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.

The paper proposes a new tool for real-time assessment of measurement sets in the context of power-system-state estimation. This tool incorporates many functions including observability analysis and restoration, as well as identification of critical measurements and critical sets. Using only network-topology data, the proposed methodology updates the qualitative characteristics of the current available measurement set in real time. The identification of critical measurements and sets is done using only network topology and is completed before running any state estimation. Mapping the system states and using information concepts, a new state space is obtained where the relationship (information) between measurements and states is straightforward. This map is easily found via triangular factorisation of the Jacobian matrix. Starting from a base-case measurement set, every time a snapshot of measurements has to be analysed, only refactorisation has to be carried out. Sparse-matrix techniques speed up the process. The method was tested in the IEEE-14-bus system as well as in the Brazilian 383-bus system, proving to be reliable, fast, easy to implement and suitable for real-time operation.

This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V-θ state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Québec TransÉnergie network.