Implementing SVPWM Technique to an Axial Flux Permanent Magnet Synchronous Motor Drive with Internal Model Current Controller

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

Bad Data Injection Attack and Defense in Electricity Market using Game Theory Study

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.Comment: To appear in IEEE Transactions on Smart Grid, Special Issue on Cyber, Physical, and System Security for Smart Gri

Attack and Defend Mechanisms for State Estimation in Smart Grid

Aging power industries together with an 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 the smart grid. Changing the traditional structure of power systems and integrating communication devices are beneficial for better monitoring and decision making by the system operators, but at the same time it increases the risk of cyber attacks. Power system blackout in 2003 created serious problems for customers in the eastern US and Canada. Although different investigations report reasons other than cyber attack for the blackout, many researchers believe a similar tragedy could happen with targeted cyber attacks. Later in 2007, researchers at the Idaho National Lab tried to attack a synchronous generator. The attack was successful and the generator was self-destroyed in a couple of minutes. This attack alarmed cyber-security decision makers, motivating them to define a critical infrastructure that is vulnerable to cyber-attack. An example of this vulnerability is the current bad data detection routine in state estimation, which is not able to detect a certain type of cyber attack called \emph{stealth attack}. Stealth attacks are able to manipulate the state estimation results in order to take economical advantages or make technical problems for power grid. In this dissertation, we analyze the cyber attack against state estimation, from both the attacker and defender points of views. We first review the structure of the electricity market, and then we present the way that the attacker alters the congestion in the ex--post market (in the desired direction) and makes financial profits. We investigate the case that attackers without prior knowledge of the power grid topology, try to make inferences through phasor observations. The inferred structural information is used to launch stealth attacks. This attack is formulated to change the price of electricity in the real-time market. Second, we look at the false data injection from the defender point of view. Because of a huge number of measurements in the network, 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 the defender, and we describe how the interest of one party (attacker or defender) can influence the other's interest. The results of this game defines the proportion of times that the attacker and defender will attack and defend different measurements, respectively. Finally, we illustrate how the normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We first propose two machine learning based techniques for stealthy attack detection. The first method utilizes the supervised learning over labeled data and trains a support vector machine. The second method requires no labeled outputs for training data and detects deviation in the measurements. In both methods, principle component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computational complexities.Electrical and Computer Engineering, Department o

Market Moods and Network Dynamics of Stock Returns:The Bipolar Behavior

The authors show that a simple mood-separable preference in a network study of stock returns captures a variety of stylized facts regarding stocks’ provisional (ab)normal behavior. These behaviors are articulated in a multistate complete Euclidean network model that specifies the existence, direction, and magnitude of a self-organized dynamics for each individual stock during abnormal market moods. In the empirical setting, the authors apply suggested model along with 2 established visual approaches (multidimensional scaling and agglomerative hierarchical clustering) for benchmark purposes. Results reveal different levels of erratic return dynamics for each stock and the entire market in different abnormal market moods. The authors model and interpret these self-organized dynamics as evidence of stocks’ and market’s bipolar behavior

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

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

Fault detection in wind turbine: a systematic literature review

Wind power has become one of the popular renewable resources all over the world and is anticipated to occupy 12% of the total global electricity generation capacity by 2020. For the harsh environment that the wind turbine operates, fault diagnostic and condition monitoring are important for wind turbine safety and reliability. This paper employs a systematic literature review to report the most recent promotions in the wind turbine fault diagnostic, from 2005 to 2012. The frequent faults and failures in wind turbines are considered and different techniques which have been used by researchers are introduced, classified and discussed

Adaptive Quickest Estimation Algorithm for Smart Grid Network Topology Error

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