Transactions of the Institute of Measurement and Control

Baazouzi, Mansour; Sayahi, Kamel; Bacha, Faouzi

doi: 10.1177/01423312231180546pmid: N/A

Fault ride-through (FRT) is a very important requirement for power grids based on voltage source converters (VSCs) to improve its operational availability of the AC grid. Grid codes require VSC stations to incorporate fault handling capabilities to prevent disconnection of converter stations from the alternating current (AC) grid for certain fault characteristics. In this paper, an FRT strategy is used in the two converter stations to handle the faults that may occur in the AC part of the grids and ensure their stability. The method improves the performance of the FRT compliance strategy during a voltage drop in the AC side. In addition, a current control that operates simultaneously on positive and negative sequence current is adopted. It also allows the injection of a reactive current to maintain the availability of the high-voltage direct current (HVDC) converter station during a fault. The performance and stability of the suggested FRT control is tested considering the direction of power flow with possible faults in balanced and unbalanced regime. For the validation of the efficiency of the FRT capacity, simulation tests are carried out with MATLAB-Simulink software, and experimental tests with a Smart-grid test bench.

Meslem, Nacim

doi: 10.1177/01423312231190193pmid: N/A

In this work, a new approach to design set-valued state estimator for linear discrete-time systems subject to additive and bounded process and measurement uncertainties is proposed. First, the system state equation is rewritten to obtain a stable numerical scheme on which an explicit reachability method is developed, based on zonotopic set computation and a re-initialization procedure. Then, to enhance the accuracy of the computed reachable set, a set-filtering technique is designed based on the system output equation and its intrinsic invariant relationships. The implementation of this filtering method is based on interval analysis coupled to contractor algorithms. The convergence property of the proposed set-valued state estimator is shown under the classical detectability assumption of linear systems. Some simulation results are presented to show the merit of the proposed new set-membership state estimation approach.

Spoor, Jan Michael; Weber, Jens; Ovtcharova, Jivka

doi: 10.1177/01423312231203030pmid: N/A

Anomalies in dynamical systems mostly occur as deviations between measurement and prediction. Current anomaly detection methods in multivariate time series often require prior clustering, training data, or cannot distinguish local and global anomalies. Furthermore, no generalized metric exists to evaluate and compare different prediction functions regarding their amount of anomalous behavior. We propose a novel methodology to detect local and global anomalies in time series data of dynamical systems. For this purpose, a theoretical density distribution is derived assuming that only noise conceals the time series. If the theoretical and the empirical density distribution yield significantly different entropies, an anomaly is assumed. For a local anomaly detection, the Mahalanobis distance using the theoretical noise distribution’s covariance is applied to evaluate sequences of predictions and measurements. In addition, the Wasserstein metric enables a comparison of predictions using the distance between the noise and empirical distribution as a measure for selecting the best prediction function. The proposed method performs well on nonlinear time series such as logistic growth and enables a useful selection of a prediction model for satellite orbits. Thus, the proposed method improves anomaly detection in time series and model selection for nonlinear systems.

Celebi, Mehmet Fatih; Tuncay Atalay, Asli; Atalay, Ozgur; Gazi, Veysel

doi: 10.1177/01423312241227252pmid: N/A

This work proposes a textile-dominated wearable actuating system utilizing textile force sensor feedback. The study explores the liquid/gas phase transition behaviour of low boiling point liquids to develop a thermally driven fluidic soft actuator. The research also focuses on obtaining feedback through capacitive textile force sensors and developing a feedback control law for a single actuator as well as sequential actuation of multiple actuators. The findings demonstrate that the proposed actuators produce the desired pressure level utilized in mechanotherapy applications. Moreover, high accuracy is achieved by the capacitive textile force sensors specifically designed for detecting the applied force exerted by the textile-based actuators. The developed system constitutes a comprehensive textile-based system encompassing heating, actuation and sensing capabilities. Following the calibration of the developed system in conjunction with its sensor, a pilot-scale implementation of sequential massage application was conducted to showcase the system’s capabilities and potential. Considering its pressure and heating properties, the developed system exhibits a great potential for utilization in mechanotherapy as well as in thermotherapy applications.

Barhoumi, Nassira; Marzougui, Hajer; Bacha, Faouzi; Boukhnifer, Moussa

doi: 10.1177/01423312241243177pmid: N/A

This work contributes to the optimization of hybrid system coupling a fuel cell (FC) to a supercapacitor (SC) for electric vehicles. The complementarity between these two energy sources allows the improvement of the global performances of the system. Our study focuses on the implementation of control and energy management techniques. Our objective is to have a better use of the storage system. In this context, our approach is to regulate the bus current and voltage and then to develop two real-time energy management strategies, one without considering and the other with considering the state of charge of the storage system. A comparison between these two strategies allowed us to understand the importance of the state of charge in the hybrid system. MATLAB/Simulink helped us in showing the performance obtained on a given mission profile of the vehicle dynamics.

