualitative knowledge of a system to
design a practical controller. Fuzzy logic controllers are nothing but rule-based controllers in which a set of rules
representing a control decision mechanism to adjust the effect of certain cases coming from power system is considered.
Further, these FLCs do not require a mathematical model of the system & can cover a wide range of operating conditions
with much robustness inherency. FLCs combined with UPFC can definitely reduce the POD in multi-machinesystems [8].
2. SINGLE LINE DIAGRAM OF POWER SYSTEM
The multi machine power system network consisting of three generators, nine bus and four loads used for the
simulation model is shown in the form of a single line diagram without and with controller in Fig. 1 & 2 respectively.
The generators 1, 2 and 3 are connected t buses 1, 5 and 8. Two UPFC‟s are used for controlling & damping the power
system oscillations in the integrated plant [9]. One is connected between bus 2 & 3 and the other is connected between
buses 6 and 7.
Three transformers T1, T2 & T3 are used in the system near the generator buses for the stepping up and stepping down
purposes. Transmission lines are connected between the buses 3-9-4-6.
Figure- 1: A 3-machine, 9-bus interconnected power system model with 4-loads without the controller
Figure 1 and 2 represents the single line diagram of 3- machine system without and with hybrid controller. The
generators 1, 2 and 3 are connected to buses 1, 5 and 8. Two UPFC‟s are used for controlling & damping the power system
oscillations in the integrated plant [9]. One is connected between bus 2 & 3 and the other is connected between buses 6
and 7 [12].
Figure -2: A 3-machine, 9-bus interconnected power system model with 4-loads & 2 POD-UPFC& the fuzzy logic
controller

3. CONTROL STRATEGY
The development of the control strategy for damping of oscillations in power system in this work is to develop UPFC-
POD Controller coordinated with Fuzzy Logic Controller. A controller is a device which brings stability to the system when
there is a disturbance or a noise or a fault, thus safeguarding the equipment from further damages, i.e., which controls each
& every operation in the system making decisions.
UPFC consists of two solid state synchronous voltage source converters coupled through a common DC link capacitor as
shown in Fig. 3, i.e., 2 voltage source inverters sharing a common DC storage capacitor. Two coupling transformers are
used. As shown in Fig. 3, the UPFC consists of a boosting transformer and an excitation transformer linked by back-to-back
converters 1 and 2. The first converter 1 is connected in shunt and the second one 2 in series with the line [10] [11].
Converter 1 can also generate or absorb reactive power and thereby provide independent shunt reactive compensation for
the line. Converter 2 provides the main function of the UPFC by injecting additional voltage with controllable magnitude
and phase angle in series with the transmission line through series transformer. The UPFCs are used at certain locations
in an integrated power system in between some buses. Fig. 3 represents the busesin theintegrated powersystemmodel.
Figure -3: A block diagram of the UPFC scheme used in
FACTS (single line diagram)
Fuzzy logic is, introduced by Lotfi A. Zadeh, a mathematical tool to dealing with uncertainty. In general, fuzzy logic
provides an inference structure that enables appropriate human reasoning capabilities. On the contrary, the tradition
binary set theory describes crisp events, that is, events that either do or do not occur. It uses probability theory to
explain if an event will occur, measuring the chance with which a given event is expected to occur. The theory of fuzzy
logic is based upon the notion of relative graded membership and so are the functions of cognitive processes. Our basic
structure of the fuzzy logic coordination controller to damp out the oscillations in the power system consists of 3
important parts, viz., fuzzification, knowledge base - decision making logic (inference system) and the defuzzification,
which are explained in brief as follows.
The necessary inputs to the decision-making unit blocks are the rule-based units and the data-based block units. The
fuzzification unit converts the crisp data into linguistic variables. The decision-making unit decides in the linguistic
variables with the help of logical linguistic rules supplied by the rule base unit and the relevant data supplied by the data
base. The output of the decision-making unit is given as input to the defuzzification unit and the linguistic variables of the
signal are converted back into the numeric form of data in the crisp form. The decision-making unit uses the conditional
rules of „IF-THEN-ELSE‟.
The inputs Pupfc1 and Pupfc2 are the active power flow through the UPFC1 and UPFC2.The output signals are command
signals adjusted to the UPFC controllers 1 and 2. In this way, the conventional POD controllers are tuned by using fuzzy-
coordination controllers. Fuzzification is a process whereby the input variables are mapped onto fuzzy variables (linguistic
variables). Each fuzzified variable has a certain membership function. The inputs are fuzzified using three fuzzy sets: B
(big), M (medium) and S (small).
The developed fuzzy rules included in the fuzzy coordinated controller are given below in the form of an algorithm as
follows:

