This study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence, developing the analytics on optimal power-distribution across pertinent power-grids are verified with the models proposed. Forecast algorithms and computational outcomes on supply-demand performance are indicated and illustratively explained using real-world data sets. This study on electric utility takes duly into considerations of both deterministic (technological factors) as well as stochastic variables associated with the available resource-capacity and demand-profile details. Thus, towards forecasting exercise as above, a representative load-curve (RLC) is defined; and, it is optimally determined using an Artificial Neural Network (ANN) method using the data availed on supply-demand characteristics of a practical power-grid. This RLC is subsequently considered as an input parametric profile on tariff policies associated with electric power product-cost. This research further focuses on developing an optimal/suboptimal electric-power distribution scheme across power-grids deployed between multiple resources and different sets of user demands. Again, the optimal/suboptimal decisions are enabled using ANN-based simulations performed on load sharing details. The underlying supply-demand forecasting on distribution service profile is essential to support predictive designs on the amount of power required (or to be generated from single and/or multiple resources) versus distributable shares to different consumers demanding distinct loads. Another topic addressed refers to a business model on a cost reflective tariff levied in an electric power service in terms of the associated hedonic heuristics of customers versus service products offered by the utility operators. This model is based on hedonic considerations and technoeconomic heuristics of incumbent systems In the ANN simulations as above, bootstrapping technique is adopted to generate pseudo-replicates of the available data set and they are used to train the ANN net towards convergence. A traditional, multilayer ANN architecture (implemented with feed-forward and backpropagation techniques) is designed and modified to support a fast convergence algorithm, used for forecasting and in load-sharing computations. Underlying simulations are carried out using case-study details on electric utility gathered from the literature. In all, ANN-based prediction of a representative load-curve to assess power-consumption and tariff details in electrical power systems supporting a smart-grid, analysis of load-sharing and distribution of electric power on smart grids using an ANN and evaluation of electric power system infrastructure in terms of tariff worthiness deduced via hedonic heuristics, constitute the major thematic efforts addressed in this research study. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Orientador: José Roberto Sanches Mantovani / Banca: Fabio Bertequini Leão / Banca: Luis Gustavo Wesz da Silva / Resumo: Neste trabalho propõe-se um algoritmo baseado na meta-heurística busca dispersa para o problema de reconfiguração de redes de distribuição de energia elétrica radiais, considerando como sistema de codificação uma estrutura denominada representação nó profundidade (RNP). O problema é modelado como não linear inteiro misto e considera como objetivo principal minimizar as perdas de potência ativa nos alimentadores do sistema de distribuição. Utiliza-se neste trabalho o modelo de cargas com potência constante, como também o modelo exponencial de cargas. O algoritmo proposto é implementado em linguagem de programação C++ e testado em quatro sistemas conhecidos na literatura, 14, 84, 136 e 202 barras. A partir dos resultados obtidos, verifica-se o bom desempenho do algoritmo, pois é capaz de gerar soluções de boa qualidade atendendo a todas as restrições físicas e operacionais do problema. / Abstract: This work proposes a scatter search algorithm to solve the electric power distribution system reconfiguration problem, considering the encoding system node depth representation. The problem is a mixed-integer nonlinear programming and the objective is to minimize the real power losses in the distribution system. It is used in the work load model with constant power, but also exponential model load. The proposed algorithm is implemented in C + +. The validity of the methodology is verified through four commonly case studies such as 14, 84, 136 and 202 bus system. Results show the effectiveness and good performance of the proposed algorithm, where it obtains the good quality solution satisfying the operational and physics constraints of problem. / Mestre

Orientador: José Roberto Sanches Mantovani / Coorientador: Luis Gustavo Wesz da Silva / Banca: Marina Lavorato de Oliveira / Banca: Marcelo Escobar de Oliveira / Resumo: Neste trabalho, propõe-se um algoritmo baseado na meta-heurística busca tabu para o problema de restauração de redes de distribuição de energia elétrica radiais com geração distribuída, considerando como sistema de codificação uma estrutura denominada representação nó-profundidade (RNP). O problema é modelado como não linear inteiro misto e considera os principais objetivos da restauração de redes de distribuição: minimizar número de consumidores sem fornecimento de energia elétrica e o número de chaveamentos. Propõe-se, também, uma sequência lógica de chaveamentos que garante os aspectos operacionais. O algoritmo desenvolvido foi implementado em linguagem de programação C++ e testado em sistemas de distribuição de 136 e 7052 barras / Abstract: This work proposes a methodology based in the meta-heuristic tabu search to distribution power system restoration considering distributed generators installed on the system, using the encoding system node depth representation. The problem is established as a mixed-integer nonlinear programming taking into account the mainly goals: to minimize both the number of consumers without supply and the number of switching. This work also proposes a logic sequence of switching operations, taking care of operational issues. The proposed algorithm was implemented in C++ programming language and tested in a 136 and a 7052 bus distribution systems / Mestre

Thesis (M.S.)--Drexel University, 2007. / Includes abstract. Includes bibliographical references (leaves 81-83).

