{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T14:49:48Z","timestamp":1761662988971,"version":"3.41.2"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2011,7,31]],"date-time":"2011-07-31T00:00:00Z","timestamp":1312070400000},"content-version":"vor","delay-in-days":211,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"name":"Department of Science and Technology, India","award":["F.No.SR\/S3\/EECE\/0064\/2009"],"award-info":[{"award-number":["F.No.SR\/S3\/EECE\/0064\/2009"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advances in Artificial Neural Systems"],"published-print":{"date-parts":[[2011,1]]},"abstract":"<jats:p>With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN) has been proposed for voltage stability margin estimation which is an indication of the power system\u2032s proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance\u2010based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6\u2010bus and IEEE 30\u2010bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.<\/jats:p>","DOI":"10.1155\/2011\/532785","type":"journal-article","created":{"date-parts":[[2011,7,31]],"date-time":"2011-07-31T19:00:27Z","timestamp":1312138827000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Genetic Algorithm\u2010Based Artificial Neural Network for Voltage Stability Assessment"],"prefix":"10.1155","volume":"2011","author":[{"given":"Garima","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laxmi","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2011,7,31]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0045\u20107906(99)00035\u2010X"},{"volume-title":"Power System Stability and Control","year":"1994","author":"Kundur P.","key":"e_1_2_7_2_2"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.141737"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.221241"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.373978"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.116988"},{"key":"e_1_2_7_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.141687"},{"key":"e_1_2_7_8_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/0378-7796(94)90065-5","article-title":"Artificial neural networks for power system steady-state voltage instability evaluation","volume":"29","author":"Jeyasurya B.","year":"1994","journal-title":"Electric Power Systems Research"},{"key":"e_1_2_7_9_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0378-7796(94)00912-N","article-title":"Online voltage stability assessment of load centers by using neural networks","volume":"32","author":"Salatino D.","year":"1995","journal-title":"Electric Power Systems Research"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.476054"},{"key":"e_1_2_7_11_2","first-page":"371","article-title":"Application of artificial neural networks to the dynamic analysis of the voltage stability problem","volume":"144","author":"Schmidt H. 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