During Damping of Low Frequency Oscillations in Power Systems with Fuzzy UPFC Controller

International Journal of Modern Engineering Research (IJMER)
www.ijmer.com Vol.2, Issue.6, Nov-Dec. 2012 pp-4215-4219 ISSN: 2249-6645

During Damping of Low Frequency Oscillations in Power Systems with
Fuzzy UPFC Controller
M.Bala Ankanna, 1 K.Harinath Reddy, 2
M.Tech (EPE), Department of EEE, AITS, Rajampet, A.P,
Assistant Professor, Department of EEE, AITS, Rajampet, A.P,

Abstract: Low Frequency Oscillations (LFO) occur in components. Devices for high power levels have been made
power systems because of lack of the damping torque in available in converters for high and even highest voltage
order to dominance to power system disturbances as an levels. The growth of the demand for electrical energy leads
example of change in mechanical input power. In the recent to loading the transmission system near their limits. Thus,
past Power System Stabilizer (PSS) was used to damp LFO. the occurrence of the LFO has increased. FACTs
FACTs devices, such as Unified Power Flow Controller Controllers has capability to control network conditions
(UPFC), can control power flow, reduce sub-synchronous quickly and this feature of FACTs can be used to improve
resonance and increase transient stability. So UPFC may power system stability. The UPFC is a FACTS device that
be used to damp LFO instead of PSS. UPFC damps LFO can be used to the LFO. The primarily use of UPFC is to
through direct control of voltage and power. A control the power flow in power systems. The UPFC
comprehensive and systematic approach for mathematical consists of two voltage source converters (VSC) each of
modelling of UPFC for steady-state and small signal them has two control parameters namely me, δe ,mb and δb
(linearize) dynamic studies has been proposed. The other [3]. The UPFC used for power flow control, enhancement
modified linearize Heffron-Philips model of a power system of transient stability, mitigation of system oscillations and
installed with UPFC is presented. For systems which are voltage regulation [3]. A comprehensive and systematic
without power system stabilizer (PSS), excellent damping approach for mathematical modelling of UPFC for steady-
can be achieved via proper controller design for UPFC state and small signal (linearized) dynamic studies has been
parameters. By designing a suitable UPFC controller, an proposed in [4-7]. The other modified linearized Heffron-
effective damping can be achieved. In this research the Philips model of a power system installed with UPFC is
linearize model of synchronous machine (Heffron-Philips) presented in [8] and [9]. For systems which are without
connected to infinite bus (Single Machine-Infinite Bus: power system stabilizer (PSS), excellent damping can be
SMIB) with UPFC is used and also in order to damp LFO, achieved via proper controller design for UPFC parameters.
adaptive neuro-fuzzy controller for UPFC is designed and By designing a suitable UPFC controller, an effective
simulated. Simulation is performed for various types of damping can be achieved. It is usual that Heffron-Philips
loads and for different disturbances. Simulation results model is used in power system to study small signal
show good performance of neuro-fuzzy controller in stability. This model has been used for many years
damping LFO. providing reliable results [10]. In recent years, the study of
UPFC control methods has attracted attentions so that
different control approaches are presented for UPFC control
Keywords: Neuro-Fuzzy Controller, Low Frequency
such as Fuzzy control [11-14], conventional lead-lag
Oscillations (LFO), Unified Power Flow Controller
control [15], Genetic algorithm approach [16], and Robust
(UPFC), Single Machine-Infinite Bus (SMIB)
control methods [17-19].
In this case study, the class of adaptive networks
I. INTRODUCTION that of the same as fuzzy inference system in terms of
Flexible AC Transmission Systems, called FACTS, performance is used. The controller utilized with the above
got in the recent years a well known term for higher structure is called Adaptive Neuro Fuzzy Inference System
controllability in power systems by means of power or briefly ANFIS [20]. Applying neural networks has many
electronic devices. Several FACTS-devices have been advantages such as the ability of adapting to changes, fault
introduced for various applications worldwide. A number of tolerance capability, recovery capability, High-speed
new types of devices are in the stage of being introduced in processing because of parallel processing and ability to
practice. In most of the applications the controllability is build a DSP chip with VLSI Technology. To show
used to avoid cost intensive or landscape requiring performance of the designed adaptive neuro-fuzzy
extensions of power systems, for instance like upgrades or controller, a conventional lead-lag controller that is
additions of substations and power lines. FACTS-devices designed in [15] is used and the simulation results for the
provide a better adaptation to varying operational power system including these two controllers are compared
conditions and improve the usage of existing installations. with each other.
The basic applications of FACTS-devices are: (i).Power
flow control, (ii) Increase of transmission capability, (iii)
Voltage control, (iv)Reactive power compensation, II. MODEL OF THE POWER SYSTEM
(v)Stability improvement, (vi) Power quality improvement, INCLUDING UPFC
(vii)Power conditioning, (viii)Flicker mitigation, UPFC is one of the famous FACTs devices that is
(ix)Interconnection of renewable and distributed generation used to improve power system stability. Fig.1 shows a
and storages. The development of FACTS-devices has single machine- infinite-bus (SMIB) system with UPFC. It
started with the growing capabilities of power electronic is assumed that the UPFC performance is based on pulse
www.ijmer.com 4215 | Page

