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8/10/2019 ABC Algorithm Based Comparative Analysis of Optimal SVC and TCSC Placement to Maximize Load Ability
54 Y. V. Balarama Krishna Rao, R. Srinivasa Rao & V. V. K. Reddy
Impact Factor (JCC): 5.9638 Index Copernicus Value (ICV): 3.0
power flow performance index was explained in [13]. A hybrid tabu search and simulated annealing was proposed to
minimize the generator fuel cost in optimal power flow control with multi-type FACTS devices [14].In some approaches
Power flow algorithm with the presence of TCSC and UPFC has been formulated and solved[15]. A hybrid GA approach
to solve optimal power flow in a power system incorporating FACTS devices has been reported [16, 17]. A Non-dominated Sorting Particle Swarm Optimization (NSPSO) is used to solve a mixed continuous discreet Multi-objective
optimization problem which consist of optimal location and size of Static VAR Compensators (SVC) and Thyristor
Controlled Series Capacitors (TCSC) in order to maximize Static Voltage Stability Margin (SVSM), reduce power losses
(PL) and minimize load Voltage Deviations (VD)[23]. In [24] Optimal placement using the sensitivity of transmission loss
with respect to the control parameters of devices using the interior point method for the minimizing real power loss and
multiply with new equation of SVC and TCSC. The new equation of SVC is sum of reactive power flow at the bus and the
new equation of TCSC is sum of real power loss in transmission line.
In this paper, application of ABC algorithm for optimal placement of FACTS devices which has minimum cost of
installation, to maximize the system loadability (MSL), while satisfying the power system constraints, for SVC and TCSC
is presented.. The variables for the optimization for each device are its location in the network, its setting and the
installation cost, in the case of single-type devices. TCSC has been modelled as a variable line reactance inserted in the line
and SVC is modelled as a reactive source injected both ends of the line.
Computer simulations were done for IEEE 6 and 30 bus systems for the base case without FACTS device and
with the insertion of SVC and TCSC. In the test cases with SVC and TCSC it is observed that SL cannot be increased
beyond a limit after placing. The maximum value of SL that can be achieved without violating the constraints is known as
maximum system loadability (MSL). The MSL, minimum number of FACTS devices required to attain the MSL and the
optimal installation cost of FACTS devices are obtained for single type of FACTS devices SVC and TCSC using ABC
algorithm.
MODELLING OF FACTS DEVICES
The power system as well as the FACTS devices is modelled using power flow equations. The FACTS devices
considered in this paper are Static Var Compensators (SVC) and Thyristor Controlled Series Compensators (TCSC), since
these FACTS devices, besides Phase Angle Regulators, are used most frequently in power systems [21].
SVC Variable Susceptance Model
An SVC is shunt-connected device with the line at both ends and influences the voltage VSVC at the bus to which
it is connected by injecting or absorbing reactive power QSVC [1]. This characteristic is modelled by a shunt-connected
variable susceptance BSVC at both ends of the transmission line as shown in figure 1.This model represents the fundamental
frequency equivalent of all shunt modules making up the SVC. This model is an improved version of SVC models. In this
paper only this model is considered for case studies
8/10/2019 ABC Algorithm Based Comparative Analysis of Optimal SVC and TCSC Placement to Maximize Load Ability
58 Y. V. Balarama Krishna Rao, R. Srinivasa Rao & V. V. K. Reddy
Impact Factor (JCC): 5.9638 Index Copernicus Value (ICV): 3.0
0i
d d P Pλ =
(14)
where λ is the loading parameter, Pd 0 and Pd
i are the
Intial system load and system load after installation of ‘i’th set of FACTS devices. Uniform loading of the load is
assumed in this paper. The optimal placement of FACT devices can be expressed mathematically as follows
min max
min max
min max
min max
min max
( , , , , , , ) 0
0 1
i i i
i i i
svc TCSC
gi gi gi
gi gi gi
i i i
SVC SVC SVC
TCSC TCSC TCSC
Max
Subject to
f V P Q X X
P P P
Q Q Q
V V V
X X X
X X X
λ
λ δ
λ
=
≤ ≤
≤ ≤
≤ ≤
≤ ≤
≤ ≤
≤ ≤ (15)
Where F=λ +W1*TL+W2*VD+W3*LFD+W4*SVC (cost) + W5*TCSC (cost).
W1-W5 are weightings for the multi objective functions.
f (V, δ, P, Q) is the power flow equations described by equations(3-6).
ARTIFICIAL BEE COLONY ALGORITHM
In the ABC model, the colony consists of three groups of bees: employed bees, onlookers and scouts. It is
assumed that there is only one artificial employed bee for each food source. In other words, the number of employed bees
in the colony is equal to the number of food sources around the hive. Employed bees go to their food source and come back
to hive and dance on this area.
The employed bee whose food source has been abandoned becomes a scout and starts to search for finding a new
food source. Onlookers watch the dances of employed bees and choose food sources depending on dances.
