Optimized Method for Locating the Source of Voltage Sags

Short-Duration Voltage Variations (SDVVs) are the power quality disturbances (PQD) that mainly affect industrial systems, and are originated for various reasons, in particular short circuits over large areas, even those originating in remote points of the electrical system. The location problem aims to indicate the area or region or distance from the substation that is connected to the source causing the voltage sags, and is a fundamental task to ensure good power quality. One of the strategies used to determine the location of sources causing SDVVs and for an implementation of machine learning algorithms in modern distribution networks, called Smart Grids. Monitoring a Smart Grid plays a key role, however mostly it generates a large volume of data (Big Data) and as a result, multiple challenges arise due to the properties of this data such as volume, variety and velocity. This work presents an optimization through genetic algorithm to select meters which already exist in the Smart Grid, using a voltage sag location method in order to reduce the data obtained and analyzed throughout the localization process. Optimization was evaluated through a comparison with a non-optimized localization method, this comparison showed a difference between the hit rates of less than 1%

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

Fast Characterization of Power Quality Events Based on Discrete Signal Processing and Data Mining

The extensive use of solid-state power electronics technology in industrial, commercial and residential equipment causes degradation of quality of electric power with the deterioration of the supply voltage. The disturbances result in degradation of the efficiency, decaying the life span of the equipment, increase in the losses, electromagnetic interference, the malfunctions of equipment and other harmful fallout. Generally, the power quality is the measurement of an ideal power supply. More over the power quality is the continuity and characteristics of the supply voltage in terms of frequency, magnitude and symmetry. The mitigation of power quality (PQ) disturbances requires detection of the source and causes of disturbances. The MODWT is a suitable method for forecasting of further occurrence of disturbance. However proper and quick detection and localization of the disturbances plays a crucial role in the power quality environment. Hence, in this thesis, a fast detection technique has been proposed along with the MODWT in order to provide time-scale representation of the signals by removing the drawback of the traditional methods like DWT and ST. Comparative analysis shows that SGWT is a best technique for localization and detection of distortions than the conventional methods. During the course of the research, it is found that suitable algorithms are required for the characterization of the disturbances for smooth mitigation of the distortions. So, data mining based classifier has been proposed for discrimination of both single and multiple disturbances. Further, the suitable features are needed for efficient characterization of the disturbances. Hence, the suitable features are extracted in order to ii reduce the number of raw data. The data normalization also plays a crucial role for efficient classification. These classification techniques are fast and able to analyze large number of disturbances. In this thesis, large numbers of signals are synthesized both in noisy and noise free environment. In the real time environment, these techniques have been performed satisfactorily. This leads to increase in the overall efficiency of the combination of the detection and classification method. In recent times, with the advancement of renewable source requires better quality of power. The important issue of the today’s distributed generation based interconnected power system is the islanding detection. Non detection zone is a good and reliable measurement of the islanding. However, failure to detect islanding situation sometimes leads to number of serious problem both for the utility and the customers. Hence, this thesis also provides a comparative analysis of the benefits and the drawbacks of aforementioned detection methods which are applied in power quality environment. The voltage signal at the PCC of the renewable distributed generation embedded with IEEE−14 bus system is captured and given as input to the analysis methods in order to extract features from the output of the analysis. The proposed SGWT properly discriminates power quality disturbances from the islanding events by introducing threshold selection. The data mining classifiers are implemented for classification of power quality as well as islanding events captured from IEEE bus system. Similar to the previous cases, the signals of same length are given to all the detection methods in ordered to compare the time of operation of each these methods. Moreover, the proposed techniques have been applied in noise free and noisy environment, bus system embedded with renewable source, real time environment etc. The overall findings of the thesis could be useful for the industrial and domestic applications. Since the detection methods are simple and faster, they could be useful for power industry and other applications such as medical science etc. Similarly, the classification can be used for application such as stock exchange, medical science etc

