Multiport power electronics circuitry for integration of renewable energy sources in low power applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering at Massey University, Palmerston North, New Zealand

The increasing demand for electricity and the global concern about environment has led energy planners and developers to explore and develop clean energy sources. Under such circumstances, renewable energy sources (RES) have emerged as an alternative source of energy generation. Immense development has been made in renewable energy fields and methods to harvest it. To replace conventional generation system, these renewable energy sources must be sustainable, reliable, stable, and efficient. But these sources have their own distinguished characteristics. Due to sporadic nature of renewable energy sources, the uninterrupted power availability cannot be guaranteed. Handling and integration of such diversified power sources is not a trivial process. It requires high degree of efficiency in power extraction, transformation, and utilization. These objectives can only be achieved with the use of highly efficient, reliable, secure and cost-effective power electronics interface. Power electronics devices have made tremendous developments in the recent past. Numerous single and multi-port converter topologies have been developed for processing and delivering the renewable energy. Various multiport converter topologies have been presented to integrate RES, however some limitations have been identified in these topologies in terms of efficiency, reliability, component count and size. Therefore, further research is required to develop a multiport interface and to address the highlighted issues. In this work, a multi-port power electronics circuitry for integration of multiple renewable energy sources is developed. The proposed circuitry assimilates different renewable sources to power up the load with different voltage levels while maintaining high power transfer efficiency and reliability with a simple and reliable control scheme. This research work presents a new multiport non-isolated DC-DC buck converter. The new topology accommodates two different energy sources at the input to power up a variable load. The power sources can be employed independently and concurrently. The converter also has a bidirectional port which houses a storage device like battery to store the surplus energy under light load conditions and can serve as an input source in case of absence of energy sources. The new presented circuitry is analytically examined to validate its effectiveness for multiport interface. System parameters are defined and the design of different components used, is presented. After successful mathematical interpretation, a simulation platform is developed in MATLAB/Simscape to conduct simulations studies to verify analytical results and to carry out stability analysis. In the final stage, a low power, low voltage prototype model is developed to authenticate the results obtained in simulation studies. The converter is tested under different operating modes and variable source and load conditions. The simulation and experimental results are compiled in terms of converter’s efficiency, reliability, stability. The results are presented to prove the presented topology as a highly reliable, stable and efficient multiport interface, with small size and minimum number of components, for integration of renewable energy sources

Predicting noise-induced hearing loss (NIHL) in TNB workers using GDAM algorithm

Noise is a form of a pollutant that is terrorizing the occupational health experts for many decades due to its adverse side-effects on the workers in the industry. Noise�Induced Hearing Loss (NIHL) handicap is one out of many health hazards caused due to excessive exposure to high frequency noise emitted from the machines. A number of studies have been carried-out to find the significant factors involved in causing NIHL in industrial workers using Artificial Neural Networks (ANN). Despite providing useful information on hearing loss, these studies have neglected some important factors. The traditional Back-propagation Neural Network (BPNN) is a supervised Artificial Neural Networks (ANN) algorithm. It is widely used in solving many real time problems in world. But BPNN possesses a problem of slow convergence and network stagnancy. Previously, several modifications were suggested to improve the convergence rate of Gradient Descent Back-propagation algorithm such as careful selection of initial weights and biases, learning rate, momentum, network topology, activation function and ‘gain’ value in the activation function. This research proposed an algorithm for improving the current working performance of Back-propagation algorithm by adaptively changing the momentum value and at the same time keeping the ‘gain’ parameter fixed for all nodes in the neural network. The performance of the proposed method known as ‘Gradient Descent Method with Adaptive Momentum (GDAM)’ is compared with ‘Gradient Descent Method with Adaptive Gain (GDM-AG)’ (Nazri, 2007) and ‘Gradient Descent with Simple Momentum (GDM)’ by performing simulations on classification problems. The results show that GDAM is a better approach than previous methods with an accuracy ratio of 1.0 for classification problems like ix Thyroid disease, Heart disease, Breast Cancer, Pima Indian Diabetes, Wine Quality, Australian Credit-card approval problem and Mushroom problem. The efficiency of the proposed GDAM is further verified by means of simulations on Noise-Induced Hearing loss (NIHL) audiometric data obtained from Tenaga Nasional Berhad (TNB). The proposed GDAM shows improved prediction results on both ears and will be helpful in improving the declining health condition of industrial workers in Malaysia. At present, only few studies have emerged to predict NIHL using ANN but have failed to achieve high accuracy. The achievements made by GDAM has paved way for indicating NIHL in workers before it becomes severe and cripples him or her for life. GDAM is also helpful in educating the blue collared employees to avoid noisy environments and remedies against exposure to excessive noise can be taken in the future to prevent hearing damage

