International Journal of Trend in Scientific Research and Development (IJTSRD)

Volume 6 Issue 3, March-April 2022 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

Analysis and Implementation of Particle Swarm Optimization

Technique for Reduction of Power Loss in Microgrid
Nilesh Pandurang1, Pramod Kumar Rathore2
1
Student, 2Assistant Professor,
1,2
RKDF College of Engineering, Bhopal, Madhya Pradesh, India

ABSTRACT How to cite this paper: Nilesh

This article will concentrate on renewable energy sources. The Pandurang | Pramod Kumar Rathore
hybridization production is made up of two sources combined: solar "Analysis and Implementation of
and wind energy. The purpose of constructing a hybrid connection is Particle Swarm Optimization Technique
to totally absorb both sources and boost network resiliency. The for Reduction of Power Loss in
Microgrid"
software MATLAB was used to make the art. The focus here is on
Published in
constructing a modest micro-grid for a specified region that will be International Journal
supplied with power generated only from renewable sources. The of Trend in
connection of this full tiny micro grid with the state utility system Scientific Research
results in reliable and sustainable power. During disturbances, and Development
producing and matching loads may be disconnected from the utility (ijtsrd), ISSN: 2456- IJTSRD49512
to protect the load of micro-grids while without affecting the 6470, Volume-6 |
transmission grid's integrity. This capacity to create energy and Issue-3, April 2022, pp.250-256, URL:
distribute it independently to a particular small area need has the www.ijtsrd.com/papers/ijtsrd49512.pdf
potential to give more local reliability than the whole power grid.
Copyright © 2022 by author(s) and
KEYWORDS: Solar (PV), Wind Power, boost converter, AC-DC International Journal of Trend in
Hybrid Systems, Battery backup, Microgrid Scientific Research and Development
Journal. This is an
Open Access article
distributed under the
terms of the Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)

1. INTRODUCTION
Traditional energy sources are rapidly disappearing. task, and microgrids hold great promise for improving
Furthermore, energy costs are growing, and power supply dependability. The micro grid is an
renewable energy sources provide potential energy sector that improves the usage of dispersed
alternatives. They are numerous, pollution-free, energy production utilizing renewable energy sources
widely distributed, and recyclable. Its high such as wind, tidal, biomass, and solar, among others.
installation costs and poor conversion efficiency are "A micro grid is a set of linked loads and dispersed
disadvantages. A micro-grid is a small-scale power energy sources inside clearly defined electrical
source, such as a wind turbine, solar array, or diesel boundaries that function as a single controlled entity
engine, that is linked to fulfilling the requirements of with regard to the grid," according to the US
local communities. To be more specific, it is a Department of Energy. As electrical distribution
distributed generation (DG) network that provides technology advances throughout the twenty-first
energy as a group or individually [2]. A micro-grid century, several developments will emerge that will
incorporates renewable energy sources such as wind alter the needs for energy supply. And these changes
turbines and PV arrays that are connected to are being pushed by both the demand side, where
traditional utilities through a bidirectional converter. more energy availability and efficiency are wanted,
This is known as the grid-connected mode, as and the supply side, where the integration of
opposed to the autonomous island mode, in which the distributed generation and peak-saving technologies
traditional utility is isolated from the micro-grid. In is required. Power systems are now experiencing
reality, to construct a Hybrid Micro-Grid, a micro- major changes in operating conditions and needs,
grid may incorporate AC sources, DC sources, or mostly as a result of deregulation and an expanding
both. (3rd) Energy sustainability is the most difficult population. Because of the increased growth of DGs

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in power systems, controlling the power of various conversion stages and their associated energy losses.
DGs and utility grids has become a key challenge. The bulk of electricity systems, however, have
Micro grids have been a widely acknowledged idea in alternating current (AC). Microgrids continue to
this industry for the improved connection of DGs to dominate, and exclusively DC microgrids are on the
power networks. AC micro grids have been created to rise. As a consequence, current research has focused
correspond to the conventional power system, and a on linking AC microgrids with DC microgrids and
number of studies have been conducted, notably on using the advantages of both microgrids. The idea is
the question of power sharing of parallel-connected to connect alternating current and direct current
sources. Because the majority of renewable energy microgrids using a bidirectional ac/dc converter to
sources generate dc power or require a dc link for grid create a hybrid ac/dc microgrid in which alternating
connection, and as a result of increasing modern dc current and direct current energy sources and loads
loads, dc micro grids have recently emerged due to can be flexibly integrated into the microgrids and
their advantages in terms of efficiency, cost, and power can flow smoothly between the two
systems that can eliminate the dc-ac or ac-dc power microgrids.

