Mário Oleskovicz

Power Transformer Differential Protection Based on Clarke's Transform and Fuzzy Systems

The power transformer is a piece of electrical equipment that needs continuous monitoring and fast protection since it is very expensive and an essential element for a power system to perform effectively. 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 affect the protection behavior and the power system stability. This paper proposes the development of a new algorithm to improve the differential protection performance by using fuzzy logic and Clarkes transform. An electrical power system was modeled using Alternative Transients Program (ATP) software to obtain the operational conditions and fault situations needed to test the algorithm developed. The results were compared to a commercial relay for validation, showing the advantages of the new method.

Load Profile Identification Interface for Consumer Online Monitoring Purposes in Smart Grids

This paper presents a method for the identification of consumer load profiles in a totally automatic way. The main objective was to develop an interface between utilities and consumers in order to obtain data from smart meters and identify the load profile. To this end, client/server software interfaces capable of transmitting and receiving data through TCP/IP sockets were developed. In this case, the consumer and utility were represented by client and server software, respectively. However, in order to be able to consider all the stages of this methodology, a database was created to store data related to consumers (meter readings, electricity bills and personal data). From this database, the utility software was able to furnish each consumers load profiles and to use this information to make decisions. Moreover, the software sends the electricity bill to the consumer by e-mail each month. Experimental and simulation results were obtained to validate the methodology and to show some benefits that can be achieved with the introduction of smart meters into power distribution systems in the context of load profile identification.

Identification of residential load profile in the Smart Grid context

This work presents an automatic method for identification of residential load profile in the Smart Grid context. Hence, in this research were used client/server software interfaces to transmit and receive data through the Internet. In this case, the residential consumers and utility were represented by client and server software, respectively. However, to consider all the stages of this method, a database was created to store fictitious data related to consumer measurements. From these data, the utility software was able to furnish information about consumers load profile and use this information to make decisions. The results were obtained using an experimental workbench that contains residential loads, a configurable power supply and an energy analyzer. It is show in an experimental way some benefits that can be achieved with the introduction of Smart Grid concept on distribution systems.

Transient analysis concerning capacitor bank switching in a distribution system

Abstract The quality of electric power has been a constant topic of study, mainly because inherent problems to it can lead to great economic losses, especially in industrial processes. Among the various factors that affect power quality, those related to transients originating from capacitor bank (CB) switching in the primary distribution systems must be highlighted. In this work, the characteristics of transients resulting from the switching of utility CBs are analyzed, as well as factors that influence their intensities. The conditions under which these effects are mitigated can then be investigated. A circuit that represents a real distribution system, 13.8 kV, from Cia Paulista de Forca e Luz (CPFL—a Brazilian utility) was simulated through the software Alternative Transients Program ( atp ) for purposes of this study. Finally, a comparison with real-life data recorded at the distribution system was performed in order to validate the present simulation.

FPGA implementation of Genetic Algorithms for frequency estimation in power systems

The main objective of this research is to present a fast and efficient method based on genetic algorithms (GAs) to measure the frequency deviation, as well as the voltage magnitude and phase angle of a noisy sinusoid wave. The frequency estimation is formulated as an optimization problem, where the aim is to minimize the estimation error. The use of GAs has an advantage of better immunity against noise disturbance present in the input data. In addition, this work investigates the implementation of a GA scheme in FPGA (field-programmable gate array) in order to obtain a device that can work properly in real time. The new approach is able to accurately estimate the frequency, voltage magnitude and phase angle, simulating an on-line frequency relay. The proposed methodology provides an efficient alternative to frequency relays having advantages compared to traditional approaches.

Estudo comparativo de ferramentas modernas de análise aplicadas à qualidade da energia elétrica

This work presents a comparative study amongst tools for the analysis of Power Quality, emphasizing the Windowed Fourier Transform (WFT), the Wavelet Transform (WT) as well as Artificial Neural Networks (ANN). From the tools mentioned, the WFT and WT are applicable to the detection, location and classification of abnormalities related to voltage waveforms in a distribution system for the diagnosis of the present situation. As it will be shown, the classification of the phenomena can also be performed using alternative methods, as the ANN. Tests show that the mentioned tools have great potentiality to be applied to the evaluation of Power Quality. Some peculiarities of each tool will be emphasized.

