COVID-19 Data Warehouse: A Systematic Literature Review

The coronavirus disease (COVID-19) affects the whole world and led clinicians to use the available knowledge to diagnose or predict the infection. Data Warehouse is one of the most crucial tools that may enhance decision-making (DW).In this paper, three main questions will be investigated according to using DW in the COVID-19 pandemic. The effect of using DW in the field of diagnosing and prediction will be investigated, besides, the most used architecture of DW will be explored. The sectors that faced a lot of researchers' attention such as diagnosing, predicting, and finding the correlations among features will be examined. The selected studies are explored where the papers that have been published between 2019-2022 in the digital libraries (ACM, IEEE, Springer, Science Direct, and Elsevier) in the field of DW that handle the COVID-19 are selected. During the research, many limitations have been detected, while some future works are presented. Enterprise DW is the most used architecture for COVID-19 DW while finding correlation among features and prediction are the sectors that had taken the researchers' attentio

A review on optimal location of distributed generation techniques in electrical network

To increase the maximum possible benefits of technical, economic and environmental aspects, the optimal sizing and sitting of distributed generation in distribution system has always been challenging for customers as well as to utilities. The objective of optimum sitting of distributed generation in a distribution system is to reach suitable operation of such systems with minimization losses of the systems, voltage profile improvement, enhanced system load ability, stability and reliability. In this paper, a review of techniques for optimum sitting of distributed generation units in distribution systems and study their effects on customers and utilities are presented. Also, a comparison was done between methods for optimum location of distributed generation in distribution systems

Optimal location and size estimation of distributed generators by employing grouping particle swarm optimization and grouping genetic algorithm

Distributed Generators is being implemented in the distribution network to improve the performance of the network by reducing the real and reactive power losses, while improving voltage profile during the operation. These advantages could be confirmed and accomplished if optimal size distribution generation units are installed at the optimal location in the distribution network. Otherwise, the problem of generation may increase when the DG is located in non-optimal location and size which can lead to increase the real and reactive power losses and high voltage deviation. Therefore, there are various algorithms that could be applied in order to integrate distribution generation units into the distribution network. These algorithms can be enhanced to increase their efficient and effective. This work is aimed to decrease the total real and reactive power losses while enhancing the voltage profile of the distribution network with less computation time by proposing two new artificial intelligence algorithms, i.e. grouping particle swarm optimization algorithm and grouping genetic algorithm. These two algorithms are compared to their original artificial intelligence algorithms, i.e. particle swarm optimization algorithm and genetic algorithm. These algorithms are used to obtain the optimal size of distributed generators units to be installed at optimal locations, which are obtained using loss sensitivity factor. Multi-objective function is the summation of three indices that are considered in these algorithms, i.e. real power loss index (PLI), reactive power loss index (QLI), and cumulative voltage index (CVD), and implemented on an IEEE 30-bus test system. This is to test the performance of the four artificial intelligence algorithms by taking into consideration the installation of 5 distributed generators units in the bus test system. It was observed that the grouping particle swarm optimization algorithm has achieved high reduction of total real and reactive power losses, by installing five distributed generators, when compared to the base case. It was observed that the voltage profile was improved with the shortest computation times demonstrated in determining the optimal size (global optimal) for the multi DG units that were installed in the IEEE 30-bus test system comparing to all the four artificial intelligence algorithms that were employed in this work. Also, it is found that case of installing four distributed generators units is not much difference from case of five distributed generators which is make it optimum to be installed in the IEEE 30-bus test system in terms of reducing the real and reactive power losses. This also relates to the stability of the voltage