Grid Voltages Estimation for Three-Phase PWM Rectifiers Control Without AC Voltage Sensors

This paper proposes a new ac voltage sensorless control scheme for the three-phase pulse-width modulation rectifier. A new startup process to ensure a smooth starting of the system is also proposed. The sensorless control scheme uses an adaptive neural (AN) estimator inserted in voltage-oriented control to eliminate the grid voltage sensors. The developed AN estimator combines an AN network in series with an AN filter. The AN estimator structure leads to simple, accurate, and fast grid voltages estimation, and makes it ideal for low-cost digital signal processor implementation. Lyapunov-based stability and parameters tuning of the AN estimator are performed. Simulation and experimental tests are carried out to verify the feasibility and effectiveness of the AN estimator. Obtained results show that the proposed AN estimator presented faster convergence and better accuracy than the second-order generalized integrator-based estimator; the new startup procedure avoided the overcurrent and reduced the settling time; and the AN estimator presented high performances even under distorted and unbalanced grid voltages

Techniques neuromimétiques pour la commande dans les systèmes électriques (application au filtrage actif parallèle dans les réseaux électriques basse tension)

Le travail présenté dans ce mémoire concerne l'élaboration d'une statégie complète d'identification et de commande neuronale d'un filtre actif parallèle (FAP). L'objectif visé est l'amélioration des performances par rapport aux systèmes classiques de dépollution des installations électriques basse tension.MULHOUSE-SCD Sciences (682242102) / SudocSudocFranceF

Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations

International audienceThis paper proposes and experimentally validates an online energymanagement system (EMS) based on improved power frequency approachto meet the load requirements and enhance operating efficiency offuel cell electric vehicle (FCEV). Within the proposed approach, afrequency-driven power splitter is used to assign lower frequenciesof power demand towards the fuel cell and higher frequencies to thesupercapacitor, which is used as a peak power unit. Subsequently,battery is directly connected with the DC bus, which behaves as anenergy buffer. To realize the limitations of storage devices, alocal supervisor is integrated in the frequency-based approach.Using the energy macroscopic representation (EMR), the details ofFCEV components and power frequency approach are described. Inaddition, the interactions and energy exchanges between sources areanalyzed. The proposed approach is experimentally validated usingOpal-RT and dSPACE systems within Hardware-In-the-Loop (HIL)platform. Based on the experimental set-up, proposed EMS is testedunder real-time conditions. Experimental results show stablevehicular operation and improved performance of FCEV in differentreal-world driving cycles

New Time-Frequency Transient Features for Nonintrusive Load Monitoring

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively