A causality based feature selection approach for data-driven dynamic security assessment

Bellizio, Federica; Cremer, Jochen; Sun, Mingyang; Štrbac, Goran

doi:10.1016/j.epsr.2021.107537

Cited by 16 publications

(12 citation statements)

References 23 publications

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“…In this work, we use the Qhull algorithm [56] to compute the  metric that measures the convex hull volume occupied by the generated samples 𝛺 𝑆 . Subsequently, the volume of the convex hull with vertices 𝑥 (1) , 𝑥 (2) , … , 𝑥 (𝑛) is (2) − 𝑥 (1) 𝑥 (3) − 𝑥 (1) ⋯ 𝑥 (𝑛) − 𝑥 (1) ) | (18) which further resolves to…”

Section: Measuring Performance Of Samplersmentioning

confidence: 99%

“…In such scenarios, ML is particularly promising as it allows for predictions in real-time [9] have shown promise to assess dynamic stability problems ranging from voltage stability [7,10], transient stability [6,9] and frequency stability [11,12]. Recent works [13][14][15][16] show real promise for real-time probabilistic DSA including considering topological changes [17,18]. There, using ML, estimating the dynamic security boundary [19] Voronoi tessellation formed by sample 𝑥 𝑖 𝑥 * an optimised operating condition 𝑦 vertex of Voronoi cell inertia grids [20] and may become possible through the increasing availability of larger amount of (PMU-) data and monitoring tools in control centers [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Split-Based Sequential Sampling for Realtime Security Assessment

Bugaje¹,

Cremer²,

Štrbac³

2022

SSRN Journal

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Section: Measuring Performance Of Samplersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Split-Based Sequential Sampling for Realtime Security Assessment

Bugaje¹,

Cremer²,

Štrbac³

2022

SSRN Journal

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“…In the domain of dataset building, several open challenges facing the research community can be identified: (1) open sourcing datasets from power system WAMS measurements, (2) addressing potential security concerns associated with WAMS data [11], (3) open sourcing existing benchmark test cases, (4) providing a unified and consistent set of benchmark test cases featuring power systems of varying sizes and levels of RES penetration, (5) providing benchmark test cases featuring hybrid AC/DC power grids, and (6) providing standardized simulated environments of power systems for the reinforcement learning. These points deal with standardization of benchmark test cases and bringing them closer to the expected future levels of RES penetration, as well as introducing the hybrid AC/DC power grids [82,83].…”

Section: Challenges and Future Research Opportunitiesmentioning

confidence: 99%

“…This shift of balance between RESs and conventional power plants exposes a major downside of the renewables today, which is a reduced system inertia (when less generators with rotating mass are in operation). This reduction of the available rotating mass will have important ramifications on the future security and stability of power system operation [1][2][3]. With the increased proportion of RESs in the generation mix, the problem of reduced system inertia will only increase.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques for Power System Transient Stability Assessment

et al. 2022

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The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.

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“…For example, Chen in [25] applied an indirect principal component analysis (PCA) as a dimensionality reduction technique for a power system transient stability assessment. Bellizio et al proposed a causality-based feature selection [26]. Gnanavignesh and Shenoy introduced a relaxation-based spatial-domain decomposition method [27].…”

Section: Introductionmentioning

confidence: 99%

Manifold Learning in Electric Power System Transient Stability Analysis

Sarajcev,

Lovric

2023

Energies

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This paper examines the use of manifold learning in the context of electric power system transient stability analysis. Since wide-area monitoring systems (WAMSs) introduced a big data paradigm into the power system operation, manifold learning can be seen as a means of condensing these high-dimensional data into an appropriate low-dimensional representation (i.e., embedding) which preserves as much information as possible. In this paper, we consider several embedding methods (principal component analysis (PCA) and its variants, singular value decomposition, isomap and spectral embedding, locally linear embedding (LLE) and its variants, multidimensional scaling (MDS), and others) and apply them to the dataset derived from the IEEE New England 39-bus power system transient simulations. We found that PCA with a radial basis function kernel is well suited to this type of power system data (where features are instances of three-phase phasor values). We also found that the LLE (including its variants) did not produce a good embedding with this particular kind of data. Furthermore, we found that a support vector machine, trained on top of the embedding produced by several different methods was able to detect power system disturbances from WAMS data.

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A causality based feature selection approach for data-driven dynamic security assessment

Cited by 16 publications

References 23 publications

Split-Based Sequential Sampling for Realtime Security Assessment

Split-Based Sequential Sampling for Realtime Security Assessment

Artificial Intelligence Techniques for Power System Transient Stability Assessment

Manifold Learning in Electric Power System Transient Stability Analysis

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