False data detection in a clustered smart grid using unscented Kalman filter
- Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78548-78556
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- Description: The smart grid accessibility over the Internet of Things (IoT) is becoming attractive to electrical grid operators as it brings considerable operational and cost efficiencies. However, this in return creates significant cyber security challenges, such as fortification of state estimation data such as state variables against false data injection attacks (FDIAs). In this paper, a clustered partitioning state estimation (CPSE) technique is proposed to detect FDIA by using static state estimation, namely, weighted least square (WLS) method in conjunction with dynamic state estimation using minimum variance unscented Kalman filter (MV-UKF) which improves the accuracy of state estimation. The estimates acquired from the MV-UKF do not deviate like WLS as these are purely based on the previous iteration saved in the transition matrix. The deviation between the corresponding estimations of WLS and MV-UKF are utilised to partition the smart grid into smaller sub-systems to detect FDIA and then identify its location. To validate the proposed detection technique, FIDAs are injected into IEEE 14-bus, IEEE 30-bus, IEEE 118-bus, and IEEE 300-bus distribution feeder using MATPOWER simulation platform. Our results clearly demonstrate that the proposed technique can locate the attack area efficiently compared to other techniques such as chi square. © 2013 IEEE.
Vulnerability assessment framework for a smart grid
- Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
- Date: 2022
- Type: Text , Conference paper
- Relation: 4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022, Cappadocia, Turkey, 14-17 June 2022, Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022 p. 449-454
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- Description: The increasing demand for the interconnected IoT based smart grid is facing threats from cyber-attacks due to inherent vulnerability in the smart grid network. There is a pressing need to evaluate and model these vulnerabilities in the network to avoid cascading failures in power systems. In this paper, we propose and evaluate a vulnerability assessment framework based on attack probability for the protection and security of a smart grid. Several factors were taken into consideration such as the probability of attack, propagation of attack from a parent node to child nodes, effectiveness of basic metering system, Kalman estimation and Advanced Metering Infrastructure (AMI). The IEEE-300 bus smart grid was simulated using MATPOWER to study the effectiveness of the proposed framework by injecting false data injection attacks (FDIA); and studying their propagation. Our results show that the use of severity assessment standards such as Common Vulnerability Scoring System (CVSS), AMI measurements and Kalman estimates were very effective for evaluating the vulnerability assessment of smart grid in the presence of FDIA attack scenarios. © 2022 IEEE.
Assessing reliability of smart grid against cyberattacks using stability index
- Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Conference paper
- Relation: 31st Australasian Universities Power Engineering Conference, AUPEC 2021, Virtual, Online 26 to 30 September 2021, Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021
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- Description: The degradation of stability index within smart grid leads to incorrect power generation and poor load balancing. The remote data dependency of the central energy management system (CEMS) causes communication delay that further leads to poor synchronization within the system. This becomes worse in the presence of cyber-attacks such as stealth or false data injection attack (FDIA). We used dynamic estimation to obtain state data after the inception of false data attack and analyzed its impact on the stability index of the smart grid. A lookup table was constructed based on the fluctuations within the voltage estimates of IEEE-Bus system. An index number was assigned to output estimates at the bus that highlights the level of severity within the grid. We used IEEE-57 Bus using MATLAB to capture and plot the results related to voltage estimates, latency, and inception time delay. The results demonstrate a clear relationship between stability index and state estimates especially when the system is under the influence of a cyber-attack. © 2021 IEEE.
Assessing trust level of a driverless car using deep learning
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Das, Rajkumar , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4457-4466
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- Description: The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people's main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car. © 2000-2011 IEEE.
State estimation within ied based smart grid using kalman estimates
- Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 15 (2021), p.
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- Description: State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Trustworthiness of self-driving vehicles for intelligent transportation systems in industry applications
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 2 (2021), p. 961-970
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- Description: To enhance industrial production and automation, rapid and faster transportation of raw materials and finished products to and from distributed factories, warehouses and outlets are essential. To reduce cost with increased efficiency, this will increasingly see the use of connected and self-driving commercial vehicles fitted with industrial grade sensors on roads, shared with normal and self-driving passenger vehicles. For its wide adoption, the trustworthiness of self-driving vehicles in the intelligent transportation system (ITS) is pivotal. In this article, we introduce a novel model to measure the overall trustworthiness of a self-driving vehicle considering on-Board unit (OBU) components, GPS data and safety messages. In calculating the trustworthiness of individual OBU components, CertainLogic and beta distribution function (BDF) are used. Those trust values are fused using both the dempster-Shafer Theory (DST) and a logical operator of CertainLogic. Results of our simulation show that our proposed method can effectively determine the trust of self-driving vehicles. © 2005-2012 IEEE.
Low-power wide-area networks : design goals, architecture, suitability to use cases and research challenges
- Authors: Buurman, Ben , Kamruzzaman, Joarder , Karmakar, Gour , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 17179-17220
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- Description: Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology's ability to meet design goals is essential in determining suitable technologies for a given application. To address these gaps, we have analysed six prominent design goals and identified the design decisions used to meet each goal in the eight LPWAN technologies, ranging from technical consideration to business model, and determined which specific technique in a design decision will help meet each goal to the greatest extent. System architecture and specifications are presented for those LPWAN solutions, and their ability to meet each design goal is evaluated. We outline seventeen use cases across twelve domains that require large low power network infrastructure and prioritise each design goal's importance to those applications as Low, Moderate, or High. Using these priorities and each technology's suitability for meeting design goals, we suggest appropriate LPWAN technologies for each use case. Finally, a number of research challenges are presented for current and future technologies. © 2013 IEEE.
Assessing transformer oil quality using deep convolutional networks
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1