Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
Renewable energy sources such as photovoltaic (PV) and wind power are widely used; however, their intermittent nature impairs power supply quality by creating frequency distortions and irregularities in voltage. Battery energy storage systems (BESS) are utilized to flatten out and relieve fluctuation issues. To prevent the need for larger storage systems and to prolong their operational life through controlled charging and discharging, a method of control for BESS charging level regulation is necessary. This study presents a solar-wind power and battery state of charge (SoC) control technique using a hydrogen electrolyzer (HE) fuel cell unit. An Intelligent Model Predictive Controller (IMPC) has been developed that utilizes a neural network (NN) plant model for predictive minimization instead of using a mathematical plant model for dynamic management of the HE output, which allows for flattened PV-wind power supply with regulated BESS charging and discharging. The IMPC accepts as inputs the intermittent renewable power and different battery attributes and intelligently manages the HE production while staying within the imposed restrictions. The NN, as opposed to a mathematical plant model, captures the dynamics of the plant with exceptionally high accuracy. Moreover, the NN plant model performance increases as the data gathered from the actual system increases. The neural network model resolves concerns with the MPC model’s mathematical intricacy that occurs as the plant becomes more complicated. According to simulation results, the IMPC greatly reduces PV-wind power fluctuations, for solar power, the IMPC reduces the peak battery SoC by 26.7% and compared to FLC, the peak SoC is reduced by 11.2%. Similarly, for wind power, the peak SoC is reduced by 7.3% and is decreased 3.2% more than the FLC. To demonstrate the power smoothing effectiveness of the IMPC, the peak solar power ramp rate is reduced by 30.3% and the peak wind power ramp rate is reduced by 91.3%. The presented method encourages green hydrogen usage in the electrical sector for supplying firmed solar and wind power....
Wind energy is one of the speedy processing technologies in the energy generation industry and the most economical methods of electrical power generation. For the reliability of system, it is wanted to improve highly appropriate wind speed forecasting methods. *e wavelet transform is a powerful mathematical technique that converts an analyzed signal into a time-frequency representation. *is technique has proven useful in a nonstationary time series forecasting. *e aims of this study are to propose a wavelet function by derivation of a quotient from two di2erent Lucas polynomials, as well as a comparison between an arti3cial neural network (ANN) and wavelet-arti3cial neural network (WNN). We used the proposed wavelet, Mexican hat, Morlet, Gaussian, Haar, Daubechies, and Coi:et to transform the wind speed data using the continuous wavelet transform (CWT). MATLAB software was used to implement the CWT and ANN. *e proposed models were applied in the meteorological 3eld to forecast the daily wind speed data that were collected from the meteorological directorate of Sulaymaniyah which is a city located in the Kurdistan region of Iraq for the period (Jan. 2011–Dec. 2020). Five di2erent performance criteria during calibration and validation, the root mean square error (RMSE), mean square error (MSE), mean absolute percentage error MAPE, mean absolute error MAE, and coeAcient of determination (R2), were evaluated. When studying, analyzing, and comparing these models, the results of the study concluded that the proposed wavelet-ANN is the best result (MSE � 0.00072, RMSE � 0.02683, MAPE � 2.32400, and R2 � 0.99983)....
Wind energy holds a leading position among other renewable energy sources in electricity production. The competitive advantage of wind turbines to be connected to every electrical grid around the world and the 2030 targets of the EU have led to their high penetration in all countries, and especially European ones. Wind power plants are installed in areas with sufficient wind conditions, which simultaneously, are exposed to lightning activity, creating risks in their smooth operation. Considering the fact that there are more wind power installations in areas with different soil and topographic characteristics and the demand for the reliable, economically efficient, and smooth operation of the wind turbines, there is a need for standardized solutions that can be adapted to project-specific characteristics. In the current work it is introduced a methodology that intends to provide modular lightning protection for wind turbines and wind power plants, with the main drivers being the techno-commerciality and high availability of the facility, which can be adopted in most of the sites having as basis the relevant international standards....
For many academics, it has proven difficult to operate a wind energy conversion system (WECS) under changeable wind speed while also enhancing the quality of the electricity delivered to the grid. In order to increase the effectiveness and performance of the DFIG-basedWind Energy Conversion System, this research suggests an updated model predictive control technique. This study intends to regulate the generator in two ways: first, to follow the reference wind speed with high precision using the rotor side and grid side converters; second, to reduce system error. The suggested approach optimizes a value function with current magnitude errors based on the discrete mathematical model to forecast the converter’s switching state. In this system, the converter switching states are used directly as control inputs. Thus, the converter may be immediately subjected to improved control action. The key advantage of the suggested strategy over current FCS-MPC methods is error reduction. The originality of this research is in the proposal of a cost function that allows for both successful results and computation time minimization. To achieve this, the system is first presented, followed by a description of the predictive control, and then this method is applied to the rotor side control and grid side control. To demonstrate the efficacy and robustness of the suggested technique, a random wind profile was used to examine the system’s performance with a unitary power factor. This was done in order to compare the results with other controls that have been reported in the literature. The simulation results, which were conducted using a 1.5 kW DFIG in the MATLAB/Simulink environment, demonstrate that the FCS-MPC technique is highly effective in terms of speed, accuracy, stability, and output current ripple....
Wind power generation is the major approach to wind energy utilization. However, due to the volatility, intermittent, and controllability of wind power, it is difficult to control and scheduling of wind power, which brings challenges to the grid-connected operation and dispatch of wind power. Therefore, accurate power prediction of the wind power generation system is worthy of indepth study. And this paper proposes a wind power prediction model based on logistic chaos atom search optimization (LCASO) optimized back-propagation (BP) neural network, aiming to achieve accurate and efficient power prediction. Moreover, this work utilizes data preprocessing to obtain more precise prediction results and related prediction evaluation indexes to quantificationally compare the effect of the proposed one with other prediction models based on GA-BP neural network and PSO-BP neural network. In contrast with the BP neural network, GA-BP neural network, and PSO-BP neural network, the simulation tests verify the comprehensive prediction performance and wider applicability of LCASO-BP neural network-based power prediction model....
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