width modulation (PWM) converters. In figure 1 me, mb and each inverter can independently generate (or absorb)
δe, δb are the amplitude modulation ratio and phase angle of reactive power at its own ac output terminal.
the reference voltage of each voltage source converter Inverter 2 provides the main function of the UPFC
respectively. These values are the input control signals of by injecting an ac voltage Vpq with controllable magnitude
the UPFC. Vpq (0≤Vpq≤Vpqmax) and phase angle (0≤≤360), at the
power frequency, in series with the line via an insertion
transformer. The injected voltage is considered essentially
as a synchronous voltage source. The transmission line
current flows through this voltage source resulting in real
and reactive power exchange between it and the ac system.
The real power exchanged at the ac terminal (i.e., at the
terminal of insertion transformer) is converted by the
inverter into dc power that appears at the dc link as positive
or negative real power demanded. The reactive power
exchanged at the ac terminal is generated internally by the
Fig.1 A single machine connected to infinite bus with
inverter. The basic function of inverter 1 is to supply or
UPFC
absorb the real power demanded by Inverter 2 at the
common dc link. This dc link power is converted back to ac
As it mentioned previously, a linearized model of the power
and coupled to the transmission line via a shunt-connected
system is used in dynamic studies of power system. In order
transformer. Inverter 1 can also generate or absorb
to consider the effect of UPFC in damping of LFO, the
controllable reactive power, if it is desired, and there by it
dynamic model of the UPFC is employed; In this model the
can provide independent shunt reactive compensation for
resistance and transient of the transformers of the UPFC can
the line.
be ignored. The Linearized state variable equations of the
It is important to note that where as there is a
power system equipped with the UPFC can be represented
closed “direct” path for the real power negotiated by the
as [8].
action of series voltage injection through Inverters 1 and 2
back to the line, the corresponding reactive power
exchanged is supplied or absorbed locally by inverter 2 and
therefore it does not flow through the line. Thus, Inverter 1
can be operated at a unity power factor or be controlled to
have a reactive power exchange with the line independently
of the reactive power exchanged by the Inverter 2. This
means there is no continuous reactive power flow through
UPFC. Operation of the UPFC from the standpoint of
conventional power transmission based on reactive shunt
compensation, series compensation, and phase shifting, the
UPFC can fulfill these functions and thereby meet multiple
Where ΔmE, ΔmB , ΔδE and ΔδB are the deviation of input control objectives by adding the injected voltage Vpq, with
control signals of the UPFC. Also in this study IEEE Type- appropriate amplitude.
ST1A excitation system was used.

