J
Johnson Asumadu
Researcher at Western Michigan University
Publications - 55
Citations - 440
Johnson Asumadu is an academic researcher from Western Michigan University. The author has contributed to research in topics: Artificial neural network & Renewable energy. The author has an hindex of 9, co-authored 55 publications receiving 311 citations.
Papers
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Journal ArticleDOI
Optimum unit sizing of hybrid renewable energy system utilizing harmony search, Jaya and particle swarm optimization algorithms
Nahar Alshammari,Johnson Asumadu +1 more
TL;DR: The study demonstrated that the ideal system with the least cost and the best performance was that which consists of thirteen solar PV systems, four biomass systems, one wind turbine, and 15 NI-Fe battery banks, with a total system present cost of $581,218 and a 0.254 $/kWh cost of energy.
Journal ArticleDOI
A Web-based electrical and electronics remote wiring and measurement laboratory (RwmLAB) instrument
Johnson Asumadu,Ralph Tanner,J. Fitzmaurice,M. Kelly,Hakeem Ogunleye,J. Belter,Song Chin Koh +6 more
TL;DR: This paper presents an instrument based on a new architecture called RwmLAB acting as a local multicircuit board on a common distributed panel on the Internet for real-time remote wiring of electrical and electronic circuits and real data acquisition over the Internet instead of using simulated data.
Proceedings ArticleDOI
PID control for improving P&O-MPPT performance of a grid-connected solar PV system with Ziegler-Nichols tuning method
TL;DR: In this article, the Ziegler-Nichols (Z-N) tuning method was combined with adjustments of the Proportional-Integral-Derivative (PID) gain parameters to achieve satisfactory improvement in the open-loop Perturb-and-Observe (P&O) maximum power point tracking (MPPT) performance of a grid-connected solar photovoltaic (PV) system.
Journal ArticleDOI
Radial Basis Function Neural Networks (RBFNN) and p-q Power Theory Based Harmonic Identification in Converter Waveforms
Eyad Almaita,Johnson Asumadu +1 more
TL;DR: Two radial basis function neural networks are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory and the small size and the robustness of the resulting network models reflect the effectiveness of the algorithm.
Journal ArticleDOI
Wind-Solar Hybrid Electrical Power Production to Support National Grid: Case Study - Jordan
Ghassan Halasa,Johnson Asumadu +1 more
TL;DR: In this paper, the authors presented the next generation of power energy systems using solar-and wind-energy systems for the country of Jordan, where the feasibility for using wind and solar energies is now when the price oil reaches US$ 100.00 per barrel.