The proposed work aims to assess the reduction in power losses and better voltage profiles in network reconfiguration through a hybrid approach that combines Optimal Power Flow (OPF) and graph theory for static power source. The layout of a distribution network can be modified by opening or closing ties and sectionalizing. The OPF is conducted on the IEEE 30-node system using optimization methodologies such as the Interior Point Method, Sequential Quadratic Programming, and Active Set methods. Feasible radial topologies are obtained through Prim?s Maximal Spanning Tree algorithm, using the parameters obtained through the OPF methodologies. This approach eliminates the need for a tedious mesh check for an IEEE 30-node system. To validate the proposed methodology, the results were compared with those of the Social Spider Optimization (SSO), Edmonds' Maximal Spanning Tree algorithm, and Kruskal?s maximal spanning tree algorithm. Simulation results for the IEEE 30-node model confirm the effectiveness of Prim?s Maximal Spanning Tree algorithm, particularly when OPF is conducted using the Interior Point Method. Results shows that Prim?s maximal spanning tree- based interior point methods outperformed the other methods with 0.71938 p.u. active power loss, 2.0062 p.u. reactive power loss voltage stability index of 0.1457 p.u., showing strong resilience against voltage fluctuations. The study is carried out using MATLsoftware (2020 version) and MATLAB- based Power System Analysis Toolbox (PSAT) for programming and analysis. 

Optimized distribution network reconfiguration using hybrid OPF and graph theory techniques

Power distribution systems, especially radial topologies like IEEE 33-bus network, commonly challenges severe problems such as high-power losses, voltage profile instability, and high voltage drops during peak load periods, primarily due to the high line resistance. Although the aim of Distributed Generation (DG) is to minimize losses and ensure voltage stability, improper placement, and sizing of DG leads to higher losses and inefficient operation. To mitigate these consequences, this work proposes an African Bison Optimization (ABO) for optimal placement and sizing of DG units in the IEEE 33-bus radial distribution system. Specifically, proposed approach utilizes Voltage Stability Index (VSI) to identify proposed buses for DG installation, and ABO accomplished to identify optimal sizes for DG units to minimize real and reactive power losses and maintaining improved voltage profiles. Simulation results demonstrate that the proposed ABO solution performs significantly well at reducing distribution losses while improving the overall voltage stability.

Power Loss Minimization and Grid Stability Enhancement in IEEE 33-Bus Network using African Bison Optimization

Manufacturing has undergone a greater change in recent years than it has over almost the entire past century. Due to the variety of products, shortening delivery times and changing customer habits, the COVID-19 pandemic has demanded greater flexibility from manufacturing companies than ever before. One of the fundamental parts of manufacturing flexibility is the design of manufacturing layouts, which has so far not followed automation and methodological developments with regard to technological areas of manufacturing. In this article, a new method for supporting traditional factory design methods with digital manufacturing tools is introduced.

Building a reusable data model to support systematic lay- out planning with discrete event simulation of flexible manufacturing systems

: In power system electricity delivery, the distribution system has the most electricity loss as the system has the highest R/X ratio and has a radial network at one time. Optimal reconfiguration of the distribution network is needed in order to reduce power losses. However, as it is also involved with multiple objectives and constraint problems such as switching frequency, voltage, and current limits, it is difficult to find the optimal solution. Hence, this paper proposes the Multi-objective Cuckoo Search (MOCS) algorithm to find the optimal reconfiguration of distribution networks by considering minimizing power losses and switch operations. Based on the simulation results on the IEEE-33 bus system, the performance of the MOCS-based scheme has been found to be significantly better than the single-objective algorithm thereby reducing approximately 33% of the power losses. kerana ia melibatkan objektif dan kekangan masalah yang pelbagai seperti kadar peralihan, had voltan serta arus, adalah sukar bagi mendapatkan bacaan yang optimum. Oleh itu, kajian ini mencadangkan Carian Cuckoo Pelbagai Objektif (MOCS) bagi mencari konfigurasi semula yang optimum bagi sistem pengagihan tenaga dengan mengambil kira pengurangan pembaziran tenaga dan kadar peralihan. Berdasarkan keputusan simulasi pada sistem bas IEEE-33, pretasi MOCS telah menunjukkan peningkatan yang ketara berbanding algoritma objektif tunggal dan pengurangan sebanyak 33% tenaga.

Optimal distribution network reconfiguration using multi-objective cuckoo search algorithm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Éla

Author Tools

Chat about Author

Papers

Building a reusable data model to support systematic lay- out planning with discrete event simulation of flexible manufacturing systems

Optimal distribution network reconfiguration using multi-objective cuckoo search algorithm