Abstract
Reactive power optimization of distribution networks is of great significance to improve power quality and reduce power loss. However, traditional methods for reactive power optimization of distribution networks either consume a lot of calculation time or have limited accuracy. In this paper, a novel data-driven-based approach is proposed to simultaneously improve the accuracy and reduce calculation time for reactive power optimization using ensemble learning. Specifically, k-fold cross-validation is used to train multiple sub-models, which are merged to obtain high-quality optimization results through the proposed ensemble framework. The simulation results show that the proposed approach outperforms popular baselines, such as light gradient boosting machine, convolutional neural network, case-based reasoning, and multi-layer perceptron. Moreover, the calculation time is much lower than the traditional heuristic methods, such as the genetic algorithm.
Highlights
Reactive power optimization is one of the widely used means to reduce power loss and improve power quality by regulating the state of equipment, such as shunt capacitor bank, on-load tap changer (OLTC), and static var compensator (SVC)
Note that the output data of maximum pooling layers is utilized as the input data to the following convolutional layers or dense layers
Different sub-models can be selected to form the proposed ensemble model, and their order should be determined by the loss function of the validation set
Summary
Reactive power optimization is one of the widely used means to reduce power loss and improve power quality by regulating the state of equipment, such as shunt capacitor bank, on-load tap changer (OLTC), and static var compensator (SVC). For model-based algorithms, they mainly include light gradient boosting machine (LightGBM), multi-layer perceptron (MLP), convolutional neural network (CNN), etc These model-based algorithms use models (e.g., deep neural networks) to project the non-linear relationship between power loads (e.g., active power and reactive power) and dispatching strategies, and their accuracy is higher than those of similarity-based algorithms, especially when the power loads change dramatically. These previous publications employ ensemble learning to map the non-linear relationship between the magnitude and phase angle of voltage and power loads They can only be used to obtain the power flow of distribution networks and cannot provide guidance for the operation state of the power equipment to achieve the optimal power flow.
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