The main objective of this thesis is to develop models for the power distribution network of high performance gigascale chips. The two main concerns in distributing power in a chip are voltage drop and electromigration-induced reliability failures. The voltage drop on the power distribution network is due to IR-drop and simultaneous switching noise. IR-drop is the voltage drop due to current passing through the resistances of the power distribution network. Simultaneous switching noise is due to varying current passing through the inductances of the power distribution network. Compact physical models are derived for the IR-drop and electromigration for different types of packages. These chip-package co-design models enable designers in the early stages of the design to estimate the on-chip interconnect resources, and also to choose type and size of the package required for power distribution. Modeling of the simultaneous switching noise requires the simulation of a large circuit with thousands of inductances. The main obstacle challenging the simulation of a simultaneous switching noise circuit model is the computing resources required to solve the dense inductance matrix. In this work, a new relative inductance matrix is introduced to solve massively coupled RLC interconnects. It is proven that the analysis using this method is accurate for a wide frequency range and all configurations. Using the new inductance matrix makes the circuit simulations significantly faster without losing accuracy.

Electric power distribution network is an important part of electrical power systems for delivering electricity to consumers. Electric power utilities worldwide are increasingly adopting the computer aided monitoring, control and management of electric power distribution systems to provide better services to the electrical consumers. Therefore, research and development activities worldwide are being carried out to automate the electric power distribution system. The power distribution system consists of a three-phase source supplying power through single-, two-, or three-phase distribution lines, switches, and transformers to a set of buses with a given load demand. In addition, unlike transmission systems, single-, two-, and three-phase sections exist in the network and single-, two-, and three-phase loads exist in the distribution networks. Further, most distribution systems are overhead systems, which are susceptible to faults caused by a variety of situations such as adverse weather conditions, equipment failure, traffic accidents, etc. When a fault occurs on a distribution line, it is very important for the utility to identify the fault location as quickly as possible for improving the service reliability. Hence, one of the crucial blocks in the operation of distribution system is that of fault detection and it’s location. The achievement of this objective depends on the success of the distribution automation system. The distribution automation system should be implemented quickly and accurately in order to isolate those affected branches from the healthy parts and to take alternative measures to restore normal power supply. Fault location in the distribution system is a difficult task due to its high complexity and difficulty caused by unique characteristics of the distribution system. These unique characteristics are discussed in the present work. In recent years, some techniques have been discussed for the location of faults, particularly in radial distribution systems. These methods use various algorithmic approaches, where the fault location is iteratively calculated by updating the fault current. Heuristic and Expert System approaches for locating fault in distribution system are also proposed which uses more measurements. Measurements are assumed to be available at the sending end of the faulty line segment, which are not true in reality as the measurements are only available at the substation and at limited nodes of the distribution networks through the use of remote terminal units. The emerging techniques of Artificial Intelligence (AI) can be a solution to this problem. Among the various AI based techniques like Expert systems, Fuzzy Set and ANN systems, the ANN approach for fault location is found to be encouraging. In this thesis, an ANN approaches with limited measurements are used to locate fault in long distribution networks with laterals. Initially the distribution system modeling (using actual a-b-c phase representation) for three-, two-, and single-phase laterals, three-, two-, and single- phase loads are described. Also an efficient three-phase load flow and short circuit analysis with loads are described which is used to simulate all types of fault conditions on distribution systems. In this work, function approximation (FA) is the main technique used and the classification techniques take a major supportive role to the FA problem. Fault location in distribution systems is explained as a FA problem, which is difficult to solve due to the various practical constraints particular to distribution systems. Incorporating classification techniques reduce this FA problem to simpler ones. The function that is approximated is the relation between the three-phase voltage and current measurements at the substation and at selected number of buses (inputs), and the line impedance of the fault points from the substation (outputs). This function is approximated by feed forward neural network (FFNN). Similarly for solving the classification problems such as fault type classification and source short circuit level classification, Radial Basis Probabilistic Neural Network (RBPNN) has been employed. The work presented in this thesis is the combinational use of FFNN and RBPNN for estimating the fault location. Levenberg Marquardt learning method, which is robust and fast, is used for training FFNN. A typical unbalanced 11-node test system, an IEEE 34 nodes test system and a practical 69- bus long distribution systems with different configurations are considered for the study. The results show that the proposed approaches of fault location gives accurate results in terms of estimated fault location. Practical situations in distribution systems such as unbalanced loading, three-, two-, and single- phase laterals, limited measurements available, all types of faults, a wide range of varying source short circuit levels, varying loading conditions, long feeders with multiple laterals and different network configurations are considered for the study. The result shows the feasibility of applying the proposed method in practical distribution system fault diagnosis.

Thesis (Ph.D.)--University of Texas at Arlington, 2009.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 139-145).

Thesis--M. Phil., University of Hong Kong. / Mimeographed.

Thesis (M.S.)--Ohio University, November, 2009. / Title from PDF t.p. Includes bibliographical references.