The electric vehicles (EVs) have gained popularity over recent years due to the stringent environmental standards on greenhouse emission and the scarcity of fossil fuels. The increased demand for plug in EVs has necessitated grid integrated bidirectional EV battery charging systems. These charging stations deployed near the parking lots, shopping malls or industries can make the EVs suitable for long-distance runs. Moreover, the off-board charging methods with grid integration and proper communication interfaces can utilise the EV to enhance demand side management. This study proposes a multifunctional, grid integrated, bidirectional charger with a single feedback loop controller. Along with the battery charge or discharge control at unity power factor operation, the system can act as an active power filter for the non-linear and reactive loads connected to the point of common coupling. Also, the bidirectional AC/DC converter is operated with reduced switching adaptive hysteresis current controller to assure high efficiency. Analysis of various modes of operation of the system is done using MATLAB/Simulink and the prototype of the low power setup is done with FPGA to validate the concepts.

Electrical load and wind power forecasting are a demanding task for modern electrical power systems because both are closely linked with the weather parameters, such as temperature, humidity, and air pressure. The conventional methods of electrical load and wind power forecasting are useful to handle dynamic and uncertainties in un-regulated energy markets. However, there is still need of relative improvement by incorporating weather parameter dependencies. Considering above, a genetic algorithm-based non-linear auto-regressive neural network (GA-NARX-NN) model for short- and medium-term electrical load forecasting is presented with relative degree of accuracy. Causality, a new modelling technique, is employed for monthly and yearly wind speed patterns predictions and long-term wind speed forecasting. Real-time historical electrical load and weather parametric data are used to critically observe the performance of the proposed models compared to various state-of-the-art forecasting schemes. Numerical simulations are conducted that validates the proposed models based on various error calculation methods, such as mean absolute percentage error, root mean-square error, and variance (). The quantitative comparison with five traditional techniques for electrical load and wind speed forecasting reveals that the GA-NARX-NN method is more accurate and reliable.

This study introduces the use of arithmetic and logical hybrid controllers between proportional–integrative–derivative (PID) controllers and adaptive neuro-fuzzy inference system (ANFIS) for voltage regulation with buck-type DC–DC converters and proposes the novel product hybrid controller. The product hybrid controller combines the advantages of a PID controller and ANFIS controller to obtain an improved response and light and heavy load efficiency for the buck converter. PIDs are known for their good response and robustness but suffer due to non-optimal tuning for non-linear systems. ANFISs, on the other hand, excel in varying and abnormal conditions. ANFIS controllers also can improve and adapt their response to the current load or inputs with time. Simulation results are presented and analysed for all the controllers to validate the controller designs. The controllers are also verified experimentally. It is observed that the hybrid controllers provide enhanced tracking and response capabilities in comparison to classical PID controllers, with the novel product hybrid improving on the steady-state error, peak efficiency, and overall light and load operation.

This study compares the performance and reliability of classical complementary metal-oxide-semiconductor (CMOS) gates with Schmitt trigger (ST) ones. The ST hysteresis, caused by the added positive feedback transistors, improves the design static noise margin (SNM) and offers noise immune operation. Hence, ST-based circuits are expected to operate more reliably than the ones implemented using classical CMOS. Although many research papers have been focused lately on using ST design concepts for implementing more reliable static random access memory (SRAM) cells, significantly less work was devoted to the application of ST concepts in the combinatorial logic domain. Moreover, available research on ST-based logic gates had only focused on the low-voltage/power applications range. The authors are going to look at the whole voltage range and performance spectrum to compare and understand not only the SNMs and the power consumption (at different frequencies and voltage levels) but also the delay and the power-delay-product of ST-based logic gates. These will be compared with classical CMOS as well as with optimally sized CMOS and ST-based logic gates. This study should give a clear picture of the potential advantages ST could offer for combinatorial logic in advanced CMOS technology nodes and of their application range.

This study presents an improved non-local total variation (NLTV) model by using the block-matching and three-dimensional filtering (BM3D) algorithm for image denoising. First, the preprocessed image is obtained with the BM3D algorithm. Then, taking the place of the noisy image, the preprocessed image is used to construct the fidelity term of the energy functional and calculate the weight function in NLTV regularisation term. Finally, the energy functional is solved by the split Bregman algorithm. Experimental results demonstrate that the proposed model achieves better denoising performance than the original NLTV model in the visual appearance and objective indices, especially for the highly degenerated images. In addition, the proposed model can effectively suppress the appearance of the false information in the flat region, which overcomes the problem faced by the BM3D algorithm.