A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Abstract

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

Reliability-Centered Maintenance Scheduling of Photovoltaic Components According to Failure Effects

“…Additionally, it is essential to the predictive maintenance model to consider the failure probabilities of PV system elements. Gradwohl et al [13] presented a method that combines physical and statistical models for optimal PV plant maintenance. Its prediction of failure through diagnosis has high accuracy, but diagnosis is not a suitable practice for small-scale PV systems.…”