Showing 8 results for Svm
M. Alizadeh Moghadam, R. Noroozian, S. Jalilzadeh,
Volume 11, Issue 3 (9-2015)
Abstract
This paper presents modeling, simulation and control of matrix converter (MC) for variable speed wind turbine (VSWT) system including permanent magnet synchronous generator (PMSG). At a given wind velocity, the power available from a wind turbine is a function of its shaft speed. In order to track maximum power, the MC adjusts the PMSG shaft speed.The proposed control system allowing independent control maximum power point tracking (MPPT) of generator side and regulate reactive power of grid side for the operation of the VSWT system. The MPPT is implemented by a new control system. This control system is based on control of zero d-axis current (ZDC). The ZDC control can be realized by transfer the three-phase stator current in the stationary reference frame into d-and q-axis components in the synchronous reference frame. Also this paper is presented, a novel control strategy to regulate the reactive power supplied by a variable speed wind energy conversion system. This control strategy is based on voltage oriented control (VOC). The simulation results based on Simulink/Matlab software show that the controllers can extract maximum power and regulate reactive power under varying wind velocities.
M Khodsuz, M Mirzaie,
Volume 11, Issue 4 (12-2015)
Abstract
This paper introduces the indicators for surge arrester condition assessment based on the leakage current analysis. Maximum amplitude of fundamental harmonic of the resistive leakage current, maximum amplitude of third harmonic of the resistive leakage current and maximum amplitude of fundamental harmonic of the capacitive leakage current were used as indicators for surge arrester condition monitoring. Also, the effects of operating voltage fluctuation, third harmonic of voltage, overvoltage and surge arrester aging on these indicators were studied. Then, obtained data are applied to the multi-layer support vector machine for recognizing of surge arrester conditions. Obtained results show that introduced indicators have the high ability for evaluation of surge arrester conditions.
A. Dameshghi, M. H. Refan,
Volume 14, Issue 4 (12-2018)
Abstract
Wind turbines are very important and strategic instruments in energy markets. Wind power production is unreliable. Wind power is weather dependent and the extreme wind speed changes make difficult to control of grid voltage and reactive power. Based on these reasons, Wind Power Prediction (WPP) is important for real applications. In this paper, a new short-term WPP method based on Support Vector Machine (SVM) is proposed. In contrast to physical approaches based on very complex differential equations, the proposed method is based on data history. Firstly, data preprocessing and normalization is done. Secondly, formulate the prediction as a regression problem. Thirdly, the prediction model is constructed using the Particle Swarm Optimization (PSO) and Least Square Support Vector Machine (LSSVM). In this paper, instead of using the conventional kernels, such as linear kernel, Polynomial and Radial basis function (RBF), the Wavelet (W) transform is used. The PSO-LS-WSVM accuracy has been tested with industrial wind energy data. This method has been compared with other methods and the experimental results based on practical data illustrate that PSO-LS-WSVM proposed method has better responses than other methods. Statistical results indicate that the predicting error of PSO-LS-WSVM is 2.98% for one look-ahead hour.
H. Benbouhenni, Z. Boudjema, A. Belaidi,
Volume 15, Issue 1 (3-2019)
Abstract
This article presents an improved direct vector command (DVC) based on intelligent space vector modulation (SVM) for a doubly fed induction generator (DFIG) integrated in a wind turbine system (WTS). The major disadvantages that is usually associated with DVC scheme is the power ripples and harmonic current. To overcome this disadvantages an advanced SVM technique based on fuzzy regulator (FSVM) is proposed. The proposed regulator is shown to be able to reduce the active and reactive powers ripples and to improve the performances of the DVC method. Simulation results are shown by using Matlab/Simulink.
