Fault detection in photovoltaic arrays: a robust regularized machine learning approach

Abstract

In this paper, a robust data-driven method for fault detection

in photovoltaic (PV) arrays is proposed. Our method is based on

the random vector functional link networks (RVFLN) which has the

advantage of randomly assigning hidden layer parameters with no

tuning. To eliminate the effect of measurement noise and overfit-

ting in the training process which reduce the fault detection ac-

curacy, the sparse-regularization method is utilized which uses l2-

norm with loss weighting factor to compute the output weights.

To attain strong robustness against the outlier samples, the non-

parametric kernel density estimation is employed to assign a loss

weighting factor. Through rigorous simulation and experimental

studies, we validate the performance of our proposed method in

detecting the short and open circuit faults based on only the out-

put current and voltage measurements of PV arrays. In addition

to stronger robustness comparing with the least square-support

vector machine, we also show that our proposed method provides

80% and 100% average detection accuracy for short circuit and

open circuit, respectively.