research on improved vsg control algorithm based on capacity-limited energy storage system

Clicks: 146
ID: 133076
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #397 of 526 articles by views in acs combinatorial science

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 526 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
A large scale of renewable energy employing grid connected electronic inverters fail to contribute inertia or damping to power systems, and, therefore, may bring negative effects to the stability of power system. As a solution, an advanced Virtual Synchronous Generator (VSG) control technology based on Hamilton approach is introduced in this paper firstly to support the frequency and enhance the suitability and robustness of the system. The charge and discharge process of power storage devices forms the virtual inertia and damping of VSG, and, therefore, limits on storage capacity may change the coefficients of VSG. To provide a method in keeping system output in an acceptable level with the capacity restriction in a transient period, an energy control algorithm is designed for VSG adaptive control. Finally, simulations are conducted in DIgSILENT to demonstrate the correctness of the algorithm. The demonstration shows: (1) the proposed control model aims at better system robustness and stability; and (2) the model performs in the environment closer to practical engineering by fitting the operation state of storage system.
Reference Key
ma2018energiesresearch Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yanfeng Ma;Zijian Lin;Rennan Yu;Shuqiang Zhao
Journal acs combinatorial science
Year 2018
DOI
10.3390/en11030677
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.