Power system contingency ranking using Newton Raphson load flow method and its prediction using soft computing techniques

The most important requirement and need of proper operation of power system is maintenance of the system security. Power system security assessment helps in monitoring and in giving up to date analysis regarding currents, bus voltages, power flows, status of circuit breaker, etc. This system assessment has been done in offline mode in which the system conditions are determined using ac power flows. The use of AC power flows is it gives information of power flows in terms of MW and MVAR , line over loadings and voltage limit violation with accurate values. Contingency selection or contingency screening is a process in which probable and potential critical contingencies are identified for which it requires consideration of each line or generator outage. . Contingency ranking is a procedure of contingency analysis in which contingencies are arranged in descending order, sorted out by the severity of contingency. Overall severity index (OPI) is calculated for determining the ranking of contingency. Overall performance index is the summation of two performance index , one of the performance index determines line overloading and other performance index determines bus voltage drop limit violation and are known as active power performance index and voltage performance index respectively. Here in this proposed work the contingency ranking has been done with IEEE 5 bus and 14 bus system. But the system parameters are dynamic in nature, keeps on changing and may affect the system parameters that are why there is need of soft computing techniques for the prediction purpose. Fuzzy logic approach has also been used. Two model of Artificial Neural Network namely, Multi Layer Feed Forward Neural Network (MFNN) and Radial Basis Function Network (RBFNN) have been considered. With these soft computing techniques the prediction method helps in obtaining the OPI with greater accuracy

Big Data Analysis application in the renewable energy market: wind power

Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións. Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)

Context sensitive neural network by overlapped systems

The use of context dependency in neural networks is an important issue in many cognitive situations. In this report we introduce a novel context dependent neural network model based on overlapped multi-neural network structures. We present a detailed study about contextual features and some of its applications in neural networks. We also present some different strategies for applying overlapping in neural networks. The generalization ability of a neural network is mainly influenced by three factors, the number and performance of the learning data samples, the complexity of the learning algorithm employed, and the network size. Neural network overlapping is one of the practical techniques of achieving a better generalization and recognition rate. This is due to its ability of decreasing the number of free weights of a neural network and providing less complexity of the neural network function. For this purpose overlapped neural networks have been used in feed-forward neural networks (MFNN) , self organizing maps (SOM) and in shared weight neural networks (SWNN). Overlapped neural networks also have the ability of performing a function localization over the neural network feature space. Among the feature space of any problem, three different types of features (from the relevance point of view ) can be distinguished: primary, contextual, and irrelevant features. Researches in the contextual features are mainly concerned with two issues. Identifying such contextual features, and managing them. We are presenting the strategy of identifying these context-sensitive features and five basic strategies for managing them. We are also presenting a context sensitive model for overcoming the slow convergence problems, and a context dependent (cd) neuron model that is considered a generalization of the traditional neuron model. We introduce a novel approach for problems regardless of sufficiency or accuracy of their historical observations or lab simulation data. Our approach is based on imposing a context of problem performance metrics into networks and gaining the enhancement towards its satisfactory state. We use an overlapped system of back propagation neural networks for our purpose. A main neural network is responsible for mapping input and output relation while a regulatory neural network evaluates the performance metrics satisfaction. We provide special training and testing algorithms for the overlapped system that guarantees a synchronized solution for both neural networks. An example of traffic control problem is simulated. The result of simulation shows a great enhancement of the solution using our approach

Design of Neural Network Filters

Emnet for n rv rende licentiatafhandling er design af neurale netv rks ltre. Filtre baseret pa neurale netv rk kan ses som udvidelser af det klassiske line re adaptive l-ter rettet mod modellering af uline re sammenh nge. Hovedv gten l gges pa en neural netv rks implementering af den ikke-rekursive, uline re adaptive model med additiv st j. Formalet er at klarl gge en r kke faser forbundet med design af neural netv rks arkitekturer med henblik pa at udf re forskellige \black-box " modellerings opgaver sa som: System identi kation, invers modellering og pr diktion af tidsserier. De v senligste bidrag omfatter: Formulering af en neural netv rks baseret kanonisk lter repr sentation, der danner baggrund for udvikling af et arkitektur klassi kationssystem. I hovedsagen drejer det sig om en skelnen mellem globale og lokale modeller. Dette leder til at en r kke kendte neurale netv rks arkitekturer kan klassi ceres, og yderligere abnes der mulighed for udvikling af helt nye strukturer. I denne sammenh ng ndes en gennemgang af en r kke velkendte arkitekturer. I s rdeleshed l gges der v gt pa behandlingen af multi-lags perceptron neural netv rket

Non intrusive load monitoring & identification for energy management system using computational intelligence approach

Includes bibliography.Electrical energy is the life line to every nation’s or continent development and economic progress. Referable to the recent growth in the demand for electricity and shortage in production, it is indispensable to develop strategies for effective energy management and system delivery. Load monitoring such as intrusive load monitoring, non-intrusive load monitoring, and identification of domestic electrical appliances is proposed especially at the residential level since it is the major energy consumer. The intrusive load monitoring provides accurate results and would allow each individual appliance's energy consumption to be transmitted to a central hub. Nevertheless, there are many practical disadvantages to this method that have motivated the introduction of non-intrusive load monitoring system. The fiscal cost of manufacturing and installing enough monitoring devices to match the number of domestic appliances is considered to be a disadvantage. In addition, the installation of one meter per household appliances would lead to congestion in the house and thus cause inconvenience to the occupants of the house, therefore, non-intrusive load monitoring technique was developed to alleviate the aforementioned challenges of intrusive load monitoring. Non-intrusive load monitoring (NILM) is the process of disaggregating a household’s total energy consumption into its contributing appliances. The total household load is monitored via a single monitoring device such as smart meter (SM). NILM provides cost effective and convenient means of load monitoring and identification. Several nonintrusive load monitoring and identification techniques are reviewed. However, the literature lacks a comprehensive system that can identify appliances with small energy consumption, appliances with overlapping energy consumption and a group of appliance ranges at once. This has been the major setback to most of the adopted techniques. In this dissertation, we propose techniques that overcome these setbacks by combining artificial neural networks (ANN) with a developed algorithm to identify appliances ranges that contribute to the energy consumption within a given period of time usually an hour interval