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New algorithm to identify faults in PV systems

Researchers from the Costa Rica Institute of Technology have developed a novel fault-detection algorithm for PV systems based on the least significant difference (LSD) test technique.

LSD is a set of individual t-tests that allow the means of two or more pre-determined varieties to be compared. T-tests are used to assess the significance of differences between groups by determining whether there is a significant difference between the means of two groups.

“The proposed fault detection algorithm is based on the idea that a set of quasi-identical PV modules produces statistical equivalent results when faced with the same stimulus such as irradiance and temperature,” the scientists said.

The proposed approach was tested in a simulated array under standard test conditions via the Simulink model. All the modules in the simulated system had a voltage sensor connected in parallel to their output terminals and each string had a current sensor. Two LSD test algorithms were applied to these voltage and current signals.

The algorithm can detect and locate a range of faults. It can also detect when there aren’t any faults affecting the solar array.

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“This is capable to detect and locate different fault types, also it detects when the fault has been solved and it does not produce false detection,” the academics said. “Another positive characteristic of this algorithm is capable to detect five different faults no matter the fault type was.”

The scientists claimed that the test showed the capacity to process current or voltage measurements to distinguish one offset from the averages, in real time.

“This doesn’t mean the algorithm is infallible, special care has to be taken when simultaneous faults of the same type occur because this situation can hide the faults,” the research group said, adding that it will resolve this issue in the next version of the algorithm.

They described the technique in “Photovoltaic Array Fault Detection Algorithm Based on Least Significant Difference Test, published in Applied Computer Sciences in Engineering.

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Source: pv magazine