Biophysical foundation and function of depolarizing afterpotentials in principal cells of the medial entorhinal cortex

Neurons in layer II of the rodent medial entorhinal cortex (MEC) encode spatial information. One particular type, grid cells, tends to fire at specific spatial locations that form hexagonal lattices covering the explored environment. Within these firing fields grid cells frequently show short high-frequency spike sequences. Such bursts have received little attention but may contribute substantially to encoding spatial information. On the other hand, in vitro recordings of MEC principal cells have revealed that action potentials are followed by prominent depolarizing afterpotentials (DAP). Their biophysical foundation and function, however, are poorly understood. The objective of this study is to understand the mechanism behind the DAP by creating a biophysical realistic model of a stellate cell and to draw a connection between DAPs and burst firing in vivo. The developed single-compartment model reproduced the main electrophysi- ological characteristics of stellate cells in the MEC layer II, that are a DAP, sag, tonic firing in response to positive step currents and resonance. Using virtual blocking experiments, it was found that for the generation of the DAP only a NaP , KDR and leak current were necessary whereby the NaP current also exhibited a resurgent component. This suggests that for the generation of the DAP a balance between several currents is needed. In addition, the persistent and resurgent sodium current might play an important role. We analyzed the relevance of DAPs in vivo using whole-cell recordings of grid cells from Domnisoru et al. (2013). We found that around 20% of the cells exhibited a DAP. However, the percentage of cells was much lower than estimates from in vitro recordings. We showed that this is partly due to the quality of the recording as selecting APs from presumably good parts of the recording improved the visibility of DAPs. To investigate the relationship between DAPs and burst firing all cells were classified into bursty and non-bursty based on the spike-time autocorrelation. All cells with a DAP were bursty except the cell with the smallest DAP. Moreover, taking the mean of the spike-triggered average of the membrane potential for all bursty and non-bursty cells respectively showed a clear DAP for bursty but not for non-bursty cells. In summary, we found that the DAP can be realized in a single-compartment model by a NaP , KDR and leak current and provided evidence for the relevance of DAPs for burst firing in vivo

THE ROLE OF SOMATOSTATIN-EXPRESSING INTERNEURONS IN ANTERIOR PIRIFORM CORTEX

At first approximation, the anterior piriform cortex (APC) appears to be a homogenous structure in terms of connectivity and sensory processing. Feedforward excitatory afferent input innervates the APC uniformly throughout the cortex, as does the local recurrent excitatory input. Odors are represented by a distributed ensemble of neurons spread throughout the APC, showing no obvious columnar structure or topography. Still, this supposed homogeneity belies the abundant diversity of inhibitory processes that may underlie sensory processing of the piriform cortex. Unfortunately, current understanding of inhibitory interneurons and their function is fairly coarse, limiting our ability to model olfactory processing. Our research was focused on clarifying the quality of inhibition in anterior piriform cortex, specifically focusing on Layer 3 somatostatin-expressing interneurons, which mediate recurrent, feedback inhibition. We first characterized the types of inhibition seen in the APC and their potential functions. Using electrical stimulation of APC fiber tracts, we measured the relative balance of feedforward and recurrent excitation and inhibition onto the three major classes of excitatory cells in piriform cortex to understand the relative roles for each type of input onto piriform principal cells. Then, we characterized the electrophysiological and functional properties of interneurons in APC, including somatostatin cells, to better understand the diversity of cells in olfactory cortex. Finally, we were interested in the role of inhibition in mediating principal cell activity. To do this, we used a novel technology - Targeted Recombination of Active Populations (TRAP) - to molecularly label active neurons during exploration of a novel odor environment. We found that neurons responding to an odor are not distributed uniformly across the APC, but rather on a gradient along the rostrocaudal axis. Using optogenetics, we found a spatial bias of inhibition onto pyramidal cells that corroborated the TRAP data, as well as discovering a subclass of interneurons that receive an opposing rostrocaudal bias of inhibition - suggesting a possible role for disinhibition in sensory processing. We determined that somatostatin cells are poised to mediate asymmetric inhibition onto interneurons, and therefore asymmetric disinhibition onto pyramidal cells

