Machine Learning Approaches in a Real Power System and Power Markets

Razmi, Peyman; Asl, Mahdi Ghaemi

doi:10.1007/978-3-030-77696-1_17

Cited by 1 publication

(1 citation statement)

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“…On the other hand, monitoring techniques and associated software have been evolving. The advancement and widespread adoption of artificial intelligence (AI) and machine learning (ML) techniques have brought about significant changes in power system monitoring techniques [7,8]. The common thread among all these developments is instrumentation.…”

Section: Introductionmentioning

confidence: 99%

Simplifying Rogowski Coil Modeling: Simulation and Experimental Verification

Mingotti,

Betti,

Tinarelli

et al. 2023

Sensors

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The integration of renewable energy sources, electric vehicles, and other electrical assets has introduced complexities in monitoring and controlling power networks. Consequently, numerous grid nodes have been equipped with sensors and complex measurement systems to enhance network observability. Additionally, real-time power network simulators have become crucial tools for predicting and estimating the behavior of electrical quantities at different network components, such as nodes, branches, and assets. In this paper, a new user-friendly model for Rogowski coils is presented and validated. The model’s simplicity stems from utilizing information solely from the Rogowski coil datasheet. By establishing the input/output relationship, the output of the Rogowski coil is obtained. The effectiveness and accuracy of the proposed model are tested using both simulations and commercially available Rogowski coils. The results confirm that the model is simple, accurate, and easily implementable in various simulation environments for a wide range of applications and purposes.

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Section: Introductionmentioning

confidence: 99%