microgrid presents an efficient and novel solution for integrating many new energy generating resources with traditional distribution networks. having an appropriate protective scheme is a fundamental challenge in achieving microgrid plans. it is proven that traditional protective plans are not efficient for protecting microgrid, since the network is dynamic and the level of fault current chang...

In this paper, a new fast and accurate method for fault detection, location, and classification on multi-terminal DC (MTDC) distribution networks connected to renewable energy and energy storages presented. MTDC networks develop due to some issues such as DC resources and loads expanding, and try to the power quality increasing. It is important to recognize the fault type and location in order ...

We consider a fixed probability µ. For any () 1, λ ∈ − ∞ , we denote by () , m λ µ the λ-Sugeno measure generated by µ. For a fixed measurable set A, the continuity of the map () , A fdm λ λ µ ∫ ֏ is proved, where the integral is either Choquet or Sugeno. The asymptotic (marginal) cases 1 λ = − and λ = ∞ are studied too. Finally, some concrete computations illustrate the previous theory.

This paper focuses on the Tennessee Eastman (TE) process and for the first time investigates it in a cognitive way. The cognitive fault diagnosis does not assume prior knowledge of the fault numbers and signatures. This approach firstly employs deterministic reservoir models to fit the multiple-input and multiple-output signals in the TE process, which map the signal space to the (reservoir) mo...

In this paper we generalize the framework of the Feasible Descent Method (FDM) to a Randomized (R-FDM) and a Randomized Coordinate-wise Feasible Descent Method (RCFDM) framework. We show that many machine learning algorithms, including the famous SDCA algorithm for optimizing the SVM dual problem, or the stochastic coordinate descent method for the LASSO problem, fits into the framework of RC-F...

Self-Organizing Maps (SOM) is an excellent method of analyzing multidimensional data. The SOM based classification is attractive, due to its unsupervised learning and topology preserving properties. In this paper, the performance of the self-organizing methods is investigated in induction motor rotor fault detection and severity evaluation. The SOM is based on motor current signature analysis (...

BACKGROUND/AIMS Documentation of conjunctival forniceal foreshortening in cases of progressive cicatrising conjunctivitis (PCC) is important in ascertaining disease stage and progression. Lower fornix shortening is often documented subjectively or semi-objectively, whereas upper forniceal obliteration is seldom quantified. Although tools such as fornix depth measurers (FDMs) have been described...