Modeling Antenna Radiation Using Artificial Intelligence Techniques

Author(s):  
Theodoros N. Kapetanakis ◽  
Ioannis O. Vardiambasis ◽  
Melina P. Ioannidou ◽  
Antonios I. Konstantaras

The forward and the inverse problem of a thin, circular, loop antenna that radiates in free space is modeled and solved by using soft computing techniques such as artificial neural networks and adaptive neuro fuzzy inference systems. On the one hand, the loop radius and the observation angle serve as inputs to the forward model, whereas the radiation intensity is the output. On the other hand, the electric field intensity and the loop radius are the input and output, respectively, to the inverse model. Extensive numerical tests indicate that the results predicted by the proposed models are in excellent agreement with theoretical data obtained from the existing analytical solutions of the forward problem. Thus, the employment of artificial intelligence techniques for tackling electromagnetic problems turns out to be promising, especially regarding the inverse problems that lack solution with other methods.

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
B. B. Bukata ◽  
R. A. Gezawa

Devolution of the power grid into smart grid was necessitated by the proliferation of sensitive load profiles into the system, as well as incessant environmental challenges. These two factors culminated into aggravated disturbances that cause serious havoc along the entire system structure. The traditional proportional-plus-integral-plus-derivative (PID) solution offered by the distribution synchronous compensator (DSTATCOM) could no longer hold. As such, this paper proposes some soft-computing framework for redesigning DSTATCOM to automatically deal with power quality (PQ) problems in smart distribution grids. A recipe of artificial neural network (ANN) and coactive neuro-fuzzy inference systems (CANFIS) was fabricated for the objective. The system was modelled, simulated, and validated in MATLAB/Simulink SimPowerSystems environment. The performance of the CANFIS against adaptive neuro-fuzzy inference systems (ANFIS), ANN and fuzzy logic controllers’ algorithms proved superior in handling PQ issues like voltage sag, voltage swell and harmonics.


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