Work

Transparent enhancement of active distribution network through FCA-based blind decomposition

Year: 2024

Type: article

Abstract: Transparency is crucial for decision-making within an active distribution network (ADN). To enhance ADN's transparency, this study develops a novel Blind Decomposition of Composite Events (BDCE) approach rooted in Free Component Analysis (FCA), with a detailed exploration of its related theorems, algorithms, and deductions. Notably, FCA employs non-commutative matrix variables instead of scalar variables, establishing a natural connection to Random Matrix Theory (RMT). By incorporating RMT, FCA-BDCE effectively utilizes spatial-temporal correlation---a matrix-derived spectrum statistic; it allows for the filtration of locally independent noises, such as individual-level measurement error, ubiquitous white noise, while retaining globally influential signals across some specified spatial-temporal span. This capability is particularly valuable when gaining insight into the complex ADN, a landscape with significant diversity and uncertainty. In general, our approach is model-free, theory-guided, and unsupervised, making it particularly suitable for ADN. A comprehensive case study validates the practical effectiveness of our FCA-BDCE approach, demonstrating its superiority over ICA-BDCE.

Source: Applied Energy

DOI: 10.1016/j.apenergy.2024.124776

Authors: Xing He, Zhuangyan Zhang, Qian Ai, Zenan Ling, Yuezhong Tang, Robert Qiu

Institutions: Shanghai Jiao Tong University, Huazhong University of Science and Technology, State Grid Shanghai Municipal Electric Power Company (China)

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,300