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Hisham Mahmood

Researcher at Florida Polytechnic University

Publications -  24
Citations -  1485

Hisham Mahmood is an academic researcher from Florida Polytechnic University. The author has contributed to research in topics: Microgrid & Voltage droop. The author has an hindex of 13, co-authored 22 publications receiving 1050 citations. Previous affiliations of Hisham Mahmood include University of Western Ontario & Lakehead University.

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Journal ArticleDOI

Accurate Reactive Power Sharing in an Islanded Microgrid Using Adaptive Virtual Impedances

TL;DR: In this article, a reactive power sharing strategy that employs communication and the virtual impedance concept is proposed to enhance the accuracy of power sharing in an islanded microgrid, where the communication is utilized to facilitate the tuning of adaptive virtual impedances in order to compensate for the mismatch in voltage drops across feeders.
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A Power Management Strategy for PV/Battery Hybrid Systems in Islanded Microgrids

TL;DR: In this paper, a power management strategy for PV/battery hybrid systems in islanded micro-grids is proposed, which enables the photovoltaic (PV)/battery unit to operate as a voltage source that employs an adaptive droop control to share the load with other sources while charging the battery.
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Reactive Power Sharing in Islanded Microgrids Using Adaptive Voltage Droop Control

TL;DR: A strategy that employs an adaptive voltage droop control to achieve accurate reactive power sharing is investigated, and the effectiveness of the proposed strategy is demonstrated on a 1.2 kVA prototype microgrid.
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Decentralized Power Management of a PV/Battery Hybrid Unit in a Droop-Controlled Islanded Microgrid

TL;DR: In this article, a control strategy is proposed to achieve decentralized power management of a PV/battery hybrid unit in a droop-controlled islanded microgrid, where the PV unit is controlled as a voltage source that follows a multi-segment adaptive power/frequency characteristic curve.
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Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

TL;DR: The proposed synthetic weather forecast is proved to embed the statistical features of the historical weather data, which results in a significant improvement in the forecasting accuracy and promotes a more efficient utilization of the publicly available type of sky forecast to achieve a more reliable PV generation prediction.