Islanded Microgrid with Decentralized Control for Optimal Generation Dispatch

Khan, M. Reyasudin Basir; Pasupuleti, Jagadeesh; Jidin, Razali

doi:10.1109/scored50371.2020.9251013

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, an effective solution for managing an ecologically friendly renewable energy resource is highly required. Although various decentralized control and operation methods are currently under extensive research [3]- [4], system operators must heavily rely on centralized control, such as the energy management system (EMS). Owing to the supervisory control and data acquisition (SCADA) capability, it continuously monitors and regulates the operational status of generators, substations, and transmission lines, making it an integral component of our energy infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Steady-State Data-Driven Dynamic Stability Assessment in the Energy Management System

Song,

Min,

Jung

2024

Preprint

View full text Add to dashboard Cite

The extensive research on dynamic security assessment stability prediction has focused on data preprocessing techniques to improve accuracy because it was assumed that high-resolution postfault data exist. For practical users, the acquisition and application of high-resolution measurement data present significant challenges. Installing phasor measurement units on all power system nodes is deemed impractical due to high costs. In this work, we aimed to develop a rotor angle stability prediction model using steady-state data that can be easily generated from current energy management system. Note that the steady-state measurement data refer to a pre-contingency operation condition characterized by real and reactive loads, generation levels, flows, as well as voltages and angles. The proposed framework comprises three stages: it finds physical meaning from the extended equal-area criterion to move away from the black-box approach, proposes a feature data extraction strategy to reduce the dimensionality of the input space in the support vector machine, and partition time-series power flow data by month to consider system topology changes. By utilizing 5-min-interval power flow data, unstable cases are determined, and two main feature data are extracted to train the support vector machine. The proposed model can be implemented in the energy management system for industrial use, and the obtained results showed the effectiveness of the proposed framework in responding to a critical line fault event in real time

show abstract

Section: Introductionmentioning

confidence: 99%