{"id":3377,"date":"2022-12-14T11:51:25","date_gmt":"2022-12-14T11:51:25","guid":{"rendered":"https:\/\/deqepub.org\/ejees\/?post_type=journal_article&p=3377"},"modified":"2025-06-17T18:18:14","modified_gmt":"2025-06-17T18:18:14","slug":"loss-minimization-of-the-nigeria-330kv-power-grid-network-using-artificial-neural-network-based-interline-power-flow-compensator","status":"publish","type":"journal_article","link":"https:\/\/deqepub.org\/ejees\/journal_article\/loss-minimization-of-the-nigeria-330kv-power-grid-network-using-artificial-neural-network-based-interline-power-flow-compensator\/","title":{"rendered":"Loss Minimization of the Nigeria 330kv Power Grid Network Using Artificial Neural Network Based Interline Power Flow Compensator"},"content":{"rendered":"

ABSTRACT<\/strong><\/p>\n

Nigeria has since suffered from poor electric generation, transmission and distribution, despite the fact that Nigeria has the largest population in Africa. This situation has impacted negatively on businesses in Nigeria which often rely on off-grid generation to run their businesses. Real power losses in the transmission lines have been identified as one of the country’s key causes of inadequate power supply. Against this backdrop, there is therefore an urgent need to address the problem of real power losses in the lines so as to boost the meager power currently available at the national grid. In view of this, this study seeks to minimize real power losses in the transmission lines of the Nigerian 47-bus transmission network using an Artificial Neural Network (ANN) based Interline Power Flow Compensator (IPFC). Thus, the Nigerian 47 bus transmission network was modeled in Simulink\/PSAT and characterized using load flow analysis. Continuation Power flow (CPF) was used to identify the weak buses in the network and the result showed that five (5) buses fell below the acceptable voltage level of 0.95pu\u2264V\u22641.05. In addition, the total real power loss on the network was obtained. ANN optimal size predictor and ANN optimal locator were created and trained using ANN fitting tool in Simulink. The trained AI agents were then converted to Simulink models and connected to the test network to ascertain the optimal size(s) and location(s) of the Interline Power Flow compensator (IPFC) modeled to minimize real power losses in the network. The simulation was carried out on the integrated network with optimally sized ANN-based IPFCs deployed at the optimal location. The result showed that the total real power losses were reduced from 0.5182pu to 0.21186pu and the magnitude of the voltage profile of the five weak buses normalized within the IEEE acceptance range of 0.95pu\u2264V\u22641.05pu. This implies that IPFC optimized with ANN will be significantly viable in minimizing real power losses for improving the voltage profile and security of the Nigerian 47-bus transmission network<\/p>\n

Keywords<\/em><\/strong>: Loss Minimization; Continuation Power Flow; Nigeria 330kv Power Grid Network; Artificial Neural Network; Interline Power Flow Compensator<\/em><\/p>\n

