We present a probabilistic assessment of power distribution systems resilience under different weather conditions. It is based on Monte Carlo simulation considering failures and repairs models obtained in previous studies for the used power distribution system allowing consideration of different weather scenarios during the resilience estimation. Our main contribution is the methodology for assessing power distribution systems under different weather scenarios, which integrates time-to-event models to estimate resilience. The assessment is carried out on distribution networks of a Brazilian power company, and using two resilience perspectives: structural, considering networks connectivity and service, quantifying energy delivered. An investigation of which features are related to the resilience loss in extreme weather is also performed. Results show that all the networks are resilient to a normal weather condition in both perspectives and weather adversities reduce their structural resilience, mainly in large networks. At the same time, service resilience loss is not linearly related to network size, but with how electricity flows over the networks, as the average load passing through vertices and the presence of lateral branches. These findings can support decision making about management and planning of such systems as resilience-oriented modifications, resources allocation, and expansion planning.

This paper presents a three-phase Distribution System State Estimator (DSSE) based on a Bayesian inference approach to manage different sampling rates of typical sources of information present in distribution networks. Such information comes from smart meters, supervisory control and data acquisition (SCADA) measurements, phasor measurement units and typical load profiles from pseudo measurements. The temporal aspect of the measurement set is incorporated in the estimation process by using a sampling layer concept, dealing separately with each group of measurements according to the respective updating rate. A Bayesian information fusion procedure provides the final estimation. The proposed DSSE consists in a multiple stage estimator that combines a prior model for the state variables, updated by new observations from measured values in each sampling layer, through Maximum a Posteriori estimation. This work also introduces an orthogonal method for the information fusion numerical solution, to tackle the severe ill-conditioning associated with practical distribution systems. Simulations with IEEE distribution test feeders and a Brazilian real distribution feeder illustrate the features of the proposed DSSE and its applicability. By exploring the concept of credibility intervals, the method is able to detect events on the grid, such as subtle load variation and contingencies, while maintaining accuracy.

This letter presents a problem of calculating load flow in distribution feeders via the conventional three-phase backward/forward sweep method when voltage regulators in the bidirectional mode are experiencing reverse flow due to power injection from distributed generation downstream the regulators. It also presents a simple but effectively modification in the algorithm of that method to overcome the problem. A modified version of the IEEE 34 buses distribution test feeder will be used to demonstrate the problem and the proposed algorithm solution.

Real time monitoring is a key tool for several automated features in distribution system. However, due to the limited number of real-time measurements, distribution system monitoring generally rely on load modeling procedures. This paper presents field results for a Real Time Monitoring Tool that was implemented on a Brazilian distribution utility to provide load values for service restoration software. The implemented tool comprises two steps. The first step consists of pre-processing routines that provide the required information for a Real Time Load Estimator that is executed in the second step. The field results provide evidence of the practical viability of the implemented Real Time Monitoring Tool, demonstrating the tool overcomes some usual limitations of real time load estimation procedures. More specifically, the implemented tool is able to handle multiple and different types of real-time measurements, treat real-time measurements on the feeder and not only in the head of the feeder, handle obviously bad data and to satisfy the performance requirements on practically sized distribution systems. Besides to propose and confirm a Real Time Monitoring Tool on a real distribution system, this paper shares valuable utility experience on providing new insights and the challenges faced for real time monitoring projects.

State Estimator is a key monitoring feature for the conception of Smart Grids. Although largely used in transmission systems, the implementation of estimators that correctly capture the Distribution Systems' (DSs) behavior is challenging, partially due to these circuits' asymmetric and unbalanced nature. As a result, a three-phase representation of the network is required. Despite some good theoretical results with this formulation, they usually use a phase angle reference for each phase, assuming balanced voltage phase angles at the Reference Bus (RB), which does not enable the estimator to encompass the unbalanced nature of those circuits. This article investigates the number of reference angles required for a three-phase Distribution System State Estimator (DSSE) from an observability point of view. Based on this concept, this study derives a condition for the use of only one phase angle as the reference. The article also introduces a method that properly represents the RB and promotes the use of one reference in most cases. Results from IEEE 34, IEEE 123, and 1058 bus Brazilian test feeders show the approach's feasibility and the negative effect of the mistreatment of the RB.

State-of-the art physics-model based dynamic state estimation generally relies on the assumption that the system’s transition matrix is always correct, the one that relates the states in two different time instants, which might not hold always on real-life applications. Further, while making such assumptions, state-of-the-art dynamic state estimation models become unable to discriminate among different types of anomalies, as measurement gross errors and sudden load changes, and thus automatically leads the state estimator framework to inaccuracy. Towards the solution of this important challenge, in this work, a hybrid adaptive dynamic state estimator framework is presented. Based on the Kalman Filter formulation, measurement innovation analytical-based tests are presented and integrated into the state estimator framework. Gross measurement errors and sudden load changes are automatically detected, identified, and corrected, providing continuous updating of the state estimator. Towards such, the asymmetry index applied to the measurement innovation is introduced, as an anomaly discrimination method, which assesses the physics-model-based dynamic state estimation process in different piece-wise stationary levels. Comparative tests with the state-of-the-art are presented, considering the IEEE 14, IEEE 30, and IEEE 118 test systems. Easy-to-implement-model, without hard-to-design parameters, build-on the classical Kalman Filter solution, highlights potential aspects towards real-life applications.