Artificial intelligience in power sysrem

AL in powersystem
ANN n fuzzy controllers

1. Artificial intelligence in power systems PRESENTED BY: SAKETH REDDY 21011A0239

2. An electric power system is a network of electrical components used to supply, transmit and use electric power. Power systems engineering is a subdivision of electrical engineering that deals with the generation, transmission, distribution and utilisation of electric power and the electrical devices connected to such systems like generators, motors and transformers. INTRODUCTION TO POWER SYSTEMS

3. Introduction to artificial intelligence What is AI? Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI encompasses a range of technologies and methods, such as machine learning, natural language processing, computer vision, and robotics, which enable machines to perform tasks that would typically require human intelligence

4. The Need for AI in Power Systems Growing Complexity Power systems are becoming increasingly complex due to the integration of renewable energy sources, distributed generation, and smart grid technologies. Data-driven Decision Making The vast amount of data generated by power systems can be leveraged using AI to make informed, data-driven decisions. Cybersecurity Challenges AI can help detect and mitigate cyber threats to power systems, ensuring the security and resilience of critical infrastructure. Sustainability Goals AI can optimize energy consumption, reduce emissions, and support the transition to a more sustainable power system.

5. Fuzzy Logic is well-suited for complex power systems with many variables and uncertainties. Unlike traditional binary logic, Fuzzy Logic uses a range of truth values to handle imprecise information. This allows for intelligent decision-making without precise numeric inputs, like in voltage/frequency control and microgrid management. AI Techniques for Power Systems Artificial Neural Networks (ANNs) and Fuzzy Logic are two powerful AI techniques used in power systems. ANNs are inspired by the human brain. They can learn from data to make predictions, recognize patterns, and make decisions. ANNs are great for applications like load forecasting, fault detection, and renewable energy integration. ANN FUZZY LOGIC

6. Artificial Neural Networks Powerful Pattern Recognition Artificial Neural Networks excel at identifying complex, nonlinear patterns in large datasets, making them well-suited for power system applications. Adaptive Learning Neural networks can continuously learn and adapt to changing conditions, enabling them to improve their performance over time. Flexibility Neural networks can be designed to handle a wide range of power system challenges, from load forecasting to fault detection and diagnosis. Complexity The intricate structure and internal workings of neural networks can make them challenging to interpret and validate, requiring specialized expertise.

7. Fuzzy Logic Uncertainty Management Fuzzy logic can effectively handle the inherent uncertainty and imprecision in power system data and operations. Intuitive Rules Fuzzy logic-based systems can be designed using human-like, linguistic rules, making them more interpretable and easier to implement. Fuzzy logic can be combined with other techniques, such as neural networks, to create hybrid systems that optimize power system performance. Optimization Capabilities

8. Practical Application Of Al Systems In Transmission Line If any fault occurs in the transmission line, the fault detector detects the fault and feeds it to the fuzzy system. Only three line currents are sufficient to implement this technique and the angular difference between fault and pre- fault current phasors are used as inputs to the fuzzy system. Fuzzy systems can be generally used for fault diagnosis.

9. AI-Driven Grid Optimization 1 Load Forecasting AI models can accurately predict electricity demand, enabling efficient load balancing and resource allocation. 2 Voltage & Frequency Control AI algorithms can dynamically adjust voltage and frequency levels to maintain grid stability and power quality. 3 Automated Switching AI-powered systems can rapidly respond to grid disturbances, rerouting power flows and mitigating outages.

10. Improving Energy Efficiency with AI Smart Buildings AI can optimize building energy management systems, reducing consumption and costs through intelligent automation. Industrial Optimization AI-powered process control and predictive maintenance can enhance energy efficiency in industrial facilities. Smart City Solutions AI-driven urban energy management can optimize street lighting, traffic signals, and other municipal systems for greater efficiency.

11. Cybersecurity and AI-Driven Threat Detection Threat Identification AI models analyze network traffic and system logs to detect anomalies and potential cyber threats in real-time. Automated Response AI-powered systems can quickly respond to security incidents, mitigating the impact and reducing the risk of data breaches. User Behavior Analytics AI algorithms learn user patterns to identify suspicious behavior, enhancing access control and insider threat detection.

12. Advantages of AI in Power Systems 1 Improved Efficiency AI can optimize power generation, transmission, and distribution, leading to reduced energy waste and lower operational costs. 2 Enhanced Reliability AI-based systems can detect and respond to faults and anomalies in the power grid more quickly, improving system reliability and resilience. 3 Predictive Maintenance AI algorithms can analyze sensor data to predict equipment failures, enabling proactive maintenance and reducing unplanned downtime.

13. The Future of AI in Power Systems Autonomous Grid Management AI-driven systems that can autonomously optimize and self-heal the power grid, adapting to changing conditions. Distributed Intelligence Edge computing and decentralized AI algorithms that enable intelligent decision-making at the grid edge, reducing latency and improving resilience. Integrated Energy Systems AI-powered integration of diverse energy sources, including renewable energy, energy storage, and electric vehicles, for a more efficient and sustainable energy ecosystem.

14. Conclusion AI is transforming the power industry, driving greater efficiency, reliability, and sustainability. As the adoption of AI technologies continues to grow, the future of power systems will be marked by increased automation, enhanced grid resilience, and a faster transition to a sustainable energy future.

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