Machine-Learning Classification of Reactive Power Capability Compliance in Wind Power Plants

Modern grid codes require wind power plants to provide sufficient reactive power capability at the point of common coupling (PCC) to maintain voltage security. Meeting these provisions is challenging because the plant-level reactive support depends on converter limits, operating voltage, and network constraints. This study proposes a two-part approach: a multi-objective genetic algorithm co-optimizes the placement and MVAr ratings of shunt capacitors and reactor banks in the collector system, and a supervised classifier enables rapid compliance screening without solving the power-flow problem for every new operating point. Candidate designs were assessed by AC power-flow analysis under turbine voltage-dependent P–Q limits, bus-voltage bounds, and branch-loading limits. A reactive power deviation metric quantifies the closeness of the PCC envelope to the required curve across the voltage and active power levels, and a knee solution was selected to balance low losses with near-boundary compliance. Using labels derived from these evaluations—infeasible, feasible with increased losses, and feasible with reduced losses—classifiers (artificial neural network, support vector machine, decision tree, bagged trees and k-nearest neighbors) were trained on only the operating condition and compact device descriptors. The classifier provides near-instant compliance screening without per-case power flow and achieves 98.7% accuracy on a stratified holdout with residual errors concentrated near the class boundaries. A case study aligned with the Turkish grid-code practice shows that minimally compliant device sizes can satisfy the PCC capability curve while maintaining low losses, enabling fast preselection of feasible operating scenarios and informed siting/sizing of reactive support.