An Integrated Scheme for Online Dynamic Security Assessment Based on Partial Mutual Information and Iterated Random Forest

Liu, Songkai; Liu, Lihuang; Fan, Youping; Zhang, Lei; Huang, Yuehua; Zhang, Tao; Cheng, Jinmin; Wang, Lingyun; Zhang, Menglin; Shi, Ruoyuan; Mao, Dan

doi:10.1109/tsg.2020.2991335

Cited by 67 publications

(22 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple ML and DL methods were investigated and evaluated for each detection model of the BDAC Analysis Engine. In particular, regarding the detection models adopting a multiclass classification, the following ML methods were used: Logistic Regression [57], Linear Discriminant Analysis (LDA) [58], Decision Tree Classifier [38], Naive Bayes [59], SVM Linear [60], SVM RBF [60], Random Forest [61], Adaboost [53], Multi-Layer Perceptron (MLP) [62], Quadratic Discriminant Analysis [63], K Nearest Neighbour (KNN) [64]. Moreover, three custom DNNs were also used in our evaluation analysis.…”

Section: Bdac Comparative Methodsmentioning

confidence: 99%

SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

et al. 2021

View full text Add to dashboard Cite

The technological leap of smart technologies has brought the conventional electrical grid in a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way communication, pervasive control and self-healing. However, this new reality generates significant cybersecurity risks due to the heterogeneous and insecure nature of SG. In particular, SG relies on legacy communication protocols that have not been implemented having cybersecurity in mind. Moreover, the advent of the Internet of Things (IoT) creates severe cybersecurity challenges. The Security Information and Event Management (SIEM) systems constitute an emerging technology in the cybersecurity area, having the capability to detect, normalise and correlate a vast amount of security events. They can orchestrate the entire security of a smart ecosystem, such as SG. Nevertheless, the current SIEM systems do not take into account the unique SG peculiarities and characteristics like the legacy communication protocols. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) SIEM, which focuses on SG. The main contribution of our work is the design and implementation of a SIEM system capable of detecting, normalising and correlating cyberattacks and anomalies against a plethora of SG application-layer protocols. It is noteworthy that the detection performance of the SPEAR SIEM is demonstrated with real data originating from four real SG use case (a) hydropower plant, (b) substation, (c) power plant and (d) smart home.

show abstract

Section: Bdac Comparative Methodsmentioning

confidence: 99%

SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

et al. 2021

View full text Add to dashboard Cite

show abstract

“…References [20,21] construct the transient stability margin index of the power system based on CCT. However, CCT needs to be tested repeatedly to determine through multiple time-domain simulations, which is cumbersome and extremely time-consuming for a large number of samples, and is difficult to be applied in actual large-scale power grids.…”

Section: Stability Margin Prediction and Risk Quantificationmentioning

confidence: 99%

Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021

Sustainability

View full text Add to dashboard Cite

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

show abstract

“…SCUC problem is first modelled as a single objective optimization problem and solved by Intelligent Algorithm (IA) [6][7][8][9], Lagrangian Relaxation (LR) algorithm [10], Interior Point (IP) method [11], Dynamic Programming (DP) [12], Mixed Integer Programming (MIP) approach [13] and so forth. Since the public has paid more attention to environmental and social issues [14], the single optimization objective problem has been gradually transformed into multiple objective problems [15,16].…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities

Yang

Xing

et al. 2021

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

This paper proposes an intelligent Deep Learning (DL) based approach for Data-Driven Security-Constrained Unit Commitment (DD-SCUC) decision-making. The proposed approach includes data pre-processing and a two-stage decision-making process. Firstly, historical data is accumulated and pre-processed. Then, the DD-SCUC model is created based on the Gated Recurrent Unit-Neural Network (GRU-NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two-stage decisionmaking process outputs the decision results based on various applications and scenarios. This approach has self-learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118-bus test system and a real power system from China showed that compared with deterministic Physical-Model-Driven (PMD)-SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.

show abstract

An Integrated Scheme for Online Dynamic Security Assessment Based on Partial Mutual Information and Iterated Random Forest

Cited by 67 publications

References 41 publications

SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

Deep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities

Contact Info

Product

Resources

About