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Computational intelligence-based diagnostic frameworks have emerged as rapidly evolving but highly efficient approaches for diagnosing faults in power grids. This work aims to build a diagnostic framework by resorting to computational intelligence techniques to improve decision-making and diagnostic accuracy. This diagnostic framework has three modules for signal processing, fault detection, and location. The signal-processing module uses the variational mode decomposition technique to extract informative time-frequency features from the voltage and frequency signals. Voltage features are then fed into the fault detection module to train a set of modular support vector machines that are used for monitoring the binary state of each node in the power grid. Once a faulty state on a node is detected, it activates the third module for identifying fault location. This module benefits from a novel zSlices-based general type-2 fuzzy fusion model for the sake of identifying the fault type as well as mitigating the false alarm rate. The exact location of the fault is then determined through a fuzzy decision support system that is equipped with a recommendation mechanism for the sake of consensus reaching. Various scenarios are simulated on the IEEE 39-bus system and on an experimental setup of a Three-Bus Two-Line transmission system, where the attained results verify the applicability, efficiency, and robustness of the proposed framework.
Hossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif; Jafar Zarei; Frede Blaabjerg. Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids. IEEE Transactions on Emerging Topics in Computational Intelligence 2021, PP, 1 -14.
AMA StyleHossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Jafar Zarei, Frede Blaabjerg. Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids. IEEE Transactions on Emerging Topics in Computational Intelligence. 2021; PP (99):1-14.
Chicago/Turabian StyleHossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif; Jafar Zarei; Frede Blaabjerg. 2021. "Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids." IEEE Transactions on Emerging Topics in Computational Intelligence PP, no. 99: 1-14.
This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called ``preparation range'' to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.
Seyede Zahra Tajalli; Abdollah Kavousi-Fard; Mohammad Mardaneh; Abbas Khosravi; Roozbeh Razavi-Far. Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval. IEEE Transactions on Cybernetics 2021, PP, 1 -14.
AMA StyleSeyede Zahra Tajalli, Abdollah Kavousi-Fard, Mohammad Mardaneh, Abbas Khosravi, Roozbeh Razavi-Far. Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval. IEEE Transactions on Cybernetics. 2021; PP (99):1-14.
Chicago/Turabian StyleSeyede Zahra Tajalli; Abdollah Kavousi-Fard; Mohammad Mardaneh; Abbas Khosravi; Roozbeh Razavi-Far. 2021. "Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval." IEEE Transactions on Cybernetics PP, no. 99: 1-14.
This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.
Hossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif; Vasile Palade. Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems. Sensors 2021, 21, 5173 .
AMA StyleHossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Vasile Palade. Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems. Sensors. 2021; 21 (15):5173.
Chicago/Turabian StyleHossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif; Vasile Palade. 2021. "Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems." Sensors 21, no. 15: 5173.
The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients. Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model. This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process. To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients’ survival chance. Secondly, a hybrid framework is designed that contains three main modules for missing data imputation, class imbalance learning, and classification, each of which employing multiple advanced techniques for the given task. Furthermore, these two approaches are compared and evaluated using a real clinical case study. The experimental results indicate the robust and superior performance of the proposed deep learning method in terms of F-measure and area under the receiver operating characteristic curve (AUC).
Ehsan Hallaji; Roozbeh Razavi-Far; Vasile Palade; Mehrdad Saif. Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients. IEEE Access 2021, 9, 73641 -73650.
AMA StyleEhsan Hallaji, Roozbeh Razavi-Far, Vasile Palade, Mehrdad Saif. Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients. IEEE Access. 2021; 9 ():73641-73650.
Chicago/Turabian StyleEhsan Hallaji; Roozbeh Razavi-Far; Vasile Palade; Mehrdad Saif. 2021. "Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients." IEEE Access 9, no. : 73641-73650.
