DSpace Collection:

Impact of Load Modeling on DG Integrated Distribution Systems

Tue, 01 Jan 2019 00:00:00 GMT

Title: Impact of Load Modeling on DG Integrated Distribution Systems Authors: Khan, Muhammad Faisal Nadeem Abstract: Renewable energy based Distributed Generation (DG) resources are crucial for sustainable energy supply infrastructure as they are non-polluting and inexhaustible. The uncertainties associated with DG resources may cause distinct economic and technical challenges which require comprehensive investigation to facilitate their integration in Distribution System (DS). Generally, DG planning studies are conducted while assuming constant generation and load models. However, such assumptions may result in misleading and inconsistent values for voltage profile, loss reduction, payback period, deferral values, and other relevant calculations. Therefore, to achieve accurate and realistic results, it is necessary to consider variations associated with generation and load. This thesis presents time varying load modeling and probabilistic solar irradiance modeling techniques and investigates their impact on Photovoltaic (PV) based DG planning. A novel Beta distribution based probabilistic generation model is proposed for solar irradiance uncertainty modeling to compute the hourly output power values produced by PV based DG. The beta distribution parameters are found by assuming the variations of irradiance patterns at consecutive time steps. Subsequently, the proposed model is employed to generate various solar irradiance generation scenarios. Then, a time varying load modeling approach is presented for PV based DG planning. Five different types of time varying load models (i.e. residential, commercial, industrial, mixed and constant) are considered. These loads are modeled by combining the time varying characteristics of residential, commercial and industrial loads with the voltage-dependent load model while assuming suitable voltage exponents. The application of these load models make the PV based DG integration more realistic as compared to the conventional model. Furthermore, a methodology has been developed to determine intermittent DG allocation for DS while considering varying load and generation. The objective is to minimize the multiobjective optimization function which involves voltage deviation, active and reactivepower loss indices. Finally, the impact of time varying load modeling approach on DG integrated DS performance has been investigated. The proposed DG planning framework has been validated on IEEE 33-bus and 69-bus standard distribution test systems in MATLAB environment. A comparative assessment of different impact indices, penetration level, active and reactive power intake, active and reactive power loss and MVA support offered by the installation of PV based DG for different time varying load models has been performed. The results demonstrate that the proposed generation model is suitable for solar irradiance modeling. Moreover, time varying load modelling approach has a significant impact on DS planning studies under uncertain scenario.

Design and Analysis of Metasurfaces for Polarization Conversion of Electromagnetic Waves

Tue, 01 May 2018 00:00:00 GMT

Title: Design and Analysis of Metasurfaces for Polarization Conversion of Electromagnetic Waves Authors: Khan, Muhammad Ismail Abstract: Control and manipulation of polarization state of electromagnetic waves has always been of interest in the scientific community due to its fundamental role in wide range of applications including contrast imaging microscopy, optical sensing, molecular biotechnology and optical and microwave communication. Although, conventional techniques are applied for polarization control using natural materials such as optical activity of crystals, Faraday-effect and solutions of chiral molecules such as sugar. However, such methods generally result in bulky volumes, narrow bandwidth and incidence angle dependent response which greatly limit their use for many practical applications. Therefore, scientists have explored the use of artificial structures in the form of ultra-thin metasurfaces to achieve miniaturized polarization control devices with wide bandwidth and angularly stable response. However, most of these designs achieve polarization conversion for normal incidence only, which practically becomes prohibitive, as incoming waves can have arbitrary incidence angles. Thus, metasurfaces with stable response for arbitrary incidence angles are highly desirable. In this perspective, there are two main objectives of this research thesis: firstly, to realize wideband metasurfaces achieving polarization conversion both for normal as well oblique incidence and secondly, to design metasurfaces which can achieve multiple functionalities through a single structure. The first three metasurface designs presented in Section I achieve angularly stable (maximum up to 60o ) wideband cross-polarization conversion in reflection mode. The cross-polarization conversion is achieved through anisotropy of the unit cell while the bandwidth is extended through multiple plasmonic resonances. Multifunctional metasurfaces are presented in Section II. These metasurfaces are extremely desirable in practical applications as they can replace multiple optical components, thus miniaturizing size and reducing the cost of the overall system. The first of the multifunctional metasurfaces presented in this thesis, not only transforms linear and circular polarization to their corresponding cross polarization, but also achieves linear-to-circular and circular-to-linear polarization conversion in different frequency regimes. Thus, it exhibits both half- and quarter-wave plate operations in different frequency bands using an ultra-thin bilayer anisotropic metasurface. The final design presented in this thesis is based on a flexible single layer anisotropic metasurface manifesting both quarter-wave plate and half-mirror (1:1 beam splitter) operation.

