This study presents a new technique for fault detecting and diagnosing (FDD) of power grids faults. Data fusion topologies are a popular practice in smart grids, offering the opportunity to fuse data to gain a new feature dependent on the complementary sensors. In fact, to gain a forecast dependent on certain detectors, likely in certain regions, when another detector failures. Related to prior measurements, associated sensors can achieve the above capabilities. The whole thesis presents the issue of finding an enhanced data fusion methodology used for optimal design and operation of fault detection systems. Discrete Wavelet transform (DWT) has a major drawback of noise sensitivity. The proposed technique solves the problems of DWT, where a high precision classification of noisy and faulty signals could be obtained. Fusion between voltage and power readings will be execute to provide a more reliable and accurate decision to determine the exact location of the fault. In this technique, the complex tree learner classifier is used, and the system is trained for multiple situations where most faults may occur. All simulations were carried out and performed on the standard IEEE 14 bus system to check the efficiency and performance of the technique proposed. In comparison, the presented technique has been extended to several study cases such as faulty or noisy signals to show the effectiveness of the enhanced methodology. Simulation results demonstrate, as will be discussed, a strong effectiveness of the suggested approach relative to others. The main feature of the proposed technique is that it can differentiate between faulty and noisy signals and recognize the fault's location quickly and with high reliability.

Breast cancer is usually treated with surgery, radiotherapy and chemotherapy. Fever-range hyperthermia treatments (HT) enhance the effect of radiotherapy and chemotherapy in terms of local tumor control and survival rates. Electromagnetic (EM) radiation is commonly used; however there is an increasing interest for using ultrasound (US) due to the larger penetration depth and better focusing capabilities. An adequate applicator for fever-range HT treatment of tumors in the entire intact breast region is not yet available. The purpose of this work is to describe the theoretical design and characterization of an ultrasound cylindrical phased-array applicator. The US applicator is used to compute heating profiles in breast tumors and the results are obtained. In this thesis, breast models are constructed from human data that is obtained from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. Design parameters such as frequency, number of physical elements, width of element, kerf (spacing between element), and display angle of ultrasound transducer are optimized to obtain the smallest possible focus. The field ii program will be used for simulating ultrasound transducer fields and ultrasound imaging using linear acoustics. The k-Wave toolbox is developed to show how the nonlinear beam pattern from an ultrasound transducer can be modeled.