The use of solar panels has some advantages over other conventional electrical generating methods, as there is no sound pollution in collecting solar energy using solar panels, and also it has a minimum need for maintenance. In addition, it helps in the greenhouse effect which does not contribute to any CO2 pollution, as the conversion of light to electricity does not contain any chemical reactions. Using photovoltaic (PV) systems that are connected to a load will require a Maximum Power Point Tracker (MPPT) to maintain the highest possible efficiency of power generated. The resistance of the PV panels is different from the load resistance, the MPPT will control the duty cycle of the Insulated Gate Bipolar Transistor (IGBT) in the DC-DC converter to match the PV and load resistance for best efficacy. However, the use of MPPT with the connection to a controller collecting the maximum power generated from the PV system. In this paper, we design and implement a Recurrent Neural Network (RNN) based MPPT method to improve the efficiency of the power observation for the PV system for any value of irradiation (G) and temperature (T). Mainly, we compare two controller methods, using 104 sets of data for an ANN controller that was designed and tested in the past, with the same 104 sets of data to train the proposed RNN controller, as ANN used prediction in its calculations to find the best output efficiency, RNN will use a recurrent connection in the hidden layers that allow information to flow from one input to another.
This paper deals with the development of a proximity-coupled-fed S-band array antenna intended for microwave imaging application. The array antenna which is designed to work at the center frequency of 3 GHz is employed as a sensing device to transmit and receive emitted electromagnetic waves. The proposed array antenna is deployed on two layers of FR4 epoxy dielectric substrate and composed of four square patches arranging in a 2 × 2 configuration. After hardware realization, the fabricated array antenna is experimentally characterized and measured in an anechoic chamber. The characterization result demonstrate that the fabricated array antenna has the reflection coefficient of −17.29 dB at its center frequency, with the fractional bandwidth of 9.3% in the frequency range from 2.92 GHz to 3.2 GHz. These results are comparable to the simulated ones and adequate for the desired application.
Industrial networks interconnect devices to support industrial processes and manufacturing systems. Industrial applications results in time-critical and/or mission-critical data traffic, which require high reliability, guarantees on the delay bounds and robust time synchronization with high precision. Time Sensitive Networking (TSN)is considered as a suitable multi-service network technology for industrial systems and applications. TSN provides greater timing accuracy, enhanced performance in reducing packet delay variation, and adds more networking determinism. However, the configurations of TSN switches presents a challenge (e.g. time-aware traffic shaping schedules). Each output port of a TSN switch has to follow timed schedule to achieve the desired performance. The schedules have to be distributed at the network initialization and have to be maintained upon new devices joining or obsolete device being removed. In this paper, we use the Software Defined Networking (SDN) paradigms to overcome the aforementioned challenges. We propose the Software-Defined Time Sensitive Networks (SD-TSN), where end devices sign contracts with the network controller, to check the availability of the network resources and to select an optimized path for the data transmission. Therefore, It achieves overall improved network resources management, while meeting QoS requirement with high reliability. Our performance analysis show that SD-TSN accurately schedule the critical time traffic resulting in a bounded end-to-end latency with very low jitter. Even in the presence of different types of data traffic, the critical data traffic meets the quality of service requirements with a maximum jitter of $\mathbf{6}\ \mu\mathbf{s}$ . Moreover, SD-TSN takes 10 seconds run-time, compared to 2 minutes run-time when traditional TSN scheduling is used, to calculate the schedules of 100 TSN critical flows.
A new Diffusion Artificial Bee Colony (DABC) heuristic algorithm is developed to estimate system parameters in Wireless Sensor Networks (WSNs). The main contribution is the incorporation of ABC algorithm in the traditional Diffusion Least Mean Square (DLMS) algorithm which leads to a better Mean Square Error (MSE) performance. The DABC algorithm shows excellent convergence beyond the noise variance boundary. In the diffusion stage, each node shares the local best cost function and corresponding local best particle position to immediate neighboring nodes. The extensive simulations show that the proposed DABC approach achieves excellent MSE improvement (MSD deterioration) in comparison to existing DLMS algorithms.
Companies throughout the world are making an innovative switch from oil and gas to renewable energy sources, such as wind and solar power. As the world transitions to renewable energy, the demand for electric vehicles (EVs) has increased significantly. EVs mainly use Lithium-ion batteries because of their durability and efficiency. However, as the number of Lithium-ion batteries increases with the goal of reduction of emission and low energy cost, it comes with a major drawback of safety which affects efficiency. To address these challenges, this study investigates ways on how to improve the storage management system in an EV. In this research, different Lithium-ion battery states of an EV were monitored to effectively improve the battery management system (BMS). Two different drive cycles, federal test procedure (FTP) 75 and wide-open throttle (WOT) simulation time of 2474 seconds are used to obtain the results. The implementation of State of charge (SOC) technique has been applied to evaluate the energy remaining in the battery as well as the aging effects/dynamic load. The results obtained show a rate that gradually slows down in a linear manner. In addition, EVs have a less likely chance of experiencing power loss due to a very sophisticated gear system and it is very similar to a hybrid electric vehicle or internal combustion engine.
