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Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka
Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.
Modified Space Vector Modulation for Cascaded H-Bridge Multilevel Inverter with Open-Circuit Power Cells
In this research, a new space vector modulation control algorithm is proposed to increase the reliability of the cascaded H-bridge multilevel inverters in case of faulty situations, where one or several power cells do not function. Methods to detect faults ensure finding open-circuit module exactly, which is fast and easy to program. By giving a detailed analysis of the impact of the faulty power cells, optimal redundant level states are chosen such that highest possible output voltage can be achieved, while the balance of the three-phase line-to-line voltage is maintained and common-mode voltage is reduced. The proposed algorithm is generalized so that it can be applied to H-bridge inverters of any level. The validity of the method is verified by numerical simulations and experiment results with an 11-level cascaded H-bridge inverter.
An Improved Feedback Network Superresolution on Camera Lens Images for Blind Superresolution
Most of the recent advances in image superresolution (SR) assume that the blur kernel during downsampling is predefined (e.g., Bicubic or Gaussian kernel), but it is a difficult task to make it suitable for all the realistic images. In this paper, we propose an Improved Superresolution Feedback Network (ISRFN) which is designed free to predefine the downsampling blur kernel by dealing with real-world HR-LR image pairs directly without downsampling process. We propose ISRFN by modifying the layers and network structures of the famous Superresolution Feedback Network (SRFBN). We trained the ISRFN with the Camera Lens Database named City100, which produced the HR and LR on the same lens, respectively, free for downsampling, so our proposed ISRFN is free to estimate the blur kernel. Due to different camera lens (smartphone and DSLR) databases, we perform two series of experiments under two camera lenses-based City100 databases, respectively, to choose the optimum network structures; experiments make it clear that different camera lens-based databases have different optimum network structures. We also compare our two ISRFNs with the state-of-the-art algorithms on performance; experiments show that our proposed ISRFN outperforms other state-of-the-art algorithms.
Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review
Having neither precise definition nor a commonly accepted scope, the term “MicroGrid” tends to be used differently across researchers and practitioners alike. The management of energy usage within a microgrid is one of the topics that was handled from numerous perspectives. This study presents systematic literature review (SLR) of research on architectures and energy management techniques for microgrids, providing an aggregated up-to-date catalogue of solutions suggested by the scientific community. The SLR incorporated 45 papers selected according to inclusion/exclusion criteria and defined a priori. The selection process was based on an automated search and covered three known digital libraries. The extraction process covers three main questions. (i) The architectures of microgrids including their components, their bus configuration, and the adopted utility grid policy. (ii) The employed methods to ensure an optimal usage of energy under uncertainty. (iii) The confronted challenges and constraints of the suggested strategies. The findings of this SLR indicate a great diversity of methods and a rich background. Finally, the SLR suggests that future research should take into account the uncertainty aspect relating to energy management rather than the direct use of historical data as it is commonly done in most research papers. A sensitivity analysis should be provided in the latter case.
Static Voltage Stability Assessment of the Kenyan Power Network
In recent years, the Kenyan Power Network has witnessed large growths in load demand. Although the increased load demand has somewhat been matched with an increase in transmission and generation capacity, the rate of expansion has not been matched with the rate of increase in load demand due to economic, environmental, and geographical constraints. This has led to the system being prone to instability since it is being operated under stressed conditions. In the recent past, several studies have been carried out on voltage stability analysis and improvement using various conventional methods. However, conventional methods have various limitations in their utilization for voltage stability analysis. One solution to overcome these limitations is to employ a combination of one or more methods so as to get more information and greater degree of accuracy in voltage stability studies. In this paper, a methodology is proposed involving the combination of QV modal analysis, sensitivity analysis (VQ) and power-voltage curves in assessing the static voltage stability analysis taking a case study of the Kenyan Power Network. V-Q sensitivity analysis and QV modal analysis have been used to identify the load regions most susceptible to voltage instability and the corresponding weak buses in the network for various V-Q responses. Reactive power loss sensitivities for branches in the network have been used to determine the critical (weak) lines in the network. Loading margins (LM) and voltage stability margins (VSM) have then been used to determine the proximity to voltage collapse of the voltage weak buses identified by QV modal analysis. The effect of tripping one the critical lines on the voltage weak buses is also investigated. The current high voltage power network under the average peak loading conditions during the year 2019 is considered for the study. The paper also reviews existing voltage stability analysis methods and their limitations.
Rolling Scheduling Method considering Shiftable Demand Respond Resources
In order to deal with the fluctuation of the renewable energy, this paper proposes rolling scheduling strategy taking into account the capacity of load-side resources. By considering the energy characteristics of shiftable loads, an improved rolling scheduling model is proposed by adopting a full-cycle scheduling and adding periodic power constraints. By this way, the accuracy of the scheduling can be improved. The testing examples verified that the proposed rolling scheduling method can reflect the long-time benefit and therefore result in better performance.