# Risk Assessment Method for CPS-Based Distributed Generation Cluster Control in Active Distribution Networks Under Cyber Attacks

**Authors:** Jinxin Ouyang, Fan Mo, Fei Huang, Yujie Chen

PMC · DOI: 10.3390/s25196053 · 2025-10-01

## TL;DR

This paper introduces a new method to assess cyber risks in wind and solar power systems under cyber attacks, improving grid stability.

## Contribution

A novel cyber risk assessment method for DG cluster control considering cyber-physical interactions and attack strategies.

## Key findings

- A probabilistic failure model for DG cluster control was developed based on control topology and master-slave logic.
- A physical consequence quantification method was introduced using power deficits at cluster PCC and internal DG network results.
- Simulation results validated the effectiveness of the proposed cyber risk assessment method.

## Abstract

In modern power systems, distributed generation (DG) clusters such as wind and solar resources are increasingly being integrated into active distribution networks through DG cluster control, which enhances the economic efficiency and adaptability of the DGs. However, cyber attacks on cyber–physical systems (CPS) may disable control links within the DG cluster, leading to the loss of control over slave DGs and resulting in power deficits, thereby threatening system stability. Existing CPS security assessment methods have limited capacity to capture cross-domain propagation effects caused by cyber attacks and lack a comprehensive evaluation framework from the attacker’s perspective. This paper establishes a CPS system model and control–communication framework and then analyzes the cyber–physical interaction characteristics under DG cluster control. A logical model of cyber attack strategies targeting DG cluster inverters is proposed. Based on the control topology and master–slave logic, a probabilistic failure model for DG cluster control is developed. By considering power deficits at cluster point of common coupling (PCC) and results in internal network of the DG cluster, a physical consequence quantification method is introduced. Finally, a cyber risk assessment method is proposed for DG cluster control under cyber attacks. Simulation results validate the effectiveness of the proposed method.

## Full-text entities

- **Genes:** DSG3 (desmoglein 3) [NCBI Gene 1830] {aka ABOLM, CDHF6, PVA}, DSC2 (desmocollin 2) [NCBI Gene 1824] {aka ARVD11, CDHF2, DG2, DGII/III, DSC3}, DSG1 (desmoglein 1) [NCBI Gene 1828] {aka CDHF4, DG1, DSG, EPKHE, EPKHIA, PPKS1}
- **Diseases:** DG (MESH:D020243), IEDs (MESH:D009471), paralysis (MESH:D010243), MMS (MESH:D000080888), power deficit (MESH:D009461), injury to (MESH:D014947)
- **Chemicals:** CPS (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12526811/full.md

---
Source: https://tomesphere.com/paper/PMC12526811