Sliding-Window-Based Real-Time Model Order Reduction for Stability Prediction in Smart Grid

Shamisa, Abdolah; Majidi, Babak; Patra, J.C.

doi:10.1109/tpwrs.2018.2868850

Cited by 37 publications

(8 citation statements)

References 40 publications

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“…Many researchers have applied sliding window algorithms to various applications. In the field of power systems, some researchers utilised sliding window algorithms for power supply load forecasting [43], transient stability prediction [44], faulty equipment detection based on image recognition [45], and IED defect classification based on text mining [40]. In the field of anomaly detection, researchers applied sliding window algorithms to detect anomalies in different applications, such as IoT networks [9,10,46], and in-vehicle networks [47,48].…”

Section: F Sliding Window Algorithmsmentioning

confidence: 99%

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

et al. 2022

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In recent decades, cyber security issues in IEC 61850-compliant substation automation systems (SASs) have become growing concerns. Many researchers have developed various strategies to detect malicious behaviours of SASs during the system operational stage, such as anomaly-based detection. However, most existing anomaly-based detection methods identify an abnormal behaviour by checking every single network packet without any association. These traditional methods cannot effectively detect "stealthy" attacks which modify legitimate messages slightly while imitating patterns of benign behaviours. In this paper, we present feature selection and extraction methods to generalise and summarise critical features when detecting insider attacks triggering from untrusted control devices within SASs. By applying a sliding window-based sequential classification mechanism, our detection method can detect anomalies across multiple devices without the need to learn datasets collected from all devices. Firstly, to generalise critical features and summarise systems' behaviours so that it is unnecessary to collect all datasets, we selected and extracted six critical network features from generic object-oriented substation events (GOOSE) messages and seven summarised physical features based on the general architecture of the primary plant of distribution substations. After that, to improve detection accuracy and reduce computational costs, we applied sliding window algorithms to divide datasets into different overlapped window-based snippets. Then we applied a sequential classification model based on Bidirectional Long Short-Term Memory networks to train and test those datasets. As a result, our method can detect insider attacks across multiple devices accurately with a false-negative rate of less than 1%.

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Section: F Sliding Window Algorithmsmentioning

confidence: 99%

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

et al. 2022

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“…Results are probabilistic which limits practical repeatability. For solving these Sliding Window Based TSA (SW-TSA) is suggested for large grids [9]. The power system is complex so nonlinear MOR is used.…”

Section: Related Workmentioning

confidence: 99%

Predicting Smart Grid Stability with Optimized Deep Models

Breviglieri¹,

Erdem

Eken

2021

SN COMPUT. SCI.

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In a smart grid, consumer demand information is collected, centrally evaluated against current supply conditions and the resulting proposed price information is sent back to customers for them to decide about usage. As the whole process is time dependent, dynamically estimating grid stability becomes not only a concern but a major requirement. Decentral Smart Grid Control (DSGC) systems monitor one particular property of the grid-its frequency. So, it ties the electricity price to the grid frequency so that it is available to all participants, i.e., all energy consumers and producers. DSGC has some assumptions to infer the behavior of participants. DSGC system is described with differential equations. In this paper, we study on optimized deep learning (DL) models to solve fixed inputs (variables of the equations) and equality issues in DSGC system. Therefore, measuring the grid frequency at the premise of each customer would suffice to provide the network administrator with all required information about the current network power balance, so that it can price its energy offering-and inform consumers-accordingly. To predict smart grid stability, we use different optimized DL models to analyze the DSGC system for many diverse input values, removing those restrictive assumptions on input values. In our tests, DL model accuracy has reached up to 99.62%. We demonstrate that DL models indeed give way to new insights into the simulated system. We have learned that fast adaptation generally improves system stability.

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“…Although the authors emphasized the algorithm’s precision, it was not clear how the clustering performance was measured in this work. Ohadi et al defined a new DBSCAN algorithm called SW-DBSCAN [ 13 ] formulated on the sliding window grid-based model [ 14 ]. Nevertheless, in this paper, the evaluation of the algorithm was measured using the Accuracy metric.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Architecture of a Clustering Hybrid Algorithm Called FA+GA-DBSCAN Using Artificial Datasets

Perafán-López

Ferrer

Nieto-Londoño

et al. 2022

Entropy

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Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN. Here, the function nearest neighbor and a genetic algorithm were used for the automation of parameters MinPts and Eps. Furthermore, the Factor Analysis (FA) method was defined for pre-processing through a dimensionality reduction of high-dimensional datasets with dimensions greater than two. Finally, the performance of the clustering algorithm called FA+GA-DBSCAN was evaluated using artificial datasets. In addition, the precision and Entropy of the clustering hybrid algorithm were measured, which showed there was less probability of error in clustering the most condensed datasets.

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Sliding-Window-Based Real-Time Model Order Reduction for Stability Prediction in Smart Grid

Cited by 37 publications

References 40 publications

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

Predicting Smart Grid Stability with Optimized Deep Models

Performance Analysis and Architecture of a Clustering Hybrid Algorithm Called FA+GA-DBSCAN Using Artificial Datasets

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