Genetic Algorithms and Machine Learning for Optimal Power Flow Solutions
Keywords:
Machine Learning, Genetic Algorithms, Power Systems Optimization, Optimal Power Flow, Convergence Speed, Total Cost Optimization.Abstract
This research examines the application of Genetic Algorithms (GAs) and Machine Learning (ML) in tackling the Optimal Power Flow (OPF) issue inside control frameworks. The study points to playing down operational costs whereas assembly operational imperatives through the optimization of control factors. Tests were conducted comparing GAs, Particle Swarm Optimization (PSO), Bolster Vector Machines (SVM), and Neural Networks (NN). The comes about uncovered that GAs reliably outflanked other calculations, illustrating predominant merging speed and accomplishing lower add up to costs. The research contributes experiences into the viability of GAs in exploring the complex and non-convex arrangement space of the OPF issue. Comparative investigations with related works assist fortified the competitive execution of the proposed Genetic Algorithm approach. This consideration not only propels the understanding of control framework optimization but also gives profitable suggestions for the broader application of GAs and ML procedures over differing spaces. The research highlights the potential for crossover approaches and the integration of real-time information to enhance versatility and vigor within the setting of keen networks and feasible vitality systems.
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ABID, M.H., ASHRAF, R., MAHMOOD, T. and FAISAL, C.M.N., 2023. Multi-modal medical image classification using deep residual network and genetic algorithm. PLoS One, 18(6),.
AIVALIOTIS-APOSTOLOPOULOS, P. and LOUKIDIS, D., 2022. Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization. PLoS One, 17(9),.
ALGHAMDI, A.S., SAEED, A., KAMRAN, M., MURSI, K.T. and WAFA, S.A., 2023. Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms. Electronics, 12(2), pp. 280.
BATAINEH, A.A., REYES, V., OLUKANNI, T., KHALAF, M., VIBHO, A. and PEDYUK, R., 2023. Advanced Misinformation Detection: A Bi-LSTM Model Optimized by Genetic Algorithms. Electronics, 12(15), pp. 3250.
CALVACHI, D., TIPÁN, L. and JARAMILLO, M., 2023. Localization and Sizing of Distributed Generation through a Genetic Algorithm to Improve Voltage Profile Using Ecuadorian Standards. Energies, 16(10), pp. 4139.
CAO, L., SHAO, C., ZHANG, Z. and CAO, S., 2023. A Novel Fusion Method for State-of-Charge Estimation of Lithium-Ion Batteries Based on Improved Genetic Algorithm BP and Adaptive Extended Kalman Filter. Sensors, 23(12), pp. 5457.
CHAI, W., ZHENG, Y., LIN, T., QIN, J. and ZHOU, T., 2023. GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting. Mathematics, 11(16), pp. 3574.
CHEN, C., LI, Z. and WEI, J., 2023. Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures. Electronics, 12(21), pp. 4433.
CHENG, Y., FA-YOU, A., YU-FENG, W., SHI-QUN YAN, CHUAN-BING ZHU and ZHANG, H., 2023. Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation. Mathematical Problems in Engineering, 2023.
CHENG, Y., SUN, Y., ZOU, Y., ZHENG, F., LIU, S., ZHAO, B., WU, M. and CUI, H., 2023. Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods. Energies, 16(16), pp. 5974.
CHOUDHURY, S., LUHACH, A.K., JOEL, J.P.C.R., AL-NUMAY, M., GHOSH, U. and DIPTENDU, S.R., 2023. A Residual Resource Fitness-Based Genetic Algorithm for a Fog-Level Virtual Machine Placement for Green Smart City Services. Sustainability, 15(11), pp. 8918.
DENG, Q., WANG, N. and LU, Y., 2023. Cloud Task Scheduling using the Squirrel Search Algorithm and Improved Genetic Algorithm. International Journal of Advanced Computer Science and Applications, 14(3),.
