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To model reactive power limited generators within power flow problems, PV-PQ switching fixes generator voltages when reactive power outputs are within limits but allows the voltages to vary with a constant reactive power injection when limits are reached. Power flow algorithms often use heuristics that iteratively modify the generators’ PV versus PQ representation as the algorithm executes. The convergence behavior and speed of power flow algorithms with these heuristics significantly depend on their initialization. To improve computational performance, we propose an approach for using neural networks to initialize PV-PQ switching heuristics. After offline training where the neural networks learn the power flow solution’s PV vs. PQ generator statuses across varying load demands, the neural networks are deployed to initialize power flow algorithms in online applications. Numerical results demonstrate the effectiveness of this approach with speedup factors of 1.55× to 4.32× over the nominal generator PV-PQ status initialization.