An Extended Ann-Based High Speed Accurate Transmission Line Fault Location for Double Phase Toearth Fault on Non-Direct-Ground

Okwudili, E.; Ezechukwu, O A; Ezema, Chukwuedozie N.

doi:10.29121/ijoest.v1.i1.2017.04

Cited by 4 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…preparation to get maintenance with economy and intrinsic reliability [8], [16]. Monte Carlo simulation and method of state enumeration are used for larger severe events under complex condition of operation.…”

Section: State Enumeration Methods and Monte Carlo Simulationmentioning

confidence: 99%

“…Live-work technologies have advantages as no power loss, reduces penalties during downtime, the problem can be attended immediately, avoids emergency outages, which helps to minimize event risk and reducing switching cycles of switchgears. Live line maintenance needs less manpower and less time required for maintenance as compared to the cold line method, this gives economic benefits and postponement of life to apparatus gives added ways to improve live line technologies [2], [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of artificial intelligence in early fault detection of transmission line-a case study in India

Mawle

Dhomane

Burade

2022

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue.</span>

show abstract

Section: State Enumeration Methods and Monte Carlo Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of artificial intelligence in early fault detection of transmission line-a case study in India

Mawle

Dhomane

Burade

2022

IJECE

View full text Add to dashboard Cite

show abstract

“…Using current and voltage data from the source end, ANFIS and ANN are used to precisely identify fault types and pinpoint the transmission line issue. Global System For Mobile Communication (GSM) and Global Positioning System (GPS) based ANN model has been developed in [16]. The challenge with such a communication-based protection system is that it could be more reliable, and there are always issues with cybersecurity threats.…”

Section: Introductionmentioning

confidence: 99%

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Kanwal,

Jiriwibhakorn

2024

IEEE Access

View full text Add to dashboard Cite

Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.

show abstract

“…Artificial neural networks (ANN), a universal function approximator, can be used to predict fault location with higher accuracy compared to other algorithms [14][15][16]. For example, Obi et al [17] employed ANN in detecting and locating faults using GPS and GSM for remote information access. However, the system is tested on fault resistance up to only 50 Ohms.…”

Section: Introductionmentioning

confidence: 99%

IoT based Fourth Generation SCADA System for High Voltage Networks Fault Diagnosis based on BSDT-ANN

Fayyad¹,

Gawad

Saleh

et al. 2023

The Egyptian International Journal of Engineering Sciences and

View full text Add to dashboard Cite

Smart Grid substations rely on conventional Supervisory Control and Data Acquisition (SCADA) systems for remote supervision and control. However, these systems are limited in the geographical area they cover. Recently, the Internet of Things (IoT) has paved the way for connecting a vast number of devices to the Internet, which would be effective and beneficial for power system automation and data acquisition. In this paper, an intelligent low-cost SCADA system based on IoT for transmission line fault diagnosis is proposed. In the first step of the proposed scheme, voltage and current signals at the relaying point are preprocessed and analyzed using Discrete Cosine Transform (DCT). Next, signal energy components are extracted and sent to a Boosted Decision Tree (BSDT), a reliable and fast ensemble classifier, to identify fault type and, accordingly, a specific ANN is activated to estimate fault location. The diagnosis data is sent to a microcontroller to be displayed, trip the load circuit, and allow communication with Cloud ThingSpeak platform via the ESP8266 wi-fi communication module which makes data available anywhere all over the world. The approach is applied on a real-world HV transmission line, located between Samalut and Cairo, Egypt, where more than 16000 faults cases are well tested. The results show the reliability, validity, and effectiveness of the proposed approach.

show abstract

An Extended Ann-Based High Speed Accurate Transmission Line Fault Location for Double Phase Toearth Fault on Non-Direct-Ground

Cited by 4 publications

References 7 publications

Application of artificial intelligence in early fault detection of transmission line-a case study in India

Application of artificial intelligence in early fault detection of transmission line-a case study in India

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

IoT based Fourth Generation SCADA System for High Voltage Networks Fault Diagnosis based on BSDT-ANN

Contact Info

Product

Resources

About