In the past few years, Integrated On-Board Battery Chargers (IOBCs) have shown a substantial growth worldwide within the electric vehicles' market. Multi-phase machines have recently been favored thanks to their outstanding merits over three-phase counterparts. However, the optimum winding configuration and control technique remains a challenging topic. This paper proposes and compares three different six-phase winding configurations through single phase charging by connecting the two neutral points of the two three-phase sets to the single phase grid terminals. The system is controlled using Predictive Current Control (PCC), which has significant advantages, such as simple algorithm, easy implementation, relatively fast response, and adequate performance. This paper presents the detailed design and construction of three six-phase winding layouts which are fulfilled from an externally reconfigured lkW 12-phase induction motor. The designed system is experimentally validated to support the theoretical findings.
Utilization of variable speed drive has been recently recommended as an excellent candidate for energy saving. This paper discusses the potential of such technology in recent automated multi-pumps water system. The paper proposes a full smart automated setup of three-pumps operated boosting station based on programmable logic controller (PLC) with suitable human machine interface (HMI) and corresponding industrial communication protocols. Proposed system performance has been simulated, using Simulink/Matlab software package, for two different operation schemes. Experimental setup has been implemented, based on Siemens Simatic S7 components and software. The obtained simulation and experimental results showed the effectiveness of the proposal for smart automated operation with energy saving opportunities; particularly for variable flow operation conditions.
Six-phase induction machine is considered one of the promising multi-phase motors, which can be used in a large number of recent applications. Its design and control circuits were presented by many researches for getting high quality performance. This paper presents the modeling, control, drive circuit and practical implementation for a new 6-phase induction motor. A specific 6-phase winding angle was chosen by the authors to eliminate the current harmonics, pulsating torque, and boost reliability. The proposed controller and its data acquisition module are designed in MATLAB environment. To smooth the output waveforms of the converter, the low-pass filter parameters are carefully selected. Besides, different control strategies are evaluated in 4-quadrant operation of the motor such as Variable Frequency Drive (VFD) and Variable Frequency Variable Voltage Drive (VFVVD). The simulation and experimental results are compared and analyzed. The proposed design lessens the stress on power semiconductor devices, reduces the power rating for converter switches, and gives more flexibility for motor.
this paper introduces a comparative performance for doubly fed induction generator (DFIG) and permanent magnet synchronous generator (PMSG) systems which are driven by wind turbines under variety of operating conditions. And compare the performance for similar ratings with other control plans. This comparison intends to present in details the variety of wind speed that each of the generators can handle, power coupled from both wind energy conversion systems (WECS) at several wind rates and converter ratings. In this article, inclusive models of wind turbine are used to figure out the performance system. Maximum power point tracking (MPPT) has also been performed to attach maximum existing power for a certain wind speed in the systems. The system models are is verified using MA TLAB/SIMULINK.
The increased deployment of renewable energy sources (RES) as distributed generation (DG) in power grids brings a challenge to the protection scheme. Due to different penetration levels of RES during a day, the fault currents at the same point of the microgrid (MG) vary significantly. Since, the MG presents two different levels of fault current according to grid-tied mode or islanded mode. Consequently, the conventional overcurrent coordination protection schemes must be developed. This paper proposes an improved centralized protection strategy for AC MG with bulky DG penetration. The proposed strategy depends on communication-based overcurrent relays with a centralized unit using an artificial neural network (ANN) with symmetrical components as feature extraction. The proposed algorithm provides fast fault detection and fault location. The evaluation of the proposed strategy is validated on IEEE 9-bus system using Matlab/Simulink software. The system is examined under different operating conditions of MG and different fault types at different fault resistance and achieved remarkable results.
A key issue in solving fuzzy optimization problems is selecting an appropriate fuzzy comparison technique. This paper introduces a novel fuzzy comparison technique that can work with any fuzzy optimization problem. The new technique is employed to determine the best maintenance schedule (MS) of electric generators. To solve the MS problem, the new fuzzy comparison technique is implemented in conjunction with the use of the evolutionary programming. Unlike other fuzzy comparison techniques, the objective of the proposed technique is to compare between fuzzy numbers based on the midpoint and the upper limits for minimization problems. Which in turn controls the upper limit of the final solution and yields a better minimum solution for the problem. In this paper, the IEEE 30-bus system is used to test the proposed technique with fuzzy load and maintenance cost. Results of comparing the proposed technique with Chen's and Yager's approaches show lower cost compared to Chen's and slightly lower cost compared to Yager's techniques.
The annual growth of grid connected wind turbines raises various challenges in power grids. Improving the control of wind turbines (WT) has a very important mission in ensuring their excellent performance. Thus, in the present paper, different optimization techniques (local unimodal sampling (LUS), harmony search algorithm (HSA), and equilibrium optimization (EO)) are presented. The optimization is applied to the proportional-integral (PI) controllers of the grid side and the rotor side converters (GSC and RSC) installed in the doubly fed induction generator (DFIG). The major goal of the current paper is to improve the grid-connected wind turbine's performance when it encounters an asymmetrical one-line to ground fault. A robustness test is performed by testing the response of the optimized controllers with different wind profiles. The results are obtained by performing simulation analyses using MATLAB/Simulink software. Results show the superiority of EO in enhancing the system's performance.
