Dealing with confounded effects in interactions, in R

Question

I am doing a regression analysis of hunting and wind power turbine data.

My response variable is hunting harvest density, and a set of wind turbine variables: height, number of turbines, distance to turbines and establishment phase (pre-construction, construction and operative.

examplemodel <- lme(harvest ~ WP_distance + WP_height + phase + phase:WP_distance + phase:WP_turbines, weight = ~ I(1/Area), data = mydata, random =~ 1 | randomvar, method="ML")

In pre-construction phase, height, turbines and distance is set as zero (not built yet).

When conducting linear mixed effects, i try to include the turbine variables and interactions phase:distance, phase:height, phase:turbines. However, when doing this i get error:

Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1

I figure it's because the variables phase and the wp-variables get confounded in this way, but how could i possibly deal with it otherwise? I want to display that the height, number of turbines and distance to the wind power parks depend on the construction phase.

Thank you!