E. Bounadja, Z. Boudjema, A. Djahbar,
Volume 15, Issue 3 (9-2019)
Abstract
This paper proposes a novel wind energy conversion system based on a Five-phase Permanent Magnetic Synchronous Generator (5-PMSG) and a Five to three Matrix Converter (5-3MC). The low cost and volume and also eliminating grid side converter controller are attractive aspects of the proposed topology compared to the conventional with back-to-back converters. The control of active and reactive power injected to the grid from the proposed system is carried out by a Direct Power Control (DPC) combined with a Space Vector Modulation (SVM). An advantage of this control, compared with the Conventional Direct Power Control (C-DPC) method, is that it eliminates the lookup table and lowers grid powers and currents harmonics through the use of a standard PI controller instead of hysteresis comparators. The efficiency of proposed whole system has been simulated by using MATLAB/Simulink environment.
M. Kamarzarrin, M. H. Refan, P. Amiri, A. Dameshghi,
Volume 18, Issue 2 (6-2022)
Abstract
One of the major faults in Doubly-Fed Induction Generator (DFIG) is the Inter-Turn Short Circuit (ITSC) fault. This fault leads to an asymmetry between phases and causes problems to the normal state between current lines. Faults diagnosis from non-stationary signals for the Wind Turbine (WT) is difficult. Therefore, the strategy of fault diagnosis must be robust against instability. In this paper, a new intelligent strategy based on multi-level fusion is proposed for diagnosis of DFIG inter-turn stator winding fault. Firstly, to overcome the non-stationary nature of the vibration signals of the WT, empirical mode decomposition (EMD) method is performed in time-frequency domains to extract best fault features from information power sensor and information current sensor. Moreover, a feature evaluation technique is used for the input of the classifier to choose the best subset features. Secondly, Least Squares Wavelet Support Vector Machines (LS-WSVM) classifier is trained to classify fault types based on feature level fusion (FLF) from different sensors. The main parameters of SVM and the kernel function are optimized by Genetic Algorithm (GA). Finally, Dempster-Shafer evidential reasoning (DSER) is used for fusing the GA-LS-WSVM results based on decision level fusion (DLF) of individual classifiers. In order to evaluate the proposed strategy, a DFIG WT test rig is developed. The experimental results show the efficiency of the proposed structure compared to other ITSC fault diagnosis methods. The results show that the classification accuracy of DSER-GA-LS-WSVM is 98.27%.
Biswapriyo Sen, Maharishi Kashyap, Jitendra Singh Tamang, Sital Sharma, Rijhi Dey,
Volume 20, Issue 2 (6-2024)
Abstract
Cardiovascular arrhythmia is indeed one of the most prevalent cardiac issues globally. In this paper, the primary objective was to develop and evaluate an automated classification system. This system utilizes a comprehensive database of electro- cardiogram (ECG) data, with a particular focus on improving the detection of minority arrhythmia classes.
In this study, the focus was on investigating the performance of three different supervised machine learning models in the context of arrhythmia detection. These models included Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF). An analysis was conducted using real inter-patient electrocardiogram (ECG) records, which is a more realistic scenario in a clinical environment where ECG data comes from various patients.
The study evaluated the models’ performances based on four important metrics: accuracy, precision, recall, and f1-score. After thorough experimentation, the results highlighted that the Random Forest (RF) classifier outperformed the other methods in all of the metrics used in the experiments. This classifier achieved an impressive accuracy of 0.94, indicating its effectiveness in accurately detecting arrhythmia in diverse ECG signals collected from different patients.
Azzedine Khati,
Volume 20, Issue 3 (9-2024)
Abstract
In this research paper, a multivariable prediction control method based on direct vector control is applied to command the active power and reactive power of a doubly-fed induction generator used into a wind turbine system. To obtain high energy performance, the space vector modulation inverter based on fuzzy logic technique (fuzzy space vector modulation) is used to reduce stator currents harmonics and active power and reactive power ripples. Also the direct vector control model of the doubly-fed induction generator is required to ensure a decoupled control. Then its classic proportional integral regulators are replaced by the multivariable prediction controller in order to adjust the active and reactive power. So, in this work, we implement a new method of control for the doubly-fed induction generator energy. This method is carried out for the first time by combining the MPC strategy with artificial intelligence represented by Fuzzy SVM-based converter in order to overcome the drawbacks of other controllers used in renewable energies. The given simulation results using Matlab software show a good performance of the used strategy, particularly with regard to the quality of the energy supplied.
