Papers by Mohammad Esmalifalak
Journal of Modern Power Systems and Clean Energy
Thermal Science and Engineering Progress
IEEE Systems Journal, 2015
... Christian B. Peel This dissertation has been read by each member of the following graduate co... more ... Christian B. Peel This dissertation has been read by each member of the following graduate committee and by majority vote has been found to be satisfactory. Date A. Lee Swindlehurst, Chair ... Date A. Lee Swindlehurst Chair, Graduate Committee Accepted for the Department ...
IEEE Transactions on Smart Grid, 2015
Demand Response (DR) programs are implemented to encourage consumers to reduce their electricity ... more Demand Response (DR) programs are implemented to encourage consumers to reduce their electricity demand when needed, e.g., at peak-load hours, by adjusting their controllable load. In this paper, our focus is on controllable load types that are associated with dynamic systems and can be modeled using differential equations. Examples of such load types include heating, ventilation, and air conditioning (HVAC), water heating, and refrigeration. In this regard, we propose a new demand response model based on a two-level differential game framework. At the beginning of each demand response interval, the price is decided by the upper level (aggregator, utility, or market) given the total demand of users in the lower level. At the lower level, for each player (residential or commercial buildings that are equipped with automated load control systems and local renewable generators), given the price from the upper level, the electricity usage of air conditioning unit and the battery storage charging/discharging schedules are controlled in order to minimize the user's total electricity cost. The optimal user strategies are derived using the stochastic Hamilton-Jacobi-Bellman equations. We also show that the proposed game can converge to a feedback Nash equilibrium. Based on the effect of real-time pricing on users' daily demand profile, the simulation results demonstrate the properties of the proposed game and show how we can optimize consumers' electricity cost in presence of time-varying prices.
IEEE Systems Journal, 2014
ABSTRACT Smart grid technologies have significantly enhanced robustness and efficiency of the tra... more ABSTRACT Smart grid technologies have significantly enhanced robustness and efficiency of the traditional power grid networks by exploiting technical advances in sensing, measurement, and two-way communications between the suppliers and customers. The state estimation plays a major function in building such real-time models of power grid networks. For the smart grid state estimation, one of the essential objectives is to help detect and identify the topological error efficiently. In this paper, we propose the quickest estimation scheme to determine the network topology as quickly as possible with the given accuracy constraints from the dispersive environment. A Markov chain-based analytical model is also constructed to systematically analyze the proposed scheme for the online estimation. With the analytical model, we are able to configure the system parameters for the guaranteed performance in terms of the false-alarm rate (FAR) and missed detection ratio under a detection delay constraint. The accuracy of the analytical model and detection with performance guarantee are also discussed. The performance is evaluated through both analytical and numerical simulations with the MATPOWER 4.0 package. It is shown that the proposed scheme achieves the minimum average stopping time but retains the comparable estimation accuracy and FAR.
2013 IEEE International Conference on Communications (ICC), 2013
Smart grids are vulnerable to cyber attacks because of the inevitable coupling between cyber and ... more Smart grids are vulnerable to cyber attacks because of the inevitable coupling between cyber and physical operations. Diagnosing such malicious false data attack has significant importance to ensure reliable operations of power grids. This task is challenging, however, when attackers inject bad data into power systems that are able to circumvent the traditional maximum residual detection method. By noticing the intrinsic low rank structure of temporal erroneous-free measurements of power grid as well as sparse nature of observable malicious attacks, we formulate the false data detection problem as lowrank matrix recovery and completion problem, which is solved by convex optimization that minimizes a combination of the nuclear norm and the l1 norm. To efficiently solve this mixed-norm optimization, the method of augmented Lagrange multipliers is applied, which offers provable optimality and convergence rate. Numerical simulation results both on the synthetic and real data validate the effectiveness of the proposed mechanism.
2012 IEEE Global Communications Conference (GLOBECOM), 2012
ABSTRACT Application of cyber technologies improves the quality of monitoring and decision making... more ABSTRACT Application of cyber technologies improves the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the prices of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for attacker and defender, respectively. This situation is modeled as a zero-sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5-Bus test system, we can show the effectiveness and properties of the studied game.