III. OPERATING PRINCIPLE OF UPFC
In the presently used practical implementation,
The UPFC consists of two switching converters, which in
the implementations considered are voltage source inverters
using gate turn-off (GTO) thyristor valves, as illustrated in
the Fig 2.1. These back to back converters labeled “Inverter
1 and “Inverter 2” in the figure are operated from a Fig2.Principle configuration of an UPFC
common dc link provided by a dc storage capacitor.
The UPFC consists of a shunt and a series
transformer, which are connected via two voltage source
converters with a common DC-capacitor. The DC-circuit
allows the active power exchange between shunt and series
transformer to control the phase shift of the series voltage.
This setup, as shown in Figure 1.21, provides the full
controllability for voltage and power flow. The series
Fig. Basic circuit arrangement of unified power flow converter needs to be protected with a Thyristor bridge.
controller Due to the high efforts for the Voltage Source Converters
and the protection, an UPFC is getting quite expensive,
This arrangement functions as an ac to ac power which limits the practical applications where the voltage
converter in which the real power can freely flow in either and power flow control is required simultaneously.
direction between the ac terminals of the two inverters and


IV. CONTROLLER DESIGN combination of linear and nonlinear parameters learning
I. Lead-Lag Controller Design algorithm. Description for learning procedure can be found
As mentioned before, in this study two different in [20]. This network is called adaptive by Jang and it is
controllers have been used to damp LFO. The first one is functionally equivalent to Sugeno type of a fuzzy system. It
conventional lead-lag controller. It consists of gain block, is not a unique presentation.
washout block, lead-lag compensator block. The washout
block is considered as a high-pass filter, with the time
constant TW. Without this block steady changes in input
would modify the output. The value of TW is not critical
and may be in the range of 1 to 20 seconds. In this study,
the parameters obtained from lead-lag controller design that
is presented in [15], were used.

II. Adaptive Neuro-Fuzzy Controller Design Figure.2. A fuzzy system defined by a neural network.
Another controller is adaptive neuro-fuzzy
controller. In this section, we will present the procedure of With regard to the explanations presented and with the help
designing of the adaptive neuro-fuzzy controller. In this of MATLAB software, adaptive neuro-fuzzy controller can
research, the neuro fuzzy controller has 2 inputs that are Δδ be designed. The rules surface for designed controller is
and Δω and it has 1 output that is f ∈ {∆𝑚E, ∆𝛿 E, ∆𝑚B, ∆𝛿 B shown in figure 3.
}. For each input 20 membership functions and also 20
rules in the rules base is considered. Figure 5 demonstrates
the structure of adaptive neuro-fuzzy controller for a sugeno
fuzzy model with 2 inputs and 20 rules [20].

Fig.3 The rules surface
The membership functions for input variable ∆𝜔 are
Fig.3 ANFIS architecture for a two-input Sugeno fuzzy
presented in figure 4.
model with 20 rules

