The urgent need to reduce carbon emissions is leading to growing use of renewable electricity, particularly from wind and photovoltaics. However, the intermittent nature of these power sources presents challenges to power systems, which need to ensure high and consistent power quality. Going forward, power systems also need to be able to respond to changes in loads, for example from EV charging. Neither production nor load changes can be predicted precisely, and so there is a degree of uncertainty or fuzziness. One way to meet these challenges is to use a kind of artificial intelligence - fuzzy logic.

• Chapter 1: Introduction
• Chapter 2: Real-time simulation applications for future power systems and smart grids
• Chapter 3: Fuzzy sets
• Chapter 4: Fuzzy inference: rule based and relational approaches
• Chapter 5: Fuzzy-logic-based control
• Chapter 6: Feedforward neural networks
• Chapter 7: Feedback, competitive, and associative neural networks
• Chapter 8: Applications of fuzzy logic and neural networks in power electronics and power systems
• Chapter 9: Deep learning and big data applications in electrical power systems