@article{MAKHILLJEAS201611913827,
    title = {Evaluation of Improved MPPT-Based ANN Controller for PV Standalone System},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {11},
    number = {9},
    pages = {1972-1980},
    year = {2016},
    issn = {1816-949x},
    doi = {jeasci.2016.1972.1980},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2016.1972.1980},
    author = {Razieh,Mohd,Shahrooz and},
    keywords = {Maximum Power Point Tracking (MPPT),Photovoltaic (PV),DC-DC boost converter,Artificial Neural Network (ANN),Perturbation and Observation (P&O),Digital Signal Processor (DSP)},
    abstract = {This study presents an improved Maximum Power Point Tracking (MPPT) controller using Artificial
Neural Network (ANN) which is evaluated under different condition of solar irradiance and cell temperature.
This intelligent method is compared with Perturbation and Observation (P&O) method which is the most
popular and commonly used conventional MPPT controller. The transient and steady state responses are
presented and compared for both high and low solar irradiations as well as the dynamic responses. The control
system is implemented on eZdsp TMF28335 Digital Signal Processor (DSP). Experimental results are provided
for both high and low irradiations, at the same condition of cell temperature and solar irradiation applied in
simulation work. The results show that ANN MPPT has smaller tracking time and provides higher efficiency
than P&O with different step-sizes, under both high and low solar irradiations. In addition, in term of dynamic
responses, the ANN MPPT controller is much faster than P&O MPPT at locating and tracking the Maximum
Power Point (MPP), in case of changing solar irradiation condition.}
    }