This article develops an event-based adaptive optimal fast terminal sliding mode control (AOFTSMC) under malicious denial-of-service (DoS) attacks. It is supposed that the transmitted measurement signals are ruined by attackers randomly. A key issue is how to design the controller parameters to keep the desirable performance of the closed-loop system under DoS attacks which are characterized by their frequencies and durations. To this end, the event-based AOFTSMC is proposed first to increase robustness against the attack and reduce the computational load. Then, an explicit effect of the duration and frequency of DoS attacks on the stability of the closed-loop systems under the presented controller is analyzed. Moreover, the scheduling of controller updating times is determined. This leads to derive the maximum bandwidth of the cyber layer which is required to guarantee the stability of the closed-loop system. Then, the designer can outline suitable controller parameters in different situations in the presence of uncertainties and DoS attacks. Finally, numerical simulation results illustrate the validation and effectiveness of the proposed methodology.
Mobin Saeedi; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif. Event-Triggered Adaptive Optimal Fast Terminal Sliding Mode Control Under Denial-of-Service Attacks. IEEE Systems Journal 2021, PP, 1 -9.
AMA StyleMobin Saeedi, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif. Event-Triggered Adaptive Optimal Fast Terminal Sliding Mode Control Under Denial-of-Service Attacks. IEEE Systems Journal. 2021; PP (99):1-9.
Chicago/Turabian StyleMobin Saeedi; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif. 2021. "Event-Triggered Adaptive Optimal Fast Terminal Sliding Mode Control Under Denial-of-Service Attacks." IEEE Systems Journal PP, no. 99: 1-9.
Many efforts have been dedicated to addressing data loss in various domains. While task-specific solutions may eliminate the respective issue in certain applications, finding a generic method for missing data estimation is rather complex. In this regard, this article proposes a novel missing data imputation algorithm, which has supreme generalization ability for a vast variety of applications. Making use of both complete and incomplete parts of data, the proposed algorithm reduces the effect of missing ratio, which makes it suitable for situations with very high missing ratios. In addition, this feature enables model construction on incomplete training sets, which is rarely addressed in the literature. Moreover, the nonparametric nature of this new algorithm brings about supreme flexibility against all variations of missing values and data distribution. We incorporate the advantages of denoising autoencoders and ladder architecture into a novel formulation based on deep neural networks. To evaluate the proposed algorithm, a comparative study is performed using a number of reputable imputation techniques. In this process, real-world benchmark datasets from different domains are selected. On top of that, a real cyber-physical system is also evaluated to study the generalization ability of the proposed algorithm for distinct applications. To do so, we conduct studies based on three missing data mechanisms, namely: 1) missing completely at random; 2) missing at random; and 3) missing not at random. The attained results indicate the superiority of the proposed method in these experiments.
Ehsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif. DLIN: Deep Ladder Imputation Network. IEEE Transactions on Cybernetics 2021, PP, 1 -13.
AMA StyleEhsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif. DLIN: Deep Ladder Imputation Network. IEEE Transactions on Cybernetics. 2021; PP (99):1-13.
Chicago/Turabian StyleEhsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif. 2021. "DLIN: Deep Ladder Imputation Network." IEEE Transactions on Cybernetics PP, no. 99: 1-13.
This paper proposes a novel adversarial scheme for learning from data under harsh learning conditions of partially labelled samples and skewed class distributions. This novel scheme integrates the generative ability of the state-of-the-art conditional generative adversarial network with the semi-supervised deep ladder network and semi-supervised deep auto-encoder. The proposed generative-adversarial based semi-supervised learning framework, named GBSS, is a triple network that aims to optimize a newly defined objective function to enhance the performance of the semi-supervised learner with the help of a generator and discriminator. The duel between the generator and discriminator results in the generation of more synthetic minority class samples that are very similar to the original minority samples (attacks and faults). Meanwhile, GBSS trains the semi-supervised model to learn the general distribution of the minority class samples including the newly generated samples in contrast to other classes and iteratively adjusts its weights. Moreover, a diagnostic framework is designed, in which GBSS and several state-of-the-art semi-supervised learners are used for learning and diagnosing attacks and faults in power grids. These methods are evaluated and compared for diagnosing attacks and faults in two different power grid cases. The attained results demonstrate the superiority of GBSS in diagnosing attacks and faults under the harsh conditions.
Maryam Farajzadeh-Zanjani; Ehsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif; Masood Parvania. Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids. IEEE Transactions on Smart Grid 2021, 12, 3468 -3478.
