Long Short-term Memory Recurrent Neural Network Research Articles

Hybrid deep recurrent neural networks for COVID-19 detection and diagnosis

Corona virus Disease (COVID-19) is an acute pandemic which has put the lives of millions of people worldwide at risk during recent times. There is a high demand to develop effective tools and methods to diagnose the COVID infection in people at an early stage to prevent the spread of the disease to a larger community. This paper aims to provide a systematic method for COVID diagnosis using machine learning and deep learning algorithms. The proposed method Hybrid Deep Recurrent Neural Network (HDRNN) is a fusion of Convolution Neural Networks (CNN) and Long Short-Term Memory-Recurrent Neural Networks (LSTM-RNN) to detect COVID infection efficiently from X-ray samples. CNN is employed in the proposed method primarily to extract the essential features from the X-ray images and LSTM is suitable to classify the COVID affected patients with more fidelity. The dataset used in this work consists of an aggregate of 3470 images including COVID affected and Pneumonia affected samples. The experimental results carried out on the collected dataset with the proposed HDRNN method demonstrated an accuracy of 99.4%, F1 Score 98.7%, Sensitivity of 99.3% and Specificity of 99.2 %.

Using a recurrent neural network with S2 characteristics, efficient identification of localised cortical dysplasia

Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM.

Short-term electrical load forecasting through heuristic configuration of regularized deep neural network

An accurate electrical load forecasting is essential for optimal grid operation. The paper presents a methodology for the short-term commercial building electrical load forecasting through a regularized deep neural network: Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). Detailed heuristic analysis regarding relevant input feature selection, the volume of training data, hyperparameter tuning and regularizer selection of an optimal LSTM-RNN network configuration is presented. The regularized LSTM-RNN is used to forecast 30-min and 24-h ahead electrical loads of two commercial buildings in Virginia, USA. The forecast is performed for one week each over four different months in 2019: January, April, July and October to represent four different seasons in North America. The performance of electrical load forecasts has been compared against actual smart meter data from the electric utility of these buildings. For the case study presented, Mean Absolute Percentage Error (MAPE%) with the regularized LSTM-RNN is 4.9%, compared to 6.4%, 9.2% and 13.3% with Shallow-ANN (Artificial Neural Network), Support Vector Regression (SVR) and Linear Regression (LR) respectively for 30-min ahead electrical load forecast. For 24-h ahead electrical load forecast, MAPE (%) is 11.6%, compared to 12.7%, 13.4% and 14.3% with shallow-ANN, SVR and LR respectively. The methodology to configure a deep neural network (LSTM-RNN) for electrical load forecasting presented in this paper can be utilized for optimal forecasting performance.

Prediction of Air Quality Using LSTM Recurrent Neural Network

Rapid increase of Industrialization and Urbanization significantly draws the interest of researchers towards the prediction of air quality. Efficient modelling of air quality parameters using deep learning methods can facilitate the imminent implications of air pollution. However, existing methods weakens at consideration of long-term dependencies for multiple parameters. The present study aims prediction of air quality of New Delhi based on concentration of multiple parameters namely PM2.5, PM10, CO, O3, NO2 and SO2. The study uses long short-term memory (LSTM) approach due to its efficiency over other deep learning methods and referred it as A-LSTM prediction model. It supports multiple layers to add more linearity to the desired output. Performance of A-LSTM is evaluated for prediction of year 2019 data. Mean absolute error, root mean squared error, precision, recall and F1-score metrics are considered for comparison with other three prediction models namely support vector regressor (SVR), SVR with LSTM and I-LSTM.

Using deep learning to detecting abnormal behavior in internet of things

<span lang="EN-US">The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.</span>

Forecasting the Total South African Unplanned Capability Loss Factor Using an Ensemble of Deep Learning Techniques

