scholarly journals Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4042
Author(s):  
Vinicius F. Vidal ◽  
Leonardo M. Honório ◽  
Felipe M. Dias ◽  
Milena F. Pinto ◽  
Alexandre L. Carvalho ◽  
...  
Keyword(s):  
Point Cloud ◽  
Electrical Power ◽  
Real Life ◽  
Electrical Power System ◽  
Thermal Cameras ◽  
Dense Point ◽  
External Sources ◽  
Improved Performance ◽  
Sensors Fusion ◽  
Thermal Inspection

Thermal inspection is a powerful tool that enables the diagnosis of several components at its early stages. One critical aspect that influences thermal inspection outputs is the infrared reflection from external sources. This situation may change the readings, demanding that an expert correctly define the camera position, which is a time consuming and expensive operation. To mitigate this problem, this work proposes an autonomous system capable of identifying infrared reflections by filtering and fusing data obtained from both stereo and thermal cameras. The process starts by acquiring readings from multiples Observation Points (OPs) where, at each OP, the system processes the 3D point cloud and thermal image by fusing them together. The result is a dense point cloud where each point has its spatial position and temperature. Considering that each point’s information is acquired from multiple poses, it is possible to generate a temperature profile of each spatial point and filter undesirable readings caused by interference and other phenomena. To deploy and test this approach, a Directional Robotic System (DRS) is mounted over a traditional human-operated service vehicle. In that way, the DRS autonomously tracks and inspects any desirable equipment as the service vehicle passes them by. To demonstrate the results, this work presents the algorithm workflow, a proof of concept, and a real application result, showing improved performance in real-life conditions.

Algorithms for Generating Adaptive Projection Patterns for 3-D Shape Measurement

2007 ◽  
Author(s):  
Tao Peng ◽  
Satyandra K. Gupta
Keyword(s):  
Point Cloud ◽  
Measurement Accuracy ◽  
Point Clouds ◽  
Fringe Projection ◽  
Shape Measurement ◽  
Digital Fringe Projection ◽  
Physical Experiments ◽  
Dense Point ◽  
Improved Performance ◽  
Fringe Patterns

Point cloud acquisition using digital fringe projection (PCCDFP) is a non-contact technique for acquiring dense point clouds to represent the 3-D shapes of objects. Most existing PCCDFP systems use projection patterns consisting of straight fringes with fixed fringe pitches. In certain situations, such patterns do not give the best results. In our earlier work, we have shown that in some situations, patterns that use curved fringes with spatial pitch variation can significantly improve the process of constructing point clouds. This paper describes algorithms for automatically generating adaptive projection patterns that use curved fringes with spatial pitch variation to provide improved results for an object being measured. In addition, we also describe the supporting algorithms that are needed for utilizing adaptive projection patterns. Both simulation and physical experiments show that, adaptive patterns are able to achieve improved performance, in terms of measurement accuracy and coverage, than fixed-pitch straight fringe patterns.

scholarly journals Nano Topological Analysis For Power System Control

2016 ◽  
Vol 15 (14) ◽  
pp. 7416-7422
Author(s):  
M.Kamel EL-Sayed
Keyword(s):  
Power System ◽  
Rough Set ◽  
Topological Analysis ◽  
Electrical Power ◽  
Real Life ◽  
System Control ◽  
Suggested Model ◽  
Electrical Power System ◽  
Lower Approximation ◽  
Power System Control

In this paper,we introduce an approach for analysis of information concerning electrical power system. The suggested method is a result of hybridizing rough set concepts with nano topology constructed on the set of all data using the boundary of uncertain decision sets and its lower approximation. Bases of nano topologies are used as indicators for selecting effective features in information system of a power control. This method is applied using the main experimental data which make the suggested model near from the real life information.

A model for calculating losses in transformer related to orders and harmonic amplitude under analysis of joule effect, eddy current and hysteresis

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Gabriel Santos ◽  
Bruno M. Laurindo ◽  
Marcio Z. Fortes ◽  
Bruno W. França ◽  
Flavio G.R. Martins
Keyword(s):  
Power Systems ◽  
Eddy Current ◽  
Distribution System ◽  
Electrical Power ◽  
Real Life ◽  
Harmonic Amplitude ◽  
Electrical Power System ◽  
Joule Effect ◽  
Non Linear ◽  
Electrical Transformers

AbstractThe electrical transformer is one of the most used equipment in electrical power systems. The non-linear electrical loads are increasing, mainly in the electrical distribution system, and the electrical power transformer is inserted in this scenario, supplying these loads. Consequently, the increasing non-linear loads affect the electrical transformers and their factors directly, like in the dependency between temperature and harmonic increase. Therefore, to study the influence of harmonics in the transformer’s temperature, one should understand how it will behave with these changes in the electrical power system. For this reason, in this article, numeric simulations and tests were performed to predict the transformer temperature behavior. The proposal is a numeric technique for coupling two equations, thermal and electromagnetic, of an electrical transformer, considering heat sources regarding joule, eddy current, and hysteresis effect. To evaluate the numeric simulation and understanding the electrical transformer behavior in real-life, tests with specific harmonic orders (3rd, 5th, and 7th) and their combinations and a test with less than 10% total harmonic distortion, according to IEEE 519 standard were performed. It is verified that the electrical transformer temperature rises with the increase in harmonic amplitude and its orders. Results show that the industry must be aware of the effects of the increasing non-linear loads when designing the next generation of transformers concerning their durability and lifetime.

