This dissertation is a systematic study of electric power distribution system reliability evaluation and improvement. Reliability evaluation of electric power systems has traditionally been an integral part of planning and operation. Changes in the electric utility coupled with aging electric apparatus create a need for more realistic techniques for power system reliability modeling. This work presents a reliability evaluation technique that combines set theory and Graph Trace Analysis (GTA). Unlike the traditional Markov approach, this technique provides a fast solution for large system reliability evaluation by managing computer memory efficiently with iterators, assuming a single failure at a time. A reconfiguration for restoration algorithm is also created to enhance the accuracy of the reliability evaluation, considering multiple concurrent failures. As opposed to most restoration simulation methods used in reliability analysis, which convert restoration problems into mathematical models and only can solve radial systems, this new algorithm seeks the reconfiguration solution from topology characteristics of the network itself. As a result the new reconfiguration algorithm can handle systems with loops. In analyzing system reliability, this research takes into account time-varying load patterns, and seeks approaches that are financially justified. An exhaustive search scheme is used to calculate optimal locations for Distributed Generators (DG) from the reliability point of view. A Discrete Ascent Optimal Programming (DAOP) load shifting approach is proposed to provide low cost, reliability improvement solutions. As weather conditions have an important effect on distribution component failure rates, the influence of different types of storms has been incorporated into this study. Storm outage models are created based on ten years' worth of weather and power outage data. An observer is designed to predict the number of outages for an approaching or on going storm. A circuit corridor model is applied to investigate the relationship between power outages and lightning activity. / Ph. D.

Interruptions in power supply are inevitable due to faults in power system distribution network. These interruptions are not only expensive for the customers but also for the distribution system operator in the form of penalties. Increase in system redundancy or the use of component-specific sensors can help in reduction of interruptions. However, these options are not always economically feasible. Therefore, there is a need to check for other possibilities to reduce the risk of outages. The data stored in substations can be used for reducing the risk of outages by deriving component importance indices followed by ranking and predicting the outages. This thesis presents component importance indices derived by identifying the critical components in the grid and assigning index based on certain criterion. The model for predicting the faults is based on the weather conditions observed during the outages in the past. Component importance indices are derived and ranked based on the de-energisation time of components, frequency and impact of outages. This helps prioritize components according to the chosen criterion and adapt monitoring strategies by focusing on the most critical components. Based on categorical Naive Bayes, a model is developed to predict the probability of fault/failure, location and component type likely to be affected for a given set of weather conditions. The results from the component importance indices reveal that each component’s rank varies based on the chosen criterion. This indicates that certain components are critical with respect to specific criterion and not all criteria. However, some components are ranked high in all the methods. These components are critical and need focused monitoring. The reliability of results from component importance indices to a great extent depends on the time frame of the outage data considered for analysis. The prediction model can alert the distribution system operator regarding the possible outages in the network for a given set of weather conditions. However, the prediction of location and component type likely to be affected is relatively inaccurate, since the number of outages considered in the time frame is low. By updating the model regularly with new data, the predictions would be more accurate. / Avbrott i strömförsörjningen är oundvikliga på grund av fel i distributionsnätet för kraftsystemet. Dessa avbrott är inte bara dyra för kunderna utan också för distributionssystemoperatören i form av påföljder. Ökad systemredundans eller användning av komponentspecifika sensorer kan hjälpa till att minska avbrott. Dessa alternativ är dock inte alltid ekonomiskt genomförbara. Därför är det nödvändigt att kontrollera om det finns andra möjligheter för att minska risken för avbrott. Data lagrade i transformatorstationer kan användas för att minska risken för avbrott genom att härleda komponentviktindex följt av rangordning och förutsäga avbrott. I denna avhandling härleds viktighetsindex genom att identifiera de kritiska komponenterna i nätet och tilldela index baserat på vissa kriterier. Felprognoserna gjordes baserat på de väderförhållanden som observerades under avbrott. komponentviktighetsindex härleds och rankas baserat på komponenternas urladdningstid, frekvens och påverkan av avbrott. Detta hjälper till att prioritera komponenter enligt det valda kriteriet och anpassa övervakningsstrategier genom att fokusera på de mest kritiska komponenterna. Baserat på kategoriska Naive Bayes utvecklas en modell för att förutsäga sannolikheten för fel / fel, plats och komponenttyp som sannolikt kommer att påverkas under en viss uppsättning väderförhållanden. Resultaten från komponentviktighetsindexen visar att varje komponents rang varierar beroende på det valda kriteriet. Vissa komponenter rankas dock högt i alla metoder. Dessa komponenter är kritiska och behöver fokuserad övervakning. Tillförlitligheten hos resultat från komponentviktindex beror till stor del på tidsramen för avbrottsdata som beaktas för analys. Prognosmodellen kan varna distributionssystemoperatören om möjliga avbrott i nätverket för en viss uppsättning väderförhållanden. Förutsägelsen av plats och komponenttyp som sannolikt kommer att påverkas är dock relativt felaktig, eftersom antalet avbrott som beaktas i tidsramen är lågt. Genom att uppdatera modellen regelbundet med nya data skulle förutsägelserna vara mer exakta.

[pt] As variações que ocorrem na estrutura da troposfera ao longo do tempo em relação à sua condição mediana provocam diversos fenômenos que fazem variar aleatoriamente o nível de sinal recebido num enlace rádio. Estas variações aleatórias são denominadas desvanecimentos. Os desvanecimentos são normalmente classificados em rápidos e lentos. Os desvanecimentos rápidos estão geralmente associados ao efeito de multipercurso atmosférico que é fortemente dependente da freqüência, sendo por isto denominados desvanecimentos seletivos, e são a principal causa de degradação do desempenho de enlaces rádio digitais de alta capacidade. Os modelos existentes para a caracterização estatística do desvanecimento por multipercurso são semi-empíricos e baseados em dados experimentais obtidos em regiões de clima temperado, acarretando uma má estimativa quando aplicados a regiões de clima tropical e equatorial. Neste trabalho é apresentada uma avaliação dos métodos existentes para previsão do desempenho de enlaces rádio digitais de alta capacidade, a partir da utilização de dados reais de desempenho extraídos de medidas em um tronco rádio de alta capacidade numa região tropical. / [en] The variations which happen in the troposphere layers throughout the time in relation to the median condition of the signal cause various phenomena that change the received signal level at digital radio relay systems randomly. The random changes are named fading. Fading is normally classified as fast or slow. The former is normally associated with the atmospheric multipath propagation and is strongly dependent on frequency; therefore, this is named selective fading and it is normally the cause of performance degradation in high capacity digital radio relays. The existing models for statistics of multipath fading are semi - empirical and based on experimental data extracts from regions the climate of which is temperate; and this gives a rough estimate with respect to the tropical and equatorial zones. This work presents an evaluation of existing methods of performance prediction for high capacity digital radio relay systems using real performance data obtained from measures of a high capacity digital radio link in operation in the tropical region.