Unplanned power plant failures have been seen to be a major cause of power shortages, and thus customer power cuts, in the South African power grid. These failures are measured as the unplanned capability loss factor (UCLF). The study of South Africa’s UCLF is almost non-existent. Parameters that affect the future UCLF are, thus, still not well understood, making it challenging to forecast when power shortages may be experienced. This paper presents a novel study of South African UCLF forecasting using state-of-the-art deep learning techniques. The study further introduces a novel deep learning ensemble South African UCLF forecasting system. The performance of three of the best recent forecasting techniques, namely, long short-term memory recurrent neural network (LSTM-RNN), deep belief network (DBN), and optimally pruned extreme learning machines (OP-ELM), as well as their aggregated ensembles, are investigated for South African UCLF forecasting. The impact of three key parameters (installed capacity, demand, and planned capability loss factor) on the future UCLF is investigated. The results showed that the exclusion of installed capacity in the LSTM-RNN, DBN, OP-ELM, and ensemble models doubled the UCLF forecasting error. It was also found that an ensemble model of two LSTM-RNN models achieved the lowest errors with a symmetric mean absolute percentage error (sMAPE) of 6.43%, mean absolute error (MAE) of 7.36%, and root-mean-square error (RMSE) of 9.21%. LSTM-RNN also achieved the lowest errors amongst the individual models.

Judgment of Athlete Action Safety in Sports Competition Based on LSTM Recurrent Neural Network Algorithm

Athlete injury has always been an important factor that plagues sports. In order to reduce the probability of athletes’ sports injury and improve the judgment of athletes’ action safety, the inherent laws of sports actions are fully excavated, the development of action safety is promoted, and learners and instructors are caused to fully understand the safety of actions. This study uses the LSTM (long short-term memory) cyclic neural network algorithm to judge the safety of athletes in sports competitions. The experiment verifies the effectiveness of the LSTM cyclic neural network algorithm in basketball segmentation and recognition. Sports injury is one of the important factors affecting the performance of all sports, and the problem of athletes’ injury is worrying, so it is very necessary to effectively prevent potential sports injuries. Through the investigation of different professional athletes, the LSTM cyclic neural network algorithm is used for the whole process of extracting an independent motion action including continuous actions. It is used to distinguish key postures and nonkey postures in an action, and to judge the correctness of the action. Basketball skills here are mainly the movements of basic skills such as moving, passing the ball, dribbling, shooting, breaking with the ball, personal defense, grabbing the ball, stealing the ball, and grabbing the ball. The research results prove that the LSTM recurrent neural network algorithm has a good effect on the safety of athletes. For athletes, 41.9% of people can improve the safety of their movements by strengthening strength training.

Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong

The water quality measurement of marine water is a key research topic for environmental and ocean modelers in the past several decades. Marine water quality is mainly described by its chemical, physical and biological properties. According to the Environmental Protection Department (EPD), Hong Kong, the chemical and physical measurements are integrative parameters for water quality measurements. The existence of undesirable components in marine water degrades the water quality. The increase of natural and anthropogenic events in coastal regions poses the marine ecosystem in danger day by day. Therefore, the prediction of marine water quality indicators is essential. Turbidity is a key indicator of marine water quality, whose prediction is difficult due to its non-linear time series behavior. Therefore, the objective of the present study is to predict turbidity in the marine environment of Hong Kong using Machine Learning (ML) approach. The artificial neural network (ANN), support vector regression (SVR), and Long short-term memory recurrent neural network (LSTM-RNN) have been used as ML tools in the marine environment. The ML model prediction results were compared and the obtained results suggest that the LSTM-RNN model outperforms the ANN and SVR model results with an accuracy of 88.45%. The neural network-based approaches give better prediction accuracy over the SVR model in this study. Thus, it can be concluded that the neural network-based algorithm results can be used to monitor water quality parameters in the marine environment.

A new Intelligent Satellite Deep Learning Network Forensic framework for smart satellite networks

By combining the Internet of Things (IoT) and Artificial Intelligence (AI), new augmentations and enhancements are realized, resulting in environment-aware systems that can enable intelligent decision making, with one such example being smart satellite networks. However, smart satellite networks attract the attention of hackers, resulting in them being targeted by severe cyber-attacks that can compromise their integrity, affect their availability, or breach the confidentiality of the data they generate, collect or rout. Lately, several prominent cyber-attacking scenarios have been observed to target IoT-enabled systems, resulting in loss, alteration, or exfiltration of data, disabling of devices, (distributed) denial of services, the formation of botnets, and lateral movement into otherwise secured networks. In this paper, we propose a network forensic framework based on Deep Learning (DL), the so-called Intelligent Satellite Deep Learning Network Forensic (INSAT-DLNF), for the detection and tracing of cyber-attack activities targeting smart satellite networks. For this framework, we trained a Long Short-term Memory Recurrent Neural Network (LSTM-RNN) and Gated Recurrent Unit (GRU) and compared their performances to five supervised and two unsupervised Machine Learning (ML) algorithms. The experimental results indicate that ML and DL algorithms can be employed effectively for the discovery and tracing of cyber-attacks, resulting in more adaptive and resilient cyber-security solutions, compared to legacy forensic tools that cannot discover zero-day attack surfaces and vectors.

