23 papers · ranked by Valyu relevance
Mirsad Ćosović, Dejan Vukobratović
—In this paper, we propose a solution to an AC state estimation problem in electric power systems using a fully distributed Gauss-Newton method. The proposed method is placed within the context of factor graphs and belief propagation algorithms and closed-form expressions for belief propagation messages exchanged along…
Siliang Zhao, Wuman Luo, Qin Shu, Fangwei Xu + 1 more
False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectively control state estimation errors and are confined to the data…
Martin Wagner, Marko Jereminov, Amritanshu Pandey, Larry Pileggi
— An equivalent circuit formulation for power system analysis was demonstrated to improve robustness of Power Flow and enable more generalized modeling, including that for RTUs (Remote Terminal Units) and PMUs (Phasor Measurement Units). These measurement device models, together with an adjoint circuit based…
Lital Dabush, Ariel Kroizer, Tirza Routtenberg
—We consider the problem of estimating the states in an unobservable power system. To this end, we propose novel graph signal processing (GSP) methods. For simplicity, we start with analyzing the DC power flow (DC-PF) model and then extend our algorithms to the AC power flow (AC-PF) model. The main assumption behind…
Paulo Bárcia, P. Castelo Ferreira, Paulo Cesar Ferreira Freitas
LAW 1: the net flow in a node of the network is zero: P j Fij + P j Fji = 0. The network topology is a graph that may be described by a branch-node incidence matrix T (composed of elements with values -1, 1 or 0 only). Nodal injections are described by a vector L. The First Law may be translated into the matrix…
Manyun Huang, Junbo Zhao, Zhinong Wei, Marco Pau + 1 more
—Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a distributed and robust state estimation (DRSE) method for hybrid AC/DC distribution systems using multiple sources of data. In the proposed…
Saleh Almasabi, Turki Alsuwian, Ehtasham Javed, Muhammad Irfan + 6 more
'Mohammed Jalalah' 'Belqasem Aljafari' 'Farid A. Harraz' 'Rafael Pastor Vargas' 'Llanos Tobarra' 'Antonio Robles-Gómez'] The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these…
Authors not listed
Acoustic measurements of batteries are known to be correlated to their state-of-charge, creating opportunities for state estimation that do not rely on electrical signals. State estimators are typically parametric models fitted from data, often from the broad toolbox of machine learning. Such models can be easily…
Iosif Szeidert, Octavian Proștean, Ioan Filip, Cristian Vașar
The paper presents issues regarding the flux estimation of induction machines. The electrical machines variable's estimation represents a major problem in the actual context of modern control approaches, especially of sensorless control strategies. There are considered several implementations of induction machine's…
Ido Amiel, Zekharya Danin, Moshe Sitbon, Moshe Averbukh
The increasingly widespread occurrences of fast-changing loads, as in, for example, the charging of electrical vehicles and the stochastic output of PV generating facilities, are causing imbalances between generated and consumed power flows. The deviations in voltage cause noteworthy technical problems. The…
Nima HajiHeydari Varnoosfaderani, Amir Khorsandi
This paper investigates the state estimation problem for an islanded DC microgrid. The microgrid consists of energy storage units, as well as wind and solar generation units, all designed to supply power to a variable DC load, specifically an electric vehicle charging station. To ensure accurate state estimation, a…
Hernan Hernandez Larzabal, David Araya, Lazara Liset Gonzalez Rodriguez, Claudio Roman + 3 more
Multivariate autoregressive models [MAR] allows estimating effective brain connectivity by considering both power and phase fluctuations of the signals involved. A MAR models brain activity in one region as a linear combination of past activations in all other regions. A Hidden Markov model, HMM, whose states’ emisions…
Izudin Džafić, Rabih A. Jabr, Bikash C. Pal
—The hybrid power system state estimation problem requires computing the state of the power network using data from both legacy and phasor measurements. Recent research has shown that the normal equations approach in complex variables is computationally advantageous, particularly in the presence of phasor measurement…
Mehdi Shafiei, Gerard Ledwich, Ghavameddin Nourbakhsh, Ali Arefi + 1 more
'Houman Pezeshki'] Abstract— In the presence of renewable resources, distribution networks have become extremely complex to monitor, operate and control. In addition, with the high cost of measurement devices, communication infrastructure and data handling, there is a strong motivation to design and employ suitable…
Zekharya Danin, Ido Amiel, Neda Miteva, Moshe Averbukh + 1 more
'Alfio Dario Grasso'] The requirement of RMS (voltage and current) measurements under a fraction of the AC period has become increasingly attractive in power systems. Some of these power applications are responsible for voltage stabilization in distribution lines when the voltage correction should be made in a short…
Loïc J. Azzalini, David Crompton, Gabriele M. T. D’Eleuterio, Frances Skinner + 1 more
Data assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter…
Mikito Ogino, Daiki Sekizawa, Jun Kitazono, Masafumi Oizumi
Investigating the dynamics of neural networks, which are governed by connectivity between neurons, is a fundamental challenge in neuroscience. Because passive (spontaneous) activity provides only limited information for estimating connectivity, perturbation-based approaches are widely applied in neuroscience, as they…
R. A. Mahmoud, O. P. Malik, W. M. Fayek
Precise evaluation of fault current model parameters is an important issue in protection and automation systems. These parameters play a crucial role in selecting protective relay settings, detecting, and compensating saturated CT waveforms, calculating AC and DC components, estimating the sub-transient and transient…
Robert Guggenberger, Maximilian Scherer, Alireza Gharabaghi
Phase-dependency of cortico-spinal excitability can be researched using TMS-EEG. Due to the large artifact, non-causal filters can smear the TMS artifact and distort the phase. However, causal filters can become biased by too high filter orders or uneven pass-bands. We explored the influence of different signal…
Authors not listed
Range anxiety remains a major concern for electric vehicle (EV) drivers due to unpredictable charge usage influenced by terrain and user behavior variations. To address this issue, we propose a data-driven approach to provide accurate trip-specific battery consumption for EV drivers. First, we present a new…
Authors not listed
Accurate prediction of battery behavior under different dynamic operating conditions is critical for both fundamental research and practical applications. However, the diversity of emerging materials and cell architectures presents significant challenges to the generalizability of conventional prognostic approaches.…
Hong U Lo, Zibo Gao, Hoi Fong Loi, Sok Kin Cheng
Surface electromyography (sEMG) interfaces require interference-resistant acquisition, faithful preprocessing, and a responsive envelope for downstream control. This work presents a reproducible, analog-aware digital conditioning pipeline comprising an eighth-order Butterworth band-pass, a narrow static notch, a…
Authors not listed
Electrochemical impedance spectroscopy (EIS) is one of the most widely deployed methods to characterise electrochemical systems such as batteries, fuel cells or electrolyzers. The distribution of relaxation times (DRT) represents a technique to simplify EIS data by deconvolution with a suitable kernel, while with…