The very idea of testing for multicollinearity is dubious or even plain wrong, see Dave Giles blog post.
However, you may still be interested in the level of multicollinearity observed in your sample for diagnostic purposes.
Should you include interaction terms in testing for multicollinearity? I would include all regressors regardless of whether they are original variables or were generated as interactions of original variables. I would do that because the effect of multicollinearity will be the same regardless of the interpretation of the particular regressors (variables' names and interpretations do not matter when multiplying or inverting matrices).
Will standardizing the variables help avoid multicollinearity? I doubt that standardizing variables (subtracting estimated mean and dividing by estimated standard deviation) will help. If two variables $y$ and $x$ are perfectly collinear, that means
$$y-\beta_0-\beta_1 x=0$$
If you define standardized variables $\tilde{y}=\frac{y-\bar{y}}{\hat \sigma_y}$ and $\tilde{x}=\frac{x-\bar{x}}{\hat \sigma_x}$, the linear relationship between the standardized variables will still hold:
$$(\hat \sigma_y \tilde y + \bar y)-\beta_0-\beta_1 (\hat \sigma_x \tilde x + \bar x)=0$$
and hence the multicollinearity will still be there. The same logic applies also for imperfect multicollinearity where equality is approximate rather than strict. However, see Remedies for multicollinearity point 6. for a special case when standardizing may help.
