scholarly journals DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

Author(s):  
Jyoti Singh ◽  
Dr. Prateek Nigam ◽  
Achie Malviya

Power transformers are essential devices for the durable and reliable performance of an electrical system. the main objective of this study is to analyze three classical diagnosis techniques to identify incipient faults in Transformer oil using Rogers’s Ratio Method, Doernenburg Ratio Method, and ANN which is a type of artificial intelligence learning method. Implementation of the system in MATLAB software for each diagnosis method and compare their accuracy and efficiency and hence design three diagnosis methods of DGA for condition assessment of Power Transformer. And the analysis on the MATLAB software shall be carried so as to detect the best method for detection of a certain type of fault and the best suited method for overall fault analysis for a certain data sets out of the three methods. This technique utilizes the learning capacity of that artificial neural network has been shown to be more efficient in detecting different mistakes. The overall error detection accuracy of such gas neural network study was found to be 73.8 percent.

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.


Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.


2021 ◽  
Vol 16 (3) ◽  
pp. 387-394
Author(s):  
Yang Su ◽  
Ming-Hui Liu ◽  
Xu-Hui Kong ◽  
Chen-Jun Guo ◽  
Jiang Zhu ◽  
...  

Power transformer is regarded as one of the crucial part of electrical power transmission and distribution system. The quality of transformer oil can directly affect the operation of the power transformer, and breakdown voltage (BDV) and water content are the two main parameters of transformer oil quality. Monitoring the BDV and water content of transformer oil is considered as an important method to evaluate the safe operation of power systems. This work proposes the measurement of BDV and water content in transformer oil using multi frequency ultrasonic and generalized regression neural network (GRNN). The BDV and water content of all 210 samples were firstly tested according to the traditional testing methods and the multi frequency ultra-sonic technology, separately. And then the 210 samples were randomly divided into training sets and test sets. The obtained multi frequency ultrasonic data were set as the input of GRNN, and the BDV and water content as the output of GRNN. Moreover, the 20-fold-cross-validation was incorporated to obtain the best smoothing factor δ for GRNN. Finally, the GRNN model was trained by the training sets with δ =4.54 and was evaluated with the test sets. All results show that the lower BDV or the higher water content of the sample will cause greater ultrasonic sound attenuation, and the prediction accuracy of the prediction model for BDV and water con-tent in oil is up to 95%. It provides a new method for evaluating the health of transformer oil.


2013 ◽  
Vol 303-306 ◽  
pp. 562-566 ◽  
Author(s):  
Chen Hu Yuan ◽  
Mu Zhang ◽  
Sheng Wei Gao ◽  
Wei Wang ◽  
Xing Tao Sun

Dry-type power transformer was used widely because of its advantages. But unplanned outage effect to construct a strong intelligent power grid because of various stress. Dry-type power transformer’s fault repair time is long and impossible to repair. So it is very important to realize state maintains of dry-type transformer through state monitor and diagnosis. Based on current diagnostic methods, this paper proposed using self-organizing neural network to realize dry-type power transformer the key point temperature parameters of grading evaluation and then to realize the real-time state evaluation and analysis of failure causes. Study results to prolong the dry-type power transformer life and its design production provide theoretical guidance, in order to reduce and avoid dry-type power transformer failure.


2011 ◽  
Vol 320 ◽  
pp. 524-529 ◽  
Author(s):  
Qin Li ◽  
Zhi Bin Li ◽  
Qi Zhang

As one of the most important electric equipment for reliable power supply, the secure operation of power transformer must be guaranteed. Three-ratio method based on the Dissolved Gases Analysis (DGA) is most widely used for transformer fault diagnosis currently. Its advantage is simple and easy to use, but its encoding is incomplete and the faults classification zone is over absolute. This paper combines rough sets and Bayesian Network. Rough sets is used to get useful characters, simplify data sets, obtain simplification rules and the minimum property sets; Bayesian Network is used to analyze the faults caused by uncertain elements in complex system. The fault diagnostic model is built by Bayesian Network Tool (BNT) in MATLAB, and the simulation result shows the validity of this method.