AMA StyleMaryam Farajzadeh-Zanjani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif, Masood Parvania. Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids. IEEE Transactions on Smart Grid. 2021; 12 (4):3468-3478.
Chicago/Turabian StyleMaryam Farajzadeh-Zanjani; Ehsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif; Masood Parvania. 2021. "Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids." IEEE Transactions on Smart Grid 12, no. 4: 3468-3478.
Removing the redundant features from massive data collected from power systems is of paramount importance in improving the efficiency of data-driven diagnostic systems. This work proposes a novel concrete feature selection based on mutual information, called CFMI, for selecting proper features to enhance diagnosing faults and cyber-attacks in power systems. The proposed technique is then compared with various state-of-the-art techniques and a comprehensive study has been performed on the selected features. All techniques are evaluated with respect to simulated scenarios on IEEE 39-bus system and a Three-Bus Two-Line experimental setup. The attained results, on one hand, verify the superiority of the proposed CFMI technique over other techniques. On the other hand, the selected features from both setups denote that current and voltage features are more informative than other features for diagnostic systems. Furthermore, the results of the comprehensive study shows that those features collected from generation buses are of higher priority for diagnostic systems.
Hossein Hassani; Ehsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif. Unsupervised concrete feature selection based on mutual information for diagnosing faults and cyber-attacks in power systems. Engineering Applications of Artificial Intelligence 2021, 100, 104150 .
AMA StyleHossein Hassani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif. Unsupervised concrete feature selection based on mutual information for diagnosing faults and cyber-attacks in power systems. Engineering Applications of Artificial Intelligence. 2021; 100 ():104150.
Chicago/Turabian StyleHossein Hassani; Ehsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif. 2021. "Unsupervised concrete feature selection based on mutual information for diagnosing faults and cyber-attacks in power systems." Engineering Applications of Artificial Intelligence 100, no. : 104150.
In this study, first, a comprehensive model is introduced to model actuator faults, and then a novel fault-tolerant control (FTC) strategy is proposed to compensate the loss of actuator's effectiveness in networked control systems (NCSs). A Markov chain is exploited to represent networked-induced random delays, and data packet dropouts as well as disorders to address the stochastic characteristic of the network issues. Accordingly, the resulting closed-loop system lies in the framework of Markovian jump systems (MJSs). Moreover, partly unknown transition probabilities are considered in the current study since the identification of the exact value of transition probabilities of the Markov chain is difficult or even impractical due to the complex structure of the network. Sufficient conditions for the stochastic stability are derived by means of the solutions of a finite set of linear matrix inequalities (LMIs) to design a novel robust FTC through the output feedback technique, which requires only the outputs. A numerical example and an engineering benchmark system are presented to verify the capability of the proposed method in practical applications.
Mohsen Bahreini; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif. Robust and Reliable Output Feedback Control for Uncertain Networked Control Systems Against Actuator Faults. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -10.
AMA StyleMohsen Bahreini, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif. Robust and Reliable Output Feedback Control for Uncertain Networked Control Systems Against Actuator Faults. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-10.
Chicago/Turabian StyleMohsen Bahreini; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif. 2021. "Robust and Reliable Output Feedback Control for Uncertain Networked Control Systems Against Actuator Faults." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-10.
In cyber-physical systems, transforming a large amount of data collected from various sensors onto informative low-dimension data is of paramount importance for efficient monitoring and safe and secure operation of the system. To this aim, this paper proposes two novel dimensionality reduction techniques, where each makes use of two duelling neural networks along with two newly defined constraints of class separability and affinity correlation. Using the original distribution of the high-dimensional data, the goal is to achieve an ideal distribution in a lower-dimensional feature space, while preserving the correlation of features and discriminating samples of distinct classes. These classes are the system states including faults and cyber-attacks. The proposed novel techniques are compared with state-of-the-art dimensionality reduction techniques over several datasets collected from a cyber-physical system. The attained results show that the proposed techniques significantly outperform other techniques.
