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.

Impact of the Kaikōura earthquake on the electrical power system infrastructure

2017 ◽  
Vol 50 (2) ◽  
pp. 300-305 ◽  
Author(s):  
Yang Liu ◽  
Nirmal-Kumar Nair ◽  
Andrew Renton ◽  
Stuart Wilson
Keyword(s):  
Power System ◽  
Electrical Power ◽  
Electrical Power System ◽  
Electrical Transmission ◽  
Earthquake Preparedness ◽  
Infrastructure Performance ◽  
Kaikoura Earthquake ◽  
The Impact ◽  
Transmission And Distribution ◽  
Background Context

This paper summarizes the impact the 2016 Kaikōura earthquakes have had on electrical transmission and distribution infrastructure performance. It also provides background context to the distribution network operator’s (i.e. MainPower’s) prior earthquake preparedness following the 2010 earthquakes in the region.

Fault Location Techniques in Electrical Power System-A Review

2017 ◽  
Vol 8 (1) ◽  
pp. 206 ◽  
Author(s):  
Hui Hwang Goh ◽  
Sy yi Sim ◽  
Mohamad Amirul Hafiz Mohamed ◽  
Abdul Khairi Abdul Rahman ◽  
Chin Wan Ling ◽  
...  
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Fault Location ◽  
Electrical Power ◽  
Electric Power System ◽  
Electrical Power System ◽  
System A ◽  
Main Challenge ◽  
Distribution Line ◽  
Continuous Power

<p>Electric fault is the main challenge in the process of providing continues electric supply. Fault can occur at anytime and anywhere. Due to the fault causes are mainly based on natural disaster or accident. Most fault occurrence hardly predicted nor avoided. Therefore, a quick response fault detection is necessary to ensure that the fault area is maintained to ensure a continuous power supply system. Hence, a system is required to detect and locate the position of the fault in the power system especially in the transmission line and distribution line. This paper will review the type of fault that possibly occurs in an electric power system, the type of fault detection and location technique that are available together with the protection device that can be utilized in the power system to protect the equipment from electric fault.</p><p> </p>

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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