Abnormal Voltage Regulation Detection in on-Grid PV-ESS System by Support Vector Machine with Principal Component Analysis

Conservation voltage reduction (CVR) is a potentially effective and efficient technique for inertia synthesis and frequency support in modern grids comprising power electronics (PE)-based components, aiming to improve dynamic stability. However, due to the complexities of PE-based grids, implementing the CVR methods cannot be performed using traditional techniques as in conventional power systems. Further, quantifying the CVR impacts in modern grids, while focusing on dynamic time scales, is critical, consequently making the traditional methods deficient. This is an important issue as CVR utilization/quantification depends on grid conditions and CVR applications. Considering these concerns, this work offers a thorough analysis of CVR applications, implementation, and quantification strategies, including data-driven AI-based methods in PE-based modern grids. To assess the CVR applications from a new perspective, aiming to choose the proper implementation and quantification techniques, they are divided into categories depending on various time scales. CVR implementation methods are categorized into techniques applied to PE-based grids and islanded microgrids (MGs) where different control systems are adopted. Additionally, to address the evaluation issues in modern grids, CVR quantification techniques, including machine learning- and deep learning-based techniques and online perturbation-based methods are evaluated and divided based on the CVR application. Concerns with the further utilizing and measuring of CVR impacts in modern power systems are discussed in the future trends section, where new research areas are suggested.

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Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI

Mofidul

Alam

Rahman

et al. 2022

Sensors

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The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end.

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Abnormal Voltage Regulation Detection in on-Grid PV-ESS System by Support Vector Machine with Principal Component Analysis

Cited by 3 publications

References 18 publications

Development of Evaluation Index System for Emergency Capability of Power Grid Under the Background of New Power System

Development of Evaluation Index System for Emergency Capability of Power Grid Under the Background of New Power System

Conservation Voltage Reduction in Modern Power Systems: Applications, Implementation, Quantification, and AI-Assisted Techniques

Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI

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