Redes neurais aplicadas a relés diferenciais para transformadores de potência

Este trabalho apresenta um sistema completo de proteção diferencial para transformadores de potência, através da teoria de Redes Neurais Artificiais (RNAs). O método proposto trata a classificação do sistema de proteção como um problema de reconhecimento de padrões e constitui um método alternativo aos algoritmos convencionais. Muitos fatores, tais como a energização do transformador e a saturação dos TCs, podem causar uma operação inadequada do relé de proteção. Um sistema de proteção completo foi desenvolvido, incluindo um módulo baseado em RNA em substituição aos filtros harmônicos, usados no algoritmo convencional. Este módulo se constituiu de uma RNA tipo MLP Backpropagation para a classificação de sinais. Abordagens baseadas na reconstrução dos sinais distorcidos causados pela saturação dos TCs são também propostas. Essa análise foi realizada através do emprego de RNAs Recorrentes de Elman, utilizadas para reconstruir os sinais distorcidos pela saturação dos TCs. Essas rotinas foram adicionadas ao algoritmo final de proteção. O desempenho dos algoritmos propostos foi comparado ao do algoritmo convencional de proteção de transformadores, em termos de velocidade e precisão de resposta. Com a utilização de uma ferramenta de inteligência artificial em um algoritmo completo de proteção de transformadores, uma solução precisa, rápida e eficiente foi obtida, se comparada aos métodos convencionais.This paper presents a complete differential protection system for power transformers, applying the Artificial Neural Network (ANN) theory. The proposed approach treat the classification of the protection system as a problem of pattern recognition and as an alternative method to the conventional algorithms. Several factors such as, for example, transformer energization and CT saturation can cause an inadequate operation of the protection relay. A complete protection system was developed, including an ANN-based device in substitution to harmonic filters in use in the conventional algorithm. This stage was carried out by a MLP Backpropagtion ANN to the signals classification. Some approaches concerning the reconstruction of the distorted signals caused by the CTs saturation are also proposed. This analysis was made by Elman recurrent ANNs used to reconstruct the distorted signals caused by CT saturation. These routines are added to the final protection algorithm. With the use of artificial intelligence tools in a complete power transformer protection algorithm, a very precise, fast and efficient solution was obtained, if compared to the conventional methods

New approach for power transformer protection based on intelligent hybrid systems

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior

Distance protection algorithm for multiterminal HVDC systems using the Hilbert–Huang transform

Multiterminal high-voltage direct current (HVDC) systems still need advances in terms of protection in order to improve their reliability. In this context, the distance protection can play a major role by adding selectivity to the existing DC fault detection algorithms. Hence, the present work proposes a non-unit DC distance protection algorithm that uses the frequency of the DC voltage transient oscillation to estimate the distance of the fault. The DC voltage transient frequency is extracted using the Hilbert–Huang transform and compared with a pre-defined frequency/distance curve. The technique was evaluated by simulating faults in a four-terminal symmetric monopole multiterminal HVDC system. In the simulation environment the algorithm was fully selective for faults within the first protection zone and had a correct operation rate of 94% or more for faults located in the second protection zone. To further validate the presented technique, the proposed algorithm was embedded in a digital signal controller, running in real-time. In all performed tests in hardware, the faults were correctly detected and identified as being internal or external. The results indicate that the proposed algorithm could be used in real-world applications, in conjunction with fault detection techniques, adding selectivity to multiterminal DC protection schemes

Non-unit distance protection algorithm for multiterminal MMC-HVDC systems using DC capacitor resonance frequency

High-voltage DC (HVDC) transmission has been largely used to interconnect asynchronous systems and integrate renewable energy resources into electrical grids. However, the high short-circuit currents and low-tolerance of power electronics equipment impose new challenges for these systems' protection. To address these challenges, a new distance protection algorithm for HVDC grids with Modular Multilevel Converters (MMCs) is proposed in this paper. The proposed algorithm identifies the faulty line/cable using the resonance frequency of a DC capacitor installed in each terminal. The technique was tested in a four-terminal HVDC grid with five cables and ten circuit breakers. The algorithm was highly selective for faults in the lines and provided fast identification, in less than 1 ms, without communication amongst terminals. The algorithm was tested in hardware under high-noise conditions and provided reliable results

Genetic algorithms applied to phasor estimation and frequency tracking in PMU development.