In Figure 3, a Sugeno type of fuzzy system has the rule
base with rules such as follows:
1. If Δδ is A1 and Δω is B1 then f1=p1 Δδ+q1 Δω+r1.
2. If Δδ is A2 and Δω is B2 then f2=p2 Δδ+q2 Δω+r2.
𝜇 𝐴𝑖  and 𝜇 𝐵 𝑖 are the membership functions of fuzzy sets Ai
and Bi for i=1,…,20. In evaluating the rules, we choose
product for T-norm (logical and). Then controller could be
designed in following steps:
1. Evaluating the rule premises:
𝑤 𝑖 = 𝜇 𝐴 𝑖 (Δ𝛿) 𝜇 𝐵 𝑖 (∆𝜔), i= 1.....20 (2) Fig.4 The membership functions for input variable ∆𝜔
2.Evaluating the implication and the rule consequences:
𝑤 1 ∆𝛿,∆𝜔 𝑓1 (∆𝛿,∆𝜔 )+⋯+𝑤 20 (∆𝛿 ,∆𝜔 )𝑓 20 (∆𝛿 ,∆𝜔 ) One of the advantages of using neuro-fuzzy controller is
f(∆𝛿, ∆𝜔) =
𝑤 1(∆𝛿 ,∆𝜔 ) +⋯+𝑤 20(∆𝛿 ,∆𝜔 ) that we can utilize one of the designed controllers for
Or leaving the arguments out instance Δme controller in place of the other controllers.
𝑤 1 𝑓1 +⋯+𝑤 20 𝑓20
While if we use conventional lead-lag controllers, for each
f =
𝑤 1 +⋯+𝑤 20
(3) control parameters, a controller must be designed.
This can be separated to phases by first defining
𝑤𝑖
V. SIMULATION RESULTS
w¯i = (4) In this research, two different cases are studied.
𝑤 1 +⋯+𝑤 20
In the first case mechanical power and in the second case
These are called normalized firing strengths. Then f can be
reference voltage has step change and deviation in ω (Δω)
written as
and deviation in rotor angle δ (Δδ) is observed. The
f = w¯1f1+......+ w20f20 (5) parameter values of system are gathered in Appendix. In
The above relation is linear with respect to pi , qi, ri first case, step change in mechanical input power is studied.
and i=1,…,20. So parameters can be categorized into 2 sets: Simulations are performed when mechanical input power
set of linear parameters and set of nonlinear parameters. has 10% increase (ΔPm=0.1 pu) at t=1 s. Simulation results
Now Hybrid learning algorithm can be applied to obtain for different types of loads and controllers ( ΔmE, ΔδE,
values of parameters. Hybrid learning algorithm is


ΔmB, ΔδB ) and step change in mechanical input power are demonstrates simulation result for step change in reference
shown in figures 5 to 9. voltage, under nominal load and for δb Controller.
Generator Parameters :
M = 2H = 8.0MJ /MVA , D = 0.0, Td0’ = 5.044S
Xd = 1.0pu , Xq = 0.6 , Xd „ = 0.3
Exciter (IEEE Type ST1): 𝑘 𝐴 = 100, TA = 0.01S
Reactances: XIE = 0.1 pu , XE = XB =0.1 pu,
XBv = 0.3 pu, XE = 0.5 pu
Operation Condition: Pe = 0.8 pu, Vt = Vb=1.0 pu
UPFC parameters: mE = 0.4013, mB = 0.0789,
δE = -85.3478° , δB = -78.2174°
DC link: Vdc = 2 pu, Cdc = 1 pu
Fig. 10 Response of angular velocity for 5% step change in
reference voltage in the case of nominal load (δb
Controller)

Consequently simulation results show that neuro-
fuzzy controller successfully increases damping rate and
decreases the amplitude of low frequency oscillations.
Results comparison between conventional lead-lag
compensator and the proposed neuro-fuzzy controller for
the UPFC indicates that the proposed neuro-fuzzy controller
has less settling time and less overshoot in comparison with
Fig.7 Angular velocity deviation during step change in the conventional lead-lag compensator.
mechanical input power for nominal load (δe Controller)
VI. CONCLUSIONS
With regard to UPFC capability in transient
stability improvement and damping LFO of power systems,
an adaptive neuro-fuzzy controller for UPFC was presented
in this paper. The controller was designed for a single
machine infinite bus system. Then simulation results for the
system including neurofuzzy controller were compared with
simulation results for the system including conventional
lead-lag controller. Simulations were performed for
different kinds of loads. Comparison. showed that the
proposed adaptive neuro-fuzzy controller has good ability
Fig.8 Angular velocity deviation during step change in to reduce settling time and reduce amplitude of LFO. Also
mechanical input power for nominal load (mb Controller) we can utilize advantages of neural networks such as the
ability of adapting to changes, fault tolerance capability,
recovery capability, High-speed processing because of
parallel processing and ability to build a DSP chip with
VLSI Technology. Fuzzy logic is a convenient way to map
an input space to an output space. Mapping input to output
is the starting point for everything.

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