In recent era, the reduction of greenhouse gas emission and fuel consumption is accompanied by adopting photovoltaic (PV) and wind turbine-based hybrid renewable energy sources (HRES). In nature, an ...intermittent characteristic of the wind speed and solar irradiation makes these sources unpredictable, and hence, energy produced by wind and PV system generates uncertain conditions in operation of microgrid. In such cases, the security and reliability of microgrid are enhanced by integration of energy storage system (ESS). This work deals with an energy management in a hybrid system incorporating PV source and permanent magnet synchronous generator (PMSG)-based wind energy system. The PV output is enhanced with a help of switched trans-quasi-Z-source (TQZS) boost converter in which cuckoo search-assisted radial basis function neural network (RBFNN) approach is used as maximum power point tracking (MPPT) technique for tracking maximum photovoltaic power. The proposed approach results in high-power tracking efficiency with reduced power loss and settling time. A battery is incorporated to address an intermittent nature of RES. Artificial neural networks (ANN), which are capable of self-learning battery dynamics, keep track of the state-of-charge (SOC) of the battery. The system thus framed is implemented using MATLAB software, and promising results are generated in terms of power management with improved efficiency of 92%, gain ratio of 1:10 and total harmonic distortion (THD) value of 2.33%, respectively.
Abstract
In wind energy systems, wind speed estimation plays an important role. For wind energy systems, accurate forecasting of wind direction is essential, but it is challenging because of its ...variability. In this paper, wind speed prediction is accomplished using a machine learning-based random forest (RF) method. For the production of wind energy, short-term wind speed prediction is a significant activity. However, it is difficult only to obtain deterministic estimation since wind supplies are erratic and unpredictable. It increases learning that helps to project future values. Average wind speed is a major feature that affects the atmosphere. This paper explains the estimation of wind speed with ML algorithm. It is valuable for assessing the prospects for abnormal climate events and wind energy in the future.
Abstract
In this paper, the bidirectional converter that performs DC-DC power conversion is integrated with the variable speed wind energy conversion system’s grid system. Generally, the voltage ...quality is reduced, and fluctuation in outputs is performed in renewable energy sources. The stable operation and power generation with fluctuation and power demand in the variable are ensured by developing the energy management system (EMS). In this proposed method, the bidirectional converter’s control is accomplished using the proportional-integral-derivative (PID) controller, which controls the power fluctuation from the WECS to maintain constant. The DC-AC voltage conversion is achieved using the model predictive controller (MPC), which controls both power and voltage. This proposed power and voltage controller enhances the stable AC voltage supply and improves the proper power flow. The proposed method, which is validated in simulation is simple, and the results are obtained using MATLAB/Simulink.
Abstract
The permanent magnet induction generator (PMSG) based wind system that integrates with dynamic voltage restorer (DVR) and the energy storage system (ESS) for backup power purpose is ...explained in this paper. The output power transmission of the wind energy generation is interfaced with the dynamic voltage restorer in series. The energy storage system and the dc-link capacitor are parallel to the PMSG based wind system. The proposed system is to control the fluctuations in wind power and compensate the disturbed grid voltages. The bidirectional converter controls the power flow in both directions as the power flow from the source to battery and battery to the voltage source inverter (VSI) compensates the grid voltage disturbances through the voltage injection performance and transformer. The DVR-based energy storage system results for the wind energy conversion system are validated using in MATLAB/Simulink.
•We developed new hybrid evolutionary algorithm for solving generator maintenance scheduling problem.•Hybrid optimization method balance overall reliability and economy.•A case study of 32 thermal ...generating units reveal the effectiveness of the hybrid method.
This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.
