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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 A Linguistic MCDM Framework for Sustainable Agriculture Design ... Uztürk, Gülçin Büyüközkan and Deniz. - : figshare, 2021 BASE Show details 2 A Linguistic MCDM Framework for Sustainable Agriculture Design ... Uztürk, Gülçin Büyüközkan and Deniz. - : figshare, 2021 BASE Show details 3 A model for ITIS project portfolio selection in the presence of uncertainty Combination of fuzzy multi-criteria decision making and fuzzy mathematical programming ... Seifbarghy, Mehdi; Reihaneh Amel Sadeghi. - : figshare, 2020 BASE Show details 4 A model for ITIS project portfolio selection in the presence of uncertainty Combination of fuzzy multi-criteria decision making and fuzzy mathematical programming ... Seifbarghy, Mehdi; Reihaneh Amel Sadeghi. - : figshare, 2020 BASE Show details 5 Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems Samara; Natsheh In: Sustainability ; Volume 12 ; Issue 5 (2020) Abstract: The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system. Keyword: artificial neural network; fault detection; fuzzy logic systems; low-cost microcontroller; modelling; photovoltaic system; renewable energy; time series prediction URL: https://doi.org/10.3390/su12052011 BASE Hide details 6 Metodologia pracy doktorskiej ... Doktorat Doktoraty. - : figshare, 2016 BASE Show details 7 Metodologia pracy doktorskiej ... Doktorat Doktoraty. - : figshare, 2016 BASE Show details 8 Student interview for Place-Based WAC/WID writing instruction in Tropical Plant and Soil Sciences, clip 7 of 14 Place-based WAC/WID Hui. - 2015 BASE Show details 9 Modelo de controlo difuso de um sistema de produção de energia com base em recursos renováveis Moreira, Miguel Luís de Babo. - 2014 BASE Show details 10 Graduate Committee Minutes Graham, Audie In: Graduate Committee Minutes (2010) BASE Show details

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