Quantum mechanics

Likelihood-ratio test

Algorithm

Gaussian

Engineering

Fault detection and isolation

Extreme learning machine

Statistics

Actuator

Computation

Computer science

Fault (geology)

Constant false alarm rate

Seismology

Geology

Physics

Pattern recognition (psychology)

Mathematics

Statistical hypothesis testing

Gaussian process

Kriging

Artificial intelligence

False alarm

Artificial neural network

Statistical model

Photovoltaic system

Machine learning

Electrical engineering

Solar Energy

In this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage, and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to evaluate the fault detection performance of the proposed approach.

Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems