Change the maintenance approach and get the best value from solar assets data
O&M activities are undertaken by the O&M contractor, which generally shares its tasks with the technical Asset Manager. The plant components maintenance is typically the one with greater costs. Due to the age of current solar plants (the first installed around 10-15 years ago), arise the need of a proper maintenance, to not reduce the plant performances. Several kinds of maintenance strategies can be followed. Preventive maintenance, that includes regular inspection on the plant. Reactive strategy, undertaking a corrective action only when a failure occurs (usually more expensive). Unlike preventive and reactive strategies, the predictive maintenance approach optimizes simultaneously the downtime periods, the lost production, and the total cost of maintenance activities.
i-EM combines the experience in the PV technical domain with data science expertise to provide an accurate predictive model
- rTo gather a huge amount of data related to both electrical and evironmental signal.
- pTo have a speedy and reliable IT infrastructure managing field data collection.
- rTo face data quality issues and validation data lack (no maintenance logbook available).
- rTo have data-drive approach in O&M activitives.
Different approaches to predict component deviations from nominal behavior and specific fault class with different time horizon
In the Predictive Maintenance, Machine Learning is a winning approach only if there is a balance between the various skills: the experience of i-EM data analysis and that of industry experts (e.g., O&M operators), to extract value and knowledge from information.
- NReduction of plant downtime
- NOptimization of energy production
- NOptimization of assets financial
- NReduction of O&M costs