Fig. 1. simple architecture of AC/DC HMG

As shown in Fig.1, both ac and dc sub grids are connected to the main grid through an Interlinking converter,
which can be either a voltage source converter (VSC) or a back-to-back converter. The interlinking converter
represents a corner stone of the ac/dc HMG. In general, the primary function of the interlinking converter is to
control the transfer of power between the ac/dc micro grids. In islanding operations, the IC is also designed to
ensure:
1. Equal loading of the ac/dc sub grids based on their own rating
2. Minimal load shedding and generation curtailment in the entire hybrid system.
2. METHODOLOGY
The objective function is evaluated by Newton trust region method and the iterative power flow solution can be
best described by the flow chart shown.

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Fig. 2FlowchartofACDCHMGpowerflowalgorithm
Particle Swarm Optimization (PSO)
In computational science, particle swarm optimization (PSO) is a computational method that optimizes a
problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves
a problem by having a population of candidate solutions, here dubbed particles, and moving these particles
around in the search-space according to simple mathematical formulae over the particle's position and velocity.
Each particle's movement is influenced by its local best known position, but is also guided toward the best
known positions in the search-space, which are updated as better positions are found by other particles. This is
expected to move the swarm toward the best solutions.
PSO is originally attributed to Kennedy, Eberhart and Shi and was first intended for simulating social
behaviour,[4] as a stylized representation of the movement of organisms in a bird flock or fish school. The
algorithm was simplified and it was observed to be performing optimization. The book by Kennedy and Eberhart
describes many philosophical aspects of PSO and swarm intelligence. An extensive survey of PSO applications
is made by Poli.[6][7] Recently, a comprehensive review on theoretical and experimental works on PSO has been
published by Bonyadi and Michalewicz.
PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search
very large spaces of candidate solutions. However, metaheuristics such as PSO do not guarantee an optimal
solution is ever found. Also, PSO does not use the gradient of the problem being optimized, which means PSO
does not require that the optimization problem be differentiable as is required by classic optimization methods
such as gradient descent and quasi- newton methods.
Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr.
Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schoolingPSO shares
many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is
initialized with a population of random solutions and searches for optima by updating generations. However,

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unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions,
called particles, fly through the problem space by following the current optimum particles. The detailed
information will be given in following sections. Compared to GA, the advantages of PSO are that PSO is easy to
implement and there are few parameters to adjust. PSO has been successfully applied in many areas: function
optimization, artificial neural network training, fuzzy system control, and other areas where GA can be applied.
Genetic Algorithm and PSO
Most of evolutionary techniques have the following procedure:
1. Random generation of an initial population
2. Reckoning of a fitness value for each subject. It will directly depend on the distance to the optimum.
3. Reproduction of the population based on fitness values.
4. If requirements are met, then stop. Otherwise go back to 2.
From the procedure, we can learn that PSO shares many common points with GA. Both algorithms start with a
group of a randomly generated population, both have fitness values to evaluate the population. Both update the
population and search for the optimum with random techniques. Both systems do not guarantee success.
However, PSO does not have genetic operators like crossover and mutation. Particles update themselves with the
internal velocity. They also have memory, which is important to the algorithm.
Compared with genetic algorithms (GAs), the information sharing mechanism in PSO is significantly different.
In GAs, chromosomes share information with each other. So the whole population moves like a one group
towards an optimal area. In PSO, only gBest (or lBest) gives out the information to others. It is a one -way
information sharing mechanism. The evolution only looks for the best solution. Compared with GA, all the
particles tend to converge to the best solution quickly even in the local version in most cases.

Figure 3 Flow diagram illustrating the particle swarm optimization algorithm.

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Results

Figure 4. Result of applied 6 Bus data set outputs.

BUS DATA SETS
0.9645 0.00 000 00 0.2752
0.9631 -0.0057 1.3781 1.4097 0.778
0.9653 0.0115 00 0 0.4128
0.9648 0 0 0 0.2630
0.9505 0 0.833 00 0.2556
0.9681 0 000 0 0.237

BUS DATA SETS RESOURCES.

Table: 1 AC-DC DATA set source

Interlinking Converter power 719.1328
Power loss 0.00039
Peak load power 2.77877
Maximum iteration 100
Table: 2 Parameter control table

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