ABSTRACT Voltage instability has become a major concern in power systems. Many blackouts have been reported where the main cause is voltage instability. This thesis deals with two specific areas of voltage stability in on-line power system security assessments: small-disturbance (long-term) and large-disturbance (short-term) voltage stability assessment. For each category of voltage stability, both voltage stability analysis and controls are studied. The overall objective is to use the learning capabilities of computational intelligence technology to build up the comprehensive on-line power system security assessment and control strategy as well as to enhance the speed and efficiency of the process with minimal human intervention. The voltage stability problems are quantified by voltage stability indices which measure the system for the closeness of current operating point to voltage instability. The indices are different for small-disturbance and large-disturbance voltage stability assessment. Conventional approaches, such as continuation power flow or time-domain simulation, can be used to obtain voltage stability indices. However, these conventional approaches are limited by computation time that is significant for on-line computation. The Artificial Neural Network (ANN) approach is proposed to compute voltage stability indices as an alternative to the conventional approaches. The proposed ANN algorithm is used to estimate voltage stability indices under both normal and contingency operating conditions. The input variables of ANN are obtained in real-time by an on-line measurement system, i.e. Phasor Measurement Units (PMU). This thesis will propose a suboptimal approach for seeking the best locations for PMUs from a voltage stability viewpoint. The ANN-based method is not limited to compute voltage stability indices but can also be extended to determine suitable control actions. Load shedding is one of the most effective approaches against short-term voltage instability under large disturbances. The basic requirement of load shedding for recovering voltage stability is to seek an optimal solution for when, where, and how much load should be shed. Two simulation based approaches, particle swarm optimization (PSO) algorithm and sensitivity based algorithm, are proposed for load shedding to prevent voltage instability or collapse. Both approaches are based on time-domain simulation.

ABSTRACT Voltage instability has become a major concern in power systems. Many blackouts have been reported where the main cause is voltage instability. This thesis deals with two specific areas of voltage stability in on-line power system security assessments: small-disturbance (long-term) and large-disturbance (short-term) voltage stability assessment. For each category of voltage stability, both voltage stability analysis and controls are studied. The overall objective is to use the learning capabilities of computational intelligence technology to build up the comprehensive on-line power system security assessment and control strategy as well as to enhance the speed and efficiency of the process with minimal human intervention. The voltage stability problems are quantified by voltage stability indices which measure the system for the closeness of current operating point to voltage instability. The indices are different for small-disturbance and large-disturbance voltage stability assessment. Conventional approaches, such as continuation power flow or time-domain simulation, can be used to obtain voltage stability indices. However, these conventional approaches are limited by computation time that is significant for on-line computation. The Artificial Neural Network (ANN) approach is proposed to compute voltage stability indices as an alternative to the conventional approaches. The proposed ANN algorithm is used to estimate voltage stability indices under both normal and contingency operating conditions. The input variables of ANN are obtained in real-time by an on-line measurement system, i.e. Phasor Measurement Units (PMU). This thesis will propose a suboptimal approach for seeking the best locations for PMUs from a voltage stability viewpoint. The ANN-based method is not limited to compute voltage stability indices but can also be extended to determine suitable control actions. Load shedding is one of the most effective approaches against short-term voltage instability under large disturbances. The basic requirement of load shedding for recovering voltage stability is to seek an optimal solution for when, where, and how much load should be shed. Two simulation based approaches, particle swarm optimization (PSO) algorithm and sensitivity based algorithm, are proposed for load shedding to prevent voltage instability or collapse. Both approaches are based on time-domain simulation.

This research introduces an image retrieval system which is, in different ways, inspired by the human vision system. The main problems with existing machine vision systems and image understanding are studied and identified, in order to design a system that relies on human image understanding. The main improvement of the developed system is that it uses the human attention principles in the process of image contents identification. Human attention shall be represented by saliency extraction algorithms, which extract the salient regions or in other words, the regions of interest. This work presents a new approach for the saliency identification which relies on the irregularity of the region. Irregularity is clearly defined and measuring tools developed. These measures are derived from the formality and variation of the region with respect to the surrounding regions. Both local and global saliency have been studied and appropriate algorithms were developed based on the local and global irregularity defined in this work. The need for suitable automatic clustering techniques motivate us to study the available clustering techniques and to development of a technique that is suitable for salient points clustering. Based on the fact that humans usually look at the surrounding region of the gaze point, an agglomerative clustering technique is developed utilising the principles of blobs extraction and intersection. Automatic thresholding was needed in different stages of the system development. Therefore, a Fuzzy thresholding technique was developed. Evaluation methods of saliency region extraction have been studied and analysed; subsequently we have developed evaluation techniques based on the extracted regions (or points) and compared them with the ground truth data. The proposed algorithms were tested against standard datasets and compared with the existing state-of-the-art algorithms. Both quantitative and qualitative benchmarking are presented in this thesis and a detailed discussion for the results has been included. The benchmarking showed promising results in different algorithms. The developed algorithms have been utilised in designing an integrated saliency-based image retrieval system which uses the salient regions to give a description for the scene. The system auto-labels the objects in the image by identifying the salient objects and gives labels based on the knowledge database contents. In addition, the system identifies the unimportant part of the image (background) to give a full description for the scene.