The main steps of the algorithm are given below: Initial food sources are produced for all employed bees Each
employed bee goes to a food source in her memory and determines a neighbour source, then evaluates its nectar amountand dances in the hive Each onlooker watches the dance of employed bees and chooses one of their sources depending on
the dances, and then goes to that source. After choosing a neighbour around that, she evaluates its nectar amount.
Abandoned food sources are determined and are replaced with the new food sources discovered by scouts. The best food
source found so far is registered until requirements are met.
In ABC, a population based algorithm, the position of a food source represents a possible solution to the
optimization problem and the nectar amount of a food source corresponds to the quality (fitness) of the associated solution.
The number of the employed bees is equal to the number of solutions in the population. At the first step, a randomly
distributed initial population (food source positions) is generated. After initialization, the population is subjected to repeat
the cycles of the search processes of the employed, onlooker, and scout bees, respectively. An employed bee produces a
8/10/2019 ABC Algorithm Based Comparative Analysis of Optimal SVC and TCSC Placement to Maximize Load Ability
modification on the source position in her memory and discovers a new food source position. Provided that the nectar
amount of the new one is higher than that of the previous source, the bee memorizes the new source position and forgets
the old one. Otherwise she keeps the position of the one in her memory. After all employed bees complete the search
process; they share the position information of the sources with the onlookers on the dance area.
Each onlooker evaluates the nectar information taken from all employed bees and then chooses a food source
depending on the nectar amounts of sources. As in the case of the employed bee, she produces a modification on the source
position in her memory and checks its nectar amount. Providing that its nectar is higher than that of the previous one,
the bee memorizes the new position and forgets the old one. The sources abandoned are determined and new sources are
randomly produced to be replaced with the abandoned ones by artificial scouts.
Procedures of ABC
• Initialize (Move the scouts)
• Move the onlookers
• Move the scouts only if the counters of the employed bees hit the limit
•
Update the memory.
•
Check the termination condition
Probability of Selecting a Nectar Source
(16)
Where Pi is the probability of selecting the ith employed bee S: The number of employed, θi is The position of
the ith employed bee and F(θ i ) is The fitness value.
Movement of the Onlookers: Calculation of the new position is given by
(17)
where χ i is The position of the onlooker bee, t is The iteration number, θk is The randomly chosen employed bee, j is The dimension of the solution and ϕ is A series of random variable in the range[0, 1].
The Movement of the Scout Bees Follows Equation
(18)
where r is a random number in the range[0.1].
CASE STUDIES
The proposed algorithm for optimal placement of SVC and TCSC has been tested on IEEE 6 and 30 bus systems.
8/10/2019 ABC Algorithm Based Comparative Analysis of Optimal SVC and TCSC Placement to Maximize Load Ability
60 Y. V. Balarama Krishna Rao, R. Srinivasa Rao & V. V. K. Reddy
Impact Factor (JCC): 5.9638 Index Copernicus Value (ICV): 3.0
Pijb and Qijb are the real and reactive power flow in the line i–j before placing FACTS device, respectively. P ija and Qija are
the real and reactive power flow in the line i–j after placing FACTS device, respectively. TL b and TLa are the transmission
loss occurring in the system before and after installation of FACTS devices SVC and TCSC.
IEEE 6 Bus System
The bus data and line data of the six bus sample system are taken from [17] and it contains three generator and
three load buses with 11 transmission lines. The location, settings of FACTS devices and optimal installation cost are
obtained using the ABC algorithm for optimal placement of SVC and TCSC devices and it is given in Table 1. To prove
the effectiveness of optimally placing SVC and TCSC in a power network three case studies are considered. They are
• Base case without any FACTS device
• with SVC
•
With TCSC
Table 1: IEEE 6 Bus System
In the SVC placement it is placed in 4 lines where as it is placed in 3 lines in [12].The MSL as well as IC
determined in this paper is better that reported in [12].The transmission loss also minimized for increased demand is an
added advantage.
In the TCSC placement it placed only in two lines where as it placed in 5 lines in [12].The MSL is as good while
cost slightly higher as in the ref[12].It has been observed that the results in [12 for TCSC placement violate the line flow
limits of lines (1-2),(1-4),(1-5)and (2-6)
IEEE 30 Bus System
The bus data and line data of the 30 bus system are taken from Matpower3.0 and this system comprises of one
slack bus, 5 PV buses, 24 PQ buses and 41 lines. The location, settings of FACTS devices and optimal installation cost areobtained using the ABC algorithm for optimal placement of SVC and TCSC devices and it is given in Table 2. To prove
the effectiveness of optimally placing SVC and TCSC in a power network three case studies are considered. They are
• Base case without any FACTS device
• with SVC
• With TCSC
8/10/2019 ABC Algorithm Based Comparative Analysis of Optimal SVC and TCSC Placement to Maximize Load Ability