Author(s):  
Hemant Rajnathsing ◽  
Chenggang Li

PurposeHuman–robot collaboration (HRC) is on the rise in a bid for improved flexibility in production cells. In the context of overlapping workspace between a human operator and an industrial robot, the major cause for concern rests on the safety of the former.Design/methodology/approachIn light of recent advances and trends, this paper proposes to implement a monitoring system for the shared workspace HRC, which supplements the robot, to locate the human operator and to ensure that at all times a minimum safe distance is respected by the robot with respect to its human partner. The monitoring system consists of four neural networks, namely, an object detector, two neural networks responsible for assessing the detections and a simple, custom speech recognizer.FindingsIt was observed that with due consideration of the production cell, it is possible to create excellent data sets which result in promising performances of the neural networks. Each neural network can be further improved by using its mistakes as examples thrown back in the data set. Thus, the whole monitoring system can achieve a reliable performance.Practical implicationsSuccess of the proposed framework may lead to any industrial robot being suitable for use in HRC.Originality/valueThis paper proposes a system comprising neural networks in most part, and it looks at a digital representation of the workspace from a different angle. The exclusive use of neural networks is seen as an attempt to propose a system which can be relatively easily deployed in industrial settings as neural networks can be fine-tuned for adjustments.


2021 ◽  
Vol 242 ◽  
pp. 03002
Author(s):  
Xinxin Mi ◽  
Gopinath Subramani ◽  
Mieowkee Chan

Through the dissolved gas analysis (DGA) in transformer oil, the fault of the power transformer can be diagnosed. However, the DGA method has the disadvantage of low accuracy because it couldn’t exactly reflect the nonlinear relationship between the characteristic gases and fault types. Radial basis function neural network (RBFNN) has the advantage of dealing with complex nonlinear problems, so it can be applied to transformer fault diagnosis based on DGA. The centers, widths and weights has important effects on the performance of the RBFNN. However, it is difficult to find the global optimal solution of these parameters when RBFNN training. This paper creatively designs a method to improve these parameters of RBFNN, firstly using the K-means algorithm to optimize the centers and widths of RBFNN, then using the genetic algorithm-backpropagation (GA-BP) algorithm optimize the weights. Finally, establish the K-means RBF-genetic backpropagation (KRBF-GBP) algorithm model through a large amount of training data. The test results show that the fault diagnosis accuracy of the KRBF-GBP algorithm is 96.4%, higher than the unoptimized RBFNN with 71.43%.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yizhou He

With the rapid development of neural network technology, we have widely used this technology in various fields. In the field of language translation, the research on automatic detection technology of English verb grammatical errors is in a hot stage. The traditional manual detection cannot be applied to the current environment. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. The experimental results show that the algorithm model studied in this paper can effectively improve the detection accuracy and feedback efficiency and is more applicable and effective than the traditional manual detection method.


In today's economic state, power transformer remains as the most expensive equipment in electrical system, in which insulation oil has been taken a significant role for performing a prominent operation. Since the insulation oil happens to degrade soon due to aging, high temperature and chemical reactions such as the oxidation, the periodic checking of oil followed by its replacement is necessary to stop the unexpected failure of the transformer. Moreover, it will be very advantageous if it happens to implement an automated model for predicting the age of transformer oil from time to time. The main intent of this paper is to develop an age assessment framework of transformer insulation oil using intelligent approaches. Here, diverse parameters associated with the transformer such as Breakdown Voltage (BDV), moisture, resistivity, tan delta, interfacial tension, and flash point is given as input for predicting the age of the insulation oil. These data have been already collected using 20 working power transformers operated at various substations in Punjab, India. In the proposed model, the collected parameters are subjected to a well-performing machine learning algorithm termed as Artificial Neural Network (ANN) in order to predict the age of the insulation oil. As a main contribution, the existing training algorithm in ANN so called as Levenberg–Marquardt (LM) is replaced by a hybrid metaheuritics algorithm. The newly developed hybrid algorithm merges the idea of Crow Search Algorithm (CSA), and Particle Swarm Optimization (PSO), and the new algorithm is termed as Particle Swarm-based Crow Search Algorithm (PS-CSA). The new training algorithm optimizes the weight of ANN using the hybrid CS-PSO updating procedure, in such a way that the difference between the predicted and actual outcome is minimum. Hence, this age prediction of transformer insulation oil will be beneficial for the environs to avoid the drastic condition.


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