Algorithms for Generating Adaptive Projection Patterns for 3D Shape Measurement

2008 ◽  
Vol 8 (3) ◽  
Author(s):  
Tao Peng ◽  
Satyandra K. Gupta
Keyword(s):  
Point Cloud ◽  
Measurement Accuracy ◽  
Point Clouds ◽  
3D Shape Measurement ◽  
Digital Fringe Projection ◽  
Physical Experiments ◽  
Dense Point ◽  
Improved Performance ◽  
Fringe Patterns ◽  
3D Shapes

Point cloud construction using digital fringe projection (PCCDFP) is a noncontact technique for acquiring dense point clouds to represent the 3D shapes of objects. Most existing PCCDFP systems use projection patterns consisting of straight fringes with fixed fringe pitches. In certain situations, such patterns do not give the best results. In our earlier work, we have shown that for surfaces with large range of normal directions, patterns that use curved fringes with spatial pitch variation can significantly improve the process of constructing point clouds. This paper describes algorithms for automatically generating adaptive projection patterns that use curved fringes with spatial pitch variation to provide improved results for an object being measured. We also describe the supporting algorithms that are needed for utilizing adaptive projection patterns. Both simulation and physical experiments show that adaptive patterns are able to achieve improved performance, in terms of measurement accuracy and coverage, as compared to fixed-pitch straight fringe patterns.

Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

2019 ◽  
Vol 14 ◽  
Author(s):  
Iyappan Murugesan ◽  
Karpagam Sathish
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Power System ◽  
Electrical Power ◽  
Daubechies Wavelet ◽  
Electrical Power System ◽  
Gradient Ascent ◽  
Neural Learning ◽  
Weight Value ◽  
Transmission And Distribution

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

scholarly journals Optical Voltage Transformer Based on FBG-PZT for Power Quality Measurement

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2699
Author(s):  
Marceli N. Gonçalves ◽  
Marcelo M. Werneck
Keyword(s):  
Power System ◽  
Power Quality ◽  
Distribution System ◽  
Electrical Power ◽  
Quality Measurement ◽  
Pockels Effect ◽  
Electrical Power System ◽  
Voltage Transformer ◽  
Technical Literature ◽  
Distribution Lines

Optical Current Transformers (OCTs) and Optical Voltage Transformers (OVTs) are an alternative to the conventional transformers for protection and metering purposes with a much smaller footprint and weight. Their advantages were widely discussed in scientific and technical literature and commercial applications based on the well-known Faraday and Pockels effect. However, the literature is still scarce in studies evaluating the use of optical transformers for power quality purposes, an important issue of power system designed to analyze the various phenomena that cause power quality disturbances. In this paper, we constructed a temperature-independent prototype of an optical voltage transformer based on fiber Bragg grating (FBG) and piezoelectric ceramics (PZT), adequate to be used in field surveys at 13.8 kV distribution lines. The OVT was tested under several disturbances defined in IEEE standards that can occur in the electrical power system, especially short-duration voltage variations such as SAG, SWELL, and INTERRUPTION. The results demonstrated that the proposed OVT presents a dynamic response capable of satisfactorily measuring such disturbances and that it can be used as a power quality monitor for a 13.8 kV distribution system. Test on the proposed system concluded that it was capable to reproduce up to the 41st harmonic without significative distortion and impulsive surges up to 2.5 kHz. As an advantage, when compared with conventional systems to monitor power quality, the prototype can be remote-monitored, and therefore, be installed at strategic locations on distribution lines to be monitored kilometers away, without the need to be electrically powered.

Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Diego A. Monroy-Ortiz ◽  
Sergio A. Dorado-Rojas ◽  
Eduardo Mojica-Nava ◽  
Sergio Rivera
Keyword(s):  
Power Systems ◽  
Power System ◽  
Model Reduction ◽  
Electrical Power ◽  
Schur Complement ◽  
Power Transfer ◽  
Electrical Power System ◽  
Admittance Matrix ◽  
Test Beds ◽  
Power Transfer Distribution Factors

Abstract This article presents a comparison between two different methods to perform model reduction of an Electrical Power System (EPS). The first is the well-known Kron Reduction Method (KRM) that is used to remove the interior nodes (also known as internal, passive, or load nodes) of an EPS. This method computes the Schur complement of the primitive admittance matrix of an EPS to obtain a reduced model that preserves the information of the system as seen from to the generation nodes. Since the primitive admittance matrix is equivalent to the Laplacian of a graph that represents the interconnections between the nodes of an EPS, this procedure is also significant from the perspective of graph theory. On the other hand, the second procedure based on Power Transfer Distribution Factors (PTDF) uses approximations of DC power flows to define regions to be reduced within the system. In this study, both techniques were applied to obtain reduced-order models of two test beds: a 14-node IEEE system and the Colombian power system (1116 buses), in order to test scalability. In analyzing the reduction of the test beds, the characteristics of each method were classified and compiled in order to know its advantages depending on the type of application. Finally, it was found that the PTDF technique is more robust in terms of the definition of power transfer in congestion zones, while the KRM method may be more accurate.

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