Electrical Engineering Fundamentals: Circuits & AC Power | ML for Power Systems, Lecture 2

This is a video of approximately unknown duration. This lecture is the second session of the "Machine Learning and Power System Applications" series, systematically covering the core foundations of electrical engineering — circuit analysis and AC power theory. As a cornerstone for subsequent machine learning methods, the lecture begins with Kirchhoff's laws and network equivalent transformations, gradually progressing to phasor representation of AC circuits, active/reactive power, and complex power calculations, paving the way for understanding power system modeling and optimization. The highlight lies in the seamless connection between traditional circuit theory and modern power system analysis, with rigorous yet intuitive formula derivations supported by engineering interpretations of key concepts. Suitable for engineering students with a solid mathematical background, machine learning practitioners looking to strengthen their electrical engineering foundation, and beginners in power systems research. It is recommended to review basic concepts in linear algebra and calculus before watching, and to work through derivations with pen and paper for best results.