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Global energy demand, which is largely based on fossil fuels, is expected to increase rapidly. An effort must be made to mitigate carbon emissions and climate change to ensure sustainable and clean development. In recent years, the increasing share of renewable energy and energy-storage systems, the development of electric vehicles (EVs), promotion of energy efficiency and demand-side management (DSM) have become today’s solution technologies. The microgrid (MG), which involves the interconnection of several generation and storage units capable of operating locally with or without connection to the power grid, is also a very useful emerging technology. This study allowed the experimental operation and performance analysis of a grid-connected photovoltaic (PV)/battery/EV MG hybrid system, which was used for maximizing PV self-consumption and DSM objectives. The entire MG consisting of two subsystems (polycrystalline PV array of 2.16 kWp for Subsystem 1, monocrystalline PV system of 2.4 kWp for Subsystem 2, EV with lithium-ion battery capacity of 6.1 kWh) was installed under outdoor conditions at the University Institute of Technology in Mulhouse, France in August 2018. The operation and behaviour of the system components, including the inverter, batteries and power grid, were analysed in both scenarios with and without EV connection. The results shows that the total cumulative energy injected into the grid during the entire system operating cycle is estimated to be 3466.82 and 5836.58 kWh for Subsystems 1 and 2, respectively. In addition, the energy produced by Subsystem 2 during its lifetime and the emissions emitted are respectively estimated at 5597.65 kWh and 4.17 tons. The other results in terms of PV output power, energy yield, feed-in power and self-consumed energy were quantified and analysed in detail.The performance of a grid-connected photovoltaic (PV)/battery/electric vehicle (EV) microgrid was analysed using experimental data with and without EV connection. Energy production, associated emissions, PV output power, energy yield, feed-in power and self-consumed energy were quantified and analysed in detail.

In this paper, a novel method for power quality (PQ) events recognition is presented. Nine types of PQ events consisting of single and multi-stage disturbances are considered for study. For this task, features observed in the time frequency (t, f) plane have been used. Synthetic PQ events are generated using mathematical models. These signals are then projected in the time-frequency plane via the ST with a Compact Support Kernel (ST-CSK) providing the time-frequency resolution, energy concentration and robustness to noise. In this plane, PQ events are localized and characterized. The extracted features are then classified using several technics. The achieved results show that an overall accuracy of 100% has been obtained with Support Vector Machines and Random Forest classifiers even with signals embedded in high Additive White Gaussian Noise level (SNR=5dB). In the same conditions, XGboost classifier accurately detects 99.72% of PQ events.

In this article, a new neural network-based virtual flux (NN-VF) estimator is proposed for sensorless control of a pulsewidth modulation rectifier under unbalanced and distorted grid conditions. In this estimator, the VF vector is reconstructed through an emulated ideal integrator. Thereafter, positive and negative sequence (PNS) VF components are extracted using an NN-based PNS components separator. A Lyapunov’s theory-based convergence analysis is performed for optimal tuning of the NN-VF estimator. Accordingly, accurate and fast estimation is achieved. A VF-based predictive direct power control (VF-PDPC) model, including the estimated VF positive sequence components, is formulated. A startup procedure is considered for smooth starting under unbalanced grid conditions. Feasibility and robustness of the developed VF-PDPC are verified by experiments. Mainly, obtained results demonstrate that the startup procedure reduces settling time and avoids over currents; the VF-PDPC achieves better current waveforms than those obtained by the conventional PDPC under unbalanced grid conditions; the NN-VF estimator presents similar dynamic performances as the second-order generalized integrator that uses grid voltages measurement.

In this paper, new neural filter (NF) based integrator (NF-I) is developed for virtual flux (VF) estimation in direct power control (DPC) of a three-phase pulse width modulation (PWM) rectifier. The main advantages of the proposed NF-I are its simple structure, accuracy and provides fast VF estimation compared to the traditional first order low-pass (FOLP) filter. The NF capability to online filtering distorted signals is exploited for extracting fundamental components of VF obtained from a pure integration. Numerical simulations of the proposed NF-I inserted in the VF based DPC (VF-DPC) are carried out under different grid voltage conditions. Steady state and dynamic performances of the NF-I are compared with those obtained with the traditional FOLP filter. Simulation results illustrate good performances of the NF-I inserted in the VF-DPC strategy.

This study proposes several high precision selective harmonics compensation schemes for an active power filter. Harmonic currents are identified and on-line tracked by novel Adaline-based architectures which work in different reference-frames resulting from specific currents or powers decompositions. Adalines are linear and adaptive neural networks which present an appropriate structure to fit and learn a weighted sum of terms. Sinusoidal signals with a frequency multiple of the fundamental frequency are synthesized and used as inputs. Therefore, the amplitude of each harmonic term can be extracted separately from the Adaline weights adjusted with a recursive LMS (Least Mean Squares) algorithm. A first method is based on the modified instantaneous powers, a second method optimizes the active currents, and a third method relies on estimated fundamental currents synchronized with the direct voltage components. By tracking the fluctuating harmonic terms, the Adalines learning process allows the compensation schemes to be well suited for on-line adaptive compensation. Digital implementations of the identification schemes are performed and their effectiveness is verified by experiments.

The aim of this paper is to improve the energy concentration of the Stockwell transform (S-transform) in the time–frequency domain. A modified S-transform is proposed with several parameters to control the width of a hybrid Gaussian window. A constrained optimization problem is proposed based on an energy concentration measure as objective function and inequalities constraints to define the bounds of the Gaussian window. An active-set algorithm is applied to resolve the optimization problem. The optimization of the energy concentration in the time–frequency plane can lead to more reliable applications for non-stationary signals. The simulation results show a significant improvement of the proposed methodology most notably in the presence of noise comparing with the standard S-transform and existing modified S-transform in the literature. Moreover, comparison with other known time–frequency transforms such as Short-time Fourier transform (STFT) and smoothed-pseudo Wigner–Ville distribution (SPWVD) is also performed and discussed. The proposed S-transform is tested also on real non-stationary signals through an example of split detection in heart sounds. [ABSTRACT FROM AUTHOR]