Now showing 1 - 3 of 3
  • Publication
    A data‐driven measurement placement to evaluate the well‐being of distribution systems operation
    (Institution of Engineering and Technology, 2021-01-05) ; ;
    The widespread integration of intelligent electronic devices has facilitated the employment of data mining methods in evaluating the operating condition of distribution systems. This possibility comes to prominence in active networks, where distributed energy resources can cause unforeseen dynamics that requires an effective monitoring infrastructure and a fast‐track procedure to convey the system operating condition in a comprehensible manner to the operator. To this end, a data‐driven approach is proposed to assess the status of system operating constraints by presenting each constraint as a classification problem. Afterwards, by exploiting the propounded presentation of the system operating condition, the measurement placement problem in distribution systems is addressed as selecting a set of features that have the most contribution to evaluating the system operating status . To do so, first, the effectiveness of the measurement units is identified through their contribution to the classification process, and then a procedure is proposed to pinpoint the measurement units with redundant information. Monte–Carlo simulations are performed to provide a comprehensive training set. Receiver operating characteristic analysis and time‐series power flows demonstrate the effectiveness of the proposed approaches.
    Scopus© Citations 3  168
  • Publication
    Distribution System Topology Identification for DER Management Systems Using Deep Neural Networks
    For DER management systems (DERMS) to manage and coordinate the DER units, awareness of distribution system topology is necessary. Most of the approaches developed for the identification of distribution network topology rely on the accessibility of network model and load forecasts, which are logically not available to DERMS. In this paper, the application of deep neural networks in pattern recognition is availed for this purpose, relying only on the measurements available to DERMS. IEEE 123 node test feeder is used for simulation. Six switching configurations and operation of two protective devices are considered, resulting in 24 different topologies. Monte Carlo simulations are conducted to explore different DER production and load values. A two-hidden layer feed-forward deep neural network is used to classify different topologies. Results show the proposed approach can successfully predict the switching configurations and status of protective devices. Sensitivity analysis shows that the positive and negative sequence components of the voltage (from DER units and substation) have the most contribution to discrimination among different switching configurations.
    Scopus© Citations 16  239
  • Publication
    Resilient Identification of Distribution Network Topology
    IEEE Network topology identification (TI) is an essential function for distributed energy resources management systems (DERMS) to organize and operate widespread distributed energy resources (DERs). In this paper, discriminant analysis (DA) is deployed to develop a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Following, to improve the TI resiliency against the interruption of communication channels, a quadratic programming optimization approach is proposed to recover the missing signals. By deploying the propounded data recovery approach and Bayes' theorem together, a benchmark is developed afterward to identify anomalous measurements. This benchmark can make the TI function resilient against cyber-attacks. Having a low computational burden, this approach is fast-track and can be applied in real-time applications. Sensitivity analysis is performed to assess the contribution of different measurements and the impact of the system load type and loading level on the performance of the proposed approach.
      257Scopus© Citations 13