Hybrid electric vehicles have received considerable attention because of their ability to improve fuel consumption compared to conventional vehicles. In this paper, a series-parallel hybrid electric vehicle is used because they combine the advantages of the other two configurations. In this paper, the control unit for a series-parallel hybrid electric vehicle is implemented using a Nonlinear Model Predictive Control (NMPC) strategy. The NMPC strategy needs to create a vehicle energy management optimization problem, which consists of the cost function and its constraints. The cost function describes the required control objectives, which are to improve fuel consumption and obtain a good dynamic response to the required speed while maintaining a stable value of the state of charge (SOC) for batteries. While the cost function is subject to the physical constraints and the mathematical prediction model that evaluate vehicle's behavior based on the current vehicle measurements. The optimization problem is solved at each sampling step using the (SQP) algorithm to obtain the optimum operating points of the vehicle's energy converters, which are represented by the torque of the vehicle components.
Arc problems are most commonly caused by electrical difficulties such as worn cables and improper connections. Electrical fires are caused by arc faults, which generate tremendous temperatures and discharge molten metal. Every year, flames of this nature inflict a great lot of devastation and loss. A novel approach for identifying residential series and parallel arc faults is presented in this study. To begin, arc faults in series and parallel are simulated using a suitable simulation arc model. The fault characteristics are then recovered using a signal processing technique based on the fault detection technique called Discrete Wavelet Transform (DWT), which is built in MATLAB/Simulink. Then came db2, and one level was discovered for obtaining arc-fault features. The suitable mother and level of wavelet transform should be used, and try to compare results with conventional methods (FFT-Fast Fourier Transform). MATLAB was used to build and simulate arc-fault models with these techniques.
Electrical issues such as old wires and faulty connections are the most common causes of arc faults. Arc faults cause electrical fires by generating high temperatures and discharging molten metal. Every year, such fires cause a considerable deal of destruction and loss. This paper proposes a new method for detecting residential series and parallel arc faults. A simulation model for the arc is employed to simulate the arc faults in series and parallel circuits. The fault features are then retrieved using a signal processing approach called Discrete Wavelet Transform (DWT) designed in MATLAB/Simulink based on the fault detection algorithm. Then db2 and one level were found appropriate mother and level of wavelet transform for extracting arc-fault features. MATLAB Simulink was used to build and simulate the arc-fault model.
The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a second- order two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable.
This article analyzes thoroughly the performance of the Multi-Pulse Diode Rectifiers (MPDRs) regarding the quality of input/output voltage and currents. Two possible arrangements of MPDRs are investigated: series and parallel. The impact of the DC side connection on the performance of the MPDRs regarding the operation parameters and rectifier indices are comprehensively examined. Detailed analytical formulas are advised to identify clearly the key variables that control the operation of MPDRs. Moreover, comprehensive simulation results are presented to quantify the performance and validate the analytical analysis. Test-rig is set up to recognize the promising arrangement of MPDRs. Significant correlation is there between simulation and practical results. The analytical results are presented for aircraft systems (400Hz), and power grid systems (60Hz). This is to study the impact of voltage and frequency levels on the topology type of MPDRs. In general, each topology shows merits and have limitations.
The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and Neuro-fuzzy structured trained. The result obtained had been compared with conventional Box-Jenkins models (ARIMA models) as a statistical method used in time series analysis. ARIMA (Autoregressive integrated moving average) is one of the statistical models that utilized in time series prediction during the last several decades. Neuro- Fuzzy Modeling was used to build the prediction system, which give effective in improving the predict operation efficiency. To train the prediction system, a historical data were used. The data representing the monthly electric consumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposed model and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give the efficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method compared with the traditional ARIMA method.
The reliance on networks and systems has grown rapidly in contemporary times, leading to increased vulnerability to cyber assaults. The Distributed Denial-of-Service (Distributed Denial of Service) attack, a threat that can cause great financial liabilities and reputation damage. To address this problem, Machine Learning (ML) algorithms have gained huge attention, enabling the detection and prevention of DDOS (Distributed Denial of Service) Attacks. In this study, we proposed a novel security mechanism to avoid Distributed Denial of Service attacks. Using an ensemble learning methodology aims to it also can differentiate between normal network traffic and the malicious flood of Distributed Denial of Service attack traffic. The study also evaluates the performance of two well-known ML algorithms, namely, the decision tree and random forest, which were used to execute the proposed method. Tree in defending against Distributed Denial of Service (DDoS) attacks. We test the models using a publicly available dataset called TIME SERIES DATASET FOR DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION. We compare the performance of models using a list of evaluation metrics developing the Model. This step involves fetching the data, preprocessing it, and splitting it into training and testing subgroups, model selection, and validation. When applied to a database of nearly 11,000 time series; in some cases, the proposed approach manifested promising results and reached an Accuracy (ACC) of up to 100 % in the dataset. Ultimately, this proposed method detects and mitigates distributed denial of service. The solution to securing communication systems from this increasing cyber threat is this: preventing attacks from being successful.
