Self-Healing Control for Unbalanced Active Distribution Networks

This book investigates innovative methodologies for enhancing the self-healing capabilities of active distribution networks (ADNs), which are pivotal in modern power systems with high integration of distributed energy resources. Readers will find particular interest in the comprehensive coverage of optimal real-time meter placement for state estimation (SE) considering system observability, interval SE to address uncertainties in photovoltaic and wind power outputs, multi-area probabilistic forecasting-aided interval SE for false data injection attack (FDIA) identification that handles measurement uncertainties and line parameter variations, single-phase ground fault line identification based on linear SE, and robust dynamic fault restoration models that account for injection power variability. These approaches tackle critical issues such as measurement optimization, uncertainty quantification, power data security and fault recovery robustness, offering practical solutions through detailed mathematical formulations and algorithmic implementations. The book incorporates illustrative diagrams, such as network architectures and optimization workflows, to facilitate comprehension. A key benefit is the empowerment of practitioners with tools to improve grid reliability, operational efficiency, and resilience against attacks and disturbances. This book primarily targets power system engineers, researchers, and graduate students focused on smart grid technologies and distribution automation.