Maryam Farajzadeh-Zanjani; Ehsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif. Generative adversarial dimensionality reduction for diagnosing faults and attacks in cyber-physical systems. Neurocomputing 2021, 440, 101 -110.
AMA StyleMaryam Farajzadeh-Zanjani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif. Generative adversarial dimensionality reduction for diagnosing faults and attacks in cyber-physical systems. Neurocomputing. 2021; 440 ():101-110.
Chicago/Turabian StyleMaryam Farajzadeh-Zanjani; Ehsan Hallaji; Roozbeh Razavi-Far; Mehrdad Saif. 2021. "Generative adversarial dimensionality reduction for diagnosing faults and attacks in cyber-physical systems." Neurocomputing 440, no. : 101-110.
This paper presents an industrial implementation of a virtual sensor in the process of fault detection of an induction motor. An ensemble-learning soft-sensor is developed to detect broken rotor bar that is essential to prevent irreparable damage. Most of the existing diagnostic methods assume that the data distribution is static and that all data is available during the training, while in real applications, the data become available as data streams. The proposed method is inspired by the ensemble learning algorithm, which is combined with a new drift detection mechanism. The advantages of the proposed approach are three-fold. First, a fair comparison with other algorithms show the effectiveness of the soft sensor scheme. Second, the presented concept change detection algorithm is capable of detecting a new class in the data stream as well as data distribution change, and last but not least, the efficacy of the proposed algorithm is demonstrated using benchmark concept drift data streams.
Zahra Hosseinpoor; Mohammad Mehdi Arefi; Roozbeh Razavi-Far; Niloofar Mozafari; Saeede Hazbavi. Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar. IEEE Sensors Journal 2020, 21, 5044 -5051.
AMA StyleZahra Hosseinpoor, Mohammad Mehdi Arefi, Roozbeh Razavi-Far, Niloofar Mozafari, Saeede Hazbavi. Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar. IEEE Sensors Journal. 2020; 21 (4):5044-5051.
Chicago/Turabian StyleZahra Hosseinpoor; Mohammad Mehdi Arefi; Roozbeh Razavi-Far; Niloofar Mozafari; Saeede Hazbavi. 2020. "Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar." IEEE Sensors Journal 21, no. 4: 5044-5051.
The aim of this study is to develop a novel distributed robust prescribed finite-time secondary control for both frequency and voltage restoration along with accurate active power sharing in islanded microgrids. The prescribed finite-time convergence property irrespective of the values of initial conditions helps to design an offline settling time that leads to power loss reduction. Moreover, thanks to the use of a piecewise-function based approach the upper-bound of convergence time is reduced. The fixed-time stability of the proposed scheme is rigorously confirmed by applying the Lyapunov theory, which results in a set of tuning rules. The main challenge is to establish the stability conditions through asymmetrical connections under a directed graph. Finally, the architecture of the experimental setup is constructed for an inverter-based microgrid consisting of six DGs. The OPAL-RT real-time simulator is exploited to verify the applicability of the proposed distributed fixed-time controller.
Neda Sarrafan; Mohammad-Amin Rostami; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif; Tomislav Dragicevic. Improved Distributed Prescribed Finite-Time Secondary Control of Inverter-Based Microgrids: Design and Real-Time Implementation. IEEE Transactions on Industrial Electronics 2020, 68, 11135 -11145.
AMA StyleNeda Sarrafan, Mohammad-Amin Rostami, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif, Tomislav Dragicevic. Improved Distributed Prescribed Finite-Time Secondary Control of Inverter-Based Microgrids: Design and Real-Time Implementation. IEEE Transactions on Industrial Electronics. 2020; 68 (11):11135-11145.
Chicago/Turabian StyleNeda Sarrafan; Mohammad-Amin Rostami; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif; Tomislav Dragicevic. 2020. "Improved Distributed Prescribed Finite-Time Secondary Control of Inverter-Based Microgrids: Design and Real-Time Implementation." IEEE Transactions on Industrial Electronics 68, no. 11: 11135-11145.
