A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning

Hudson, Nathaniel; Hossain, Jakir; Hosseinzadeh, Minoo; Khamfroush, Hana; Rahnamay-Naeini, Mahshid; Ghani, Nasir

doi:10.1109/icccn52240.2021.9522360

Cited by 24 publications

(23 citation statements)

References 41 publications

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“…Studies have shown that lower dimensionality in the distributed estimation process results in a low communication cost [43]. In [9,44], it is also discussed that distributed state estimation enabled by new technologies, such as edge computing, can reduce the computational cost to lower than centralized processing of data. Moreover, it can be observed that non-linear KF models, particularly UKF, perform better estimation by better enabling the capture of non-linear dynamics of the system.…”

Section: Resultsmentioning

confidence: 99%

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Hossain

Naeini

2022

Energies

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Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.

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Section: Resultsmentioning

confidence: 99%

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Hossain

Naeini

2022

Energies

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“…To mitigate privacy leakage caused by direct data sharing, FL has been applied to many applications in the smart grid [15][16][17]. Hudson et al [18] proposed a deep recurrent network for nonintrusive load monitoring via FL. Wang et al [19] proposed a horizontal FL approach to study electricity consumer characteristic identifcation, where privacy-preserving PCA is used to reduce the dimension.…”

Section: Federated Learningmentioning

confidence: 99%

Full Data-Processing Power Load Forecasting Based on Vertical Federated Learning

Mao

Huang

et al. 2023

Journal of Electrical and Computer Engineering

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Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations’ data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks.

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“…Typically, the efficiency of DPFL in alleviating the privacy risks in energy‐efficient applications has been studied by developing an NILM platform, which streamlines models' training in a privacy‐preserving way. While in Reference [135] FL has been deployed to promote privacy preservation in edge‐computing‐based smart distribution grids. Following, its efficiency has been demonstrated by approaching an NILM problem, where it was used to train a deep RNN architecture in 2‐tier and 3‐tier manners.…”

Section: Overview Of Recent Smart Nilm Algorithmsmentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning

Cited by 24 publications

References 41 publications

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Full Data-Processing Power Load Forecasting Based on Vertical Federated Learning

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

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