An Intelligent Algorithm an Approach for Distribution System
Solution
P Ravi Babu
1
a
, D Sathish Naik
1
b
,Sai Charith
1
c
and Seshadri Nagajyothi
1 d
1
Department of Electrical and Electronics Engineering., Sreenidhi Institute of Science and Technology, Telangana,
India
Keywords: Reconfiguration, Electrical Distribution System, Genetic Algorithm, Real Power Losses.
Abstract: The minimum loss reconfiguration trouble in radial distribution networks is one of the complex combination
optimizations because it is difficult to accurately determine the open sectionalizing switches for the given
network. The genetic algorithm serves best to achieve configurations with optimal actual losses in the test
system. The proposed method can easily handle any complex system for an optimal reconfigured network.
Through genetic algorithms, the paper primarily focuses on active losses and voltage drops. The test system
calculations confirm the validity and productivity of the proposed methodology. The numerical results of the
IEEE 33 bus test system are obtained through a MATLAB program
1 INTRODUCTION
India is a nation where people rely on power every
now and then. In recent times de-regulated
environmental extension of coal or resources used for
resulting in increased demand is important to manage
the electricity demand with various methods (G.
Poorna Chandra Rao and P. Ravi Babu,2023)(
Mulusew Ayalewet al.,2022). In other words,
electricity is an important aspect of modern
civilization. Usage of power in our homes, industries,
etc. completes our daily life (Juan Wen et
al.,2022),(Zhu et al.,2020),(Ravi Babu et al.,2017).
The process of generating, transmitting, and utilizing
that power is done in an actual way. This step-by-step
process happens with some electrical losses to
produce great efficiency in the power systems. In an
aspect, these efficiencies will also get disturbed or
reduced due to more electrical losses (S. Ganesh et
al.,2016) (DusharlaVenkata Sunil et al.2017),(A.V
Sudhakar Reddy et al.,2017) Due to that reduction,
there will be a decrease in performance, an increase
in maintenance, and other environmental
consequences too. One of the optimal methods to
reduce losses while distributing the power
a
https://orcid.org/0009-0001-2307-3080
b
https://orcid.org/0009-00001-3092-8622
c
https://orcid.org/0009-0008-4282-7723
d
https://orcid.org/0009-0002-9623-7549
can be done by understanding and alleviating the
power losses to attain sustainable energy
conservation. In India, power losses are a significant
factor, accounting for around 23% of distribution
losses, which is why it is crucial to reduce them
(Surender Reddy Salkuti,2019) (Zeba Khan et
al,2017) (Sushma Pasunuru et al.,2017) (G.
Poornachandra Rao et al.,2022). The purpose of this
paper is to delve into the complexities of power
losses, their causes, and their effects. Before getting
into this topic, we aim to provide a comprehensive
understanding of electrical losses and their objectives
(S Arun kumar et al,2022) (G. Poornachandra Rao et
al.,2021),(Kiran Mai B et al.,2018) We mainly
focused on strategies for reducing distribution losses.
The cause for these enormous is low network voltages
that results in huge current and resistive losses (Ravi
Baby P et al.,2020) (Duerr et al,2020).
Distribution channels in India are commonly
radial distribution systems and in order to enhance but
most effective way. Feeder reconfiguration is nothing
manipulating the existing system using open switches
credibility and wholesomeness, reconfiguration is the
and closed switches to attain desired radial network.
Babu, P., Naik, D., Charith, S. and Nagajyothi, S.
An Intelligent Algorithm an Approach for Distribution System Solution.
DOI: 10.5220/0012534800003808
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2023), pages 155-160
ISBN: 978-989-758-689-7
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
155
In this method of reconfiguration, the radiality of the
system should be maintained for which mathematical
formulas and several techniques are developed
through which we can find out the amount of change
in power loss. And some of the other techniques use
Kirchhoff's laws to solve the distribution network loss
calculations that requires closing all the open
switches to form a mesh and then operating the closed
switches to achieve radiality with minimum power
losses(Neda et al &Guo et al.,2020).