In this work, the phase lock loop PLL-based controller has been adopted for tracking the resonant frequency to achieve maximum power transfer between the power source and the resonant load. The soft switching approach has been obtained to reduce switching losses and improve the overall efficiency of the induction heating system. The jury’s stability test has been used to evaluate the system’s stability. In this article, a multilevel inverter has been used with a series resonant load for an induction heating system to clarify the effectiveness of using it over the conventional full-bridge inverter used for induction heating purposes. Reduced switches five-level inverter has been implemented to minimize switching losses, the number of drive circuits, and the control circuit’s complexity. A comparison has been made between the conventional induction heating system with full bridge inverter and the induction heating system with five level inverter in terms of overall efficiency and total harmonic distortion THD. MATLAB/ SIMULINK has been used for modeling and analysis. The mathematical analysis associated with simulation results shows that the proposed topology and control system performs well.
Self-organizing systems arise in many different fields. In this work we analyze data from social and biological systems. A central question is to demonstrate the presence of the determinism in time-series extracted from such systems that appear apparently not correlated but that are two good benchmarks for the study of complexity in real systems. We will apply the Kaplan test and we will define an order parameter for the biological data to characterize the complexity of the system.
This work focuses on the use of the Linear Quadratic Gaussian (LQG) technique to construct a reliable Static VAr Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), and Excitation System controller for damping Subsynchronous Resonance ( SSR ) in a power system. There is only one quantifiable feedback signal used by the controller (generator speed deviation). It is also possible to purchase this controller in a reduced-order form. The findings of the robust control are contrasted with those of the "idealistic" full state optimal control. The LQG damping controller's regulator robustness is then strengthened by the application of Loop Transfer Recovery (LTR). Nonlinear power system simulation is used to confirm the resilience of the planned controller and demonstrates how well the regulator dampens power system oscillations. The approach dampens all torsional oscillatory modes quickly while maintaining appropriate control actions, according to simulation results.
Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.
In this paper, a model of PM DC Motor Drive is presented. The nonlinear dynamics of PM DC Motor Drive is discussed. The drive system shows different dynamical behaviors; periodic, quasi-period, and chaotic and are characterized by bifurcation diagrams, time series evolution, and phase portrait. The stabilization of chaos to a fixed point is adopted using slide mode controller (SMC). The chaotic dynamics are suppressed and the fixed point dynamics are observed after the activation of proposed controller. Numerical simulation results show the effectiveness of the proposed method of control for stabilization the chaos and different disturbances in the system.
Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday.
In this paper the dynamic behavior of linear induction motor is described by a mathematical model taking into account the end effects and the core losses. The need for such a model rises due to the complexity of linear induction motors electromagnetic field theory. The end affects are modeled by introducing a speed dependent scale factor to the magnetizing inductance and series resistance in the d-axis equivalent circuit. Simulation results are presented to show the validity of the model during both no-load and sudden load change intervals. This model can also be used directly in simulation researches for linear induction motor vector control drive systems.
Multidimensional Online analytical processing (MOLAP) technology is considered a good tool to produce meaningful and quality results by using a multidimensional cube. The term “multidimensional cube” is used to refer to the multiple layers of data that are used to show the result. This result is identified by high-level management to increase the Iraqi court work and to improve its quality. The Iraqi court needs an analytical report to make a strategic decision on case date, case type, case state, judge, criminal age, and criminal gender. Currently, MOLAP is known as the best and strongest technique because it provides rapid, dynamic, and multiple analyses of data; presents knowledge from different perspectives; comes up with data in time series intervals; and drills down into multiple levels of data layers to present different types of details. The SQL Server Reporting Service (SSRS) presents analysis reports based on the MOLAP cube. This paper focuses on designing and developing the analysis reports of the court data system of the Republic of Iraq by using SSRS with SQL Server Analysis Service to create the MOLAP cubes.
CMOS stack circuits find applications in multi-input exclusive-OR gates and barrel-shifters. Specifically, in wide fan-in CMOS NAND/NOR gates, the need arises to connect a relatively large number of NMOS/PMOS transistors in series in the pull-down network (PDN)/pull-up network (PUN). The resulting time delay is relatively high and the power consumption accordingly increases due to the need to deal with the various internal capacitances. The problem gets worse with increasing the number of inputs. In this paper, the performance of conventional static CMOS stack circuits is investigated quantitatively and a figure of merit expressing the performance is defined. The word “performance” includes the following three metrics; the average propagation delay, the power consumption, and the area. The optimum scaling factor corresponding to the best performance is determined. It is found that under the worst-case low-to-high transition at the output (that is, the input combination that results in the longest time delay in case of logic “1” at the output), there is an optimum value for the sizing of the PDN in order to minimize the average propagation delay. The proposed figure of merit is evaluated for different cases with the results discussed. The adopted models and the drawn conclusions are verified by comparison with simulation results adopting the 45 nm CMOS technology.