i. If (input_1 is mf1) and (input_2 is mf1) then (output_1 is mf3)(1)
ii. If (input_1 is mf1) and (input_2 is mf2) then (output_1 is mf2)(1)
iii. If (input_1 is mf1) and (input_2 is mf3) then (output_1 is s1)(1)
iv. If (input_1 is mf2) and (input_2 is mf1) then (output_1 is mf3)(1)
v. If (input_1 is mf2) and (input_2 is mf2) then (output_1 is mf2)(1)
vi. If (input_1 is mf2) and (input_2 is mf3) then (output_1 is s1)(1)
vii. If (input_1 is mf3) and (input_2 is mf1) then (output_1 is mf3)(1)
viii. If (input_1 is mf3) and (input_2 is mf2) then (output_1 is mf2)(1)
ix. If (input_1 is mf3) and (input_2 is mf3) then (output_1 is s1)(1)
Figure -4: Membership function used in the fuzzification process
Figure 4 represents the membership function used in the fuzzification process. The membership functions are taken as
Small (mf1), Medium (mf2) and Big (mf3). The 9 fuzzy rules has already been discussed above.
Table 1 : The 9-fuzzy rules used for determiningthe output decisions (Inference table) rule base used for the control
purposes
Control decisions are made based on the fuzzified variables. Inference involves rules for determining output decisions. Due
to the input variables having three fuzzified variables, the fuzzy-coordination controller has nine rules for each UPFC
controller. The rules can be obtained from the system operation and the knowledge of the operator. To improve the
damping of oscillations in power systems, supplementary control laws can be applied to existing power system. These
supplementary actions are referred to as power oscillation damping (POD) controller. Commonly the POD controllers
involve a transfer function consisting of an amplification link, a washout link and two lead-lag links shown in Fig.6. In this
paper the active power of the transmission line is used as input signal. For dynamic performance of a multi-machine system,
the behavior of the controllers must be coordinated. Otherwise the power system will be deteriorated [14].
PUPFC1 Small
set S
mf1
Medium set M
mf2
Big Set B mf3
PUPFC2
Small set S mf1 B M S
Medium set M mf2 B M S
Big Set B mf3 M S S

Figure -5: UPFC-POD controllers
4. SIMULATION STUDY
Simulink model for the multi machine power system with and without controller has been developedin MATLAB. Fig.7 and
Fig. 8 shows the simulation model of 3-generator, 9- bus system with 3 phase faults in the system at generator sites one
and two respectively. Fig.9 shows the simulation model of the power system installed with two Fuzzy-POD- UPFC
controllers, i.e., first one between 2nd and 3rd bus and the second one is at 6th and 7th bus [12].
The power system can have any fault like 3- phase to ground fault, L-G fault, L-L fault and L-L-G fault. The system is
investigated under all types of fault and at all generator sites. The 3 phase to ground symmetrical fault and S-L-G fault has
been made to occur near the first generator for duration of 66 ms from the first cycle to the fifth cycle in this paper.
Figure 8 represents Simulink model of 3 machine 9-bus system when the fault has been occurred at generator 2. The
conventional POD controller coordinated with Fuzzy controller acting as a pre-amplifier for the system, whose output is
given as the input signal to the UPFC. This is done in order to compare the effectiveness of the incorporated controller in
the model when the fault takesplace with the model without the controller when the fault takes place [13].
A set of 9 fuzzy rules are written and called in the form of a file in the developed Simulink model with the controller. Figure
9 represents the Simulink model of the power system under study with fuzzy coordinated UPFC-POD controller for
damping of low frequency oscillations.

Figure -6: The developed Simulink model of a 3- machine, 9-bus system under 3-phase fault at generator 1
without Fuzzy based UPFC-POD (without controller)
Figure -7: The developed Simulink model of a 3- machine, 9-bus system under 3-phase fault at generator 2
without Fuzzy based UPFC-POD (without controller)
Figure -8: The developed Simulink model of a 3- machine,9-bus system with Fuzzy-POD-UPFC (with controller)
5. SIMULATION RESULT & DISCUSSION
The three phase to ground fault is made to occur near the first generator for duration of 66 ms i.e., from first cycle to fifth
cycle. The simulations have been carried out for 1 second with and without controller under fault condition using
MATLA10 software. The effectiveness of the controller can be seen by observing the power angle characteristics which
clearly shows that the oscillation due to occurrence of fault has reduced appreciably.
The dynamic performance of the power system has improved with the incorporation of the fuzzy coordination scheme. It
has also been observed that with the controller, the power angle characteristics curves exhibit very less overshoots &
undershoots, the oscillationsare also damped out in a lesser time.

Figure -9: Simulation result of power angle vs. time curve under 3 phase fault (without Fuzzy-POD-UPFC)
The maximumdeviation of power angle under three phase fault in the system is about 78 degree, which can be seen in figure
9. There is oscillations during the fault condition which has been minimized using controllers.
Figure -10: Simulation result of power angle vs. time curve under 3 phase fault (with UPFC-POD & fuzzy control)
The hybridcontroller consistsof Fuzzy-UPFC-POD hasbeen used to damp the oscillations due to fault in the system. Figure
10 shows the power angle curve for the system under 3-phase fault with hybrid controller. The controller greatly reduces
the oscillations in the system. The maximum deviation of the power angle with controller under 3 phase fault is 11
degree. In case of L-G fault the maximum power angle deviation is about 76 degree as shown in figure 11.
Figure -11 Simulation result of power angle vs. time curve under L-G fault (without Fuzzy-POD –UPFC)

The systemunderL-G fault with hybridcontroller improves the power flow by reducing the oscillations due to fault, shown
in figure 13. The maximum power angle curve for this system is observed as 10 degree. So, it improves the performance of
the system.
Figure -12: Simulation result of power angle vs. time curve under L-G fault (with UPFC-POD & fuzzy control)
6. CONCLUSION
The above simulations shows that the system considered when used without any damping controller is underdamped in
nature. The fuzzy controller is designed for this UPFC controller. The simulation results shows that when there is a small
perturbance in the power system, the proposed UPFC based fuzzy controller is effectively damping the oscillations.
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IRJET- Fuzzy Control Scheme for Damping of Oscillations in Multi Machine Power System with UPFC

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IRJET- Fuzzy Control Scheme for Damping of Oscillations in Multi Machine Power System with UPFC