This study is proposing a data-driven approach for fault location of distribution systems with distributed generations (DGs) by utilizing smart meters at low voltage (LV) networks and remote fault indicators (RFIs) at medium voltage (MV) networks. The determined fault location assists system operators with expedited service restoration, thus improving system reliability and resiliency. To quickly locate a fault, an enhanced escalation method is proposed to use outage reports from smart meters for prediction of the outage region. The determined outage region together with overcurrent notifications from RFIs with directional elements is jointly used to pinpoint the faulty line section. To this end, a new analytical model based on mixed integer linear programming (MILP) is proposed and each hypothetical fault location is modeled as decision variables. The result is an algorithm that is capable to support decision-making of single or multiple faulted line section(s) with incorrect and incomplete data from smart meters and RFIs for accurate fault location. In addition, an engineering way is presented to configure “power outage recognition time” of smart meters and logics for outage escalation are proposed in this study. Simulation results based on a utility feeder validate the proposed methodology for fault location.

This paper proposes a fully distributed approximate dynamic programming (FD-ADP) algorithm framework for real-time economic dispatch of a microgrid (MG). Firstly, a modified distributed consensus-based algorithm (DCBA) framework with power restriction factors is proposed to distributedly obtain the real-time ED decision of MG containing distributed energy resources with linear cost functions, which improves the convergence stability compared with the traditional DCBA framework. Then, combined with the approximate dynamic programming (ADP) theory, a FD-ADP algorithm is proposed to deal with data uncertainties. To this end, the piece-wise linear (PWL) function is applied to approximate the value function, and a distributed slope update approach is proposed to embed empirical knowledge into the value function while ensuring the distributed property in off-line training. With the well-trained PWL function assisting in decision, the distributed real-time ED strategy of MG with approximate global optimality is obtained by the proposed FD-ADP algorithm during the entire schedule horizon (e.g., 24 hours). Finally, case studies on the modified IEEE 6-bus MG system and IEEE 123-bus MG system demonstrate the superiority of solution convergence, validity of distributed training, and optimality of distributed schedule of the proposed FD-ADP algorithm.

Current distribution system outage management is to separately use digital relays at the substation for estimation of fault location and use meter information to infer the activated protection device. The lack of holistic utilization of available data in the distribution operating center results in lost opportunities for accurate fault diagnosis. To solve this issue, this study proposes a data-driven probabilistic fault location methodology based on comprehensive sensing measurement from digital relays at substations, Intelligent Electric Devices (IEDs) along primary feeders, SCADA sensors in the feeder circuit, and smart meters at customers’ premises. Statistics of historical fault location accuracies by digital relays and IEDs are used to estimate fault location errors with probability in real time. Multiple-hypothesis analysis is implemented to handle the uncertainties from SCADA sensors and smart meters. The spatial correlation between the potential fault location and collected sensor data is modeled as a mixed integer linear programming (MILP) problem. By solving the proposed optimization for generated hypotheses, a list of potential fault locations with possibilities is provided to system operators in decision-making for facilitated fault isolation and service restoration. Simulation results with a utility feeder validate the efficacy of the proposed approach for fault diagnosis.

The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed.

With smart fault indicators (SFIs) being widely deployed in distribution networks, availability of SFIs statuses and their current measurement provides an opportunity to locate distribution system faulted line sections in real-time. However, the integrity of SFIs data together with complex fault scenarios poses a challenge for fault diagnosis, especially with the presence of distributed generators (DGs). To solve this issue, this paper proposes a new method based on mixed integer linear programming (MILP) for identification of the faulted line sections using incomplete SFIs statuses. By minimizing the discrepancy between the expected SFI statuses and the available evidence at the distribution operating center, the proposed approach is able to handle complex fault scenarios with multiple faults and failures or malfunction of SFIs. When DGs are present, the current measurement of SFIs and their statuses are jointly used. The result is a robust algorithm for detection of faulted line sections of distribution systems with DGs in real-time. In addition, a new technique is proposed to convert nonlinear logical constraints into a linear combination of decision variables. The resulting linearity of the developed optimization model ensures its optimality. Simulation results based on a utility feeder demonstrate the effectiveness of the proposed approach.

When multiple uncertain resources (e.g., renewable energy and EV charging stations) are integrated into distribution systems, the uncertainties of correlated power supplies and demands pose a challenge for reliable grid operation. This challenge is compounded by the fact that this correlation cannot be modeled by standard pair copulas, e.g., Gaussian, Clayton, and Frank, as observed from the actual measurement data. To address these issues, this paper proposes a data-driven approach based on Gaussian Mixture Model (GMM) and Polynomial Chaos Expansion (PCE) for efficient grid impact studies with multiple correlated uncertain resources. The GMM is applied to segment these uncertain resources into multiple clusters, and the joint probability density within each cluster is approximated by multivariate Gaussian functions. The result is a feasibility for applying Gaussian Copula iteratively to handle the nonlinear correlation and the avoided construction of polynomial bases from arbitrary probability distributions for improved performance. Subsequently, the k-medoids clustering is utilized to strategically select experimental samples of PCE for the fast uncertainty quantification. The high efficiency and accuracy of the proposed approach has been demonstrated with real-world distribution feeder circuits together with actual measurement data from EV charging stations and solar farms.