Mustafa Manap

Current control of BLDC drives for EV application

This paper presents a current blocking strategy of brushless DC (BLDC) motor drive to prolong the capacity voltage of batteries per charge in electric vehicle applications. The BLDC motor employs a simple torque hysteresis control (THC) that can offer a robust control and quick torque dynamic performance. At first, a mathematical modeling of BLDC motor and principle of torque hysteresis control will be described, so that the benefit offered by the proposed current blocking strategy can be highlighted. It can be shown that the current control method naturally provides current limitation, in which the current error (or ripple) is restricted within the pre-defined band-gap furthermore provide current protection. The benefit of proposed current blocking strategy will be highlighted such that it can prevent the current drained from the batteries when the torque demand is released to set to 0 Nm. The control scheme is validated and verified by the simulation and experimental results.

Comparison performance of 3-Level and 5-Level Cascaded H-Bridge multilevel inverter of DTC of Induction Machine

Development of electrical vehicle (EV) and hybrid electrical vehicle (HEV) gives many opportunities and challenges in power electronic drive, especially in the development of traction motor drive that requires advanced power electronic inverter. Multilevel inverter was first proposed by Nabae in early 1980. The general structure of the multilevel inverter is to synthesis a sinusoidal voltage out of several levels of dc voltages. This paper shows comparison between the 3-Level Cascaded H-Bridge Multilevel Inverter and 5 -Level Cascaded H-Bridge Multilevel Inverter. It is found that by using 5-level Cascaded H-Bridge Multilevel Inverter (CHMI) can reduce torque and flux ripples compare to 3-Level CHMI and hence can improve DTC performances.

Modeling and simulation of torque hysteresis controller for brushless DC motor drives

This paper presents modeling and simulation of a torque hysteresis controller for brushless DC motors. Brushless DC (BLDC) motors can offer great advantageous compared to other machines used in industrial applications due to its compactness, high torque density, simpler controller and lower maintenance. At first the mathematical modeling of BLDC motor that is suitable to analyze the dynamic performance will be given. A method of torque hysteresis controller will be adapted to drive the motor such that the current (or torque) ripple can be restricted within the predefined band-gap. Moreover, a new current blocking strategy is proposed to prevent the current drained from DC supply when the torque demand is set to zero, that can prolong the capacity voltage of batteries. Some simulation results were carried out using MATLAB/Simulink to verify the proper modeling as well as functionality of the controller.

Short-circuit switches fault analysis of voltage source inverter using spectrogram

The identification of faults in voltage source inverter is highly required to ensure the reliability of the inverter. Early detection of the faults can greatly assist in maintenance of the system by avoiding possibly harmful damage borne out. This paper presents the analysis of short-circuit switches fault of voltage source inverter (VSI) using time frequency distribution (TFD). The TFD which is spectrogram represents current signal of the VSI in time-frequency representation (TFR) that provides temporal and spectral information of the signal. From the TFR, parameters of the signal are estimated such as average current, root mean square (RMS), total harmonic distortion (THD), total waveform distortion (TWD) and total non-harmonic distortion (TnHD). The results shows that the analysis using spectrogram gives information of the current signal of the VSI to identify short-circuit switches and can be implemented for faults detection system.

Improved performance of Direct Torque Control of induction machine utilizing 3-level Cascade H-Bridge Multilevel Inverter

This paper presents the advantages of using multilevel inverter which can provide greater number of voltage vector selection in minimizing the output torque ripple for Direct Torque Control (DTC) of induction machines. By employing a 3-level Cascaded H-Bridge Multilevel Inverter (CHMI), it provides 19 effective voltage vectors where each vector may give different effects on control performance for various operating conditions. This paper reports a simple switching strategy based on the hysteresis controllers and lookup table for DTC utilizing a 3-level CHMI. The torque hysteresis controller with 7-level of hysteresis band is introduced to determine the most appropriate voltage vectors which inherently suitable for different motor operating conditions. It can be shown that the selection of the proposed voltage vectors using multilevel inverter has better DTC performances in terms of torque ripple and quality of output waveforms (i.e. THD) as compared to the DTC with the conventional 2-level inverter.