Probing Stochastic Gravitational Wave Background from SU(5)×U(1)χSU(5) \times U(1)_{\chi} Strings in Light of NANOGrav 15-Year Data

A realistic model of $SU(5) \times U(1)_{\chi}$, embedded in $SO(10)$ supersymmetric grand unified theory, is investigated for the emergence of a metastable cosmic string network. This network eventually decays via the Schwinger production of monopole-antimonopole pairs, subsequently generating a stochastic gravitational wave background that is compatible with the NANOGrav 15-year data. In order to avoid the monopole problem in the breaking of both $SO(10)$ and $SU(5)$, a non-minimal Higgs inflation scenario is incorporated. The radiative breaking of the $U(1)_{\chi}$ symmetry at a slightly lower scale plays a pivotal role in aligning the string tension parameter with the observable range. The resultant gravitational wave spectrum not only accounts for the signal observed in the most recent pulsar timing array (PTA) experiments but is also accessible to both current and future ground-based and space-based experiments.Comment: 22 pages and5 figures

Carcinosarcoma of the esophagus

Carcinosarcoma of the esophagus is a rare neoplasm characterized histologically by presence of carcinomatous and sarcomatous elements. Case report of carcinosarcoma of the esophagogastric junction whose morphological and immunohistochemical features makes it quite distinctive from other tumours is presented. It was an ulcerated lesion diagnosed in an elderly Afghan lady located 34 cms from the incisor teeth. The patient was a smoker

Noise-Induced Hearing Loss (NIHL) Prediction in Humans Using a Modified Back Propagation Neural Network

Noise-Induced Hearing Loss (NIHL) has become a major source of health problem in industrial workers due to continuous exposure to high frequency sounds emitting from the machines. In the past, several studies have been carried-out to identify NIHL industrial workers. Unfortunately, these studies neglected some important factors that directly affect hearing ability in human. Artificial Neural Network (ANN) provides very effective way to predict hearing loss in humans. However, the training process for an ANN required the designers to arbitrarily select parameters such as network topology, initial weights and biases, learning rate value, the activation function, value for gain in activation function and momentum. An improper choice of any of these parameters can result in slow convergence or even network paralysis, where the training process comes to a standstill or get stuck at local minima. Therefore, this current study focuses on proposing a new framework on using Gradient Descent Back Propagation Neural Network model with an improvement on the momentum value to identify the important factors that directly affect the hearing ability of industrial workers. Results from the prediction will be used in determining the environmental health hazards which affect the workers health

An improved back propagation leaning algorithm using second order methods with gain parameter

Back Propagation (BP) algorithm is one of the oldest learning techniques used by Artificial Neural Networks (ANN). It has successfully been implemented in various practical problems. However, the algorithm still faces some drawbacks such as getting easily stuck at local minima and needs longer time to converge on an acceptable solution. Recently, the introduction of Second Order Methods has shown a significant improvement on the learning in BP but it still has some drawbacks such as slow convergence and complexity. To overcome these limitations, this research proposed a modified approach for BP by introducing the Conjugate Gradient and QuasiNewton which were Second Order methods together with ‘gain’ parameter. The performances of the proposed approach is evaluated in terms of lowest number of epochs, lowest CPU time and highest accuracy on five benchmark classification datasets such as Glass, Horse, 7Bit Parity, Indian Liver Patient and Lung Cancer. The results show that the proposed Second Order methods with ‘gain’ performed better than the BP algorithm