Big Data Platform for Real-Time Oscillatory Stability Predictive Assessment Using Recurrent Neural Networks and WAProtector's Records

Cepeda, Jaime C.; Aguaded, Ignácio; Calero, Fabian; Vaca, Angel

doi:10.1109/sgsma51733.2022.9806014

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Such a strategy not only curtails operational costs but also substantially reduces downtime. Another noteworthy trend is the implementation of remote monitoring and control systems, which facilitate real-time equipment surveillance and remote maintenance operations [7]. The progression of grid maintenance methodologies is largely driven by bolstering system reliability, cutting expenses, and adhering to ever-tightening environmental regulations.…”

Section: IImentioning

confidence: 99%

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Kiran Thatikonda

2024

jes

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This study presents a novel framework for the predictive maintenance of electric grid infrastructure, leveraging an Adaptive Predictive Maintenance Optimization (APMO) algorithm. Our comprehensive system architecture integrates IoT sensors and drone surveillance, harnessing grid computing for data processing and machine learning for analytics. The APMO algorithm, underpinned by reinforcement learning, dynamically refines maintenance schedules, enhancing operational efficiency. Results from simulated sensor data exhibit a negligible correlation, indicating a multi-faceted approach to predictive analytics. Specifically, the Health Index distribution showed a wide range of 0.7 to 1.0, while the electrical current maintained a stable mean at 10 A. Further, the APMO algorithm demonstrated a promising improvement in maintenance efficiency from 40% to 75% over a year. This research introduces a scalable, robust system for grid management, paving the way for smarter infrastructure maintenance.

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Section: IImentioning

confidence: 99%

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Kiran Thatikonda

2024

jes

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“…Although accurate estimates are provided by the employment of these methods, they still suffer several drawbacks like an inaccurate estimation of the mode amplitudes, incorrect identification of near frequency oscillations and being computationally expensive, especially in the presence of multiple oscillations. The recent developments in artificial intelligence and machine learning techniques have given researchers the opportunity to benefit from the ability to capture inherent patterns and features T contained in the data to enhance a power system's small-signal stability [14], [15]. For example, a time series model is trained using recurrent neural networks (RNN) for predicting the small-signal stability status [15].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The recent developments in artificial intelligence and machine learning techniques have given researchers the opportunity to benefit from the ability to capture inherent patterns and features T contained in the data to enhance a power system's small-signal stability [14], [15]. For example, a time series model is trained using recurrent neural networks (RNN) for predicting the small-signal stability status [15]. The oscillatory modes are estimated in real-time using ELPROS which relies on propriety modal identification algorithm and is applied to collected active powers from PMUs.…”

Section: B Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Models for Predicting Poorly Damped Low-Frequency Modes of Oscillations

Muhammed

Isbeih

Moursi

et al. 2024

IEEE Trans. Power Syst.

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This work proposes a real-time deep learning-based model for predicting the small-signal stability of an electrical network. The trained models equip power system operators with an accurate and fast monitoring tool which can be used during online operation. To achieve this objective, three different model architectures are employed in this research; stacked long short-term memory (LSTM), convolutional neural network (CNN)-LSTM and Convectional LSTM (Conv-LSTM). These models are trained using datasets which contain the oscillatory parameters (frequency and damping ratio) of both local and inter-area modes of oscillations. In addition, the voltage phasors at different buses are taken as the model input where the output comprises the real-time oscillatory patterns of the modes. Furthermore, the overall performance of proposed models is shown for the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area, and IEEE 50-machine, 145-bus benchmark test cases. The main findings show that training CNN-LSTM and Conv-LSTM models provide better performance compared with the stacked-LSTM model. The former models have less number of parameters and thus shorter training time. In addition, CNN_LSTM and Conv-LSTM models are less prone to overfitting problems in the network and have a better ability in capturing spatial and temporal features inherent in input data.

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“…A scheme which provides offline training, update and online predicting is proposed in Liu et al (2021). In Cepeda et al (2022), deep learning-based assessment scheme is proposed which only uses system frequency as the input. An optimal control strategy using thyristor-controlled series capacitors (TCSC) to damp power system oscillations is proposed in Ernst et al (2004) and in this method Reinforcement Learning (RL) is utilized to obtain the control policy.…”

Section: Oscillatory Stabilitymentioning

confidence: 99%

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Porawagamage,

Dharmapala,

Chaves

et al. 2024

Front. Smart Grids

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Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.

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Big Data Platform for Real-Time Oscillatory Stability Predictive Assessment Using Recurrent Neural Networks and WAProtector's Records

Cited by 6 publications

References 10 publications

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Deep Learning-Based Models for Predicting Poorly Damped Low-Frequency Modes of Oscillations

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

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