A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Alazab, Mamoun; Khan, Suleman; Somayaji, Siva Rama Krishnan; Pham, Quoc-Viet; Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy

doi:10.1109/access.2020.2991067

Cited by 176 publications

(102 citation statements)

References 36 publications

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“…Moreover, features are extracted in time-domain without the application of any computationally complex transformation. This confirms the superiority of the suggested solution over the existing ones [ 9 , 16 , 23 , 43 ]. The used SPADC is of much inferior, 4-Bit, resolution.…”

Section: Discussionsupporting

confidence: 79%

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Qaisar

2021

PLoS ONE

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Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

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Section: Discussionsupporting

confidence: 79%

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Qaisar

2021

PLoS ONE

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“…DL is suitable to solve complex problems particularly well and quickly, by employing black-box models that can increase the overall performance (i.e., increase the accuracy or reduce the error rate). Because of this, DL is getting more and more widespread in several complex domains of different nature (Alom et al, 2019;Yang, Venkatraman & Alazab, 2018;Alazab et al, 2020;Vasan et al, 2020;Zhaoet al, 2020).…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Continuous authentication using deep neural networks ensemble on keystroke dynamics

Bernardi

Cimitile

Pecori

2021

PeerJ Computer Science

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During the last years, several studies have been proposed about user identification by means of keystroke analysis. Keystroke dynamics has a lower cost when compared to other biometric-based methods since such a system does not require any additional specific sensor, apart from a traditional keyboard, and it allows the continuous identification of the users in the background as well. The research proposed in this paper concerns (i) the creation of a large integrated dataset of users typing on a traditional keyboard obtained through the integration of three real-world datasets coming from existing studies and (ii) the definition of an ensemble learning approach, made up of basic deep neural network classifiers, with the objective of distinguishing the different users of the considered dataset by exploiting a proper group of features able to capture their typing style. After an optimization phase, in order to find the best possible base classifier, we evaluated the ensemble super-classifier comparing different voting techniques, namely majority and Bayesian, as well as training allocation strategies, i.e., random and K-means. The approach we propose has been assessed using the created very large integrated dataset and the obtained results are very promising, achieving an accuracy of up to 0.997 under certain evaluation conditions.

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“…Among all these aforementioned applications, one of the most significant application is sensor data collection, where sensors are used to monitor and collect various aspects of the surrounding environment and transfer the data towards upstream nodes for further processing ( Alazab et al, 2020b ; Tao et al, 2020 ; Zhang et al, 2020 ; Rahman et al, 2019a ). However, sensors can be easily spoiled due to technical constraints and environmental impacts such as equipment failure, noises, absence of sensor calibration, and security concerns ( Rahman et al, 2019a ; Bhuiyan, Wang & Choo, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

In-network generalized trustworthy data collection for event detection in cyber-physical systems

Rahman

Wang

Bhuiyan

et al. 2021

PeerJ Computer Science

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Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

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A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Cited by 176 publications

References 36 publications

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Continuous authentication using deep neural networks ensemble on keystroke dynamics

In-network generalized trustworthy data collection for event detection in cyber-physical systems

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