RESEARCH PAPER
A PSO-HMM-Based Intelligent Defect Identification Method for Power Grid Security and Stability Control System
1
Southwest Branch of State Grid Corporation of China, China
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-01-05
Final revision date: 2026-04-24
Acceptance date: 2026-05-09
Online publication date: 2026-07-10
KEYWORDS
Power systemHidden Markov modelSafety and stability control systemIdentification modelDefect feature quantity
TOPICS
ABSTRACT
The security and stability control system (SSCS), as the second line of defense for power grid security, relies primarily on manual judgment for defect identification currently, which suffers from low efficiency and limited accuracy. Traditional methods are restricted by local optimization during training, while the complex inter-device coupling and scarce historical defect data of SSCS further hinder effective defect identification. Therefore, an SSCS defect identification method based on PSO-HMM is proposed. First, SSCS defects are sorted and classified into 5 categories, and 10 key characteristic quantities are mined to characterize SSCS operational states. To overcome the local optimization problem, the PSO algorithm is introduced to optimize the initial observation probability matrix of HMM, and a PSO-HMM-based SSCS defect identification model is established. The model is validated using 2500 defect samples collected from a regional power grid SSCS. Results show that the proposed method enables accurate real-time identification of SSCS defects.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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