Review of major approaches to analyze vulnerability in power system

Vulnerability analysis is a key issue in power systems since power transmission grids play a crucial role as a critical infrastructure. The power grid structure (number of nodes and lines, their connections, and their physical properties and operational constraints) is one of the main factors to assure power system security. Complex network theory as a promising topological approach for the structural vulnerability analysis has been widely used in many different fields. Recently, many complex network metrics have been proposed to assess the topological vulnerability of power transmission grids. However, these approaches are purely topological and fail in capturing the specific features of power systems. In this paper, an extended topological approach which incorporates electrical features such as flow path, line flow limits, etc., is presented. Three new metrics, net-ability, electrical betweenness and entropy degree are provided and used to assess structural vulnerability in power transmission grids.

A Perspective overview of topological approaches for vulnerability analysis of power transmission grids

Survivability evaluation and importance analysis for cyber–physical smart grids

Power System Analysis

Short term load prediction plays a critical role in the planning and operations of electric power systems especially in the modern days with high emphasis on integration of renewable energy resources. In this work, a hybrid deep learning model for short term load forecasting (STLF) is presented. The proposed method first decomposes the time series data into several intrinsic mode functions (IMF) using Empirical Mode Decomposition (EMD) and a reconstruction of the original series is obtained by suppressing the irrelevant IMFs. Detrended fluctuation analysis (DFA) is applied to each IMF to determine their scaling exponents for robust denoising performance. The denoised data is then used as input to the Deep Belief Network (DBN) model for modeling and prediction. Real data which represents hourly load consumption from the Electric Reliability Council of Texas (ERCOT) was used to evaluate the efficacy of the proposed method.

Short-Term Load Forecasting Based on a Hybrid Deep Learning Model

The aim of this paper is to evaluate the ability of the line-graph based model (LGBM) to assess the vulnerability of power systems as part of complex network theory. The nodes in the line graph are the transmission lines, thus emphasizing their vulnerability. We derive and compute line-graph corresponding to several standard IEEE test cases and well as to the Western Systems Coordinating Council (WSCC) power grid. In each case, we compute various graph-theoretic measures, and we compared to the measures corresponding to standard topologies of complex networks (random, scale-free, and small world). We also compare LGBM topological characteristics with those of the bus-based model (BBM). Moreover, we examine the vulnerability of the LGBM nodes (transmission lines) are removed randomly or targeted by a malicious opponent. In both the IEEE models and the WSCC, simulations show that when the network size is large, and the BBM behaves like a scale-free network, the pattern of LGBM node degree distribution is distinct from that of BBM. The LGBM seems to possess a higher clustering coefficient, almost double, suggesting that transmission lines are more tightly connected and interdependent than buses, at least from a topological point of view. In general, our simulations show that attacking transmission lines seem to be more detrimental to the power grid than attacking buses.

Line-graph based modeling for assessing the vulnerability of transmission lines

In this paper we validate the use of degree centrality measures based on power traffic to assess the vulnerability of power systems and to predict 70% brownouts and blackouts using Line Graph Based Model (LGBM) which is best to describe the removal of transmission lines as opposed to buses. Monte-Carlo simulations were employed for IEEE systems test cases. Different events were evaluated, including the removal of randomly selected elements of the network. The AC power flow formulation was employed to calculate the degree centrality measures. The simulations demonstrated the consistency of the degree centrality measures based on the power traffic matrix to indicate the total unsatisfied load ratio resulting from node or link removals. We studied the comparison between bus failure and link failuer. Finally, it was observed that a blackout is ensured if the sum of centrality scores of the nodes or links removed exceeds a threshold.

Vulnerabilities of Power Grid Due to Line Failures Based on Power Traffic Centrality of the Line Graph

The Internet of Things (IoT) is a technological revolution that enables human-to-human and machine-to-machine communication for virtual data exchange. The IoT allows us to identify, locate, and access the various things and objects around us using low-cost sensors. The Internet of Things offers many benefits but also raises many issues, especially in terms of privacy and security. Appropriate solutions must be found to these challenges, and privacy and security are top priorities in the IoT. This study identifies possible attacks on different types of networks as well as their countermeasures. This study provides valuable insights to vulnerability researchers and IoT network protection specialists because it teaches them how to avoid problems in real networks by simulating them and developing proactive solutions. IoT anomalies were detected by simulating message queuing telemetry transport (MQTT) over a virtual network. Utilizing DDoS attacks and some machine learning algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR), as well as an artificial neural network, multilayer perceptron (MLP), naive Bayes (NB) and decision tree (DT) are used to detect and mitigate the attack. The proposed approach uses a dataset of 4998 records and 34 features with 8 classes of network traffic. The classifier RF showed the best performance with 99.94% accuracy. An intrusion detection system using Snort was implemented. The results provided theoretical proof of applicability and feasibility.

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Cyber Threat Intelligence for IoT Using Machine Learning

Abstract The aim of this paper is the identification of the most harmful malicious attacks in a smart grid with basis on the removal of buses in a particular sequence. For that, we define the Electrical Most Damaging Element (EMDE) and the Iterated Centrality Measure (ICM). The EMDE is the element that leads to the largest unsatisfied load increase after removed, in the current state of the smart grid. The ICM is a meaningful scaled centrality for iterated attacks. Attack strategies such as the IEMDE (Iterated Electrical Most Damaging Element) and the Iterated Most Central Element (IMCE) are proposed as references for evaluating the impact of failure sequences by comparison. For each fault strategy approach, the vulnerability curves as well as a scalability analysis are presented. It is demonstrated that the IEMDE approximated the $$N-k-\varepsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>-</mml:mo> <mml:mi>k</mml:mi> <mml:mo>-</mml:mo> <mml:mi>ε</mml:mi> </mml:mrow> </mml:math> algorithm, but with reduced computational expense. Furthermore, the IMCE approach provided an efficient fault profile close to the performance of the IEMDE. Although this framework is applied in this paper to failures in buses, it can similarly be applied to other elements. Future research will be focused in applying these concepts to transmission lines. 

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Empirical framework for identification of the most harmful malicious attacks on a smart grid

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Aiman Albarakati

Papers

Line-graph based modeling for assessing the vulnerability of transmission lines

Vulnerabilities of Power Grid Due to Line Failures Based on Power Traffic Centrality of the Line Graph

Cyber Threat Intelligence for IoT Using Machine Learning

Empirical framework for identification of the most harmful malicious attacks on a smart grid

Empirical framework for identification of the most harmful malicious attacks on a smart grid