DUAN, H., YUE, S., LIU, Y., HE, H., ZHANG, Z. and ZHAO, Y., 2023. A deep learning-based method for predicting the low-cycle fatigue life of austenitic stainless steel. Materials Research Express, 10(8), pp. 086506.
FARIDI, S., ZAJ, M.M., DANESHVAR, A., SHAHVERDIANI, S. and ROODPOSHTI, F.R., 2023. Portfolio rebalancing based on a combined method of ensemble machine learning and genetic algorithm. Journal of Financial Reporting and Accounting, 21(1), pp. 105-125.
GKIKAS, D.C., THEODORIDIS, P.K., THEODORIDIS, T. and GKIKAS, M.C., 2023. Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification. Informatics, 10(3), pp. 63.
GOUVEIA, H.T.V., SOUZA, M.A., FERREIRA, A.A., DE ALBUQUERQUE, J.,C., NETO, O.N., MILDE MARIA DA, S.L. and RONALDO R B DE, A., 2023. Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting. Energies, 16(6), pp. 2635.
Bani Ahmad, A. Y. A. ., Kumari, D. K. ., Shukla, A. ., Deepak, A. ., Chandnani, M. ., Pundir, S. ., & Shrivastava, A. . (2023). Framework for Cloud Based Document Management System with Institutional Schema of Database. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 672–678.
P. William, Anurag Shrivastava, Upendra Singh Aswal, Indradeep Kumar, Framework for Implementation of Android Automation Tool in Agro Business Sector, 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 10.1109/ICIEM59379.2023.10167328
P. William, Anurag Shrivastava, Venkata Narasimha Rao Inukollu, Viswanathan Ramasamy, Parul Madan, Implementation of Machine Learning Classification Techniques for Intrusion Detection System, 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 10.1109/ICIEM59379.2023.10167390
N Sharma, M Soni, S Kumar, R Kumar, N Deb, A Shrivastava, Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market, ACM Transactions on Asian and Low-Resource Language Information Processing.
Ajay Reddy Yeruva, Esraa Saleh Alomari, S Rashmi, Anurag Shrivastava, Routing in Ad Hoc Networks for Classifying and Predicting Vulnerabilities, Cybernetics and Systems, Taylor & Francis, 2023
P William, OJ Oyebode, G Ramu, M Gupta, D Bordoloi, A Shrivastava, Artificial intelligence based models to support water quality prediction using machine learning approach, 2023 International Conference on Circuit Power and Computing Technologie
J Jose, A Shrivastava, PK Soni, N Hemalatha, S Alshahrani, CA Saleel, An analysis of the effects of nanofluid-based serpentine tube cooling enhancement in solar photovoltaic cells for green cities, Journal of Nanomaterials 2023
K Murali Krishna, Amit Jain, Hardeep Singh Kang, Mithra Venkatesan, Anurag Shrivastava, Sitesh Kumar Singh, Muhammad Arif, Deelopment of the Broadband Multilayer Absorption Materials with Genetic Algorithm up to 8 GHz Frequency, Security and Communication Networks
P Bagane, SG Joseph, A Singh, A Shrivastava, B Prabha, A Shrivastava, Classification of malware using Deep Learning Techniques, 2021 9th International Conference on Cyber and IT Service Management (CITSM).
A Shrivastava, SK Sharma,Various arbitration algorithm for onchip (AMBA) shared bus multi-processor SoC, 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science, SCEECS 509330
Gandomi, M. Haider, “Beyond the hype: Big data concepts, methods, and analytics”, International Journal of Information Management, vol. 35, no. 2, pp. 137-144, 2015.
Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022
Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219
Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729
Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321
Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774
Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721
P. Srivastava, P. Choudhary, S. A. Yadav, A. Singh and S. Sharma, A System for Remote Monitoring of Patient Body Parameters, International Conference on Technological Advancements and Innovations (ICTAI), 2021, pp. 238-243,
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