In this paper, a proposed control strategy is presented to improve the performance of the pulse width modulation (PWM) boost type rectifier when operating under different supply voltage conditions (balanced, unbalanced, and distorted three-phase supply voltages). The proposed control strategy is divided into two parts, the first part is voltage controller and the second part is current controller. In the voltage controller, Repetitive Controller (RC) is used to reduce the even order harmonics in the regulated output dc voltage so small output capacitor (filter) is used instead of large capacitor. RC also reduces the even order harmonics which appear in the reflected dc current (I MAX ), this leads to reduce the odd order harmonics which appear in the input currents. While in the current controller, Enhanced Phase Locked Loop (EPLL) technique is used to obtain sinusoidal and balanced three phases, to construct the reference currents, which are in phase with the fundamental supply voltages. Therefore, the supply-side power factor is kept close to unity. A proportional controller is used to give excellent tracking between the line and the reference currents. The complete system with the proposed control strategy are simulated using Matlab/Simulink. The results for the complete system using repetitive voltage controller are obtained and compared to the results of the system with the conventional voltage controller (Proportional-Integral (PI) controller connected in series with a Low Pass Filter (LPF)). The results with the repetitive controller show better response and stable operation in the steady state under different input voltage conditions, as well as in the transient response under changing the load condition. — Enhanced Phase Locked Loop,Repetitive Controller,Three-Phase PWM Boost Rectifier, Proportional-Integral controller. I. INTRODUCTION The boost type PWM rectifier has been increasingly employed in recent years since it offers the possibility of a low distortion line current withnear unity power factor for any load condition. Another advantage over traditional phase-controlled thyristor rectifiers is its capability for nearly instantaneous reversal of power flow. Unfortunately, the features of the PWM boost type rectifier are fully realized only when the supply three phase input voltages are balanced. It has been shown that unbalanced input voltages cause an abnormal second order harmonic at the dc output voltage, which reflects back to the input causing third-order harmonic current to flow. Next, the third-order harmonic current causes a fourth-order harmonic voltage on the dc bus, and so on. This results in the appearance of even harmonics at the dc output and odd harmonics in the input currents. An attempt was made to reduce low order harmonics at the input and the output of the PWM Boost Type Rectifier under unbalance input voltages [1]. The authors in [2] used two synchronous reference frames: a positive- sequence current regulated by a
voltage sags represent the greatest threat to the sensitive loads of industrial consumers, the microprocessor based-loads, and any electrical sensitive components. In this paper, a special topology is proposed to mitigate deep and long duration sags by using a modified AC to AC boost converter with a new control method. A boost converter is redesigned with a single switch to produces an output voltage that is linearly proportional to the duty cycle of the switch. On the other hand, the proposed control system is based on introducing a mathematical model that relates the missing voltage to the duty cycle of the boost converter switch. The simulation results along with the system analysis are presented to confirm the effectiveness and feasibility of the proposed circuit.
In light of the widespread usage of power electronics devices, power quality (PQ) has become an increasingly essential factor. Due to nonlinear characteristics, the power electronic devices produce harmonics and consume lag current from the utility. The UPQC is a device that compensates for harmonics and reactive power while also reducing problems related to voltage and current. In this work, a three-phase, three-wire UPQC is suggested to reduce voltage-sag, voltage-swell, voltage and current harmonics. The UPQC is composed of shunt and series Active Power Filters (APFs) that are controlled utilizing the Unit Vector Template Generation (UVTG) technique. Under nonlinear loads, the suggested UPQC system can be improved PQ at the point of common coupling (PCC) in power distribution networks. The simulation results show that UPQC reduces the effect of supply voltage changes and harmonic currents on the power line under nonlinear loads, where the Total Harmonic Distortion (THD) of load voltages and source currents obtained are less than 5%, according to the IEEE-519 standard.
Induction Motor (IM) speed control is an area of research that has been in prominence for some time now. In this paper, a nonlinear controller is presented for IM drives. The nonlinear controller is designed based on input-output feedback linearization control technique, combined with sliding mode control (SMC) to obtain a robust, fast and precise control of IM speed. The input-output feedback linearization control decouples the flux control from the speed control and makes the synthesis of linear controllers possible. To validate the performances of the proposed control scheme, we provided a series of simulation results and a comparative study between the performances of the proposed control strategy and those of the feedback linearization control (FLC) schemes. Simulation results show that the proposed control strategy scheme shows better performance than the FLC strategy in the face of system parameters variation.
This paper presents the PLC-HMI based simulation of electrical-based PV cell/array model in laboratory platform to give the opportunity to students and users who haven't clear knowledge to study PV cell and array behavior with respect to change of environment conditions and electrical parameters. This simulation process covers the cell models under ideal and non-ideal ones. In non-ideal one, the series resistance and the shunt resistance are covered.