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Showing 4 results for Dameshghi

M H Refan, A Dameshghi, M Kamarzarrin,
Volume 9, Issue 4 (December 2013)

Differential base station sometimes is not capable of sending correction information for minutes, due to radio interference or loss of signals. To overcome the degradation caused by the loss of Differential Global Positioning System (DGPS) Pseudo-Range Correction (PRC), predictions of PRC is possible. In this paper, the Support Vector Machine (SVM) and Genetic Algorithms (GAs) will be incorporated for predicting DGPS PRC information. The Genetic Algorithm is employed to feature subset selection. Online training for real-time prediction of the PRC enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy in Real Time DGPS. Given a set of data received from low cost GPS module, the GASVM can predict the PRC precisely when the PRC signal is lost for a short period of time. This method which is introduced for the first time for prediction of PRC is compared to other recently published methods. The experiments show that the total RMS prediction error of GASVM is less than 0.06m for on step and 0.16m for 10 second ahead cases
A. Dameshghi, M. H. Refan,
Volume 14, Issue 4 (December 2018)

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.

M. H. Refan, A. Dameshghi,
Volume 16, Issue 2 (June 2020)

Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the Time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.

M. Kamarzarrin, M. H. Refan, P. Amiri, A. Dameshghi,
Volume 18, Issue 2 (June 2022)

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%.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.