Performance Comparison of VSI Switches Faults Analysis Using STFT and S Transform

Switches fault in power converter has become compelling issues over the years. To reduce cost and maintenance downtime, a good fault detection technique is an essential. In this paper, the performance of STFT and S transform techniques are analysed and compared for voltage source inverter (VSI) switches faults. The signal from phase current is represented in jointly time-frequency representation (TFR) to estimate signal parameters and characteristics. Then, the degree of accuracy for both STFT and S transform are determined by the lowest value of mean absolute percentage error (MAPE). The results demonstrate that S transform gives better accuracy compare to STFT and is suitable for VSI switches faults detection and identification system.

Comparative study of current control strategy for DC motors

A brushed DC motorist well-known to provide simple control and superior torque dynamic performance. This is mainly due to the fact that the electromagnetic torque can be directly controlled by regulating the armature current using full bridge DC-DC converter. Note that the developed torque is proportionally to the armature current and the use of full bridge converter permits the motor performs into 4-quadrant operation. This paper will present a comparative study of current/torque performances for three different current control strategies which are as follows; 1) hysteresis-based with bipolar switching technique, 2) carrier-based with bipolar switching technique and 3) carrier-based with unipolar switching technique. The comparison performances of the switching techniques will be analyzed through experimental results.

A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4 . The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique.The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.This paper presents a novel technique for numeral reading in Indian language speech synthesis systems using the rule-based Concatenative speech synthesis technique. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. To analyze the performance of the proposed technique, a set of numerals are considered in different context and a comparison of the proposed technique with an existing numeral reading method is also presented to show the effectiveness of the proposed technique in producing intelligible speech out of the entered text.This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data com- ing directly from Two EUMETSAT Metop satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feed forward ar- tificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of interest of Morocco.Advanced Communication Systems are wideband systems to support multiple applications such as audio, video and data so and so forth. These systems require high spectral efficiency and data rates. In addition, they should provide multipath fading and inter-symbol interference (ISI) free transmission. Multiple input multiple output orthogonal frequency division multiplexing (MIMO OFDM) meets these requirements Hence, MIMO-OFDM is the most preferable technique for long term evaluation advanced (LTE-A). The primary objective of this paper is to control bit error rate (BER) by proper channel coding, pilot carriers, adaptive filter channel estimation schemes and space time coding (STC). A combination of any of these schemes results in better BER performance over individual schemes. System performance is analyzed for various digital modulation schemes. In this paper,adaptive filter channel estimated MIMO OFDM system is proposed by integrating channel coding, adaptivefilter channel estimation, digital modulation and space time coding. From the simulation results, channel estimated 2×2 MIMO OFDM system shows superior performance over individual schemes.Electricity markets are different from other markets as electricity generation cannot be easily stored in large amounts and in order to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a large extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks in order to predict short-term (72 hours ahead) wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. Implementing an in-house self-adaptive model should yield good results in the dynamic uncertain Irish energy market. We have identified the features that such a model demands and outline it here.Received May 2, 2018 Revised Jul 9, 2018 Accepted Aug 2, 2018 Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4. The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique. Keyword:This paper presents the use of Simelectronics Program for modeling and control of a two degrees-of freedom coupled mass-spring-damper mechanical system.The aims of this paper are to establish a mathematical model that represents the dynamic behaviour of a coupled mass-spring damper system and effectively control the mass position using both Simulink and Simelectronics.The mathematical model is derived based on the augmented Lagrange equation and to simulate the dynamic accurately a PD controller is implemented to compensate for the oscillation sustained by the system as a result of the complex conjugate pair poles near to the imaginary axis.The input force has been subjected to an obstacle to mimic actual challenges and to validate the mathematical model a Simulink and Simelectronics models were developed, consequently, the results of the models were compared. According to the result analysis, the controller tracked the position errors and stabilized the positions to zero within a settling time of 6.5sec and significantly reduced the overshoot by 99.5% and 99. 7% in Simulink and Simelectronics respectively. Furthermore, it is found that Simelectronics model proved to be capable having advantages of simplicity, less time-intense and requires no mathematical model over the Simulink approach.

