scholarly journals A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 255
Author(s):  
Lei Wang ◽  
Yigang He ◽  
Lie Li
Keyword(s):  
Deep Learning ◽  
Transmission Lines ◽  
Fault Location ◽  
Short Term Memory ◽  
Engineering Application ◽  
Energy Operator ◽  
Sampling Frequency ◽  
Learning Technology ◽  
Long Distance ◽  
Hvdc Transmission

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.

scholarly journals SL-BiLSTM: A Signal-Based Bidirectional LSTM Network for Over-the-Horizon Target Localization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wanting Yu ◽  
Hongyi Yu ◽  
Ding Wang ◽  
Jianping Du ◽  
Mengli Zhang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Technology ◽  
Long Distance ◽  
Position Information ◽  
Performance Loss ◽  
Low Snr ◽  
Regularization Techniques ◽  
Positioning Method ◽  
Lstm Network

Deep learning technology provides novel solutions for localization in complex scenarios. Conventional methods generally suffer from performance loss in the long-distance over-the-horizon (OTH) scenario due to uncertain ionospheric conditions. To overcome the adverse effects of the unknown and complex ionosphere on positioning, we propose a deep learning positioning method based on multistation received signals and bidirectional long short-term memory (BiLSTM) network framework (SL-BiLSTM), which refines position information from signal data. Specifically, we first obtain the form of the network input by constructing the received signal model. Second, the proposed method is developed to predict target positions using an SL-BiLSTM network, consisting of three BiLSTM layers, a maxout layer, a fully connected layer, and a regression layer. Then, we discuss two regularization techniques of dropout and randomization which are mainly adopted to prevent network overfitting. Simulations of OTH localization are conducted to examine the performance. The parameters of the network have been trained properly according to the scenario. Finally, the experimental results show that the proposed method can significantly improve the accuracy of OTH positioning at low SNR. When the number of training locations increases to 200, the positioning result of SL-BiLSTM is closest to CRLB at high SNR.

Detecting faults in VSC-HVDC systems by deep learning and K-means

2020 ◽  
Vol 13 ◽  
Author(s):  
Roohollah Sadeghi Goughari ◽  
Mehdi Jafari Shahbazzadeh ◽  
Mahdiyeh Eslami
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Transmission Lines ◽  
Fault Location ◽  
Learning Algorithm ◽  
Hvdc Transmission ◽  
Deep Learning Algorithm ◽  
Processing Techniques ◽  
Signal Processing Techniques

Background: In this paper, two methods and their comparison used to determine the fault locaton in VSC-HVDC transmission lines. Fast and reliable control are features of these systems. Methods: Additionally, wavelet transform from advanced techniques of signal processing is employed for the purpose of extracting important characteristics of fault signal from both sides of the line by PMU. To do so, Deep learning is used to identify the relation between the extracted features from wavelet analysis of the fault current and variations under fault conditions. As such, wavelet transform and advanced signal processing techniques are used to extract important features of fault signal from both sides of the line by the PMU. Results: The results show the high accuracy of finding fault location by the deep learning algorithm method compared to the k-means algorithm with an error rate of <1%. Conclusion: Studies on the 50 kV VSC-HVDC transmission line with a length of 25 km in MATLAB have been simulated.

A Novel Fault-location Method for HVDC Transmission Lines Based on Concentric Relaxation Principle and Wavelet Packet

2020 ◽  
Vol 13 (5) ◽  
pp. 705-716
Author(s):  
Congshan Li ◽  
Ping He ◽  
Feng Wang ◽  
Cunxiang Yang ◽  
Yukun Tao ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Traveling Wave ◽  
Fault Location ◽  
Wave Attenuation ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Evaluation Process ◽  
Location Method ◽  
Hvdc Transmission ◽  
Instantaneous Energy

Background: A novel fault location method of HVDC transmission line based on a concentric relaxation principle is proposed in this paper. Methods: Due to the different position of fault, the instantaneous energy measured from rectifier and inverter are different, and the ratio k between them is the relationship to the fault location d. Through the analysis of amplitude-frequency characteristics, we found that the wave attenuation characteristic of low frequency in the traveling wave is stable, and the amplitude of energy is larger, so we get the instantaneous energy ratio by using the low-frequency data. By using the method of wavelet packet decomposition, the voltage traveling wave signal was decomposed. Results: Finally, calculate the value k. By using the data fitting, the relative function of k and d can be got, that is the fault location function. Conclusion: After an exhaustive evaluation process considering different fault locations, fault resistances, and noise on the unipolar DC transmission system, four-machine two-area AC/DC parallel system, and an actual complex grid, the method presented here showed a very accurate and robust behavior.

