Abstract
In this chapter, we introduce various applications for artificial neural networks in the context of power systems. Due to a fast pace of development in recent years, multiple libraries for setting up and training artificial neural networks are available as open-source software. In the field of power system analysis, the open-source software pandapower enables broad-scale automation of power flow calculations. Based on these developments, we present multiple applications for grid planners and grid operators that are based on supervised learning. The first application is the approximation of power flows, including line contingencies, in annual time series simulations. It enables grid planners to detect violations of operational constraints quickly. Secondly, a monitoring method trained on a yearly time series uses a low number of measurements to deliver real-time insights into the grid’s state to grid operators. Similarly, grid operators can use artificial neural networks for building grid equivalents that provide information about external grids under dynamic conditions. Lastly, artificial neural networks have proven well-suited to determine grid loss as a function of topological features like line length, distributed generation, etc.
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Menke, JH., Dipp, M., Liu, Z., Ma, C., Schäfer, F., Braun, M. (2020). Applications of Artificial Neural Networks in the Context of Power Systems. In: Sayed-Mouchaweh, M. (eds) Artificial Intelligence Techniques for a Scalable Energy Transition. Springer, Cham. https://doi.org/10.1007/978-3-030-42726-9_13
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