Both principal curve analysis and kernel PCA provide the ability to find nonlinear PCA. Kernel PCA does this by finding principal components in a higher dimensional space. Principal curve analysis is done by finding a curve such that the distance between the points and the curve is minimized. Since the underlying intuition of two methods is different, I think they will give different results.
In what situations should one prefer one technique over another?