A Critical Review of Time-Frequency Distribution Analysis for Detection and Classification of Harmonic Signal in Power Distribution System

Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4 . The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique.The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.This paper presents a novel technique for numeral reading in Indian language speech synthesis systems using the rule-based Concatenative speech synthesis technique. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. To analyze the performance of the proposed technique, a set of numerals are considered in different context and a comparison of the proposed technique with an existing numeral reading method is also presented to show the effectiveness of the proposed technique in producing intelligible speech out of the entered text.This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data com- ing directly from Two EUMETSAT Metop satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feed forward ar- tificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of interest of Morocco.Advanced Communication Systems are wideband systems to support multiple applications such as audio, video and data so and so forth. These systems require high spectral efficiency and data rates. In addition, they should provide multipath fading and inter-symbol interference (ISI) free transmission. Multiple input multiple output orthogonal frequency division multiplexing (MIMO OFDM) meets these requirements Hence, MIMO-OFDM is the most preferable technique for long term evaluation advanced (LTE-A). The primary objective of this paper is to control bit error rate (BER) by proper channel coding, pilot carriers, adaptive filter channel estimation schemes and space time coding (STC). A combination of any of these schemes results in better BER performance over individual schemes. System performance is analyzed for various digital modulation schemes. In this paper,adaptive filter channel estimated MIMO OFDM system is proposed by integrating channel coding, adaptivefilter channel estimation, digital modulation and space time coding. From the simulation results, channel estimated 2×2 MIMO OFDM system shows superior performance over individual schemes.Electricity markets are different from other markets as electricity generation cannot be easily stored in large amounts and in order to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a large extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks in order to predict short-term (72 hours ahead) wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. Implementing an in-house self-adaptive model should yield good results in the dynamic uncertain Irish energy market. We have identified the features that such a model demands and outline it here.Received May 2, 2018 Revised Jul 9, 2018 Accepted Aug 2, 2018 Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4. The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique. Keyword:This paper presents the use of Simelectronics Program for modeling and control of a two degrees-of freedom coupled mass-spring-damper mechanical system.The aims of this paper are to establish a mathematical model that represents the dynamic behaviour of a coupled mass-spring damper system and effectively control the mass position using both Simulink and Simelectronics.The mathematical model is derived based on the augmented Lagrange equation and to simulate the dynamic accurately a PD controller is implemented to compensate for the oscillation sustained by the system as a result of the complex conjugate pair poles near to the imaginary axis.The input force has been subjected to an obstacle to mimic actual challenges and to validate the mathematical model a Simulink and Simelectronics models were developed, consequently, the results of the models were compared. According to the result analysis, the controller tracked the position errors and stabilized the positions to zero within a settling time of 6.5sec and significantly reduced the overshoot by 99.5% and 99. 7% in Simulink and Simelectronics respectively. Furthermore, it is found that Simelectronics model proved to be capable having advantages of simplicity, less time-intense and requires no mathematical model over the Simulink approach.

Comparison of Open and Short-Circuit Switches Faults Voltage Source Inverter (VSI) Analysis Using Time-Frequency Distributions

Voltage source inverter (VSI) plays an important roles in electrical drive systems. Consistently, expose to hash environmental condition, the lifespan of the electronic component such as insulated-gate bipolar transistor (IGBT) may shorten and many faults related to the inverter especially switches can be occur. The present of VSI switches faults causing equipment failure and increased the cost of manufacturing process. Therefore, faults detection analysis is mandatory to identify the VSI switches faults. This paper presents the analysis of VSI switches faults using time-frequency distributions (TFDs) which are short times Fourier transform (STFT) and spectrogram. From time-frequency representation (TFR) obtained by using the TFDs, parameters of the faults signal are estimated such as instantaneous of average, root mean square (RMS), fundamental, Total Waveform Distortion (TWD), Total Harmonics Distortion (THD) and Total non-Harmonic Distortion (TnHD) of current signals. Then, based on the characteristics of the faults calculated from the signal parameters, VSI switches faults can be detected and identified. The performance of TFD for the faults analysis is also demonstrated to obtain the best TFD for switches faults detection and identification system. The results show that, STFT is the best technique to classify and identify VSI switches faults and can be implemented for automated system.