Renewable energy sources are naturally pollution-free, and solar energy meets the world's energy demand. Photovoltaic (PV) systems are used to harvest solar energy, where a power converter is needed to regulate and control the harvested solar power and achieve the required output voltage for various applications. In this paper, four different DC-DC converters commonly used in research and industry are designed and developed to provide the required voltage of the PV systems, which are Buck, Synchronous Buck, Single-Ended Primary Inductor Converter (SEPIC), and Flyback converters. These DC-DC converters are modeled, simulated, and analyzed using LTspice software. The simulation studies and analysis quantify the system performance of these converters and the parameter values for the experiments. These converters are built and tested in different conditions using a standalone test bench to validate these converters for PV application systems. The test development sequences are conducted for open-loop and closed-loop systems of the power converters, where a variable power supply is used to mimic the solar panel output in the test bench. A proportional integral (PI) controller is designed to regulate the output voltage of the power converters to power load or charge the battery while solar radiation level, load, and other variables change. The Experimental test results show that the system performance profiles of these converters are different in ripple, efficiency, and other values. The maximum efficiency value achieved by the Synchronous Buck converter and less ripple output voltage is from the Flyback converter. Finally, the best proposed DC-DC converter is connected to a solar panel, where a customized Internet of Things (IoT) system is deployed into the PV system to supervise and monitor the parameters of the proposed PV system.
This paper investigates the effect of fault repairing periods on the estimation process of technical energy losses (TELs) in distribution networks with tie-switches. Comparisons between TELs calculations considering normal operation periods (NOP) only and those considering both NOP and repairing faults periods (RFP) are presented. Different repairing times (RTs) are assumed to highlight the impact of maintenance and /or repairing time on the estimation process with a wide range of fault cases. The IEEE 33-bus system with tie-switches is chosen as the test system in this paper. Load flow analysis (LF) of the case studies is implemented by ETAP program. Results are given for distribution networks with tie-switches including radial and ring schemes regarding the effect of different repairing and or maintenance time. The results assure the importance of utilizing tie switches to reduce the effect of faults repairing time on the estimation process of TELs.
Theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. There are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. This paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. There variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences; exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. The power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). The RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively.
In standalone or grid-connected PV systems, maximum power point tracking (MPPT) methods are essential since they control DC boost converter's duty cycles, thereby extracts the maximum power possible from such systems. Moreover, the power supplied by the PV array is a nonlinear function of its terminal voltage and current, which require a successful MPPT to pursue the maximum power point (MPP) regardless of operating conditions. This paper suggests a Transient Search Optimization (TSO) based on self-tuning fuzzy-Proportional Integral (PI) controller for MPP pursuing in the standalone PV system. The PI regulator is utilized to regulate to the PV voltage in relation to a reference voltage generated by TSO method in which its gain parameters are tuned using a fuzzy expert system. The simulation was done using MATLAB/SIMULINK for standard and different test conditions (STC and DTC). The obtained results reveal that the maximum power delivered to the load has been achieved with an efficiency of 100% at STC, while it was achieved concerning variable irradiation at DTC
Peer-to-peer (P2P) energy trading refers to the possibility that users have of transacting energy with each other. More prosumers are now able to generate, store, trade, sell, or distribute energy utilizing digital technologies due to the expansion of decentralized energy resources like solar and battery resources. P2P trading allows prosumers, who are producing more energy than they need, to trade with others for profit or donate energy. Recently, P2P energy trading has seen a sharp increase in projects and trials all over the world. The paper reviews some of those projects. The industry and P2P researchers will benefit from this contribution to knowledge by better understanding the similarities and differences between the various P2P trading business models used worldwide.
This paper represents a comparative study of autotuned proportional-integral-derivative (PID) controller based on different sliding mode control (SMC) schemes for the DC motor speed control such as traditional SMC, fuzzy SMC (Fuzzy-SMC), fuzzy terminal SMC (Fuzzy-TSMC) and fuzzy fast terminal SMC (Fuzzy-FTSMC) schemes to treat uncertainties existing in the DC motor model in practical validation. The DC motor have little change in its parameters, in addition, surrounding conditions vary from one time to another, this makes a very difficult to choose an appropriate controller, so that the researchers resort to using an adaptive controller to control the DC motor to improve the performance and increase the stability against the uncertainties. The auto tuned PID controller automatically compensates for variations in system dynamics by adjusting the controller characteristics so that the overall system performance remains the same, or rather maintained at optimum level. The experimental results were obtained to verify the applicability and effectiveness of the proposed control scheme against external disturbance and model uncertainties with a comparative study.