EDLA-EFDS: A Novel Ensemble Deep Learning Approach For Electrical Fault Detection Systems

Early detection of electrical faults is a very essential research area due to its positive influence on network stability and customer satisfaction. Despite of the electrical fault detection problem has been researched during the last decade, the existence of an intelligent fault detection system is still rare in real-world applications. Therefore, this study proposes a novel Ensemble Deep Learning Approach for Electrical Fault Detection Systems (EDLA-EFDS) that resolves the limitations of existing systems such as automation, validation, and overfitting. The proposed approach benefits from two phases prior to the training phase, namely, data preprocessing and pre-training. Whereas the data preprocessing phase manages data by executing all elementary operations on the raw data, the pre-training phase selects both optimal features and hyperparameters by exploiting a double Particle Swarm Optimization (PSO) metaheuristic. Thereafter, a bagging ensemble system is deployed from three different deep learning paradigms, namely, Deep Neural Networks (DNN), Long Short-Term Memory recurrent Neural Networks (LSTM-RNN), and Deep Belief Networks (DBN). The ensemble system is followed by a majority voting engine to produce the final decision. Moreover, the performance of the ensemble system leveraged by the proposed approach is measured on the VSB dataset which is a modern and realistic dataset for power line fault detection. Finally, the analysis of the results using various scenarios and aspects such as the Receiver Operating Characteristic (ROC) curves and Friedman test is provided. The experimental results confirm the effectiveness of the proposed approach in solving the electrical fault detection problem.

Research on prediction of daily admissions of respiratory diseases with comorbid diabetes in Beijing based on long short-term memory recurrent neural network.

To compare the performance of generalized additive model (GAM) and long short-term memory recurrent neural network (LSTM-RNN) on the prediction of daily admissions of respiratory diseases with comorbid diabetes. Daily data on air pollutants, meteorological factors and hospital admissions for respiratory diseases from Jan 1st, 2014 to Dec 31st, 2019 in Beijing were collected. LSTM-RNN was used to predict the daily admissions of respiratory diseases with comorbid diabetes, and the results were compared with those of GAM. The evaluation indexes were calculated by five-fold cross validation. Compared with the GAM, the prediction errors of LSTM-RNN were significantly lower [root mean squared error (RMSE): 21.21±3.30 vs. 46.13±7.60, <0.01; mean absolute error (MAE): 14.64±1.99 vs. 36.08±6.20, <0.01], and the value was significantly higher (0.79±0.06 vs. 0.57±0.12, <0.01). In gender stratification, RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting female admission (all <0.05), but there were no significant difference in predicting male admission between two models (all >0.05). In seasonal stratification, RMSE and MAE of LSTM-RNN were lower than those of GAM in predicting warm season admission (all <0.05), but there was no significant difference in value (>0.05). There were no significant difference in RMSE, MAE and between the two models in predicting cold season admission (all >0.05). In the stratification of functional areas, the RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting core area admission (all <0.05). has lower prediction errors and better fitting than the GAM, which can provide scientific basis for precise allocation of medical resources in polluted weather in advance.

Spatiotemporal Dynamic Forecasting and Analysis of Regional Traffic Flow in Urban Road Networks Using Deep Learning Convolutional Neural Network

