Stability analysis and robust damping of multiresonances in distributed-generation-based islanded microgrids

International audienc

Deep Brain Stimulation in Moroccan Patients With Parkinson's Disease: The Experience of Neurology Department of Rabat

Introduction: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is known as a therapy of choice of advanced Parkinson's disease. The present study aimed to assess the beneficial and side effects of STN DBS in Moroccan Parkinsonian patients.Material and Methods: Thirty five patients underwent bilateral STN DBS from 2008 to 2016 in the Rabat University Hospital. Patients were assessed preoperatively and followed up for 6 to 12 months using the Unified Parkinson's Disease Rating Scale in four conditions (stimulation OFF and ON and medication OFF and ON), the levodopa-equivalent daily dose (LEDD), dyskinesia and fluctuation scores and PDQ39 scale for quality of life (QOL). Postoperative side effects were also recorded.Results: The mean age at disease onset was 42.31 ± 7.29 years [28–58] and the mean age at surgery was 54.66 ± 8.51 years [34–70]. The median disease duration was 11.95 ± 4.28 years [5–22]. Sixty-three percentage of patients were male. 11.4% of patients were tremor dominant while 45.71 showed akinetic-rigid form and 42.90 were classified as mixed phenotype. The LEDD before surgery was 1200 mg/day [800-1500]. All patients had motor fluctuations whereas non-motor fluctuations were present in 61.80% of cases. STN DBS decreased the LEDD by 51.72%, as the mean LEDD post-surgery was 450 [188-800]. The UPDRS-III was improved by 52.27%, dyskinesia score by 66.70% and motor fluctuations by 50%, whereas QOL improved by 27.12%. Post-operative side effects were hypophonia (2 cases), infection (3 cases), and pneumocephalus (2 cases).Conclusion: Our results showed that STN DBS is an effective treatment in Moroccan Parkinsonian patients leading to a major improvement of the most disabling symptoms (dyskinesia, motor fluctuation) and a better QOL

A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method

Active resonance damping and harmonics compensation in distributed generation based islanded microgrids

International audienc

Modeling of complex resonances in islanded Microgrids

International audienc

VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets

Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in oceanic environments with high humidity, saline-alkali water, and impacts of marine organisms and waves, and consequently malfunctions can happen along with the need for expensive inspection and maintenance. In order to achieve effective fault diagnosis of MCGSs in events of failure, this paper focuses on fault detection and diagnosis (FDD) of MCGSs based on five-phase permanent magnet synchronous generators (FP-PMSGs) with the third harmonic windings (THWs). Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. The effectiveness of the proposed VPSO-SVM method was validated by simulation results analysis and comparisons

Analysis of the effect neutral connection mode for permanent magnet synchronous generator-vienna rectifier set

In this paper, it was considered to investigate a new electrical architecture for the conversion of mechanical energy from renewable sources into electrical energy, fault tolerant and high energy and dynamic performance for the exploitation of marine renewable energy (MRE). The architecture to be investigated concerns a three-phase permanent magnet synchronous generator combined with a Vienna rectifier, with a topology that minimizes the number of active switches to increase the reliability of the energy conversion chain. Despite the high non-linearity of this architecture, this control is made possible through to the dynamic performance and control of the maximum switching frequency of the self-oscillating controller called the Phase-Shift Self-Oscillating Current-Controller (PSSOCC). The study of the impact of the connection of the PMSG neutral to the mid-point of the DC bus is being investigated