Comparison of conventional and improved two-level converter during AC faults

An improved two-level converter (I2LC) is a practical compromise between the conventional two-level converter (C2LC) and modular multilevel converter (MMC), recently proposed for dc transmission system for relatively lower dc voltages and rated powers. The I2LC inherent the ac and dc fault behaviors of the MMC and relative simplicity of C2LC. Therefore, this paper presents a detailed quantitative comparison between the ac and dc responses of the C2LC and I2LC to symmetrical and asymmetrical ac faults. It has been showing that unlike the C2LC, the I2LC provides better controllability than the C2L at system level during asymmetrical ac faults, including two operational objectives simultaneously such as balanced output currents and ripple-free dc-link current. Index Terms—ac and dc faults, two-level converter, improved two-level converter, medium and high-voltage direct current (HVDC) transmission systems

Hourly Price-Based Demand Response for Optimal Scheduling of Integrated Gas and Power Networks Considering Compressed Air Energy Storage

Gas-fired plants are becoming an optimal and practical choice for power generation in electricity grids due to high efficiency and less emissions. Such plants with fast start-up capability and high ramp rate are flexible in response to stochastic load variations. Meanwhile, gas system constraints affect the flexibility and participation of such units in the energy market. Compressed air energy storage (CAES) as a flexible source with high ramp rate can be an alternative solution to reduce the impact of gas system constraints on the operation cost of a power system. In addition, demand response (DR) programs are expressed as practical approaches to overcome peak-demand challenges. This study introduces a stochastic unit commitment scheme for coordinated operation of gas and power systems with CAES technology as well as application of an hourly price-based DR. The introduced model is performed on a six-bus system with a six-node gas system to verify the satisfactory performance of the model

Personalized Route Planning System Based on Driver Preference

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness

Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete

The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate six standalone machine learning models, namely, artificial neural network (ANN), extreme machine learning (ELM), the group method of data handling (GMDH), multivariate adaptive regression splines (MARS), least square-support vector machine (LSSVM), and Gaussian process regression (GPR). The hybrid ensemble (HENS) model was subsequently built, employing the combined and trained predicted outputs of the ANN, ELM, GMDH, MARS, LSSVM, and GPR models. In comparison with the standalone models employed in the current investigation, it was observed that the suggested HENS model generated superior predicted accuracy with R2 (training = 0.9783, testing = 0.9287), VAF (training = 97.83, testing = 92.87), RMSE (training = 0.0300, testing = 0.0613), and MAE (training = 0.0212, testing = 0.0443). Using the training and testing dataset to assess the predictive performance of all models for IFB prediction, it was discovered that the HENS model had the greatest predictive accuracy throughout both stages with an R2 of 0.9663. According to the findings of the experiments, the newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL

IMMUNIZATION COVERAGE AND HESITANCY IN SAUDI ARABIA

One of the greatest contributions to public health is vaccination. The majority of affluent nations have high rates of childhood vaccination, indicating that vaccination is still largely regarded as a public health policy in these nations. But there might be many individuals who are under-vaccinated. The rise of VPD outbreaks, such as measles, poliomyelitis, and pertussis, and under- or non-vaccinated communities have been linked in several nations. The World Health Organization (WHO) has identified vaccine hesitancy as one of the top 10 health hazards for 2019. Recently, there have been reports of vaccine reluctance in Saudi Arabia. Currently, the need for awareness campaigns about the benefits of vaccination is needed more than ever. In this review we will be looking at immunization hesitancy in Saudi Arabia, its definition, factors, and possible solutions