M. S. Saad

Delaying instability and voltage collapse in power systems using SVCs with washout filter-aided feedback

This paper introduces washout filter-aided feedback in the design of static var compensator (SVC) control to increase the range of stable operation of a power system susceptible to voltage collapse. The use of such a control automatically maintains the open-loop steady state operating conditions. In contrast to static state feedback designs, washout-filter based designs are more efficient because no control energy is spent on needlessly maintaining an unintentionally shifted system equilibrium. The control design is illustrated in a sample power system model that undergoes a Hopf bifurcation and voltage collapse as the reactive power demand is increased to a critical value. It is seen that the control meets the goals of delaying (or eliminating) the Hopf bifurcation while at the same time maintaining the open-loop operating condition.

ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal

This paper develop a novel, non-intrusive approach for fault-detection and diagnosis scheme of bearing faults for three-phase induction motor using stator current signals with particular interest in identifying the outer-race defect at an early stage. The most common bearing problem is the outer race defect in the load zone. The empirical mode decomposition (EMD) technique is proposed for analysis of non-stationary stator current signals. The stator current signal is decomposed in intrinsic mode function (IMF) using empirical mode decomposition. The extracted IMFs apply on the wigner-ville distribution (WVD) to have the contour pattern of WVD. Then, artificial neural network is used for pattern recognition that can effectively detect outer-race defects of bearing. The experimental results show that stator current-based monitoring with winger-ville distribution based on EMD yields a high degree of accuracy in fault detection and diagnosis of outer-race defects at different load conditions, also, a more significant and reliable indicator for detection and diagnosis of outer-race defects using artificial neural network. Experimental investigation is done and reported in the paper.

Robust adaptive fuzzy logic power system stabilizer

This paper introduces a robust adaptive fuzzy controller as a power system stabilizer (RFPSS) used to damp inter-area modes of oscillation following disturbances in power systems. In contrast to the IEEE standard multi-band power system stabilizer (MB-PSS), robust adaptive fuzzy-based stabilizers are more efficient because they cope with oscillations at different operating points. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, components that ensure robust and adaptive performance are included in the control law to compensate for modelling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the systems nonlinearities. The second system is an adaptive one that compensates for modelling errors. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature.

Exfoliation of graphene sheets via high energy wet milling of graphite in 2-ethylhexanol and kerosene

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Fault Tolerance of Stator Turn Fault for Three Phase Induction Motors Star-Connected Using Artificial Neural Network

This paper proposes the possibility of developing incipient fault diagnosis and remedial operating strategies, which enable a fault tolerant induction motor star-connected winding with neutral point earthed through a controllable impedance using artificial neural network (ANN). The fault detection and diagnosis is achieved by using a strategy that detects stator turn fault, isolates the faulty components, identifies fault severity and reduces the propagation speed of the incipient stator winding fault. The fault tolerance is obtained by controlled neutral grounding resistor. This allows for continuous free operation of the induction motor even with stator winding faults. The advantage of this strategy is that it does not require any change in the standard drive system. Experimental results demonstrate the validity of the proposed technique.

Open and closed-loop motor control system with incipient broken rotor bar fault detection using current signature

Motor drive system is considered the most important asset in industrial applications. Detection of broken rotor bars has long been important but difficult job in detection area of incipient motor faults. The need for highly efficient motor control drive systems becomes more and more important. Motors are controlled in closed-loop or open-loop modes of operation. This paper develops a novel approach for fault-detection scheme of broken rotor bar faults for three-phase induction motor using stator current signal. The empirical mode decomposition (EMD) combined with Wigner-Ville distribution (WVD) has been employed for the analysis of stator current signal. Artificial neural network is then used for pattern recognition of broken rotor bar signature. The proposed algorithm offers high performance in detecting broken rotor bar fault. Both simulation and experimental results show that stator current-based monitoring in conjunction with Winger-Ville distribution based on EMD yields a reliable indicator for detection and diagnosis of broken rotor bar faults using artificial neural network. All simulations in this paper are conducted using finite element analysis software. Experimental results validate the simulation and analytical results.

A novel method for type-2 fuzzy logic controller design using a modified biogeography-based optimization

In this paper we apply the modified biogeography-based Optimization (MBBO) to design type-2 fuzzy logic controller (T2FLC) to improve the performance of the plant control system. Biogeography-based optimization (BBO) is a novel evolutionary algorithm that is based on the mathematical models of biogeography. Biogeography is the study of the geographical distribution of biological organisms. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. A modified version of the BBO is applied to design the T2FLC to get the optimal parameters of the membership functions of the controller. We test the optimal T2FLC obtained by modified biogeography-based Optimization (MBBO) using benchmark plants and is compared with Particle swarm optimization (PSO).

Towards a Conceptual Framework for Early Warning Information Systems (EWIS) for Crisis Preparedness

This paper highlights the need of many organizations nowadays for early warning information systems (EWIS) that can predict the future and help prevent crises or reduce their negative effects. These EWIS should be based on a reliable and consistent framework. The frameworks currently available are mostly deterministic, simplified or inconsistent in application and assumption; thus making them unreliable and impractical. The goal of this paper is twofold. Firstly, it provides guidelines for system analysts, designers, engineers and experts seeking to deal with crisis or disaster information systems. Secondly, it aims to present a novel framework for EWIS that can be adapted to the dynamic needs of the field of crisis management, and that can also be used efficiently in crisis preparedness. Finally, the paper will describe a case study in the law enforcement sector as a proof-of-concept for the conceptual framework; to demonstrate both the theoretical and practical approaches.

A new remedial strategy for permanent magnet synchronous motor based on artificial neural network

This paper proposes an effective approach to detect, isolate, and identify fault severity and post fault operation of permanent magnet synchronous motors (PMSM) in the presence of stator winding turn fault. The paper proposes fault tolerant operation of PMSM under post condition with stator winding turn fault by using grounded neutral point through controllable impedance using artificial neural network (ANN). The fault detection and diagnosis is achieved by using a strategy based on the analysis of the ratio of third harmonic to fundamental waveform obtained from Fast Fourier Transform (FFT) of magnitude components of the stator currents. The strategy helps to detect stator turn fault, isolate the faulty components, and estimate different insulation failure percentages and remedial operation of PMSM in the presence of stator winding turn fault. The model of PMSM with stator winding turn fault is simulated at different load conditions using a (2-D) Finite Element Analysis (FEA). Experimental results demonstrate the validity of the proposed technique.

Mode in output participation factors for linear systems

The common definition of modal participation factors was intended to provide a measure of participation of modes in states and of states in modes for linear time-invariant (LTI) systems. In this paper, recent work by the authors that revisited this common definition is extended to yield definitions and calculations for quantifying the relative contribution of system modes in system outputs. When the system outputs are simply the system states, the mode-in-output participation factors are found to reduce to the original mode-in-state participation factors. A numerical example is given to illustrate the issues addressed and the results obtained.