scholarly journals Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yuling Hong ◽  
Qishan Zhang
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Evaluation Method ◽  
Short Term Memory ◽  
Selection Model ◽  
Learning Technology ◽  
Prevention Measures ◽  
Learning Methods ◽  
Indicator Selection ◽  
The Social

Purpose. The purpose of this article is to predict the topic popularity on the social network accurately. Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP). Design/Methodology/Approach. Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up. Findings. The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%. The training speed is higher and the prediction precision is higher. Practical Implications. The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities. Moreover, its future popularity can be predicted by deep learning methods. At the same time, a new application field of deep learning technology has been further discovered and verified. Originality/Value. This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.

scholarly journals Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 186
Author(s):  
Aleena Swetapadma ◽  
Shobha Agarwal ◽  
Satarupa Chakrabarti ◽  
Soham Chakrabarti ◽  
Adel El-Shahat ◽  
...  
Keyword(s):  
Direct Current ◽  
Transmission Lines ◽  
Fault Location ◽  
Ensemble Method ◽  
Percentage Error ◽  
Communication Link ◽  
Half Cycle ◽  
Optimal Method ◽  
Hvdc Transmission ◽  
Hvdc Transmission Lines

Most of the fault location methods in high voltage direct current (HVDC) transmission lines usemethods which require signals from both ends. It will be difficult to estimate fault location if the signal recorded is not correct due to communication problems.Hence a robust method is required which can locate fault with minimum error. In this work, faults are located using boosting ensembles in HVDC transmission lines based on single terminal direct current (DC) signals. The signals are processed to obtain input features that vary with the fault distance. These input features are obtained by taking maximum of half cycle current signals after fault and minimum of half cycle voltage signals after fault from the root mean square of DC signals. The input features are input to a boosting ensemble for estimating the location of fault. Boosting ensemble method attempts to correct the errors from the previous models and find outputs by combining all models. The boosting ensemble method has been also compared with the decision tree method and thebagging-based ensemble method. Fault locations are estimated using three methods and compared to obtain an optimal method. The boosting ensemble method has better performance than all the other methods in locating the faults. It also validated varying fault resistance, smoothing reactors, boundary faults, pole to ground faults and pole to pole faults. The advantage of the method is that no communication link is needed. Another advantage is that it allowsreach setting up to 99.9% and does not exhibitthe problem of over-fitting. Another advantage is that the percentage error in locating faults is within 1% and has a low realization cost. The proposed method can be implemented in HVDC transmission lines effectively as an alternative to overcome the drawbacks of traveling wave methods.

scholarly journals Flutter speed prediction by using deep learning

2021 ◽  
Vol 13 (11) ◽  
pp. 168781402110622
Author(s):  
Yi-Ren Wang ◽  
Yi-Jyun Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Technology ◽  
Memory Space ◽  
Flutter Speed ◽  
Aeroelastic Flutter ◽  
Speed Prediction ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Method

Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.

A Fault Location Method for HVDC Transmission Lines

2014 ◽  
Vol 556-562 ◽  
pp. 2723-2727 ◽  
Author(s):  
Lu Hua Xing ◽  
Qing Chen ◽  
Bing Lei Xue
Keyword(s):  
Transmission Lines ◽  
Traveling Wave ◽  
Fault Location ◽  
Propagation Characteristic ◽  
Location Method ◽  
Hvdc Transmission ◽  
High Voltage Direct Current ◽  
Current Transmission ◽  
Hvdc Transmission Lines ◽  
Transition Resistance

A fault location method for HVDC (High Voltage Direct Current) transmission lines is proposed in this paper, using voltages and currents measured at two terminals of dc lines in time domain. Fault traveling waves propagate from the fault point to both terminals along the faulted line. The position that the traveling wave head arrives at some moment after the fault can be used to calculate the fault location. To determine the arrival positions of traveling wave head at each time indirectly, propagation characteristic curves of traveling wave heads at local and the remote terminals are calculated with distribution currents using the stationary wavelet transform. The accuracy of fault location will not be affected by transition resistance and fault position. Simulation results show that the presented fault location method can achieve quick and accurate fault location on the whole line under probable operation modes of a bipolar HVDC transmission system.

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