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In a photovoltaic permanent magnet synchronous motor pumping system (PVPMSM), both photovoltaic modules and switching-mode converters present nonlinear and time-variant characteristics, which result in a difficult control problem. This paper presents an in-depth analysis of the operating point dynamics. A potential trade-off exists between maximum power and stability of the system operating point. Analysis of parametric singularities based on bifurcation theory permits to optimize the maximum power-stability trade-off. Tracing the bifurcation structures related to the parameter set (T, G), we can determine the maximum power point(MPP) in midway and far enough from the bifurcation points from these structures. An algorithm aiming to determine the MPP within the range of stability through power bifurcation analysis is proposed in this paper. Our methodology was validated with the simulation that was carried out on MATCONT/MATLAB, and the results are demonstrated. [ABSTRACT FROM AUTHOR]

The paper presents a detailed numerical analyse of the complex bifurcation sets of a typical three-phase induction motor (IM). In fact, the dynamical properties of this electrical machine exhibit a rich behavior due to significant non-linearities and many motor parameters can vary significantly over the operating temperature range and saturation level. Moreover, the faults that occur in this kind of motor as well as the inter-turn short circuit fault in the stator windings can cause an abruptly change of parameters values. Therefore, all parameters are assumed to vary as a function of these conditions. So it is necessary to examine the effects of the parameters variation on the dynamic behavior of the motor. The study of the motor dynamics steady state is expected under both zero-load torque and nominal load torque motor tests. The main focus of this work is to present a numerically based study of the stability proprieties and the bifurcation phenomena concerning these limit points. Thus, structural stability is analyzed through the fluctuation of motor parameters such as rotor-stator resistors, inductors and mutual inductance. Therefore, it is observed that Saddle-node, Hopf and Generalized-Hopf bifurcations are the singularity subsets of the overall bifurcation phenomena that this machine may exhibit under parameters vary. Moreover, varieties of phenomena, such as jump and hysteresis, which may lead in these bifurcations, are carried out. [ABSTRACT FROM AUTHOR]

Summary: ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases. In the window‐based analysis of low‐frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20‐30 minutes prior to the actual occurrence of seizures. [ABSTRACT FROM AUTHOR]