Siemens has filed a patent for a protection device that monitors energy supply grids using a single neural network to carry out multiple protection functions. The device acquires measured values to determine the grid’s operating state, providing a classification of permissible or impermissible states. GlobalData’s report on Siemens gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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According to GlobalData’s company profile on Siemens, Smart factory applications was a key innovation area identified from patents. Siemens's grant share as of January 2024 was 56%. Grant share is based on the ratio of number of grants to total number of patents.

Protection device for monitoring energy supply grid using neural network

Source: United States Patent and Trademark Office (USPTO). Credit: Siemens AG

The patent application (Publication Number: US20240039269A1) describes a protection device designed to monitor an electrical energy supply grid. The device includes a measured value acquisition device to record electrical states at measuring points and an evaluation device formed as a single neural network. This neural network is trained to perform various protection functions based on the measured values and determine if the grid is in a permissible or impermissible state. The device can classify events, detect fault types, and determine fault locations, with the ability to operate locally or centrally within the grid.

Furthermore, the protection device can be trained using different learning methods, such as deep learning and reinforcement learning, to enhance its classification capabilities. It can be specifically trained for different installation locations and is equipped with a communication interface for calibration parameters. The method outlined in the patent involves acquiring measured values, transmitting them to the protection device, and using the neural network to classify the grid's operating states. The device can be trained to carry out all protection functions together and can operate in a two-stage learning method for local monitoring or a one-stage learning method for centralized monitoring of the energy supply grid.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.