Sushma Pasunuru et al, explained reconfiguration
method under several real-time constraints and the
reconfiguration strategy is modelled through a
decision-making tree and tree search algorithm which
increases the probability of finding the best switches
to be operated for minimal losses. Ravi Babu has
made an attempt by applying a genetic algorithm
which is an artificial intelligence program where node
population is initialized on the basis of natural
selection.
The selection of a particular switch through this
algorithm is done through operations; they are
reproduction, crossover, and mutation. Using this
algorithm, we can operate the switches efficiently and
effectively. It is a great technique for searching for an
optimum and global result for the problem
respectively
2 GENETIC ALGORITHMS
The genetic algorithm (GA) is the process of natural
selection and evaluation to discover the best response
for a given problem respectively, and these groups of
potential solutions can be called an individual which
is represented as strings called Chromosomes or more
likely genes. GA mainly depends on the natural
selection theory which was founded by Charles
Darwin. Natural selection gives us a deep explanation
of how the genetic traits of any species change over a
time period and that results in the formation of new
distinct or unique species. GA is started randomly by
initializing the population of individuals. As said
before each individual is the potential solution, these
will be evaluated on the basis of fitness which
measures how well the individual performs for the
optimization and those selected individuals will be
sent for reproduction. These step-by-step iterations
are called Generations. Three procedures/rules are
primarily utilized in each generation to produce a new
generation of the existing population. This involves
selection, crossover, and mutation.
Selection: It is the process of selecting any two
chromosomes from a given population.
Crossover: It is a mixing operation where any
random two chromosomes are swapped with some
crossover rate to form new individuals.
Mutation: It is introduced for small changes in the
genetic algorithm for selected individuals
respectively. It maintains diversity in the individuals
and prevents premature convergence to sub-optimal
and better solutions.
It is important to note that the above steps will be
repeated iteratively until the termination criteria are
satisfied and the final solutions can be extracted based
on the fitness values. Fig. 1 shows the iterations of
GA mainly in five steps i.e., calculating fitness,
Selection, Mating, Crossover, and Mutation. At the
final termination, a new population is created. These
are the step-by-step processes for achieving a better
solution for the previous population.
Figure.1. Basic Structure of GA
3 PROBLEM STATEMENT
The distribution system plays a significant role in
the effectiveness and efficiency of electricity supply
for Indian consumers. The main problem is the
significant amount of power loss during the
transmission and distribution process, which results
in a loss of economy and efficiency of the power
system, which is the primary problem. This work
mainly focuses on proposed algorithms to enhance
the performance of electric distribution
ISPES 2023 - International Conference on Intelligent and Sustainable Power and Energy Systems
156
systems(EDS) by reducing the present active power
losses which are achieved through feeder
reconfiguration.
The reconfiguration has to be made such that
radiality in the existing system is not compromised
and also voltages maintain certain distinctive levels.
The voltage in any given branch should be the
level of:
------ (1)
Where V
min
and V
max
are minimum and maximum
permissible levels of voltages respectively, between
which V
n
is varying.
Across the next branch, the voltage can be
calculated using eq.(1).
〖 〗 ------ (2)
Where V(n+1)is the voltage found in the target
branch, i(b
n
) is the current through the branch from
sending end to target branch and Z
n
is the impedance
of the respective branch which is calculated with
Resistance (R
n
) and Reactance (X
n
) at branch n of the
network.
Equation(1) can be written in a generalized form
as:
------ (3)
Where,
------ (4)
〖
〗
------ (5)
It is very important to calculate load current
which is given by the equation below:
------- (6)
Where IL(n) is the load current of that branch, P
n
is active load power and Q
n
is reactive load power.
Then the active power loss at a particular branch is
given by:
------ (7)
And,
------ (8)
P
(T)
be the total active power loss.
