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Unlocking the Future of Power Quality: A Guide to Multi-Step Predictions

Unlocking the Future of Power Quality: A Guide to Multi-Step Predictions


Power quality is an important aspect of the electrical grid, as it affects the performance and reliability of various devices and equipment. Power quality problems, such as voltage sags, harmonics, flicker, and interruptions, can cause damage, malfunction, or reduced lifespan of sensitive loads. Therefore, it is desirable to monitor and predict the power quality parameters in real time, and take corrective actions if necessary.

One of the challenges in power quality prediction is to select the appropriate input features that can capture the dynamics and patterns of the power quality signals. Using too many features can lead to overfitting and high computational cost, while using too few features can result in poor accuracy and generalization. Moreover, different power quality parameters may have different dependencies on the input features, and these dependencies may change over time.

In this blog post, we propose a novel method for power quality multi-step prediction that can automatically select and update the input features based on their relevance and importance. Our method uses machine learning and regression techniques to learn the nonlinear relationships between the input features and the power quality parameters, and to generate accurate and robust predictions for multiple steps ahead. We also introduce a gradually increasing scheme that adds new input features at each prediction step, based on their correlation with the previous prediction errors. This way, we can enhance the prediction performance by incorporating more information as the prediction horizon increases.

We evaluate our method on a real-world dataset of power quality measurements from a distribution network in Italy. We compare our method with several baseline methods that use fixed or random input features, or that use feature selection techniques based on mutual information or genetic algorithms. We show that our method outperforms the baselines in terms of prediction accuracy, especially for longer prediction horizons. We also demonstrate that our method can adapt to changing conditions and select different input features for different power quality parameters and time periods.

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