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Nima Safari
Researcher at University of Saskatchewan
Publications - 23
Citations - 373
Nima Safari is an academic researcher from University of Saskatchewan. The author has contributed to research in topics: Electric power system & Wind power. The author has an hindex of 7, co-authored 22 publications receiving 238 citations. Previous affiliations of Nima Safari include Amirkabir University of Technology & SaskPower.
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Journal ArticleDOI
Novel Multi-Step Short-Term Wind Power Prediction Framework Based on Chaotic Time Series Analysis and Singular Spectrum Analysis
TL;DR: In this paper, the authors proposed a novel decomposition approach to take the chaotic nature of wind power time series into account and to improve WPP accuracy by separating wind power TS into several components with different time-frequency characteristics by means of ensemble empirical mode decomposition.
Journal ArticleDOI
A Fuzzy Adaptive Probabilistic Wind Power Prediction Framework Using Diffusion Kernel Density Estimators
TL;DR: This paper proposes a framework based on the concept of bandwidth selection for a new and flexible kernel density estimator that is equipped with a fuzzy inference system and a tri-level adaptation function to adaptively capture the uncertainties of both the prediction model and wind power time series in different seasons.
Proceedings ArticleDOI
Reliability assessment of microgrid with renewable generation and prioritized loads
TL;DR: This paper evaluates the reliability of a microgrid containing prioritized loads and distributed RES through a hybrid analytical-simulation method and indicates the reliability enhancement of the overall system in the presence of the microgrid topology.
Journal ArticleDOI
A Hybrid Fault Cluster and Thévenin Equivalent Based Framework for Rotor Angle Stability Prediction
TL;DR: This paper addresses a novel approach for rotor angle stability prediction in power systems by introducing a fault cluster concept to divide an electrical network into several disparate zones and assigns a stability prediction model to each FC.
Journal ArticleDOI
A Quantile Regression-Based Approach for Online Probabilistic Prediction of Unstable Groups of Coherent Generators in Power Systems
TL;DR: This paper proposes a novel framework for probabilistic data-driven prediction of unstable groups of coherent generators in interconnected power systems that can offer power system operators wider flexibility to select a corrective control strategy.