4 TEST SYSTEM
Electricity network is a vital component of the
electrical network, and the efficient distribution and
transmission of electrical power is a crucial aspect of
the network. The voltage regulation and stability of
the power system are also a concern due to active
power losses, which not only contribute to energy
wastage but also impact its stability. These active
power losses are caused by the resistive components
in the distribution system. The test system aims to
provide accurate results by evaluating different
parameters and reconfiguration of lines for loss
reduction. Fig. 2 provides us with the basic Radial 33
bus system/network which is a base case to us.
By applying the proposed technique on the given
above standard 33 bus system, we get load current
IL(n) using eq.(5) and the True power losses PL(n) at
each branch respectively from eq.(6). The total active
power losses at every branch of the system are
calculated from eq(7) given above.
From the above-given figure.3 we can identify the
process for working on the proposed method using
GA. This process works in a loop of iterations until
the desired results are obtained. By analyzing the
given bus system in figure.2, the Tie switches
available in the bus system enable us to reconfigure
the distribution system in a way that is feasible. In the
very first case, the system has to calculate the Active
power losses for the base condition which helps to use
as a reference to the next configured results. Taking
the base case as a reference to check the optimal
results, the system starts the iteration while it chooses
the possible Tie switches for optimal reconfiguration
of the distribution system and calculates the active
power losses. Thus on comparing the obtained result
with the base one, if it is within the range/limit from
the base result then our method is satisfied for
acquiring less active power loss. If not GA comes into
the scene.
Figure.2. Basic 33 bus system
An Intelligent Algorithm an Approach for Distribution System Solution
157
5 TEST SYSTEM RESULTS
AND DISCUSSION
The IEEE 33 bus test system is considered a test
system for real power minimization. The technique
that was suggested was applied to this test system.
This system consisting of five tie switches has a
starting node voltage of 12.66 kV, base MVA-100,
total true and reactive. 3715 kW and 2300 kVar
respectively.
Before reconfiguration - The sectionalizing
switches are S1 to S32 while the tie switches are S33
to S37. Both types of switches are used to section. For
this combination, the power losses acquired are as
follows 202.6771 kW- real power loss, 135.1510
kVar- reactive power loss, 243.6 kVA- apparent
power loss.
After reconfiguration- S29, S33, S34, S35, and
S37 are used to obtain the optimal configurations by
using the rest of the sectionalizing switches
(represented by dotted lines) shown in the figure.6
given below. For this configuration, the resulting
losses are 109.6065kW- real power loss, and 78.031
kVar- reactive power loss, and 134.5495 kVA
apparent power loss. The resultant power losses are
46% less than the previous network.
Figure.3. Flowchart of working of the proposed method
The proposed method is coded using MATLAB
version R2017 for power flow calculations, GA is
carried out with the best results as shown in Table 4.
Table 1: Results Validation for 33 Bus Test System
Method
Tie Switch
Tr
ue Loss
(kW)
%
of Loss
reductio
n
Propose
d
Method
S29,S33,S34,S35,
S37
108.206
2
46.5816
J.Z.Zhu
[4]
S7,S9,S14,S32,S3
3
139.532
31.1555
MajidJa
mil et al.
[6]
S2,S13,S16,S33,S
37
112.68
44.4041
Firas
M.F.Flai
h et al. [7]
S7,S9,S14,
S32,S37
139.55
31.1466
Poorna
Chandra
et al [19]
S29,S33,S34,S35,
S37
109.606
5
45.9206
Figure.4: 33 Bus Optimal Reconfiguration system
6 CONCLUSIONS
The reinstatement and minimization of Ohmic
losses in the electrical power distribution system are
the two main objectives of this paper. A Genetic
Algorithm techniques is applied on standard IEEE 33
bus test systems to achieve the optimal solution for
Ohmic loss minimization minimizing the Ohmic
losses and these Ohmic losses are reduced to
46.5816% and found good among when compared to
other authors relevant work
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