The purpose is to explore the spatial-temporal dynamic prediction performance of urban road network traffic flow based on convolutional neural networks (CNN) of deep learning. A dynamic prediction model of road network traffic flow based on STGCN-BiLSTM (spatial-temporal graph convolution network Bi-directional Long Short-Term Memory) is designed in view of the complex and highly nonlinear traffic data in the actual environment. Finally, the simulation experiment is conducted on the constructed model to verify its performance which is compared with that of the LSTM (Long Short-Term Memory) model, CNN model, RNN (Recurrent Neural Network) model, AlexNet model and STGCN model. The results show that the root mean square error, mean absolute error and mean absolute percentage error of the proposed algorithm model are 4.60%, 5.46% and 7.73%, respectively. The training time is stable at about 45s, the test time is stable at about 33s, and the delay is close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.86\times 10 ^{-4}\text{s}$ </tex-math></inline-formula> . Additionally, the traffic flow of the test section in the next 15 min, 30 min, 1 h and 2 h is further predicted. Compared with other algorithms, the predicted value of the proposed algorithm is the closest to the actual value, along with the best prediction effect. Therefore, the constructed dynamic prediction model of road network traffic flow can achieve high accuracy and better robustness under the premise of low error, which can provide experimental basis for the spatial-temporal dynamic digital development of transportation in smart cities.

End-to-End Modeling and Transfer Learning for Audiovisual Emotion Recognition in-the-Wild

As emotions play a central role in human communication, automatic emotion recognition has attracted increasing attention in the last two decades. While multimodal systems enjoy high performances on lab-controlled data, they are still far from providing ecological validity on non-lab-controlled, namely “in-the-wild” data. This work investigates audiovisual deep learning approaches to emotion recognition in in-the-wild problem. Inspired by the outstanding performance of end-to-end and transfer learning techniques, we explored the effectiveness of architectures in which a modality-specific Convolutional Neural Network (CNN) is followed by a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) using the AffWild2 dataset under the Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol. We deployed unimodal end-to-end and transfer learning approaches within a multimodal fusion system, which generated final predictions using a weighted score fusion scheme. Exploiting the proposed deep-learning-based multimodal system, we reached a test set challenge performance measure of 48.1% on the ABAW 2020 Facial Expressions challenge, which advances the first-runner-up performance.

Sparse based recurrent neural network long short term memory (rnn-lstm) model for the classification of ecg signals

ABSTRACT In recent years, with the advancement of classical signal processing approaches, numerous works have been performed on the automatic ECG detection schemes for the enhancement of the effectiveness of the identification of the type of ECG heartbeats. One common issue faced by the previous works is the complexity of signal processing. In order to resolve the computational and complexity issues of existing techniques of signal processing, this research work introduces Sparse representation technique for extracting feature. In this study, adaptive thresholding technique combined with Sparse-based Recurrent Neural Network – Long Short Term Memory (RNN-LSTM) model is employed for the classification of ECG signals. P-QRS-T peaks are identified by employing Adaptive thresholding technique. Statistical features are obtained for each signal and are employed in the process of dictionary learning of sparse decomposition. Sparse representations of the incoming ECG signals are used in training the RNN-LSTM network. The trained classifier will give a classified result on giving a test ECG input signal. The performance indices for the process of classification such as accuracy, precision, error rate, sensitivity, and F-score are calculated. The performance of the proposed Sparse based RNN-LSTM classifier is found to be better in comparison with the existing RNN classifier, K-Nearest Neighbor (K-NN) classifier, and Decision Tree (DT) classifier. Furthermore, for validating the performance of the proposed framework, this approach is tested experimentally with real-world raw ECG data acquired using the AD8232 single-lead ECG sensor. The performance of the proposed experimental setup is compared with the existing state-of-the-art approaches.

Machine translation of English speech: Comparison of multiple algorithms

Abstract In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length.

Long Short‐Term Memory Recurrent Neural Network for Predicting the Return of Rate Underframe the Fama‐French 5 Factor

The multifactor approach helps determine the linear connection between a diversified portfolio’s return and risk; however, the efficacy of the model models is still limited in the experiment. Algorithms in machine learning have recently grown in popularity to compensate for some of the shortcomings of theoretical models. This study applied a machine learning technique to compare the performance of the Fama‐French 5‐factor model (FF5). Two approaches are employed in the Fama‐French model: Long Short Term Memory Recurrent Neural Network (LSTM‐RNN) and Maximum Likelihood Estimation (MLE). From January 1, 2010, through March 3, 2022, the stock market in Ho Chi Minh City was experimentally researched. The rolling window approach is used in combination with the Root Mean Square Error (RMSE), and the results of the FF5 model with the LSTM‐RNN algorithm are more efficient in prediction error than the MLE methodology. This contribution encourages investors and hedge fund managers to use the LSTM‐RNN algorithm to boost forecasting efficiency.