Combined Wavelet-neural Clasifier For Power Distribution Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2002Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2002Bu çalışmada, dağıtım sistemlerinde hibrid “Dalgacık-Yapay Sinir ağı (YSA) tabanlı” bir yaklaşımla arıza sınıflama işlemi gerçeklenmiştir. 34.5 kV “Sağmalcılar-Maltepe” dağıtım sistemi PSCAD/EMTDC yazılımı kullanılarak arıza sınıflayıcı için gereken veri üretilmiştir. Tezin amacı, on farklı kısa-devre sistem arızalarını tanımlayabilecek bir sınıflayıcı tasarlamaktır. Sistemde kullanılan arıza işaretleri 5 kHZ lik örnekleme frekansı ile üretilmiştir. Farklı arıza noktaları ve farklı arıza oluşum açılarındaki hat-akımları ve hat-toprak gerilimlerini içeren sistem arızaları ile bir veritabanı oluşturulmuştur. “Çoklu-çözünürlük işaret ayrıştırma” tekniği kullanılarak altı-kanal akım ve gerilim örneklerinden karakteristik bigi çıkarılmıştır. PSCAD/EMTDC ile üretilen veri bu şekilde bir ön islemden geçirildikten sonra YSA-tabanlı bir yapı ile sınıflama islemi gerçekleştirilmiştir. Bu yapının görevi çeşitli sistem ve arıza koşullarını kapsayan karmaşık arıza sınıflama problemini çözebilmektir. Bu çalışmada, Kohonen’in öğrenme algoritmasını kullanan bir “Kendine-Organize harita” ile “eğitilebilen vektör kuantalama” teknikleri kullanılmıştır. Bu “dalgacık-sinir ağı” tabanlı arıza sınıflayıcı ile eğitim kümesi için % 99-100 arasında ve sınıflayıcıya daha önce hiç verilmemiş test kümesi ile de %85-92 arasında sınıflama oranları elde edilmiştir. Elde edilen başarım oranları literatürdeki sonuçlara yakındır. Geliştirilen birleşik “dalgacık-sinir ağı” tabanlı sınıflayıcı elektrik dağıtım sistemlerindeki arızaların belirlenmesinde iyi sonuçlar vermiş ve iyi bir performans sağlamıştır.In this study an integrated design of fault classifier in a distribution system by using a hybrid “Wavelet- Artificial neural network (ANN) based” approach is implemented. Data for the fault classifier is produced by using PSCAD/EMTDC simulation program on 34.5 kV “Sagmalcılar-Maltepe” distribution system in Istanbul. The objective is to design a classifier capable of recognizing ten classes of three-phase system faults. The signals are generated at an equivalent sampling rate of 5 KHz per channel. A database of line currents and line-to-ground voltages is built up including system faults at different fault inception angles and fault locations. The characteristic information over six-channel of current and voltage samples is extracted by the “wavelet multi-resolution analysis” technique, which is a preprocessing unit to obtain a small size of interpretable features from the raw data. After preprocessing the raw data, an ANN-based tool was employed for classification task. The main idea in this approach is solving the complex fault (three-phase short-circuit) classification problem under various system and fault conditions. In this project, a self-organizing map, with Kohonen’s learning algorithm and type-one learning vector quantization technique is implemented into the fault classification study. The performance of the wavelet-neural fault classification scheme is found to be around “99-100%” for the training data and around “85-92%” for the test data, which the classifier has not been trained on. This result is comparable to the studied fault classifiers in the literature. Combined wavelet-neural classifier showed a promising future to identify the faults in electric distribution systemsYüksek LisansM.Sc

Measurements, Models, Systems and Design

531 s.