Grissa, Haytham; Farhani, Slah; Bacha, Faouzi

doi: 10.1177/01423312241252289pmid: N/A

This work deals with an efficient control of the photovoltaic water pumping system. The suggested method is designed by an indirect field-oriented control based on sliding mode control. The sliding mode control is chosen thanks to its various advantages like robustness versus perturbations and uncertainties as well as high accuracy tracking. The contribution is to attest its good performance with the application of a 1-day irradiation profile to achieve a pumping rate with the same variation to prove the control robustness to variation system parameter such as resistor and inductor rotor variation. The least-squares method algorithm is used for the maximum power point tracking (MPPT) adopted to calculate the speed reference of the induction motor drive of the pumping system from the value of illumination. Finally, to improve sliding mode control efficiency with simulation in MATLAB/SIMULINK environment and experimental results, obtained by real-time implementation in a dSPACE 1104 board.

Marzougui, Hajer; Kadri, Ameni; Martin, Jean-Philippe; Bacha, Faouzi; Pierfederici, Serge

doi: 10.1177/01423312241264058pmid: N/A

This paper deals with a current topic addressing the issue of energy management. Adequate energy management consists of finding the best harmony between different sources intended to supply the vehicle to satisfy the power required by the traction motor while ensuring good performances. Energy management strategy makes it possible to find the adequate distribution of the energy flow between power source elements by generating power references to be given by each source to satisfy the required power while respecting the constraints on these sources. In this context, a new energy management strategy based on the combination of flatness theory and the frequency separation principle is developed. This strategy determines the powers that the energy sources must supply and provides power source references based on their dynamics, imposing constraints to be taken into account. This novel hybrid strategy is applied to a hybrid vehicle configuration that uses a fuel cell as the primary source with a hybrid storage device (supercapacitor and battery). The implementation using dSPACE DS1005 will be presented to demonstrate the effectiveness of the proposed strategy.

Kadri, Ameni; Marzougui, Hajer; Bacha, Faouzi

doi: 10.1177/01423312241273815pmid: N/A

This research paper focuses on the comparison of two distinct strategies for direct power control (DPC) of a doubly fed induction generator (DFIG) in wind energy conversion systems (WECSs). The study places particular emphasis on algorithms based on low-pass filters for the estimation of stator and rotor flux, which have demonstrated good performance characteristics. Detailed theoretical analysis and implementation procedures of the proposed methods are presented. To evaluate and compare the performance of the control strategies, MATLAB/Simulink software is utilized for comprehensive analysis. The obtained results, both quantitative and qualitative, from this comparative analysis are expected to be of great interest to researchers engaged in the field of DFIG-based WECSs. Furthermore, real-time validation is conducted to demonstrate the effectiveness of the proposed control strategy, specifically focusing on the DPC approach based on stator flux estimation. A dSpace 1104 real-time card is employed and tested with a wind turbine emulator. The experimental results obtained through this validation process provide evidence of the efficiency and validity of the proposed control strategy. Overall, this research contributes to the advancement of DPC strategies for DFIG-based WECS by comparing and evaluating different control methods, providing in-depth theoretical analysis, and validating the proposed approach through real-time experimentation. The findings affirm the efficiency and effectiveness of the proposed control strategy, further establishing its value in practical applications.

Sen, Sena Yagmur; Cura, Ozlem Karabiber; Yilmaz, Gulce Cosku; Akan, Aydin

doi: 10.1177/01423312241267046pmid: N/A

Alzheimer’s dementia (AD) is a predominant neurological disorder arising from corruptions in brain functions and is characterized by a chronic or progressive nature. While the precise etiology of dementia remains incompletely elucidated, its manifestation is frequently associated with discernible structural and chemical alterations in the brain. Living with dementia significantly impacts individuals’ daily lives due to the resultant loss of cognitive functions. This study presents a novel method to monitor and detect AD using advanced signal processing applied to electroencephalography (EEG) signals. The intrinsic time-scale decomposition (ITD) algorithm is employed to extract proper rotation components (PRCs) from EEG signals, utilizing a 5-second EEG segment duration. The proposed method is compared with the detection of 5-second raw EEG segments using a custom one-dimensional convolutional neural network (1D CNN). Additionally, four different quartiles (Quartile 1 (Q1), Q2, Q3, and Q4) of EEG signals are considered to identify the most significant contributor to AD. Experimental results demonstrate that the ITD-based approach yields better detection performance compared to using raw EEG signals. The most promising result is achieved by the EEG-PRCs method in Q1, with an accuracy of 94.00%, sensitivity of 93.50%, and specificity of 93.90%. In contrast, the highest-performing result of the raw EEG segments method is in Q2, with an accuracy of 88.40%, sensitivity of 89.10%, and specificity of 87.60% in terms of detecting AD.

Dieulot, Jean-Yves

doi: 10.1177/01423312241262079pmid: N/A

Long Short-Term Memory (LSTM) neural networks are well suited for representing time series as, compared to other neural networks, their structure avoids vanishing or exploding gradients. LSTM has been embedded into Model Predictive Control algorithms in order to forecast the behavior of nonlinear systems. The new algorithm presented in the paper is of a different nature, as the LSTM network approximates the inverse of the system over a receding horizon and provides a sequence of future inputs as a function of a specified output trajectory. The main advantage of the method appears when the desired output trajectory is generated from a small set of parameters, for example, a convergence rate. The Model Predictive control optimizes its criterion with respect to this small set of variables, and the LSTM supplies the corresponding future control inputs. Eventually, the modeling error of the LSTM can be compensated by feeding the control sequence to the forward model and updating the controller according to the output deviation. The algorithm allows to design Model Predictive controllers for nonlinear systems in a generic way, using a very small number of decision variables even with a long receding horizon.