Automated Stock Price Prediction using LSTM Recurrent Neural Network

In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume. A correct prediction of stocks can lead to huge profits for the seller and the broker. Frequently, it is brought out that prediction is chaotic rather than random, which means it can be predicted by carefully analyzing the history of the respective stock market. Machine learning is an efficient way to represent such processes. It predicts a market value close to the tangible value, thereby increasing the accuracy. The introduction of machine learning to the area of stock prediction has appealed to many researchers because of its efficient and accurate measurements. In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and more authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high, and volume. Stock market prediction is a major exertion in the field of finance and establishing businesses. The stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g.national elections, natural calamities etc. Investors in the stock market seek to maximize their profits for which they require tools to analyze the prices and trends of various stocks. Machine learning algorithms have been used to devise new techniques to build prediction models that can forecast the prices of stock and tell about the market trend with good accuracy. Many prediction models have been proposed to incorporate all the major factors affecting the price of stocks. Due to the correlated nature of stock prices, conventional batch processing methods cannot be utilized efficiently for stock market analysis. We propose an online learning algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short Term Memory (LSTM), where the weights are adjusted for individual data points using stochastic gradient descent. This will provide more accurate results when compared to existing stock price prediction algorithms. The network is trained and evaluated for accuracy with various sizes of data, and the results are tabulated. A comparison with respect to accuracy is then performed against an Artificial Neural Network.

Mutation Prediction for Coronaviruses Using Genome Sequence and Recurrent Neural Networks

The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARS-Cov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences, there is still limited methods on what new viruses can be generated in future due to mutation. This research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM (Recurrent Neural Network-Long Short-Term Memory) and RNN-GRU (Gated Recurrent Unit) so that in the future, several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the virus. After the process of testing the model, the F1-recall came out to be more than 0.95. The mutation detection’s accuracy of both the models come out about 98.5% which shows the capability of the recurrent neural network to predict future changes in the genome of virus.

Shallow artificial networks with morphokinetic time-lapse parameters coupled to ART data allow to predict live birth.

PurposeThe purpose of this work was to construct shallow neural networks (SNN) using time‐lapse technology (TLT) from morphokinetic parameters coupled to assisted reproductive technology (ART) parameters in order to assist the choice of embryo(s) to be transferred with the highest probability of achieving a live birth (LB).MethodsA retrospective observational single‐center study was performed, 654 cycles were included. Three SNN: multilayers perceptron (MLP), simple recurrent neuronal network (simple RNN) and long short term memory RNN (LSTM‐RNN) were trained with K‐fold cross‐validation to avoid sampling bias. The predictive power of SNNs was measured using performance scores as AUC (area under curve), accuracy, precision, Recall and F1 score. ResultsIn the training data group, MLP and simple RNN provide the best performance scores; however, all AUCs were above 0.8. In the validating data group, all networks were equivalent with no performance scores difference and all AUC values were above 0.8.ConclusionCoupling morphokinetic parameters with ART parameters allows to SNNs to predict the probability of LB, and all SNNs seems to be efficient according to the performance scores. An automatic time recognition system coupled to one of these SNNs could allow a complete automation to choose the blastocyst(s) to be transferred.

Identification of Oscillatory Modes in Power System Using Deep Learning Approach

The increase in electric power demand pushes the modern power system for more interconnected networks. It leads to a lack of inertia and creates more critical disturbances in the power system. When this oscillation isn’t damped out, it results in cascade tripping. Immediate detection of low-frequency oscillatory modes and their parameters will help the power system operator to act on a particular event without consuming much time. This research paper proposes novel strategies for identifying low-frequency modes using deep learning techniques, and the model can predict the LFO modes in different topologies. This work presents the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) approach to predict the instantaneous mode oscillatory parameters in the power system. Once the LSTM-RNN model is trained for different power disturbance situations, it can be used for any events associated with the system. Simulation results are verified using two area Kundur systems at various disturbance conditions. The simulations are performed using MATLAB software and python tensor flow library. The results are validated using statistical methods, and it confirms the superior viability and adaptability of the proposed approach in predicting the instantaneous mode parameters.