Pre­dic­ti­ve Main­ten­an­ce for Wind Tur­bi­nes: Ener­gy Data Space White­pa­per

In off­shore wind tur­bi­nes, hund­reds of sen­sors con­ti­nuous­ly pro­vi­de data on envi­ron­men­tal con­di­ti­ons and func­tion. And lar­ge amounts of data are gene­ra­ted during plan­ning, pro­duc­tion, instal­la­ti­on and main­ten­an­ce, as well as during the inte­gra­ti­on of wind power into the power grid.

Nevertheless, the ope­ra­ti­on of wind farms is expen­si­ve, unplan­ned break­downs and repairs must be avoided. Pre­dic­ti­ve main­ten­an­ce should use the data from the plant and its envi­ron­men­tal con­di­ti­ons to detect ano­ma­lies in ope­ra­ti­on and impen­ding faults. This redu­ces the risk of down­ti­me, makes ope­ra­ti­ons more effi­ci­ent and incre­a­ses the ser­vice life.

To make this a rea­li­ty, a trust­worthy data eco­sys­tem is nee­ded that inclu­des con­sis­tent agree­ments on data design, data pro­vi­si­on and data use as well as a com­mon under­stan­ding of data con­trol and data sov­er­eig­n­ty.

All this is pro­vi­ded by the Ener­gy Data Space. This data space, desi­gned by Fraun­ho­fer IML and Fraun­ho­fer IEE, is dedi­ca­ted to the pre­dic­ti­ve main­ten­an­ce of wind tur­bi­nes. It builds on the refe­rence archi­tec­tu­re of the Inter­na­tio­nal Data Spaces. The IDS refe­rence archi­tec­tu­re pro­vi­des the stan­dard for com­mon data agree­ments and data sov­er­eig­n­ty in a com­plex data eco­sys­tem.

The white paper writ­ten in Ger­man gives an indepth intro­duc­tion of the Ener­gy Data Spaces. For the white paper.