Measurement, 2014
Rolling-element bearing failures are the most frequent problems in rotating machinery, which can ... more Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called Anomaly Detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.
2013 IEEE Global Communications Conference (GLOBECOM), 2013
Aging power industries together with increase in the demand from industrial and residential custo... more Aging power industries together with increase in the demand from industrial and residential customers are the main incentive for policy makers to define a road map to the next generation power system called smart grid. In smart grid, the overall monitoring costs will be decreased but at the same time, the risk of cyber attacks might be increased. Recently a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the traditional bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We propose two machine learning based techniques for stealthy attack detection. The first method utilizes the supervised learning over labeled data and trains a distributed support vector machine. The design of the distributed SVM is based on the Alternating Direction Method of Multipliers, which offers provable optimality and convergence rate. The second method requires no training data and detects deviation in measurements. In both methods, principle component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computation complexities. The results of the proposed detection methods on the IEEE standard test systems demonstrate the effectiveness of both schemes.
2008 5th International Conference on the European Electricity Market, 2008
This paper presents a method for market oriented reactive power expansion. In this method first I... more This paper presents a method for market oriented reactive power expansion. In this method first ISO proposes some locations for reactive power expansion based on system requirements and operator's experiences. The investors determine the optimal locations for reactive power expansion by computing annual expansion profit of different candidates. Finally, the presented method is applied to 8-bus PJM power system to determine optimal amount and location for reactive power expansion.
IEEE Transactions on Smart Grid, 2000
State estimation in electric power grid is vulnerable to false data injection attacks, and diagno... more State estimation in electric power grid is vulnerable to false data injection attacks, and diagnosing such kind of malicious attacks has significant impacts on ensuring reliable operations for power systems. In this paper, the false data detection problem is viewed as a matrix separation problem. By noticing the intrinsic low dimensionality of temporal measurements of power grid states as well as the sparse nature of false data injection attacks, a novel false data detection mechanism is proposed based on the separation of nominal power grid states and anomalies. Two methods, the nuclear norm minimization and low rank matrix factorization, are presented to solve this problem. It is shown that proposed methods are able to identify proper power system operation states as well as detect the malicious attacks, even under the situation that collected measurement data is incomplete. Numerical simulation results both on the synthetic and real data validate the effectiveness of the proposed mechanism.
IEEE Transactions on Smart Grid, 2000
Applications of cyber technologies improve the quality of monitoring and decision making in smart... more Applications of cyber technologies improve the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the price of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero-sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5-Bus test system, we can show the effectiveness and properties of the studied game.
IEEE Communications Magazine, 2000
Communications Magazine, IEEE, 2013
This paper presents a study of axial flux permanent magnet synchronous motor (AFPMSM) drive syste... more This paper presents a study of axial flux permanent magnet synchronous motor (AFPMSM) drive system. An internal model control (IMC) strategy is introduced to control the AFPMSM drive through currents, leading to an extension of PI control with integrators added in the off-diagonal elements to remove the cross-coupling effects between the applied voltages and stator currents in a feed-forward manner. The reference voltage is applied through a space vector pulse width modulation (SVPWM) unit. A diverse set of test scenarios has been realized to comparatively evaluate the state estimation of the sensor-less AFPMSM drive performances under the implemented IMCbased control regime using a SVPWM inverter. The resulting MATLAB simulation outcomes in the face of no-load, nominal load and speed reversal clearly illustrate the well-behaved performances of IMC controller and SVPWM technique to an Axial Flux PM Motor Drive system.
In this paper generation and transmission expansion planning of IEEE 300-BUS TEST SYSTEM using eq... more In this paper generation and transmission expansion planning of IEEE 300-BUS TEST SYSTEM using equivalent load duration curve is studied. To this end, equivalent load duration curve for each electricity region is calculated for years 1991 and 1996 using the expansion of the probability random distribution series. The criteria loss of load probability, expected energy not supplied, and capacity shortage are computed using equivalent load duration curves. Equivalent load duration curves and the above-mentioned criteria are calculated using convolution method to validate the results. Finally, generation and transmission expansion planning of the system is studied considering the computed criteria. We can suppose that random variable x is equal to x i with probabilityܲ . Ȗ, order moment of random variable x, is defined as follows:
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Papers by Mohammad Esmalifalak