English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper proposes a probabilistic transient stability-constrained preventive dispatching method for power systems under a high inclusion of wind power. First, a set of instability mode (IM)-categorized probabilistic transient stability constraints (PTSCs) are constructed, which facilitate the development of a dispatching plan against various fault scenarios. Next, to avoid massive transient stability simulations in each dispatching operation, a machine learning-based model is trained to predict the critical clearing time (CCT) and IM for all preconceived fault scenarios. Based on the predictions, the system operation plan is assessed with respect to the PTSCs. Then, the sensitivity of the probabilistic level of the CCT is calculated to the active power generated from the critical generators for each IM category. Accordingly, the implicit PTSCs are converted into explicit dispatching constraints, and the dispatch is rescheduled to ensure the probabilistic stability requirements of the system are met at an economical operating cost. The proposed approach is validated on two modified IEEE test systems, reporting high computational efficiency and high-quality solutions.

A Preventive Dispatching Method for High Wind Power-Integrated Electrical Systems Considering Probabilistic Transient Stability Constraints