Solar energy is the most promising green energy resource, as there is an enormous supply of solar power. It is considered a good potential solution for energy crises in both domestic and industrial sectors. Nowadays, many types of solar systems are used for harvesting solar energy. Most of the research is focused on direct absorption solar collectors (DASCs) due to their ability to capture more solar energy. The effectiveness of DASCs is dependent on various factors, such as working fluid properties, geometry, and operating parameters. This review summarizes the impact of different design and operating parameters on the performance of DASCs. Many effective parameters are considered and their impact on optical and thermal properties is summarized. The influence of working fluid parameters, such as base fluid type, nanoparticle type, nanoparticle size, nanoparticle shape, and nanoparticle concentration on heat transfer performance, was discussed and their optimum range was suggested. The effects of collector dimensions and many novel design configurations were discussed. The effect of the most important operating parameters, such as temperature, flow rate, flow regime, and irradiance on collector performance, was briefly summarized.

Renewable energy sources are continually increasing their share in the energy system. The introduction of Hybrid Renewable Energy Sources (H.R.E.S.) in the electrical power system has gained momentum. The increased share of Renewable sources has resulted in an increase in Power Quality (P.Q.) disturbances at the user and consumer levels. This research will engineers dedicate the P.Q. disturbances produced in the electrical system due to Hybrid Renewable energy sources, especially solar and wind. The research will predict what type of P.Q. disturbances are introduced during power production. The study will focus on a stand-alone Hybrid Renewable Energy System (H.R.E.S.). The energy system data will be collected and used to produce a Fuzzy Logic (F.L.) algorithm to improve the mitigating techniques to reduce P.Q. disturbances. The suggested algorithm analyzes the stored and continuous data from different sources. The data will be collected and used to drive the hardware to take appropriate actions at the mitigation level. The hardware used in the system consists of a Multiplexer (MUX) and different types of filters. The output of the multiplexer chooses the filters. The algorithm will improve the system's efficiency and help the designers improve the system's design capabilities. The monitoring system will help predict what type of P.Q. disturbances are produced when different energy sources are used to produce power. The proposed system monitors these P.Q. disturbances and classifies them according to their severity.

No abstract