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 plant-level reactive support depends on converter limits, operating voltage, and network constraints. This paper proposes a two-part approach: a multi-objective genetic algorithm co-optimizes the placement and MVAr ratings of shunt capacitor and reactor banks in the collector system, and a supervised classifier enables rapid compliance screening without solving a power-flow problem for every new operating point. Candidate designs are 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 how closely the PCC envelope tracks the required curve across voltage and active-power levels, and a knee solution is 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, k-nearest neighbors) are 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 class boundaries. A case study aligned with Turkish grid-code practice shows that minimally compliant device sizes can satisfy the PCC capability curve while keeping losses low, enabling fast pre-selection of feasible operating scenarios and informed siting/sizing of reactive support.