Multi-resolution Transform based Feature Extraction Techniques for Differentiating Glioma Grades using MRI Images

Mon, 01 Jan 2018 00:00:00 GMT

Title: Multi-resolution Transform based Feature Extraction Techniques for Differentiating Glioma Grades using MRI Images Authors: Zia, Razia Abstract: Medical image processing is one of the most attention gaining research areas that utilizes the technology for improving the quality of human life through a more precise and rapid diagnosis systems. This thesis focuses on computer assisted diagnosis of brain neoplasms which is amongst the most fatal cancers. Though, their exact cause is still unknown but early detection and diagnosis of correct neoplasm type is very important for patient’s life and further treatment planning. Currently, the treatment of brain neoplasm depends on clinically observed symptoms, appearance of radiological tests, and often the microscopic examination of neoplasm’s tissues (histopathology or biopsy report). Magnetic Resonance Imaging (MRI) is the state of art technique to diagnose brain neoplasms and monitor their treatment. It provides a noninvasive way to improve the quality of the patient’s life through a more accurate and fast diagnosis and with minor side-effects, leading to an effective overall treatment. However, MRI does not provide any information about exact type and grade of neoplasm. The final decision is based on biopsy report of patient which is considered as gold standard, despite all risks associated with surgery to obtain a biopsy. With rapid advancement in technology, the researchers are continuously working on computerized techniques or computer assisted diagnostic tools to provide fast identification, correct diagnosis and effective treatment of brain neoplasm. The aim of the present thesis is to design, implement, and evaluate a software classification system for discriminating three grades of brain neoplasm on MRI. Limited brain neoplasm image data is one of the biggest issues in this research area because collection of this type of data requires years and years. Normally, we find studies working on images of some specific hospital or website. In addition, direct comparison of these studies is not possible because each study had worked on different types of neoplasm and various sizes of image data. We have addressed this issue by proposing a new image cropping technique for handling images of different dimension for the same classifier. This new system is capable of handling image datasets from different institutions with various image sizes and resolutions for comparing, regulating and sharing of research. It is also observed, that lesser training and testing images in a particular class of neoplasm badly effect the classification accuracy. By using this generalized system, more image samples of a neoplasm class can be taken from other institutions or websites to improve the classification accuracy. For classification of MRI images, majority of the researchers have worked on statistical features of neoplasm region but multi-resolution transforms for feature extraction, are not much explored. Besides this, classification of normal and pathological brain is mostly addressed but very few studies are found on multi-classification of different neoplasm types. The main objective of this thesis is to explore the performance of different multi-resolution transform based feature extraction techniques for multi-classification problem of brain neoplasm type (grade II, grade III and grade IV gliomas). Discrete Wavelet Transform (DWT) is one of the most popular multi resolution transform, extensively used as feature extraction technique for binary (normal vs abnormal brains) brain neoplasm classification systems. In this thesis, a stationary and time invariant Non Subsampled Contourlet Transform (NSCT) with Gray Level Co-occurrence Matrix (GLCM) is used for computation of feature vector in brain neoplasm classification system. This NSCT-GLCM based classification system is also compared with conventional DWT-GLCM based classification system, for the same experimental setup. It is found that NSCT-GLCM based system perform better than DWT-GLCM based system. For further improvement in neoplasm discrimination accuracy, in last algorithm, a multi resolution transform based hybrid feature extraction technique is introduced. This hybrid technique is comprised of conventional DWT, NSCT and GLCM. The quantitative performance analysis showed that hybrid feature extraction technique per- formed much better than the previous two techniques (DWT-GLCM and NSCT-GLCM) with the highest accuracy of 88.88%. The developed brain neoplasm classification techniques can better assist the physician’s ability to classify and analyze pathologies leading for a more reliable diagnosis and treatment of disease.

