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Fault diagnosis and location play a pivotal role in expediting fault restoration and enhancing power system resilience. However, integrating distributed generation and diverse load profiles has led to more complicated distribution networks, intensifying fault diagnosis and location challenges. Hence, there is a need to transform traditional distribution networks into resilient active ones that actively guide and utilize distributed energy resources, possessing active control and operational capabilities. This paper comprehensively reviews fault diagnosis and location techniques in distribution networks, focusing on faulted line selection, fault section estimation, and accurate fault location. It provides an in-depth analysis of current fault diagnosis and location techniques, along with an assessment of their challenges, summarizing and evaluating the current research status at both local and international levels. To address existing fault diagnosis and location technologies limitations, a method utilizing micro-phasor measurement units (μPMUs) to enhance situational awareness and state estimation of active distribution networks is proposed. The paper also provides a comprehensive analysis of the optimized configuration strategy for achieving network observability and fault diagnosis using limited μPMU, in addition to comparing and analyzing the advantages and disadvantages of typical solution algorithms for the optimal PMU placement (OPP) problem. In response to extreme disturbance scenarios in active distribution networks, resilience enhancement strategies are proposed from three perspectives: fault prevention, fault response, and fault restoration. Finally, a summary and outlook on current research methods for fault diagnosis and location, as well as the challenges and future research directions in resilience enhancement strategies, are provided. [Display omitted] • Review fault diagnosis, section estimation, and accurate fault location methods. • Improving power system resilience through fault diagnosis and location. • Strategies using limited μPMUs to enhance distribution network situation awareness. • Comprehensive methods for power system resilience in extreme disasters. [ABSTRACT FROM AUTHOR]

Reliability improvement is regarded as a crucial task in modern distribution network expansion planning. Compared to previous works, this paper presents a bi-level optimization model to optimize the planning of the distribution network complying with multiple renewable energy and energy storage system (ESS) functionalities to guarantee the economical and reliable operation of the distribution network. The candidate assets include substations, distribution lines, renewable energy-based distributed generations (DGs), and ESSs are systematically involved. The load level affected by seasonal change and the multiple uncertainties, including renewable energy, load fluctuation, and contingency outage, are comprehensively considered. The uncertainties caused by the stochastic of renewable energy and load demand are described using Latin Hypercube Sampling (LHS) method. To address the computational burden and complexities associated with non-linear AC power flow, the mixed-integer linear programming (MILP)-based bi-level model is proposed via piecewise linearization methodology. Therein, the upper-level optimization model is proposed to minimize the total present value cost of the planning scheme in normal operating conditions. The lower level model, which is constrained to investment decision-making of the upper-level framework, aims to minimize the total cost of expected energy not supplied (EENS) considering the uncertainties of the single contingency outage. The effectiveness of the proposed bi-level planning model is validated by numerical studies to guarantee economic and reliability improvement for distribution network. • Multiple uncertainties are comprehensively considered in the proposed model. • Branches and multi-type generators are incorporated to the joint planning model. • Optimal allocation and reliability assessment are coordinated to build bi-level model. • The benefits from ESS for reliability enhancement is analyzed. [ABSTRACT FROM AUTHOR]