Authorship<\/strong><\/p>\n

Okechukwu, U. K. & Onoh, G. N.<\/p>\n

DOI: https:\/\/doi.org\/10.5281\/zenodo.7437085<\/a> | FULL PDF<\/a><\/p>\n

\n","protected":false},"author":1,"template":"","journal_article_cats":[170],"class_list":["post-3377","journal_article","type-journal_article","status-publish","hentry","journal_article_cat-vol-6-no-5"],"yoast_head":"\nLoss Minimization of the Nigeria 330kv Power Grid Network Using Artificial Neural Network Based Interline Power Flow Compensator - European Journal of Engineering and Environmental Sciences<\/title>\n<meta name=\"description\" content=\"Nigeria has since suffered from poor electric generation, transmission and distribution, despite the fact that Nigeria has the largest population in Africa. This situation has impacted negatively on businesses in Nigeria which often rely on off-grid generation to run their businesses. Real power losses in the transmission lines have been identified as one of the country's key causes of inadequate power supply. Against this backdrop, there is therefore an urgent need to address the problem of real power losses in the lines so as to boost the meager power currently available at the national grid. In view of this, this study seeks to minimize real power losses in the transmission lines of the Nigerian 47-bus transmission network using an Artificial Neural Network (ANN) based Interline Power Flow Compensator (IPFC). Thus, the Nigerian 47 bus transmission network was modeled in Simulink\/PSAT and characterized using load flow analysis. Continuation Power flow (CPF) was used to identify the weak buses in the network and the result showed that five (5) buses fell below the acceptable voltage level of 0.95pu\u2264V\u22641.05. In addition, the total real power loss on the network was obtained. ANN optimal size predictor and ANN optimal locator were created and trained using ANN fitting tool in Simulink. The trained AI agents were then converted to Simulink models and connected to the test network to ascertain the optimal size(s) and location(s) of the Interline Power Flow compensator (IPFC) modeled to minimize real power losses in the network. The simulation was carried out on the integrated network with optimally sized ANN-based IPFCs deployed at the optimal location. The result showed that the total real power losses were reduced from 0.5182pu to 0.21186pu and the magnitude of the voltage profile of the five weak buses normalized within the IEEE acceptance range of 0.95pu\u2264V\u22641.05pu. This implies that IPFC optimized with ANN will be significantly viable in minimizing real power losses for improving the voltage profile and security of the Nigerian 47-bus transmission network\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/deqepub.org\/ejees\/journal_article\/loss-minimization-of-the-nigeria-330kv-power-grid-network-using-artificial-neural-network-based-interline-power-flow-compensator\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Loss Minimization of the Nigeria 330kv Power Grid Network Using Artificial Neural Network Based Interline Power Flow Compensator\" \/>\n<meta property=\"og:description\" content=\"Nigeria has since suffered from poor electric generation, transmission and distribution, despite the fact that Nigeria has the largest population in Africa. This situation has impacted negatively on businesses in Nigeria which often rely on off-grid generation to run their businesses. Real power losses in the transmission lines have been identified as one of the country's key causes of inadequate power supply. Against this backdrop, there is therefore an urgent need to address the problem of real power losses in the lines so as to boost the meager power currently available at the national grid. In view of this, this study seeks to minimize real power losses in the transmission lines of the Nigerian 47-bus transmission network using an Artificial Neural Network (ANN) based Interline Power Flow Compensator (IPFC). Thus, the Nigerian 47 bus transmission network was modeled in Simulink\/PSAT and characterized using load flow analysis. Continuation Power flow (CPF) was used to identify the weak buses in the network and the result showed that five (5) buses fell below the acceptable voltage level of 0.95pu\u2264V\u22641.05. In addition, the total real power loss on the network was obtained. ANN optimal size predictor and ANN optimal locator were created and trained using ANN fitting tool in Simulink. The trained AI agents were then converted to Simulink models and connected to the test network to ascertain the optimal size(s) and location(s) of the Interline Power Flow compensator (IPFC) modeled to minimize real power losses in the network. The simulation was carried out on the integrated network with optimally sized ANN-based IPFCs deployed at the optimal location. The result showed that the total real power losses were reduced from 0.5182pu to 0.21186pu and the magnitude of the voltage profile of the five weak buses normalized within the IEEE acceptance range of 0.95pu\u2264V\u22641.05pu. This implies that IPFC optimized with ANN will be significantly viable in minimizing real power losses for improving the voltage profile and security of the Nigerian 47-bus transmission network\" \/>\n<meta property=\"og:url\" content=\"https:\/\/deqepub.org\/ejees\/journal_article\/loss-minimization-of-the-nigeria-330kv-power-grid-network-using-artificial-neural-network-based-interline-power-flow-compensator\/\" \/>\n<meta property=\"og:site_name\" content=\"European Journal of Engineering and Environmental Sciences\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-17T18:18:14+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/deqepub.org\/ejees\/journal_article\/loss-minimization-of-the-nigeria-330kv-power-grid-network-using-artificial-neural-network-based-interline-power-flow-compensator\/\",\"url\":\"https:\/\/deqepub.org\/ejees\/journal_article\/loss-minimization-of-the-nigeria-330kv-power-grid-network-using-artificial-neural-network-based-interline-power-flow-compensator\/\",\"name\":\"Loss Minimization of the Nigeria 330kv Power Grid Network Using Artificial Neural Network Based Interline Power Flow Compensator - European Journal of Engineering and Environmental Sciences\",\"isPartOf\":{\"@id\":\"https:\/\/deqepub.org\/ejees\/#website\"},\"datePublished\":\"2022-12-14T11:51:25+00:00\",\"dateModified\":\"2025-06-17T18:18:14+00:00\",\"description\":\"Nigeria has since suffered from poor electric generation, transmission and distribution, despite the fact that Nigeria has the largest population in Africa. This situation has impacted negatively on businesses in Nigeria which often rely on off-grid generation to run their businesses. Real power losses in the transmission lines have been identified as one of the country's key causes of inadequate power supply. Against this backdrop, there is therefore an urgent need to address the problem of real power losses in the lines so as to boost the meager power currently available at the national grid. In view of this, this study seeks to minimize real power losses in the transmission lines of the Nigerian 47-bus transmission network using an Artificial Neural Network (ANN) based Interline Power Flow Compensator (IPFC). Thus, the Nigerian 47 bus transmission network was modeled in Simulink\/PSAT and characterized using load flow analysis. Continuation Power flow (CPF) was used to identify the weak buses in the network and the result showed that five (5) buses fell below the acceptable voltage level of 0.95pu\u2264V\u22641.05. In addition, the total real power loss on the network was obtained. ANN optimal size predictor and ANN optimal locator were created and trained using ANN fitting tool in Simulink. The trained AI agents were then converted to Simulink models and connected to the test network to ascertain the optimal size(s) and location(s) of the Interline Power Flow compensator (IPFC) modeled to minimize real power losses in the network. The simulation was carried out on the integrated network with optimally sized ANN-based IPFCs deployed at the optimal location. The result showed that the total real power losses were reduced from 0.5182pu to 0.21186pu and the magnitude of the voltage profile of the five weak buses normalized within the IEEE acceptance range of 0.95pu\u2264V\u22641.05pu. 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This situation has impacted negatively on businesses in Nigeria which often rely on off-grid generation to run their businesses. Real power losses in the transmission lines have been identified as one of the country's key causes of inadequate power supply. Against this backdrop, there is therefore an urgent need to address the problem of real power losses in the lines so as to boost the meager power currently available at the national grid. In view of this, this study seeks to minimize real power losses in the transmission lines of the Nigerian 47-bus transmission network using an Artificial Neural Network (ANN) based Interline Power Flow Compensator (IPFC). Thus, the Nigerian 47 bus transmission network was modeled in Simulink\/PSAT and characterized using load flow analysis. Continuation Power flow (CPF) was used to identify the weak buses in the network and the result showed that five (5) buses fell below the acceptable voltage level of 0.95pu\u2264V\u22641.05. 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