Artificial intelligence (AI) in engineering and technology
Abstract
From traditional civil engineering and aerospace engineering to large portions of electrical, electronics, and computer engineering, and most importantly, the medical and clinical sciences, artificial intelligence (AI) is one of the most critical and useful tools for research. The human brain’s ability to interpret sensory data, solve issues, learn from past experiences and tests, store and retrieve data, etc., is where the current discipline starts. In particular, the power sector is on the cusp of inevitable change as a result of numerous restructuring efforts, and the power science community requires artificial intelligence resources for effective planning, operation, and control of the power system. Almost every AI method is logically conceived of as an optimization or decision-making problem. Power utilities can benefit from these AI methods because they provide novel approaches to efficient evaluation, effective management, and astute decision-making. We have learned more about the design process and created higher-quality goods and artifacts thanks in large part to the widespread use of AI techniques and approaches over the past few decades. When these disciplines work together, they produce cutting-edge architectures that can solve a wider range of design problems at once.
References
1. Khalid S. Applications ofArtificial Intelligence in Electrical Engineering. IGI Global; 2020.
2. AL-Kandari AM, EL-Naggar KM. A genetic-based algorithm for optimal estimation of input-output curve parameters of thermal power plants. Electrical Engineering 2007; 89: 585–590. doi: 10.1007/s00202-006-0047-x
3. Baklrtzis AG, Petridis V, Kiartzis SJ et al. A neural network short-term load forecasting model for the greek power system. IEEE Transactions on Power Systems 1996; 11(2): 858–863. doi: 10.1109/59.496166
4. Karaboga D, Basturk B. An artificial bee colony (ABC) algorithm for numeric function optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium; 2006; Indianapolis, USA. pp. 888–893.
5. Khalid S. THD, and compensation time analysis of three-phase shunt active power filter using adaptive spider net search algorithm (ASNS) for an aircraft system. Journal of Machine Intelligence 2017; 2(1): 1–6. doi: 10.21174/jomi.v2i1.98
6. Sarkar D, Bali R, Sharma T. Practical Machine Learning with Python: A Problem-Solvers Guide to Building Real-World Intelligent Systems, 1st ed. Berkely; 2018. doi: 10.1007/978-1-4842-3207-1
7. Khalid S. Applied Computational Intelligence and Soft Computing in Engineering. IGI Global; 2017. doi: 10.4018/978-1-5225-3129-6
8. Bakirtzis AG, Biskas PN, Zoumas CE, Petridis V. Optimal power flow by enhanced genetic algorithm. IEEE Transactions on Power Delivery 2002; 17(2): 229–236. doi: 10.1109/TPWRS.2002.1007886
9. Khalid S, Verma S. THD and compensation time analysis of three-phase shunt active power filter using adaptive mosquito blood search algorithm (AMBS). International Journal of Energy Optimization and Engineering 2019; 8(1): 25–46. doi: 10.4018/ijeoe.2019010102
10. Sur C, Shukla A. Discrete cuckoo search optimization algorithm for combinatorial optimization of vehicle route in graph-based road network. In: Advances in Intelligent Systems and Computing, Proceeding of the third International Conference on Soft Computing for Problem Solving; 2014. Springer, New Delhi; 2014. Volume 258, pp. 307–320.