This paper develops a novel multi-objective controller to regulate the power converters of a class of direct current (DC) microgrids (MGs) connected to nonlinear constant power loads (CPLs) and linear resistive loads. The suggested control approach uses the non-dominating sorting binary genetic algorithm (NSBGA-II) to directly design the on/off switching signal of the converters without using the pulse width modulation (PWM) technique. The multi-objective controller minimizes the tracking error of the DC bus voltage and at the same time tries to reduce the total number of switching actions. Thereby, the developed controller tracks the desired reference with a reduced converter switching action and power loss by using a proper Pareto solution. Moreover, by employing the NSBGA-II algorithm, it is feasible to involve the switching frequency in the design procedure to enhance the performance. Exploiting the binary genetic algorithm (BGA) instead of conventional GA, turns a continuous surface searching into a binary one, which not only makes it more compatible with the nature of the power converter control but also decreases the online computational burden. To illustrate the superiority of the proposed approach, real-time OPAL results are provided.
Arezoo Vafamand; Navid Vafamand; Jafar Zarei; Roozbeh Razavi-Far; Tomislav Dragicevic. Intelligent Multiobjective NSBGA-II Control of Power Converters in DC Microgrids. IEEE Transactions on Industrial Electronics 2020, 68, 10806 -10814.
AMA StyleArezoo Vafamand, Navid Vafamand, Jafar Zarei, Roozbeh Razavi-Far, Tomislav Dragicevic. Intelligent Multiobjective NSBGA-II Control of Power Converters in DC Microgrids. IEEE Transactions on Industrial Electronics. 2020; 68 (11):10806-10814.
Chicago/Turabian StyleArezoo Vafamand; Navid Vafamand; Jafar Zarei; Roozbeh Razavi-Far; Tomislav Dragicevic. 2020. "Intelligent Multiobjective NSBGA-II Control of Power Converters in DC Microgrids." IEEE Transactions on Industrial Electronics 68, no. 11: 10806-10814.
In this article, a new approach is proposed for stability analysis and controller design of nonlinear discrete-time positive systems by means of the Takagi-Sugeno fuzzy model. The closed-loop stability and the positivity constraint are guaranteed by synthesizing a linear co-positive Lyapunov function and by applying the parallel distributed compensation controller. In contrast to the state-of-the-art approaches for ensuring the ℓ₁-stability of the positive system which are based on bilinear matrix inequalities, the proposed optimal robust control design under ℓ₁-induced performance is derived based on linear programming framework. It has been shown that the computational complexity of the proposed optimization problem can be effectively reduced. Finally, a numerical example and the Leslie population model are adopted to show the capabilities of the proposed method.
Elham Ahmadi; Jafar Zarei; Roozbeh Razavi-Far. Robust ℓ₁-Controller Design for Discrete-Time Positive T-S Fuzzy Systems Using Dual Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, PP, 1 -10.
AMA StyleElham Ahmadi, Jafar Zarei, Roozbeh Razavi-Far. Robust ℓ₁-Controller Design for Discrete-Time Positive T-S Fuzzy Systems Using Dual Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020; PP (99):1-10.
Chicago/Turabian StyleElham Ahmadi; Jafar Zarei; Roozbeh Razavi-Far. 2020. "Robust ℓ₁-Controller Design for Discrete-Time Positive T-S Fuzzy Systems Using Dual Approach." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-10.
In this work, a novel nonlinear approach is proposed for the stabilization of microgrids with constant power loads (CPLs). The proposed method is constructed based on the incorporation of a pseudo-extended Kalman filter into stochastic nonlinear model predictive control (MPC). In order to achieve high-performance and optimal control in DC microgrids, estimating the instantaneous power flow of the uncertain constant power loads and the available power units is essential. Thus, by utilizing the advantages of the stochastic nonlinear model predictive control and the pseudo-extended Kalman filter, an effective control solution for the stabilization of DC islanded microgrids with CPLs is established. This technique develops a constrained controller for practical application to handle the states and control input constraints explicitly; furthermore, as it estimates the current by using the pseudo-EKF, it is a current-senseless approach. As noisy measurements are taken into account for the state estimation, it leads to a less conservative control action rather than the classical robust MPC, whereas it guarantees the global asymptotic stability in the presence of noisy measurements and parameter uncertainty. To validate the performance of the proposed controller, the attained results are compared to state-of-the-art controllers. Furthermore, the implementability of the proposed method is validated using real-time simulations on dSPACE hardware.
