arXiv subjects

Ghavameddin Nourbakhsh

Publications and source records attributed to Ghavameddin Nourbakhsh.

5 recordsLinked to original sources

arXiv2025

Grid impedance estimation based Kalman Filter

Modern power systems face new operational hurdles due to the increasing adoption of inverter-coupled distributed energy resources, which impact system stability and control. Central to these challenges is the dynamic nature of grid impedance. To address this, a novel real-time estimation algorithm based on the Discrete Fourier Transform is proposed. This algorithm is embedded within an Advanced Angle Estimation Kalman Filter framework that employs a Linear Quadratic Regulator for current control (AAEKF-LQR). The impedance data directly informs and refines the controller's phase angle estimation. Simulation analyses demonstrate robust collaboration between the estimator and controller, sustaining system stability under weak grid conditions. The technique proves capable of delivering swift and accurate impedance updates during grid variations, which is crucial for maintaining stable inverter operation

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arXiv2025

Symbolic Equation Modeling of Composite Loads: A Kolmogorov-Arnold Network based Learning Approach

With increasing penetration of distributed energy resources installed behind the meter, there is a growing need for adequate modelling of composite loads to enable accurate power system simulation analysis. Existing measurement based load modeling methods either fit fixed-structure physical models, which limits adaptability to evolving load mixes, or employ flexible machine learning methods which are however black boxes and offer limited interpretability. This paper presents a new learning based load modelling method based on Kolmogorov Arnold Networks towards modelling flexibility and interpretability. By actively learning activation functions on edges, KANs automatically derive free form symbolic equations that capture nonlinear relationships among measured variables without prior assumptions about load structure. Case studies demonstrate that the proposed approach outperforms other methods in both accuracy and generalization ability, while uniquely representing composite loads into transparent, interpretable mathematical equations.

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arXiv2025

Synchronising DER inverters to weak grid using Kalman filter and LQR current controller

Grid-following (GFL) inverters are commonly used for integrating renewable energy sources into power grids. However, the dynamic performance of GFL models can be significantly impacted by the Phase-Locked Loop (PLL) in a weak grid, leading to instability due to inaccuracies in grid source phase angle estimation. The proposed method in this manuscript replaces the PLL with an Advanced Angle Estimation based Kalman Filter including a Linear Quadratic Regulator (LQR) controller of the GFL. This method is robust in incorporating grid impedance terms as part of state space models in the Kalman Filter approach to estimate instantaneous phase angle using α-\b{eta} Synchronous Reference Frame equations. The stability performance of the proposed approach is validated through eigenvalue analysis in a two-source case. Additionally, an LQR controller is employed to regulate capacitor voltage, inverter current, and the current at the Point of Common Coupling (PCC). The proposed controller surpasses existing approaches in terms of accuracy and distortion reduction under abrupt grid impedance increases. Moreover, drop compensation is integrated into the Kalman Filter to enhance robustness of the inverter against external oscillation disturbances from a synchronous machine connected to the GFL via the PCC. The results in this paper demonstrate substantial improvement in oscillation damping across a range of frequencies compared with published research works.

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arXiv2018

Single Iteration Conditional Based DSE Considering Spatial and Temporal Correlation

The increasing complexity of distribution network calls for advancement in distribution system state estimation (DSSE) to monitor the operating conditions more accurately. Sufficient number of measurements is imperative for a reliable and accurate state estimation. The limitation on the measurement devices is generally tackled with using the so-called pseudo measured data. However, the errors in pseudo data by cur-rent techniques are quite high leading to a poor DSSE. As customer loads in distribution networks show high cross-correlation in various locations and over successive time steps, it is plausible that deploying the spatial-temporal dependencies can improve the pseudo data accuracy and estimation. Although, the role of spatial dependency in DSSE has been addressed in the literature, one can hardly find an efficient DSSE framework capable of incorporating temporal dependencies present in customer loads. Consequently, to obtain a more efficient and accurate state estimation, we propose a new non-iterative DSSE framework to involve spatial-temporal dependencies together. The spatial-temporal dependencies are modeled by conditional multivariate complex Gaussian distributions and are studied for both static and real-time state estimations, where information at preceding time steps are employed to increase the accuracy of DSSE. The efficiency of the proposed approach is verified based on quality and accuracy indices, standard deviation and computational time. Two balanced medium voltage (MV) and one unbalanced low voltage (LV) distribution case studies are used for evaluations.

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arXiv2018

Layered Based Augmented Complex Kalman Filter for Fast Forecasting-Aided State Estimation of Distribution Networks

In the presence of renewable resources, distribution networks have become extremely complex to monitor, operate and control. Furthermore, for the real time applications, active distribution networks require fast real time distribution state estimation (DSE). Forecasting aided state estimator (FASE), deploys measured data in consecutive time samples to refine the state estimate. Although most of the DSE algorithms deal with real and imaginary parts of distribution networks states independently, we propose a non iterative complex DSE algorithm based on augmented complex Kalman filter (ACKF) which considers the states as complex values. In case of real time DSE and in presence of a large number of customer loads in the system, employing DSEs in one single estimation layer is not computationally efficient. Consequently, our proposed method performs in several estimation layers hierarchically as a Multi layer DSE using ACKF (DSEMACKF). In the proposed method, a distribution network can be divided into one main area and several subareas. The aggregated loads in each subarea act like a big customer load in the main area. Load aggregation results in a lower variability and higher cross correlation. This increases the accuracy of the estimated states. Additionally, the proposed method is formulated to include unbalanced loads in low voltage (LV) distribution network.

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