AN EFFICIENT SCHEME FOR LUNG NODULE DETECTION

Sun, 01 Apr 2018 00:00:00 GMT

Title: AN EFFICIENT SCHEME FOR LUNG NODULE DETECTION Authors: Shaukat, Furqan Abstract: Lung cancer has been one of the major threats to human life for decades in both developed and under developed countries with the smallest rate of survival after diagnosis. The survival rate can be increased by early nodule detection. Computer Aided Detection (CAD) can be an important tool for early lung nodule detection and preventing the deaths caused by the lung cancer. In this dissertation, we have proposed a novel technique for lung nodule detection using a hybrid feature set. The proposed method starts with pre-processing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multi scale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine (SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected to optimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K-Nearest-Neighbour (KNN), Decision Tree and Linear Discriminant Analysis (LDA) have also been used for performance comparison. The extracted features have also been compared class-vise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium (LIDC) dataset and k-fold cross validation scheme. The main research work done in this dissertation is summarized in the following section. 1. The proposed method starts with the segmentation of lung volume from pre-processed input CT images. Lung segmentation has a critical importance as it is pre-requisite to the nodule detection. Any in-accurate lung volume segmentation can lead to the low accuracy of whole system. In this dissertation, we propose a fully automated segmentation method for lung volume from CT scan images which consists of series of steps. Initially, the CT image is segmented by using optimal thresholding and the lung volume is obtained using connected component labeling method and other irrelevant information is removed at this stage. The resultant image at this stage contains holes which is filled with the hole filling algorithm e.g. morphological operations. Finally, the lung contour is smoothed by rolling ball algorithm to include any juxta pleural nodules. 2. After lung segmentation, image enhancement is done to detect the low-density nodules. Image enhancement plays an important role in detection of these nodules by enhancing them and reducing false positives by weakening the other structures in lung region. In this thesis, a multi scale dot enhancement filter is used to detect these low-density nodules which may remain undetected in the absence of any enhancement algorithm and can affect the accuracy of the system. In the first step, a Gaussian smoothing on all the corresponding 2D slices is performed to reduce the noise and sensitivity effect. After Gaussian smoothing, Hessian matrix and its eigen values |𝜆2|<|𝜆1| are calculated for every pixel to determine the local shape of the structure. The suspected pulmonary nodule region exhibits the form of a circular or oval object whereas vascular tissue structures presents a line-like elongated structure. Therefore, this property can be used to distinguish different shape structures present in lung region. This process is repeated for different scales and finally we integrate the filter’s output values to obtain the maximum value for the best enhanced effect and generate the resultant image. After image enhancement, lung nodule candidates are detected using optimal thresholding. Then a rule-based analysis has been made based on some initial measurements like area, diameter and volume whether to keep or discard the detected nodule candidate. The advantage of rule-based analysis is that it eliminates the objects which are too small or too big to be considered as a nodule candidate and thus reduces the workload for the next stage. 3. A hybrid feature set is obtained after rigorous experimentation which increases the classification accuracy and reduces the false positive per scan considerably. The proposed feature set plays a crucial role in the overall performance of the CAD system. We selected a large pool of features initially and then trimmed down the set on the basis of accuracy and false positive per scan and ultimately obtained the proposed hybrid feature set. 4. The classification of pulmonary nodules is done using SVM algorithm. In the classification phase, the suspected pulmonary nodules are divided into true pulmonary nodules and false pulmonary nodules. SVM as a high-dimensional multi-feature hyperplane differentiation algorithm performs considerably well in a situation where it must decide only between the two classes i.e., nodule or non-nodule and the features of the suspected pulmonary nodules refer mainly to the two classes and the Gaussian Radial Basis Function (RBF) kernel function can increase its linear separability which makes the detection and classification of pulmonary nodules more accurate. 5. We have done an extensive evaluation of our proposed system on Lung Image Database Consortium (LIDC). LIDC is a publicly available database accessible from The Cancer Imaging Archive (TCIA). We have considered the 850 scans (LIDC-IDRI-0001 to LIDC-IDRI0844) of this dataset, which contains nodules of size 3-30 mm fully annotated by four expert radiologists in two consecutive sessions. K-fold cross-validation scheme is used for model selection and validation whereas the k value varies for 5, 7 and 10. An exhaustive grid search has been used to tune the hyperparameters of SVM classifier. Some other classifiers have also been used for classification of lung nodule candidates. An attempt has also been made to determine the most relevant feature class for lung nodule detection system. The achieved sensitivities at detection and classification stages are 94.20% and 98.15%, respectively, with only 2.19 FP/scan. The results of our proposed method show the superiority of our scheme as compared to other systems with increased sensitivity and reduced FP/scan. The main contribution of this dissertation is the presentation of a relatively simple nodule detection scheme that has a very good performance in an extensive experimental analysis. In addition, the proposed feature set has helped in reducing the false positives significantly and has increased the sensitivity of the proposed system. Moreover, a comparison has been made to determine the most relevant feature class in extracted feature set. The overall sensitivity has been improved compared to the previous methods and FP/scan have been reduced significantly.