Predictive Fault Diagnosis for Ship Photovoltaic Modules Systems Applications

Abstract

In this paper, an application for the management and supervision by predictive fault diagnosis (PFD) of solar power generation systems is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for measuring and supervising the parameters inherent to solar power generation and renewable energy supply are applied. The importance of renewable power generation systems in ships is discussed, as well as the causes of photovoltaic modules (PVMs) aging due to superimposed causes of degradation, which is a natural and inexorable phenomenon that affects photovoltaic installations in a special way. In ships, PVMs are doubly exposed to inclement weather (solar radiation, cold, rain, dust, humidity, snow, wind, electrical storms, etc.), pollution, and a particularly aggressive environment in terms of corrosion. PFD techniques for the real-world installation and safe navigation of PVMs are discussed. A specific method based on the online analysis of the time-series data of random and seasonal I–V parameters is proposed for the comparative trend analyses of solar power generation. The objective is to apply PFD using as predictor symptom parameter (PS) the generated power decrease in affected PVMs. This PFD method allows early fault detection and isolation, whose appearance precedes by an adequate margin of maneuver, from the point of view of maintenance tasks applications. This early detection can stop the cumulative degradation phenomenon that causes the development of the most frequent and dangerous failure modes of solar modules, such as hot-spots. It is concluded that these failure modes can be conveniently diagnosed by performing comparative trend analyses of the measured power parameters by NMEA sensors.

Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators

“…In a previous work [ 11 ], the partial shading fault inductive method was applied in the context of PWM regulators with a main objective of establishing the minimum threshold value of the predictive symptom (MTVPS) that could be used to diagnose faults. That method considered the restriction that the quantitative values of the I-V parameters would only be observable at the PWM output regulator in the battery part.…”

“…The comparative analysis was carried out systematically online for each of the synchronized samples of each of the four intervening series. In order to be able to make the best possible comparison with the analysis carried out in [ 11 ], the same positioning of the solar panels was maintained with respect to the displacement of the sun while trying to find the same shading effects on the panels.…”

“…For this reason, to carry out the comparative analysis, the standard element represented by the maximum value of the quantitative values systematically obtained at each instant of synchronized sampling obtained from each of the intervening series was used. The application of this algorithm in [ 11 ] was based on the hypothesis that the possibility that all the panels were partially shaded at the same time was very unlikely.…”

“…In [ 11 ], a specific method based on the online analysis of the time-series data of random and seasonal I–V parameters was proposed for the comparative trend analyses of solar power generation focused on isolated installations using PWM charge regulators…”

“…Many of the most common faults and the cause of degradation in solar panels are manifested through a reduction in the power generated in the solar panel part. For this reason, when using MTTP regulators, supervision and parametric observation are carried out in the right place, and more precisely because the solar panel is the element susceptible to developing faults due to partial shading [ 11 , 16 ]. On the contrary, PWM regulators could mask and delay the detection of possible symptoms or small incipient faults that are developing in the solar panel part.…”