Vol: 1 Issue: 1
LOAD FLOW ANALYSIS OF 132KV CALABAR TRANSMISSION SYSTEM FOR IMPROVED PERFORMANCE USING GLOBAL BEST ARTIFICIAL BEE COLONY (GABC) ALGORITHM
Simeon Ebahe Okachi, Eko James Akpama
Introduction
An important role for load flow analysis is played in the design, management, and control of power system networks. In the recent few decades, a number of optimisation algorithms have been introduced, including the evolutionary algorithm, particle swarm optimisation, harmony search, artificial bee colony, and ant colony optimisation. Its performance was evaluated using the benchmark optimisation function, and its development was inspired by modelling the intelligent foraging behaviours of honey bees in their colony. The following are only a few of GABC's key advantages over other optimisation algorithms:
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Simplicity, flexibility and robustness of modelling
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Use of fewer control parameters when compared to another search algorithm.
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Ease of hybridization with other optimization algorithms.
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Ability to handle the objective cost with stochastic nature.
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Ease of implementation with basic mathematical and logical operations.
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Accuracy, strong convergence and reliability.
This work has examined a sufficient amount of the literature, and other scholars have written about and reported their findings, therefore it is reasonable to assume that the matter has been resolved. However, the amount of study in this field is limitless due to technological advancements, the usage of effective calculation tools, greater sensitivity, and simplicity of convergence. The innovative Global Best Artificial Bee Colony (GABC) Algorithm will therefore be the approach and methodology for this effort.
In the past, load flow issues were examined using the Newton-Rahpson (NR) approach. However, NR has certain intrinsic flaws such the inability to handle heavily loaded networks, the need for starting value assumptions, and anomalous operating situations. The load flow problem for the Calabar 132KV 179 bus real transmission network, is solved using the Global Best Artificial Bee Colony algorithm (GABC), a recently invented swarm intelligence-based technique, to eliminate the limitations of the existing NR method.
The Calabar 132KV sub-transmission infrastructure is carefully examined, and it becomes clear that the transmission companies are unable to adequately control the reactive electrical power flow of the network, which results in voltage losses and load shedding [1]. These networks are linked to insufficient electricity distribution and insufficient generation capacity. Inadequate or non-existent reserves and insufficient control infrastructure are other significant factors impacting stability. Therefore, to ensure effective performance, a thorough network analysis, planning, optimisation, operation, and control must be made as soon as possible to prevent a complete system failure. Similar to how the most recent collapse of the national power grid was witnessed.
This study aims to evaluate the existing 132KV Calabar transmission system's load flow performance for the stability of the network and overall efficiency. Thus, providing the network with a framework for medium-term operation.
The following goals are the focus of this study on the load flow evaluation of the 132KV, 179-buses making up the actual Calabar sub-transmission network:
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To create a standard swarm intelligence model based on GABC to calculate the voltage magnitudes, phase angle of load buses, actual and reactive power flows, and other parameters of the transmission line.
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To assess the transmission lines' present condition using the data at hand.
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Using the algorithm known as the Global Best Artificial Bee Colony (GABC) Algorithm, model, modify, and simulate the network.
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To make better use of the base-case simulation's findings in order to reduce loss of power across the 132KV Sub-transmission system and to improve the overall actual and reactive power flow.
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To confirm that the Global Best Artificial Bee Colony (GABC) optimisation technique used in this research has significant benefits over other traditional algorithms.
The significance of this research study lies in the solutions it will provide for efficiently reducing real and reactive power losses and appropriately injecting reactive and active electrical power into the networks in an effort to boost performance and provide better service. Once more, the evaluation's findings will offer helpful data for future growth. Modelling and simulating unique Gbest ABC algorithms for the 132KV Calabar zonal transmission system is the goal of this research project. This is necessary in order to increase the quality of the power being transmitted and distributed throughout the metropolis of Calabar and its surroundings. The model being proposed is an accurate technological approach that is extremely effective, efficient, and high convergence for studying a transmission network that is under a lot of stress, such as the Calabar network. To analyse this system using GABC, for the network under consideration real time data was sourced and simulated in a MATLAB environment.
2.0 Literature Review
Due to its strength and simplicity of use, the ABCA has gained a lot of popularity since it was first introduced. It has been effectively used by numerous researchers to solve issues in a variety of application areas. The ABCA was initially used to solve issues with numerical optimization (Karaboga, 2005). In (Karaboga, & Basturk, 2007a), the ABCA was expanded to include constrained optimization problems. It was also used to train neural networks, classify medical patterns, and solve clustering issues (Akay, et al., 2008). In order to suggest relevant supplementary materials for a learner based on their style of learning, interests, and difficulties, most recently (Hsu et al., 2012) employed ABC and suggested a customized auxiliary material system for recommendations on Facebook. The goal of the suggested strategy was to find appropriate learning materials quickly. In order to enhance the ABCA's capacity for global search and use it to address TSP issues, (Xing, et al., 2007) further investigated the mechanism that controls the selection of local optimal solutions.
Singh, (2009) compared the ABCA strategy to GA, ACO, and tabu search (TS) while solving the leaf-constrained minimum spanning tree (LCMST) problem. In order to reduce real power loss, enhance voltage profile, and balance feeder load subject to the radial network topology in which all loads must be powered on, (Rao, et al., 2008) used the ABCA to network reconfiguration problem in a radial distribution system. In terms of the quality of the answer and computation as a whole, the ABCA's outcomes outperformed the other methods tested in the study. Direct linear transformation (DLT), one of the camera calibration methods, was solved by (Bende, & Ozkan, 2008) using the ABCA by creating a relationship between 3D object coordinates plane linearly. Results produced by the ABCA were compared against those of the DE algorithm.