This paper presents an efficient intelligent tool applied to phasor measurements and frequency tracking\ud of fundamental components for PMU application. The estimation task is modeled as an optimization\ud problem in order to use genetic algorithms to search for optimal solutions. Very promising results are\ud presented. This approach is compared to traditional methods considering the IEEE C37.118 standard\ud and the results show that this intelligent tool offers better performance, especially during transient\ud events, considering traditional methods. The proposed approach is implemented in hardware using\ud FPGAs to take advantage of the intrinsic parallelism of genetic algorithms, making it applicable to realtime\ud estimationsCNPqFAPES

Short-circuit analytical model for modular multilevel converters considering DC cable capacitance

Developing analytical short-circuit models for Modular Multilevel Converters (MMC) is not straightforward due to their switching and blocking characteristics. Short-circuit models for MMCs have been developed previously in the literature. However, there is a lack of understanding regarding the dynamics in the short-circuit model when the DC cable capacitance is taken into account. Therefore, this work proposes an analytical pole-To-pole short-circuit model for half-bridge MMCs that considers the cable capacitance and terminal capacitors and accounts their contribution to fault dynamics. An approximated analytical model has been derived separating the system solutions in different natural frequencies. The proposed model provides an excellent approximation for a vast range of realistic system parameters. The analytical model reproduced the behaviour of the variables in the time domain and provided a clear basis for interpreting the dynamics of the voltages and currents involved

An approximated analytical model for pole-to-ground faults in symmetrical monopole MMC-HVDC systems

Developing pole-to-ground (PG) fault models for Modular Multilevel Converters (MMC) is not straightforward due to the fault asymmetry and converter switching concerning blocking characteristics. Various studies have been carried out regarding transient simulation of PG faults. However, there is a lack of analytical models for the first stage of the fault. Therefore, this work proposes an approximated analytical model for PG faults in half-bridge MMCs. Closed-form expressions for the MMC contribution to the fault and the fault current are derived. We show that separating the solutions in different resonant frequencies represents the system dynamics and facilitates the interpretation of the phenomena. When compared to system calculated by Ordinary Differential Equations (ODEs), the proposed model provided a good approximation for a wide range of parameters. When compared to the full PSCAD solution, the analytical model was able to precisely calculate the peak fault current value, which confirmed its validity

A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

In this paper, a novel method for power quality signal decomposition is proposed based on Independent Component Analysis (ICA). This method aims to decompose the power system signal (voltage or current) into components that can provide more specific information about the different disturbances which are occurring simultaneously during a multiple disturbance situation. The ICA is originally a multichannel technique. However, the method proposes its use to blindly separate out disturbances existing in a single measured signal (single channel). Therefore, a preprocessing step for the ICA is proposed using a filter bank. The proposed method was applied to synthetic data, simulated data, as well as actual power system signals, showing a very good performance. A comparison with the decomposition provided by the Discrete Wavelet Transform shows that the proposed method presented better decoupling for the analyzed data. (C) 2012 Elsevier Ltd. All rights reserved.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior)Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)FAPEMIG (Fundacao de Amparo a Pesquisa do Estado de Minas Gerais

Experimental Platform for Controlled Faults on Synchronous Generator Armature Windings

An experimental platform that allows application of internal faults on the armature windings of a specially modified synchronous generator in a controlled environment is described. It allows recording and studying current and voltage waveforms of internal fault conditions that may occur in a synchronous generator. Thus, traditional and new protection functions can be tested by using real data, and the transient response of the machine due to internal faults can be analyzed more closely. The hardware-software platform is described in detail, as well as all its control functions. The results can contribute significantly in new protection developments, as well as for educational purposes.Conselho Nacional de Desenvolvimento Cientifico e TecnologicoConselho Nacional de Desenvolvimento Cientifico e TecnologicoCoordenadoria de Aperfeicoamento de Pessoal de Nivel SuperiorCoordenadoria de Aperfeicoamento de Pessoal de Nivel SuperiorFundacao de Amparo a Pesquisa do Estado de Sao PauloFundacao de Amparo a Pesquisa do Estado de Sao Paul

Artificial neural network model of discharge lamps in the power quality context

This paper presents a methodology for modeling high intensity discharge lamps based on artificial neural networks. The methodology provides a model which is able to represent the device operating in the frequency of distribution systems, facing events related to power quality. With the aid of a data acquisition system to monitor the laboratory experiment, and using $$\text{ MATLAB }^{\textregistered }$$ software, data was obtained for the training of two neural networks. These neural networks, working together, were able to represent with high fidelity the behavior of a discharge lamp. The excellent performance obtained by these models allowed the simulation of a group of lamps in a distribution system with shorter simulation time when compared to mathematical models. This fact justified the application of this family of loads in electric power systems. The representation of the device facing power quality disturbances also proved to be a useful tool for more complex studies in distribution systems. © 2013 Brazilian Society for Automatics - SBA