Elham Kowsari; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif; Tomislav Dragicevic; Mohammad-Hassan Khooban. A Novel Stochastic Predictive Stabilizer for DC Microgrids Feeding CPLs. IEEE Journal of Emerging and Selected Topics in Power Electronics 2020, 9, 1222 -1232.
AMA StyleElham Kowsari, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif, Tomislav Dragicevic, Mohammad-Hassan Khooban. A Novel Stochastic Predictive Stabilizer for DC Microgrids Feeding CPLs. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2020; 9 (2):1222-1232.
Chicago/Turabian StyleElham Kowsari; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif; Tomislav Dragicevic; Mohammad-Hassan Khooban. 2020. "A Novel Stochastic Predictive Stabilizer for DC Microgrids Feeding CPLs." IEEE Journal of Emerging and Selected Topics in Power Electronics 9, no. 2: 1222-1232.
This article focuses on the design of a hierarchical framework for locating faults in smart grids by resorting to only modal components of three-phase voltage measurements. The search space for identifying the faulty lines is first limited to the impacted regions by the fault, which is determined through an improved graph analytic-based algorithm by contributing the system topology and attribute affinities. The faulty lines within the faulty regions are then identified by employing a heuristic index extracted from the wavelet multiresolution analysis of corresponding modal components. The fault location on the faulty lines is finally estimated by the regression analysis of two novel graph regularization-based learning models. This fault location proposal has been evaluated over numerous simulated scenarios on the IEEE 39-bus system with the measurements subject to sampling rate, fault resistance, and noise issues. The attained results validate the efficiency of the proposed framework.
Hossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif; Gerard-Andre Capolino. Regression Models With Graph-Regularization Learning Algorithms for Accurate Fault Location in Smart Grids. IEEE Systems Journal 2020, 15, 2012 -2023.
AMA StyleHossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Gerard-Andre Capolino. Regression Models With Graph-Regularization Learning Algorithms for Accurate Fault Location in Smart Grids. IEEE Systems Journal. 2020; 15 (2):2012-2023.
Chicago/Turabian StyleHossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif; Gerard-Andre Capolino. 2020. "Regression Models With Graph-Regularization Learning Algorithms for Accurate Fault Location in Smart Grids." IEEE Systems Journal 15, no. 2: 2012-2023.
This paper proposes a clustering-based hierarchical framework that includes a consensus decision support system for locating faults in smart grids. Frequency measurements are initially collected by distributed frequency disturbance recorders, and, then, decomposed in the time-frequency domain. Extracted time-frequency variational modes are further analyzed through statistical analysis. The resulted features are then used by the affinity propagation clustering technique to partition the power grid. The faulty partition is determined by evaluating a heuristic index, and, is then fed to a zNumber--based multicriteria group decision support system to decide on the fault location. The effect of various preferences on affinity propagation clustering has been handled by resorting to an aggregation scheme, which considers multiple criteria into account. The feasibility and effectiveness of the proposed framework have been validated through a comprehensive study on the IEEE 39-bus system.
Hossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif. Fault Location in Smart Grids Through Multicriteria Analysis of Group Decision Support Systems. IEEE Transactions on Industrial Informatics 2020, 16, 7318 -7327.
AMA StyleHossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif. Fault Location in Smart Grids Through Multicriteria Analysis of Group Decision Support Systems. IEEE Transactions on Industrial Informatics. 2020; 16 (12):7318-7327.
Chicago/Turabian StyleHossein Hassani; Roozbeh Razavi-Far; Mehrdad Saif. 2020. "Fault Location in Smart Grids Through Multicriteria Analysis of Group Decision Support Systems." IEEE Transactions on Industrial Informatics 16, no. 12: 7318-7327.
