Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs

Omar, Madiah Binti; Ibrahim, Rosdiazli; Mantri, Rhea; Chaudhary, Jhanavi; Selvaraj, Kaushik Ram; Bingi, Kishore

doi:10.3390/s22124342

Cited by 17 publications

(4 citation statements)

References 73 publications

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“…In [43][44][45], the researchers developed an enhanced form of artificial neural network which uses a two-step process to identify, locate, and measure the extent of damage in plate structures made of functionally graded materials, petroleum products, and other electrical systems. In the first stage, a damage indicator based on the frequency response function is utilized to forecast which components of the material have been affected.…”

Section: Arithmetic Optimization Algorithmmentioning

confidence: 99%

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

Devan

Ibrahim

Omar

et al. 2023

Sensors

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A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.

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Section: Arithmetic Optimization Algorithmmentioning

confidence: 99%

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

Devan

Ibrahim

Omar

et al. 2023

Sensors

Self Cite

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“…The technique suggested in [3] aimed to address the difficulty of stability prediction in the presence of missing data. The loss of a sensor, network link, or other system might account for this missing variable.…”

Section: Introductionmentioning

confidence: 99%

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

Lakshmanarao,

Srisaila,

Srinivasa Ravi Kiran

et al. 2024

IJEECS

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<span>A smart grid is a modern power system that allows for bidirectional communication, driven mostly by the idea of demand responsiveness. Predicting the stability of the smart grid is necessary for improving its dependability and maximizing the efficacy and regularity of electricity delivery. Predicting smart grid stability is difficult owing to the various elements that impact it, including consumer and producer engagement, which may contribute to smart grid stability. This research work proposes machine learning (ML) and deep learning (DL) approaches for predicting smart grid sustainability. Five ML algorithms, namely support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), were applied for the prediction of smart grid stability. Later, the stacking ensemble and voting ensemble of ML algorithms were also applied for prediction. To further increase accuracy, a novel fusion model with DL artifical neural networks (ANN) and ML SVM was applied and achieved an accuracy of 98.92%. The experiment results show that the proposed model outperformed existing models for smart grid stability prediction.</span>

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“…Therefore, the abundance of unnecessary and irrelevant data generated during this process poses a significant problem [4]. To address this issue, the performance of SGs can be enhanced using artificial intelligence (AI) techniques by integrating various machine learning (ML) and deep learning (DL) classifiers [5][6][7][8][9]. These algorithms leverage the knowledge derived from collected data to refine the understanding of the system, optimize demand forecasts, and anticipate potential fluctuations.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

Alaerjan,

Jabeur,

Ben Chikha

et al. 2024

Symmetry

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It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

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Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs

Cited by 17 publications

References 73 publications

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

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