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Solar Photovoltaic Array's Shadow Evaluation Using Neural Network with On-Site Measurement

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3 Author(s)
Nguyen, D.D. ; Northeastern Univ., Boston, MA ; Lehman, B. ; Kamarthi, S.

This paper proposes a method to accurately predict the maximum output power of the solar photovoltaic arrays under the shadow conditions by using neural network, a combined method using the multilayer perceptrons feed forward network and the backpropagation algorithm. Using the solar irradiation levels, the ambient temperature and the sun's position angles as the input signals, and the maximum output power of the solar photovoltaic array as an output signal, the training data for the neural network is received by measurement on a particular time, when solar panel is shaded. After training, the neural network model's accuracy and generalization are verified by the test data. This model, which is called the shading function, is able to predict the shadow effects on the solar PV arrays for long term with low computational efforts.

Published in:

Electrical Power Conference, 2007. EPC 2007. IEEE Canada

Date of Conference:

25-26 Oct. 2007