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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Incremental Unsupervised Learning Algorithm for Power Fluctuation Event Detection in PV Grid-Tied Systems
verfasst von : Ken Weng Kow, Yee Wan Wong, Rajparthiban Kumar Rajkumar, Rajprasad Kumar Rajkumar, Dino Isa
Erschienen in: 9th International Conference on Robotic, Vision, Signal Processing and Power Applications
Verlag: Springer Singapore
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Abstract
Obstacles for solar photovoltaic (PV) system to be a reliable energy source is its intermittent and stochastic output power. The randomness output power could trigger power fluctuation event. Subsequently, more power quality issues such as frequency fluctuation, voltage variation and harmonic distortion could happen. Thus, this paper introduces a Self-Organizing Incremental Neural Network (SOINN) to predict the output power and subsequently detect the power fluctuation events in order to enhance the reliability of a PV grid-tied system. The SOINN is developed from the growing neural gas and competitive hebbian learning. It could be trained without predefined the structure of the network. To train the SOINN, input data to the PV system such as irradiance and temperature are used. The trained SOINN will be compared with the Self-Organizing Map (SOM) network. Results show that the SOINN prediction engine achieves an accuracy of 100 % in identifying power fluctuation event through predicted output power.