A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912554676125696 |
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| author | Varmazyari, Hamid Nazari, Masoud H. |
| author_facet | Varmazyari, Hamid Nazari, Masoud H. |
| contents | This paper presents a machine-learning based Stochastic Hybrid System (SHS) modeling framework to detect contingencies in active distribution networks populated with inverter-based resources (IBRs). In particular, this framework allows detecting unobservable contingencies, which cannot be identified by normal sensing systems. First, a state-space SHS model combining conventional and IRB-based resources is introduced to formulate the dynamic interaction between continuous states of distribution networks and discrete contingency events. This model forms a randomly switching system, where parameters or network topology can change due to contingencies. We consider two contingency classes: (i) physical events, such as line outages, and (ii) measurement anomalies caused by sensor faults. Leveraging multivariate time series data derived from high-frequency sampling of system states and network outputs, a time series-based learning model is trained for real-time contingency detection and classification. Simulation studies, carried out on the IEEE 33-bus distribution system, demonstrate a 96% overall detection accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18500 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources Varmazyari, Hamid Nazari, Masoud H. Systems and Control This paper presents a machine-learning based Stochastic Hybrid System (SHS) modeling framework to detect contingencies in active distribution networks populated with inverter-based resources (IBRs). In particular, this framework allows detecting unobservable contingencies, which cannot be identified by normal sensing systems. First, a state-space SHS model combining conventional and IRB-based resources is introduced to formulate the dynamic interaction between continuous states of distribution networks and discrete contingency events. This model forms a randomly switching system, where parameters or network topology can change due to contingencies. We consider two contingency classes: (i) physical events, such as line outages, and (ii) measurement anomalies caused by sensor faults. Leveraging multivariate time series data derived from high-frequency sampling of system states and network outputs, a time series-based learning model is trained for real-time contingency detection and classification. Simulation studies, carried out on the IEEE 33-bus distribution system, demonstrate a 96% overall detection accuracy. |
| title | A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.18500 |