Shady S. Refaat

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.

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.

Implementation of smart residential energy management system for smart grid

Residential sector represents a substantial increase of electricity consumption, due to substantial growth of electrical residential appliances. Electrical energy management is a major key toward reducing energy consumption and improving efficiency, decreasing costs of energy use, and decreasing the carbon footprint. Therefore, this paper proposes a novel embedded real-time, non-intrusive, smart, simple and effective residential energy management system that is capable to monitor and control energy consumption of the residential loads, balance electric power supply, reduce peak demand, and reduce energy bill while considering residential households preferences and comfort level. Therefore, the presented algorithm aims to reach those set goals by assigning the residential load according to utilities power supply events. The proposed system is investigated using Matlab/Simulink software to both verify the analytical procedure accuracy in the power grid and to obtain basic characteristics of the residential loads during peak demand. The proposed energy management system does not require any changes in the residential electrical panel. Experimental results and real-time implementation demonstrate the validity of the system proposed.

ANN-based diagnosis of incipient stator winding turn faults for three-phase induction motors in the presence of unbalanced supply voltage

Perfectly balanced supply voltages are not possible in practice. Therefore, detection, discrimination and diagnosis of stator winding turn fault in the presence of unbalanced supply voltages for three-phase induction motors is needed. In this paper a novel approach is presented for stator winding turn incipient faults detection in the presence of different levels of voltage unbalance and at different load conditions. The proposed method investigates and utilizes the ratio between third harmonic and fundamental voltage and current waveform. Fast Fourier Transform (FFT) magnitude components of the stator currents and voltages are utilized for detection and estimation of different insulation failure percentages in the presence of unbalanced supply voltages. The method uses artificial neural networks (ANN) and is tested through simulation and experimental investigations. The proposed approach presents a high degree of accuracy in detection and diagnosis of stator winding turn faults in the presence of unbalanced supply voltages condition. The method discriminates between the effect of incipient stator winding turn fault and those due to unbalanced supply voltage. In addition, the proposed approach gives a more significant and reliable indicator for detection and diagnosis of stator winding turn faults in the presence of unbalanced supply voltages conditions.

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 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.

Residential load management system for future smart energy environment in GCC countries

Electricity consumption has increased substantially over the last decade. According to the Gulf Research Center (2013), residential sector represents the largest portion of electricity consumption (about 50%) in the Gulf Cooperation Council (GCC) region, due to substantial growth of electrical residential appliances. Therefore, we present a novel online smart residential load management system that is used to online monitor and control power consumption of the loads toward optimizing energy consumption, balancing electric power supply, reducing peak demand, and minimizing energy bill, while considering residential customer preferences and comfort level. The presented online algorithm manages power consumption by assigning the residential load according to utilities power supply events. The input data to the management algorithm is set based on the categorized loads according to: importance (vital, essential, and non-essential electrical loads), electrical power consumption, electricity bill limitation, utilities power limitation, and load priority. The data are processed and fed to the presented algorithm, which accurately manages the power of dwelling loads using external controlled disconnectors. The proposed online algorithm yields to improve the overall grid efficiency and reliability, especially during the demand response periods. Simulation results demonstrate the validity of the proposed algorithm.

Investigation into the effect of unbalanced supply voltage on detection of stator winding turn fault in PMSM

In recent years, permanent magnet synchronous motors (PMSM) are becoming popular in industrial applications. It is difficult to have perfectly balanced 3 phase ac supply voltages in power system. In fact, line supply voltages typically differ by a few volts or more, and hence unbalanced supply voltages can exist anywhere in a three-phase power distribution system. This paper presents a study of PMSM with stator winding turn fault and different load conditions under balanced and various unbalanced supply voltages. Three-phase PMSM is simulated for healthy and faulty conditions with stator turn fault at different loads using finite element analysis. Experimental laboratory results validate the analysis and demonstrate that the proposed approach can be used for both balanced and unbalanced supply voltage, as a reliable indicator for detecting different insulation failure percentages of stator winding at different load conditions.

Transient stability impact of large-scale photovoltaic system on electric power grids

Transient stability studies are the key for providing secured operating configurations in power-grid networks. Penetration of a large-scale photovoltaic energy into an existing power grid is expected to highly increase, which will affect operational characteristics. This paper investigates transient stability analysis and the impact of large scale grid-connected photovoltaic (PV) system on electric power-grid networks. Three different scenarios, with their relevant dynamic models with centralized PV farm, are considered at medium voltage level without voltage regulation capabilities. Conducted simulation results show the impact of increased PV penetration on both steady state and transient stability performance. Results in this paper are conducted using a power system software for the design, simulation, operation, and automation of power network and large scale photovoltaic system.

Big data, better energy management and control decisions for distribution systems in smart grid

Big Data is an essential element for energy management and control decision toward improved energy security, efficiency, and decreasing costs of energy use. Power distribution network is required to deliver electric energy reliability with reduced complexity and to be part of future smart grid. Therefore, in this paper Big Data related to the distribution generation systems will be discussed and illustrated within the context of smart grid principle. The paper work is to study the impact of adopting big data on energy management systems and to show the importance of the big data in strategic decision-making. The paper will highlight the Big Data issues and challenges associated with it in the energy management and control decisions in power distribution networks.