Photovoltaic (PV) technology allows large scale investments in a renewable power-generating system at
competitive levelized cost of energy (LCOE) and low environmental impact. Large scale PV installations operate in a highly competitive market environment where even small performance losses have 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 failure detection methodologies. Performance losses caused by instant failures or gradual degradations must be prevented by identifying the causes of failures in a quick and reliable manner. The identification of failures and root causes requires an integrated concept for plant monitoring and failure detection, which was realised
as part of the Austrian OptPV4.0 research project. In this paper we present an integrated approach on model-based fault detection, diagnosis and prognosis for optimized maintenance activities 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 strings were detected reliably and possible root causes were identified. Overall, the integrated approach shall contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and shall be applied within the OptPV4.0 research project for monitoring photovoltaic plants.