A Multi-Objective Approach For Dg And Capacitor Placement Using Harmony Search Algorithm

Kona, Venkateshan; Kollu, Ravindra

doi:10.1109/icoac.2017.8441380

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…Alam, Gupta, Bindal, Siddiqui, & M. Zaid, 2018;Amraj, Boonrach, & K. Bhumkittipich, 2021;Siregar, Yuwaldi, Tarmizi, & Akhyar, 2023;Roy, Srivastava, & Dixit, 2020;Venkatraman & Khaitan, 2016;Sridevi, Rani, & B. L. Rao, 2019), and integrated approaches combining both capacitor and DG placement (Imran & Kowsalya, 2014;Chege, K. Murage, & K. Kihato, 2018;Jamil, 2023;Kaur & Jain, 2016). Capacitor placement and DG placement studies have employed methods such as genetic algorithms (Siregar et al, 2023), Tabu search (Rugthaicharoencheep et al, 2012), and harmonic search algorithms (Kona & Kollu, 2017), with a strong emphasis on renewable energy solutions. DG placement literature includes hybrid optimization approaches (Muhtazaruddin et al, 2014), particle swarm optimization (Lee et al, 2015), selective particle swarm optimization (Sarfaraz et al, 2016), Local Search Optimization (Kaushal & Tomar, 2017) and improved multi-objective harmony search algorithms (Kona & Kollu, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Mahato,

Poudel,

Mandal

et al. 2024

AAES

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The growing demand for electricity has raised concerns about power dissipation in distribution systems. To mitigate these losses, capacitors and distributed generator (DGs), particularly solar PV are strategically placed within the system. This project is committed to reducing power losses and improving the voltage profile through an in-depth analysis, optimizing the placement of capacitor and DG along the distribution feeder. The application of forward and backward sweep (FBS) algorithms assists load flow analysis in distribution networks with high R/X ratios, while the incorporation of the Genetic Algorithm (GA) within MATLAB identifies optimal locations & size for capacitors and DGs inside the large solution space of this complex, nonlinear optimization problem. Test outcomes, conducted on an IEEE 33-bus test system as its convincing representation of medium sized distribution network providing a versatile platform for evaluating proposed methodologies with practical implementation, showcase load flow examination, improvements in voltage profiles and minimized energy dissipation. The methodology is further applied to the real distribution network of the Sallaghari-Thimi 11 kV feeder in Bhaktapur, Nepal, sustaining the approach's effectiveness in mitigating power losses and increasing voltage profiles. Distributed generation with capacitor outperforms capacitors, and DG integration in the power system results in significant reductions of 72.91% in real power loss and 63.45% in reactive power loss, with a notable 6.542% increase in voltage magnitude. Application of these strategies in the Thimi Sallaghari 11 kV feeder demonstrates significant power loss saving (up to 82.72%) and worthy improvements in voltage profiles (up to 5.32%), focusing on their effectiveness in enhancing operational efficiency. This approach provides a practical solution for optimizing capacitor and solar PV distributed generator placement in distribution networks considering various case scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Mahato,

Poudel,

Mandal

et al. 2024

AAES

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“…𝑃 𝑚𝑑 is the PV module maximum power defined in (7), 𝑁 𝑠 is the series connected PV modules, 𝑁 𝑝 is the number of parallel connected PV modules. Maximum module power is as defined in (8) where 𝑉 𝑜𝑐 represents voltage at the output terminals of the PV module during open circuit while 𝐼 𝑠𝑐 denotes current during short circuit of the PV module. Fill factor (FF) in ( 4) is the light sensitive area in the solar panel.…”

Section: Solar Dg Modelingmentioning

confidence: 99%

“…One of the solutions [7] employs a particle swarm optimization (PSO)-based optimization strategy to reduce loss and facilitate optimal DG placement. The DG placement is a multivariate optimization problem, where methods like harmony search are being utilized over IEEE 33-bus that results minimization of the power loss [8]. The minimization of load consumption by maintaining a threshold voltage is the core objective of a popular energy saving scheme, namely the integration of DG and conservation voltage reduction (CVR).…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

Rekha¹,

Byalihal²

2023

IJECE

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<span lang="EN-US">Power demand in the current days is increasing more and more where the conventional power generation systems are failing to meet these power demands due to less availability of non-renewable resources. Hence, many of the researchers are working on the distributed generation (DG) by using renewable resources like wind and solar. The penetration towards wind, solar DG faced challenging situations during power generation due to uncertainty in the wind speed and solar radiation. Recent studies have predicted that the combination of both solar and wind can lead to better performance. However, the sizing and placement of DG systems is necessary to achieve efficiency otherwise the systems may lead to adverse effects in distribution networks. This paper introduced the solar DG, wind DG and hybrid (solar and wind) DG system. The particle swarm optimization technique is used to size and place the DG because of its parallel search capability. Also, the combination of wind-solar DG gives better DG sizing in the respective DG location. The voltage profile of these DG systems has shown better results for the efficient power system. In comparison to conventional DG systems, the suggested hybrid DG system is capable of minimizing power loss and maintaining voltage profile.</span>

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“…Reduce dmax adaptively using (32) Keep the calculated nondominated solutions in the Pareto archive Remove dominated solutions from the archive…”

Section: Yesmentioning

confidence: 99%

“…In [31], the whale optimization algorithm (WOA) and the moth swarm algorithm (MSA) are used and compared to solve the optimal allocation problem of shunt capacitors and DG units; however, WOA results in lower quality solutions and MSA has complicated objective functions. In [32], an improved multiobjective harmony search algorithm (IMOHSA) is used to find the optimal allocation of DG and capacitor; however, it has a high number of iterations. In [33], a combined approach is proposed to solve the proposed issue; however, complicated objective functions are used.…”

Section: Introductionmentioning

confidence: 99%

Capacitor Allocation Using Multiobjective Water Cycle Algorithm and Fuzzy Logic

El-Saeed

Abdel-Gwaad

Farahat

2022

ELEKTRON ELEKTROTECH

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Radial distribution systems are susceptible to a lack of voltage profile and increase system losses, particularly at the distant ends of the distribution feeder. This manuscript proposes an approach to solve the optimal capacitor placement problem in radial distribution networks to minimize system losses, improve the voltage profile of all buses, promote total voltage stability, and improve net savings. The optimal capacitor placement problem is solved in two stages. Firstly, normalized loss sensitivity factor and voltage magnitude are used as inputs to build fuzzy expert rules to arrange the most candidate buses for capacitor placement. Secondly, a multiobjective water cycle algorithm is applied to determine the optimal sizes and locations of capacitors within the predefined search space using fuzzy expert rules. The multiobjective function is formulated with operational constraints considering fixed and switched capacitors. To validate the effectiveness of this methodology, it is demonstrated on IEEE 33 and IEEE 94-bus radial distribution networks. Clearly, the findings show the improvement in the voltage profile and static voltage stability, the significant reduction in system losses, as well as the enhancement in overall savings. Furthermore, a comprehensive evaluation is also carried out by comparing the numerical results with other methods such as interior point algorithm, a combination fuzzy real coded genetic algorithm method, water cycle algorithm for IEEE 33-bus system and artificial bee colony algorithm for IEEE 94-bus system which prove the viability and effectiveness of the proposed methodology.

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A Multi-Objective Approach For Dg And Capacitor Placement Using Harmony Search Algorithm

Cited by 5 publications

References 9 publications

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

Capacitor Allocation Using Multiobjective Water Cycle Algorithm and Fuzzy Logic

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