Description:FIELD OF THE INVENTION
This invention relates to Federated Learning-Based Edge Computing Framework for Phasor Measurement Units in Smart Grids
BACKGROUND OF THE INVENTION
Phasor Measurement Units are used for real-time monitoring and controlling of power systems in smart grids. Conventional cloud-based processing methods exhibit high latency, security risk, and bandwidth restrictions. To overcome these challenges edge computing approaches have been used. Federated Learning (FL) uses decentralized learning which enhances preserving data privacy, low latency, improving communication, resource management, and grid resilience.
Existing methods use cloud-based PMU data processing, but this approach faces challenges like high latency, and lack of preserving data.US Patent 10,892,489 B2 – "Edge Computing method for Smart Grid Applications
Present commercial trends emphasize cloud-based data collection, synchrophasor integration of SCADA and PMU, and AI-based predictive analysis; however, they do not incorporate decentralized solutions, privacy preservation, or low latency.
Existing solutions depend on centralized cloud computing, which results in significant latency, bandwidth problems, and potential security liabilities, whereas edge computing does not provide privacy-preserving features. Present Federated Learning (FL) applications enhance cybersecurity and low-latency, secure, and decentralized processing of PMU data within smart grids.
Feature Proposed Solution Previous Solutions
Real-Time PMU Monitoring Real-time data monitoring of PMU, enhancing low latency and data preservation
Majorly concentrates load forecasting and cybersecurity without real-time PMU data processing.
Privacy Preservation Enhances privacy preservation by using decentralized learning and edge nodes.
By using cloud-based aggregation, facing potential security risks during data transmission.
Edge Computing Integration By the integration of Edge computing and Federal learning approaches, low latency and data preservation are obtained. These cloud-based methods attain high latency and insecurity of data preservation.
Scalability and Efficiency Scalable and efficient compared to previous methods. Scalability depends on centralized systems.

SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Federated Learning-Based Edge Computing Framework for phasor measurement units in smart grid enhances high latency, preserving the data, and computational inefficiencies by integration of edge computing and Federated Learning. FL exhibits decentralized model training, where edge devices train machine learning models on local data and only share model updates, preserving data privacy. The Edge node processes the PMU data and reduces latency and securing of data preservation.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Federated Learning-Based Edge Computing Framework for phasor measurement units in smart grid enhances high latency, preserving the data, and computational inefficiencies by integration of edge computing and Federated Learning. FL exhibits decentralized model training, where edge devices train machine learning models on local data and only share model updates, preserving data privacy. The Edge node processes the PMU data and reduces latency and securing of data preservation.
NOVELTY:
The novelty is the hybrid mode of Federated Learning with edge computing for real-time PMU monitoring in smart grids, enhancing the privacy-preserving decentralized learning with low-latency analytics.
 
, Claims:1. A system for processing phasor measurement unit (PMU) data in a smart grid, comprising:
a plurality of edge nodes, a federated learning module and a central aggregation server.
2. The system as claimed in claim 1, wherein the system reduces communication latency, preserves data privacy, and improves real-time analysis of smart grid conditions.
3. The system as claimed in claim 1, wherein the plurality of edge nodes configured to receive and locally process PMU data from one or more sensors.
4. The system as claimed in claim 1, wherein the federated learning module executed on each of the edge nodes, the module configured to train a local machine learning model on the PMU data and generate model updates.
5. The system as claimed in claim 1, wherein the central aggregation server configured to receive model updates from the plurality of edge nodes and aggregate the updates to form a global model, wherein raw PMU data is not transmitted from the edge nodes.