6 papers
Health-Focused Optimal Power Flow
Logesh Kumar, Parikshit Pareek, Sivakumar Nadarajan +3
In this paper, we propose a novel Health-Focused Optimal Power Flow (HF-OPF) to take into account the equipment health in operational and physical constraints. The health condition…
Gaussian Process Learning-based Probabilistic Optimal Power Flow
Parikshit Pareek, Hung D. Nguyen
In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and load uncertainties of arbitrary d…
Non-parametric Probabilistic Load Flow using Gaussian Process Learning
Parikshit Pareek, Chuan Wang, Hung D. Nguyen
In this work, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on the Gaussian Process (GP) learning to understand the power system behavior under uncer…
Probabilistic Robust Small-Signal Stability Framework using Gaussian Process Learning
Parikshit Pareek, Hung D. Nguyen
While most power system small-signal stability assessments rely on the reduced Jacobian, which depends non-linearly on the states, uncertain operating points introduce nontrivial h…
A Sufficient Condition for Small-Signal Stability and Construction of Robust Stability Region
Parikshit Pareek, Konstantin Turitsyn, Krishnamurthy Dvijotham +1
The small-signal stability is an integral part of the power system security analysis. The introduction of renewable source related uncertainties is making the stability assessment…
Computationally Efficient Day-Ahead OPF using Post-Optimal Analysis with Renewable and Load Uncertainties
Parikshit Pareek, Ashu Verma
This paper presents a method to handle renewable source and load uncertainties in Dynamic Day-ahead Optimal Power Flow (DA-OPF) using post-optimal analysis of linear programming pr…