This article concerns with enhancing the dynamics of a hybrid wind/PV/battery system feeding an isolated load. The complete system components are firstly modeled and described in detail. The paper contribution is presented through designing an efficient control topology for managing the dynamics of the wind-driven generator and comparing its performance with one of the existent control algorithms to visualize its advantages. The study also adopts a power management procedure for achieving the optimal power flow in the system. An effective control procedure is also utilized to control the battery performance. The performance of PV system is optimally managed to maintain the maximum power extraction. The obtained results confirm the advantages of the designed controller used with the wind generation system in comparison with the existent control approach. It also approves the validity of the power management procedure in stabilizing the power transfer and achieving an optimal exploitation of wind and solar energies.
this research deals with the optimal power flow (OPF) problem from the uncertainty perspective which arises due to the high penetration levels of renewable energy sources (RESs) in recent years. In this work, RESs are represented by wind and solar PV generators and their uncertain outputs are modeled by weibull and lognormal probability density functions (PDFs), respectively. From economic point of view, the uncertain output of wind and solar power is translated into the total power cost in form of reserve or penalty cost based on the situation of their output. The IEEE-30 bus and 57 bus power systems are adjusted to involve wind and solar PV generators. Gradient based optimization (GBO) algorithm is employed for solving the OPF problem in these circumstances. The obtained results have been compared with the results of other optimization algorithms presented in literature. GBO has achieved the minimum total power cost for both modified IEEE-30 and 57 bus power systems, 781.5504 $/h, and 20233.5012 $/h, respectively with low computation time and fast convergence of solution.
The adoption of electric vehicles (EVs) is steadily growing as a result of rising environmental fears and petrol prices. However, EVs operation only becomes completely ecologically friendly when the power they are using originates from a renewable energy source. This paper presents a design of a hybrid charging station that can be used for ac and dc plug-in EVs. The dc charger can charge two types of EVs batteries with 48V and 240V, while the ac charger has two outlets with line-line voltages of 220V and 380V for EVs with on-board charger (OBC). Three different types of energy sources to ensure fulltime operation all over the day and night are considered: Photovoltaic (PV) cells as the main energy source, battery storage system (BSS), and the electrical grid. Different operating modes are described in detail. An energy management system is proposed to control the power flow based on the operating mode. Several simulation results validating the effectiveness of the proposed design are presented.
The protection of microgrids can be challenging if traditional protective relays are used due to the change in the short circuit level, bidirectional power flow, and changes in system topology and operating conditions. This paper proposes a new protection scheme for fault detection in radial DC microgrids based on the magnitude of power. The proposed scheme is applied to a DC microgrid consisting of renewable energy sources and energy storage systems. The system under study is composed of two microgrids connected to two different buses on a radial DC feeder, while the load is connected to a third bus on the feeder. Each microgrid is composed of PV and battery energy storage. Power amplitude is used to detect the DC microgrid faults with a high degree of dependability and security. The proposed protection scheme is evaluated through different fault conditions.
FACTS devices are playing an essential role in modern power systems, as they have the ability to control the parameters of a system's transmission lines so that the transferability of the system is improved, and the overall performance is enhanced. However, it is crucial to identify the best allocation and size of the incorporated FACTS device to achieve the best results considering the objective required. In this paper, several FACTS devices are incorporated into the IEEE 30-bus standard system, where the optimal allocation and size of such devices are required regarding the cost of generation and power losses as a single objective function of each, considering the integration of wind turbines in the system. The optimization is performed using several recent optimizers including Moth Flame Optimizer (MFO), Whales Optimization Algorithm (WOA), Skill Optimization Algorithm (SOA), and Equilibrium Optimizer (EO), investigating the best results concluded by such optimizers. Simulation results reveal that the EO algorithm is more efficient and superior for optimal allocation and size of FACTS devices solution compared with the other recent algorithms.
optimal design of load frequency controller (LFC) is an important issue in stabilizing power system. This paper presents PI controller based Archimedes optimization algorithm (AOA) to enhance the performance of load frequency controller. A two interconnected non-reheat thermal power plants has been used to investigate the performance of the proposed system. AOA has been used to minimize the integral time multiplied absolute error (ITAE) through tuning the parameters of PI controller. Consequently, the frequency deviation and power mismatch between generation and load demand under different loading conditions is minimized. Two load disturbances lasting for 400 msec are applied, 0.02 and 0.04 p.u. These load disturbances were applied at the two areas, area 1 and area 2. The obtained results were compared to conventional case without optimizing the controller. The obtained results shows the effectiveness of the proposed controller.
This paper discusses the control of four-switches split-source inverter utilizing a generalized modulation (PWM) technique. Even in voltage fluctuations across the two capacitors, the suggested vector PWM offers a straightforward approach for choosing three or four vectors that successfully make the required output voltage by synthesis. The method uses the scalar form of the so-called space vector modulation. A comparison of several vector combinations. Using already existing stationary reference frame stator voltages, a normalized space vector modulation (SVPWM) technique is suggested for a four-switch three-phase (FSTP) voltage source inverter (VSI). Finally Results from simulations are offered to confirm this study's viability.