DC shipboard power systems are usually under the threat of instability due to the incremental negative impedance of the constant power loads connected to the DC bus. The instantaneous power flow, which moves along the time-varying uncertain constant power loads, is required to be estimated to enhance the control efficiency of the shipboard power grid. In this paper, at first, the estimation of the load power variation of uncertain constant power loads in a shipboard DC microgrid is conducted in the finite time by adopting the finite-time disturbance observer method. The estimated load power is then received by the fixed-time terminal sliding mode controller to stabilize the entire marine power grid as well as tracking the reference voltage of the DC bus in the fixed time independent of initial conditions. A Lyapunov-based stability analysis rigorously proves the fixed-time convergence of the proposed disturbance observer-based controller. Finally, Model-in-the-Loop real-time simulations using dSPACE emulator are carried out under various operating cases to evaluate the practical implementation of the presented disturbance observer-based scheme.
Neda Sarrafan; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif; Mohammad-Hassan Khooban. A Novel On-Board DC/DC Converter Controller Feeding Uncertain Constant Power Loads. IEEE Journal of Emerging and Selected Topics in Power Electronics 2020, 9, 1233 -1240.
AMA StyleNeda Sarrafan, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif, Mohammad-Hassan Khooban. A Novel On-Board DC/DC Converter Controller Feeding Uncertain Constant Power Loads. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2020; 9 (2):1233-1240.
Chicago/Turabian StyleNeda Sarrafan; Jafar Zarei; Roozbeh Razavi-Far; Mehrdad Saif; Mohammad-Hassan Khooban. 2020. "A Novel On-Board DC/DC Converter Controller Feeding Uncertain Constant Power Loads." IEEE Journal of Emerging and Selected Topics in Power Electronics 9, no. 2: 1233-1240.
Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.
Mojtaba Kordestani; Mehrdad Saif; Marcos E. Orchard; Roozbeh Razavi-Far; Khashayar Khorasani. Failure Prognosis and Applications—A Survey of Recent Literature. IEEE Transactions on Reliability 2019, 70, 728 -748.
AMA StyleMojtaba Kordestani, Mehrdad Saif, Marcos E. Orchard, Roozbeh Razavi-Far, Khashayar Khorasani. Failure Prognosis and Applications—A Survey of Recent Literature. IEEE Transactions on Reliability. 2019; 70 (2):728-748.
Chicago/Turabian StyleMojtaba Kordestani; Mehrdad Saif; Marcos E. Orchard; Roozbeh Razavi-Far; Khashayar Khorasani. 2019. "Failure Prognosis and Applications—A Survey of Recent Literature." IEEE Transactions on Reliability 70, no. 2: 728-748.
While the quality of the synchronized measurements is of paramount importance for real-time monitoring and protection of the power grids, collected measurements often contain missing values. This paper proposes a scheme for diagnosing attacks and faults in the presence of missing measurements in power grid data. The proposed scheme contains four modules for clustering, missing data imputation, decision-making, and optimization. This paper develops a novel technique for missing data imputation based on the correlation-connected clusters that consider local correlation among the measurements in estimating missing data, handle high-dimensional data, and tolerate high missing ratios. The optimization module ties the imputation process to diagnostic performance. The proposed novel imputation technique is compared with other state-of-the-art techniques within the diagnostic scheme. The achieved results show that the proposed technique significantly outperforms other competitors.
Roozbeh Razavi-Far; Maryam Farajzadeh-Zanjani; Mehrdad Saif; Shiladitya Chakrabarti. Correlation Clustering Imputation for Diagnosing Attacks and Faults With Missing Power Grid Data. IEEE Transactions on Smart Grid 2019, 11, 1453 -1464.
AMA StyleRoozbeh Razavi-Far, Maryam Farajzadeh-Zanjani, Mehrdad Saif, Shiladitya Chakrabarti. Correlation Clustering Imputation for Diagnosing Attacks and Faults With Missing Power Grid Data. IEEE Transactions on Smart Grid. 2019; 11 (2):1453-1464.
Chicago/Turabian StyleRoozbeh Razavi-Far; Maryam Farajzadeh-Zanjani; Mehrdad Saif; Shiladitya Chakrabarti. 2019. "Correlation Clustering Imputation for Diagnosing Attacks and Faults With Missing Power Grid Data." IEEE Transactions on Smart Grid 11, no. 2: 1453-1464.