Details

Autor(en) / Beteiligte

• Liang, Yingqi
• Zhao, Junbo
• Sampath Kumar, Dhivya
• Srinivasan, Dipti

Titel

Real-time and consistent sparse estimation of power system distribution factors using online adaptive elastic-net

Ist Teil von

• International journal of electrical power & energy systems, 2022-11, Vol.142, p.108361, Article 108361

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •A novel Online Adaptive Elastic-net method is proposed for real-time and consistent sparse estimation of distribution factors in large-scale power systems.•It promotes the data-driven, sparsity-aware, and computationally efficient sensitivity analysis of active power line flows.•The proposed method can broadly facilitate real-time situational awareness and decision-making for improving power system security. Distribution factors (DFs) are vital for power system monitoring and secure operation. However, it is challenging to accurately estimate dominant DFs while promoting the sparsity of DFs in real time, especially in large-scale power systems with high-dimensional data. This paper proposes a novel Online Adaptive Elastic-net (AdaEnet) method to address this challenge, which allows the real-time and consistent sparse estimation of DFs in large-scale power systems. The problem is formulated as a sparse regression problem using data measured by phasor measurement units. The AdaEnet estimator is advocated to promote the sparsity of DFs, and a LARS-AdaEnet algorithm is developed for the real-time estimation of DFs. Thanks to the oracle properties and the grouping effect of the AdaEnet, the method yields unbiased dominant estimates with stability to collinear predictor variables, even for underdetermined systems. Moreover, due to the online updated adaptive weights and the early-stopping property of the LARS-AdaEnet, the method efficiently solves the estimator using only a few latest samples. Test results on the IEEE 300- and the Polish 3375-bus systems demonstrate that the proposed method achieves high estimation consistency and real-time adaptability in the presence of high-dimensional data (i.e., massive collinear predictor variables of small sample size). Hence this method promotes the data-driven, sparsity-aware, and computationally efficient sensitivity analysis of active power line flows in large-scale power systems. It can broadly facilitate real-time situational awareness and decision-making for improving power system security.