Browsing by Author "Malik, Om Parkash"

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    Automatic Operation of a Stand-Alone Hybrid Power System with Frequency Regulation by Fuzzy Logic and Binary Control
    (2013-09-20) Manjarres, Pamela Astrid; Malik, Om Parkash
    Hybrid power systems must be self-sufficient in terms of frequency and voltage control due to their islanded operation. A control strategy to combine the operation of a wind generator, a diesel generator, a battery energy storage system and a dump load for frequency regulation is proposed in this thesis. The proposed strategy partitions the control task into two subtasks: a) choosing the element to be operated on, b) providing frequency regulation. A global controller, based on an IF-THEN inference engine, chooses the element to operate. The frequency regulation is provided by separate individual controllers. In this thesis, a hybrid power system has been modelled and the proposed control strategy has been tested. By monitoring the system's power management and frequency, it is shown that the proposed control strategy operates efficiently. The proposed strategy also reduces the number of measurements required and facilitates the integration of renewable energy sources.
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    Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis
    (2019-01-22) Lu, Na; Zhang, Guangtao; Xiao, Zhihuai; Malik, Om Parkash
    Feature extraction is a key procedure in the fault diagnosis of rotating machinery. To obtain fault features with lower dimensionality and higher sensitivity, a feature extraction method based on adaptive multiwavelets transform (AMWT) and local tangent space alignment (LTSA) is proposed in this paper. AMWT is first used to obtain multiple features from the vibration signals of the machine under test to form a high-dimensional feature set. Then, in order to avoid the adverse effect of the irrelevant features in this high-dimensional feature set on the fault diagnosis result, a detection index (DI) is investigated to evaluate the sensitivity of the features and those with lower sensitivity are removed. After that, LTSA is applied for feature fusion to reduce the redundant features in the high-dimensional feature set. To validate the proposed method, performance of four feature extraction schemes based on (i) wavelet and LTSA, (ii) Geronimo, Hardin, and Massopust (GHM) multiwavelets and LTSA, (iii) AMWT and principal component analysis (PCA), and (iv) AMWT and multidimensional scaling (MDS) is compared with the proposed method. The feature extraction results by these methods are then fed into K-medoids classifier to discriminate the faults. The results show that the proposed method can improve the sensitivity of the extracted features and obtain higher fault recognition rate.