The recent trend of encouragement to renewable integration has resulted in an inverter-dominated hybrid grid. The time-scale of such integrated grid activities and its response time varies in a wide range. In such a scenario, the first challenge is to have a model that can capture both the dynamics of fast as well as slow components, and the second is stability analysis without compromising its time-varying impact. To address these issues, the paper includes the Port-Controlled Hamiltonian (PCH) Inverter formulation related to a interdisciplinary model for voltage stability analysis of inverter-dominated distribution grids. The interactions between slow network, load dynamics, intermediate converter-control dynamics, and fast electromagnetic states can be captured in the model. The PCH formulation provides an energy-based representation of the inverter dynamics, enabling systematic analysis of transient voltage response and post-disturbance recovery in inverter-integrated power systems. MATLAB is used to validate the proposed methodology in weak-grid and load-varying scenarios. A new PCH-based controller has been designed to tune the inverter PWM signal so that voltage stability can be supported to maintain the grid voltage profile.
Madhumita Patil, Aakanksha Mane, Rayyan Petkar et al.· International Conference on...· 0 citations
Nonlinear electrical and electromechanical systems pose significant challenges for observer-based control design. Conventional observer approaches require accurate mathematical models, which often fail under physical irregularities such as sensor noise, measurement delays, and parameter variations. These changes decrease estimation accuracy and degrade control action. This paper proposes a Hybrid ARX– Observer framework to overcome these limitations. It combines the stability of a traditional model-based observer with the flexibility of a data-driven ARX estimator. To guarantee input-to-state stability (ISS), observer gains are computed using a matrix-multiplier technique based on linear matrix inequalities (LMI). The ARX component employs Recursive Least Squares (RLS) adaptation to attenuate measurement noise and capture residual nonlinearities. Both estimates are then fused together using a fusion gain α. This framework is tested on a robotic arm and the results show that the hybrid framework improved the robustness and reduced estimation error up to 4% compared to the conventional observer based estimation, while remaining computationally light compared to fully data-driven alternatives.
Tanmay Wankhade, Saish Pakhare, Aakanksha Mane et al.· International Conference on...· 0 citations
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