This study demonstrates an application of Facebook for higher education in science (STEM), and it clarifies its impact on students’ learning in a formal online learning setting. A combined qualitative and quantitative approach was used. Messages posted on Facebook were classified by qualitative transcript analysis. The perception and experience of students with Facebook were recorded by means of pre- and post-tests, using a treatment/control group approach. The results show that an integral Community of Inquiry (CoI) was formed on Facebook within the regular online course, encompassing all relevant CoI interactions leading to a powerful educational experience. Additionally, a type of CoI interaction “student–community” is observed, which was not reported before. The results show that the use of Facebook had positive effects on students’ learning, only when the activities on Facebook were constructively integrated in a course design, and a moderator was present. More research is needed to include larger group sizes and other course designs.

The world today is plagued with problems of increased transmission and distribution (T&D) losses leading to poor reliability due to power outages and an increase in the expenditure on electrical infrastructure. To address these concerns, technology has evolved to enable the integration of renewable energy sources (RESs) like solar, wind, diesel and biomass energy into small scale self-governing power system zones which are known as micro-grids (MGs). A de-centralised approach for modern power grid systems has led to an increased focus on distributed energy resources and demand response. MGs act as complete power system units albeit on a small scale. However, this does not prevent them from large operational sophistication allowing their independent functioning in both grid-connected and stand-alone modes. MGs provide greater reliability as compared to the entire system owing to the large amount of information secured from the bulk system. They comprise numerous sources like solar, wind, diesel along with storage devices and converters. Several modeling schemes have been devised to reduce the handling burden of large scale systems. This paper gives a detailed review of MGs and their architecture, state space representation of wind energy conversion systems & solar photovoltaic (PV) systems, operating modes and power management in a MG and its impact on a distribution network.

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An advanced topology comprising a wind-turbine driven synchronous generator (SG) and solar photovoltaic (PV) array to address the power quality (PQ) concerns and the uncertainty in renewable energy generation is proposed in this paper. The fluctuations in wind speed and insolation are soaked up by utilizing successively interfaced power electronic converters (PECs), namely, SG side converter (SGC) and utility grid side converter (UGC) integrated with a DC link capacitor and a PV array. SGC is controlled by estimating the rotor position from the stator voltage and current measurement data and maintains unity power factor (UPF) at stator terminals. UGC is controlled by employing filtered X least mean fourth (FXLMF) control, which ensures superior convergence performance and reduced computational complexity. Besides, it performs operations like reactive power compensation, enhancement of PQ, load and power balancing during steady-state and dynamic conditions. The notion of optimal fuzzy reasoning is instilled which generates a fuzzy scheme with inherent optimal-tuning characteristics to bring about considerable improvement in system performance. A water cycle algorithm (WCA) tuned fuzzy logic controller (FLC) is employed for dynamic stability enhancement leading to the generation of accurate loss component, i loss , which bolsters the ability of VSC to accurately extricate weight signals and the fundamental load current component. This improves regulation of DC link voltage during dynamic load, insolation and wind conditions. The veracity of the proposed model is verified under wind gusts and the bell-shaped irradiance curve. Extensive simulation results obtained in MATLAB corroborate the efficacy of the system.filtered X least mean fourth (FXLMF) control, fuzzy logic controller (FLC), power quality (PQ), SG utility grid side converter (UGC), side converter (SGC), solar photovoltaic (PV) array, synchronous generator (SG), water cycle algorithm, wind energy conversion system (WECS)

This paper proposes a hybrid learning algorithm based super twisting sliding mode control (STSMC) of a hybrid wind/photovoltaic (PV) power system for grid connected applications. The gating pulses of the voltage source converter (VSC) are generated by employing adaptive reweighted zero attracting least mean square (RZA-LMS) algorithm. The control law acquiring the super-twisting algorithm generates a continuous and saturated control signal to regulate a hybrid system influenced by disturbances. The proposed control injects sinusoidal currents into the grid with low total harmonic distortion (THD) which improves the steady state & dynamic performance of the system by mitigating power system problems like harmonic injections besides giving satisfactory results under dynamic loading, varying wind speeds and solar insolation. It is a chattering free control which enhances the quality of disturbance rejection and sensitivity to parameter variation. It also caters to abnormal conditions like voltage distortions, DC link variations and reduces the latter by a factor of 80 V besides reducing switch stress by a factor of 5 V. This control exhibits robustness against model uncertainties and external disturbances. Also, the loss component is reduced which decreases the unmodelled losses. It also ensures efficient power flow between the grid, hybrid source and the load. The efficacy of the system is verified in MATLAB/Simulink. Improvements are also observed during dynamic conditions in terms of reduced fluctuations, steady state error and peak overshoot.