Multi-layer neural networks applied to distance relaying

This paper demonstrates the use of Artificial Neural Networks (ANNs) as pattern classifiers for a distance relay operation of transmission lines. Two different types of ANN architectures, concerning the input data, are taken into account. One approach utilises the first five post-fault samples as inputs. The other one employs the magnitudes of the three-phase voltage and current phasors (including the zero sequence) as inputs. A comparison of how well the schemes performed is carried out. An improvement concerning the use of ANNs for protection purposes is found.

Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances

Graphical abstractDisplay Omitted HighlightsAn adaptive threshold is determined for segmentation of power quality signals.The power quality signals are based on mathematical models and acquired in field.Wavelet transforms are used to decompose the signals.The intersections between the adaptive threshold and the wavelet transform detail curves determine the start and the end of the segments.The adaptive threshold was accurate in more than 96 percent of the signals. Detecting discontinuities in electrical signals from recorded oscillograms makes it possible to segment them. This is the first step in implementing automated methods which will ensure disturbances in electrical power systems are detected, classified and stored. In this context, this paper presents a way of determining an adaptive threshold based on the decomposition of electrical signals through the Discrete Wavelet Transform (DWT) using Daubechies family filter banks, allowing for the segmentation of signals and, as a consequence, the analysis of disturbances related to Power Quality (PQ). Considering this, the proposed approach was initially evaluated for signals originating from mathematical models representing short-term voltage fluctuations, transients (impulsive and oscillatory) and harmonic distortions. In the synthetic signal database, either single or combined occurrences of more than one disturbance were considered. By applying the DWT, the amount of energy and entropy of energy were then calculated for the leaves of the second level of decomposition. Based on these calculations, a unique adaptive threshold could be determined for each analyzed signal. Afterwards, the amount of existing intersections between the threshold and the curve of details obtained for the second level of decomposition was then defined. Thus, the intersections determine the beginning and end of the segments. In order to validate the approach, the performance of the proposed methodology was analyzed considering the signals obtained from oscillograms provided by IEEE 1159.3 Task Force, as well as real oscillograms obtained from a regional distribution utility. After these analyses, it was observed that the proposed approach is efficient and applicable to automatic segmentation of events related to PQ.

Assessment of voltage fluctuations induced by electromechanical oscillations in distributed generation systems

This paper focuses on analyzing the impact of electromechanical oscillations caused by distributed synchronous generators on power distribution systems from the power quality point of view. The electromechanical oscillations are natural responses of synchronous generators to a mismatch in their net torques, and thus, are present in every distribution system equipped with such generators. In this work, a methodology is established to analyze the induced phenomena, which are mainly related to voltage fluctuations, providing a complete framework for a thorough power quality analysis concerning the electromechanical oscillations. This methodology is used to the power quality assessment of a real power distribution system from a Brazilian utility. The obtained results show that electromechanical oscillations in distribution systems can indeed be a significant source of power quality problems, specially regarding voltage fluctuations.

A genetic based algorithm for frequency relaying using FPGAs

This work presents an accurate and precise Genetic Algorithm (GA) for frequency estimation of Electrical Power System (EPS) signals. The problem of estimating the frequency of a distorted electrical signal is modeled as an optimization problem. The advantages of GAs in this approach include the use of coding for a number of solutions which facilitates computer implementation, as well as the search for an appropriate solution from a population of possible solutions. The GA is programmed in a FPGA (Field-Programmable Gate Array) device and the estimation procedure is performed in real-time. This is made possible due to (a) the implicit parallelism of FPGAs in computing their instructions and (b) the suitable choice of steps of GAs to explore this parallelism. To evaluate the performance of the proposed method, an EPS was simulated having typical operation conditions. The resulting signals were analyzed by the proposed GA-FPGA approach and promising results were compared to a commercial relay.