Browsing by Author "Foudeh, Husam"

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    Advanced quadrotor control strategies for health monitoring of overhead power lines.
    (Cranfield University, 2021-07) Foudeh, Husam; Luk, Patrick Chi-Kwong; Whidborne, James F.
    Research into autonomous control and behavior of mobile vehicles has become increasingly widespread. In particular, unmanned aerial vehicles (UAVs) have seen an upsurge of interest and of the many UAVs available, the multirotor has shown significant potential in monitoring and surveillance tasks. The objective of this research’s programme is to develop novel control that enable quadrotors to track and inspect on high voltage electricity networks. This is a research application that has elicited little attention. This thesis provides a succinct and comprehensive literature research in both state-of-art overhead power lines (OPL) inspection technologies, and quadrotor design and control. It proceeds to motivate, develop and evaluate a learning algorithms controller which exploit the repeated nature of the fault-finding task. Very few iterative learning control (ILC) algorithms have been implemented in this area, and no analysis or practical results exist to specifically investigate UAV performance to modelling uncertainty and exogenous disturbances. In particular, novel contributions are made in ILC algorithms are derived and validated by experimental results on an AscTec Hummingbird quadrotor. It has taken a robust comparisons among several ILC approaches (gradient-based, norm optimal and Newton method ICLs), and the comparisons are largely based on analytical calculated results. In the case of optimal ILC approaches, a new algorithm for nonlinear MIMO systems is developed to cope with exogenous disturbances and noise severely affect UAV as well as a novel tuning method for bnew variation is formulated and applied to the problem of reference tracking for a 6-degree-of-freedom UAV with a two-loop structure. The first loop addresses the system lag and another tackles the possibility of a disturbance commonly encountered when inspection of OPL. The new algorithm contributes to good trajectory tracking and very good convergence speed while minimizing disturbance effects. A linearisation design approach has been extended to enable new updates using quadcopters dynamics. Then constraints have embedded to meet the application demands. After overcoming this deficiency, the ILC controller is further extended based on point-to-point through a straight conductor to fulfil the full task and perform a 2-3 sequence of operations. Finally, the ILC development results are given follow-up using 3D analysis approach where these results are the first ever in this key area.
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    Forecasting and modelling the uncertainty of low voltage network demand and the effect of renewable energy sources
    (MDPI, 2021-04-12) Alasali, Feras; Foudeh, Husam; Ali, Esraa Mousa; Nusair, Khaled; Holderbaum, William
    More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model