ABSTRACT
Significant research in power system techniques and controls has revolutionized the world. Now, it is time to integrate artificial intelligence (AI) techniques into the power sector for even more intelligent and efficient systems. In the last few decades, power engineers have effectively designed intelligent controllers to mitigate power quality problems and challenges. Application of power electronics to power systems, renewable energy integration, and design of high-voltage DC transmission systems, flexible AC transmission systems, and custom power devices has been a remarkable journey.
The new norm is power quality (PQ) improvement and mitigation of problems using AI techniques. The search for fast, cost-effective solutions rests on new learning techniques for solving perennial PQ problems. It includes designing neural networks (NN), fuzzy-based solutions, and hybrid combinations such as ANFIS. The literature review suggests several new and effective control techniques designed to mitigate PQ problems.
This chapter starts with some conventional and new AI-based solutions for PQ improvement. Several newly developed functional NN techniques are discussed briefly, such as basic FLANN, trigonometric FLANN, Legendre’s NN, and time-delayed recurrent NN. After that, the application of the designed NN controllers in reactive power compensation and PQ problem mitigation is discussed. The PQ problems discussed in the chapter 74include power factor (pf) correction, load balancing, and reactive power (Q) compensation. Two case studies are included – (i) without PV integration and (ii) with PV integration. Precise control and MATLAB models are discussed, and results are presented for a single/three-phase power distribution system encountering several PQ problems. Further, the chapter discusses new NN techniques that can be explored for PQ mitigation in future work.