An intelligent, Adaptive Neuro-Fuzzy Inference System (ANFIS) based Xilinx Power Management system is proposed for the Hybrid renewable energy sources (HRES). The Xilinx Power Management generator ...block set (ANFIS-XL-PMS) controller is implemented for the purpose to control and avoid the non-linear nature opted in the HRES power system. Solar (PV), Wind Energy (WE), and Fuel cell (FC) are the primary power sources of the system, and a battery storage system (BES) is used as a backup. A comprehensive power management strategy is intended for the proposed system to control the flow of power between the various energy sources and the system's storage device. The simulation model for the HRES is elegantly designed using the MATLAB/Simulink platform and the Switching patterns are generated using the XLPMS controller. The system performance and output responses of the SMC-XL-ANFIS controller have been verified by carrying out the simulation studies and compared with the existing controller like Sliding Mode Controller (SMC-XL-PMS), and Artificial Neutral Network (ANN-XL-PMS). Simulation results show good performance in terms of voltage and current transient, settling time, load power efficiency, and power quality Also a Prototype model with a PIC microcontroller is designed and the output responses are analyzed.
While potential for battery-powered electric vehicles (BEVs) is increasing as the need to minimise greenhouse gas pollution and the utilization of energy production, the restricted driving range and ...the uninviting market price constitute BEV obstacles to reliable assessment as a conventional car. This paper suggests a double-motor multispeed direct-drive BEV drivetrain to improve average engine running performance in everyday travelling whilst raising any production or control sophistication, effectively saving minimal battery resources and production costs. The characteristics of the proposed drivetrain are first defined through quantitative and visual measurements, which divide the conventional one-engine propulsion to two with different permanent gears to optimise engine performance. Centred on dynamic powertrain modelling in Simulink, the economic change strategy and circulatory system consisting transfer control were planned and checked. Based on the simulation performance, it is stated that substantial increases in energy quality can be accomplished. Thanks to the optimised torque transfer control technique, the vehicle’s incredibly low pull is registered during the shifting phase. Finally, it can be inferred that the suggested dual-motor drivetrain is better than the conventional single-motor equivalent in terms of fuel efficiency, driving range, and expense. There is substantial increase in total machine running areas of affordable to solitary machine performance and megacity driving ages, similar to WLTC, FTP-75, and JP 10-15, with 5 to 8 percentage performance enhancement being achieved.
The emerging trend of micro grid enabled technologies with renewable energy sources are used to satisfy the high demand of energy requirement and are preferred over conventional energy sources. ...However, it has disadvantages like uncertainty in energy on hand with renewable energy sources and unpredictable demand. Optimal scheduling of power generation among the available renewable energy sources is necessary to achieve minimum cost of energy generation with consideration of power loss. In this work, we have used Cuckoo search optimization algorithm with enhanced local search using Tabu search for optimal energy scheduling. This approach is compared with other evolutionary algorithm and existing approaches and results show that our proposed approach performs well.
PhotoVoltaic (PV) systems play a promising role in renewable energy resources (REs) as it provides environmentally friendly energy without pollution. PV systems under Partial Shading conditions (PSc) ...can significantly drop off their productivity of power where the arrays are shaded for various reasons, which lead to the output power that has many peaks, so there is a chance for local minima. Maximum Power Point Tracking (MPPT) methods will reduce the loss created over the PSc. Even though many conventional and soft computing approaches are commonly employed for the MPPT problem, conventional approaches show inadequate efficiency owing to fixed step size, and most of the existing soft computing approaches are limited by complicated rules implementation and require reinitialization during changing environmental conditions. So in this work, Deep learning based Radial Basis Function Network (D-RBFN) is proposed for the MPPT method as this neural network does not oscillate nearby the maximum power point region and performs well in nonlinear and rapid changing condition. Also, RBFN is optimized with the proposed BOosted Salp Swarm optimization (BOSS) approach to attaining higher accuracy and convergence speed by removing the random weights. Moreover, this proposed BOSS-D-RBFN method uses a DC–DC boost converter for faster transient response. The proposed method is evaluated against the existing classical MPPT, neural network-based MPPT, and fuzzy logic-based MPPT method to validate the performance. The results from the simulation study proved that our proposed BOSS-D-RBFN performs better compared to other existing methods with respect to global MPPT and MPPT power efficiency.
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