International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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Artificial Intelligence and Optimization Techniques for Intelligent Power Systems: Fault Detection, Energy Management, and Grid Stability Er. Aman Choudhary1*, Er Ankush Pathania 2 1,2Assistant Professor, Baddi University of Emerging Sciences & Technology Makhnumajra, Baddi, Distt. Solan, H.P.-
173205, India. ------------------------------------------------------------------------***------------------------------------------------------------------------ABSTRACT There is an increasing need for intelligent, adaptive and resilient control strategies under the new paradigm of modern power systems decentralization, sustainability and digitalization. Simple rules and static optimization of traditional, centralized models are not sufficient to manage growing amounts of renewable energy sources (RES), distributed energy resources (DERs) and bi directional energy flows. To this challenge, the application of Artificial Intelligence (AI) and advanced optimization techniques have been used as powerful tools for increasing the level of real time monitoring, fault detection, energy forecasting and system stability. In this review, we synthesize the application of this range of AI-driven models (machine learning, deep learning, reinforcement learning and hybrids) to key power system functions. For example, fault detection speeds of under 5 ms with 98.5% accuracy have been demonstrated, using wavelet transform integrated with support vector machines, for multi terminal DC (MTDC) grids. Applying artificial neural networks (ANNs), the classification accuracy was 97.8% and localization error was below 5%. CNNs, using deep learning based on CNNs also went beyond 99% accuracy in the fault diagnosis of underground cables. Recurrent neural networks (RNNs) and long short term memory (LSTM) models improved the shortterm load forecasting accuracy up to 15% and ANN-PSO algorithms improved the V2G scheduling with minimum battery wear and peak shaving. The paper also recognizes critical research gaps (e.g., explainability, data privacy, edge deployment, cybersecurity) that need to be addressed for robust and impactful deployment of ML. Integrating these intelligent systems into power networks allows utilities to move closer to that of resilient, efficient and sustainable energy infrastructures. With this review, researchers and engineers can have a basis to lead future developments in smart grid intelligence and energy optimization.
Keywords: Smart Grids, Artificial Intelligence, Fault Detection, Optimization, Renewable Energy, Energy Management, Machine Learning, Deep Learning, V2G, Load Forecasting. Because of the move to sustainable and resilient energy systems, designing, operating and managing today’s power networks has faced various new difficulties and benefits. Conventional energy systems which worked by a single plan and at a central place, are now moving towards being complex, digital and more flexible smart grids. They have significant shares of RES, involve power generation that is far from centralized, two-way energy flow and a high use of EVs and data technology. Because of their importance, AI and optimization techniques help these systems better manage control, run efficiently and adapt, depend on and give back to the environment. 1.1 Background: The Evolution of Power SystemsHistorically, power was generated at few central locations and moved only one way to users who consumed it. Most systems used rules to manage power which gave predictable results under regular load conditions. There has been a rise in demand for clean energy and reducing carbon in the energy segment, so solar PV, wind turbines and small-scale hydroelectric energy are now being integrated. Now, the grid works as an interactive system, linking several stakeholders, solar and wind generators, storage systems and any entities that create energy. On the technical side, complex sensors, devices using the Internet of Things (IoT), advanced meters (AMI) and new communication methods have brought us the Smart Grid which can monitor itself, collect data and make its own decisions. But these developments bring serious problems to the management of the grid, mostly related to stability, coordination, security and efficiency. Handling the unpredictable and erratic qualities of modern power systems now depends on AI and optimization tools..
2. AI Applications in Power System Monitoring and Fault Detection Previously, electricity flowed from central power plants down to consumers along a one directional process. Most control systems, working under continuous load, operated according to set rules to direct actions. Now, however, people want clean energy and want to reduce the carbon footprint of the energy industry and so more solar PV, wind turbines and
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