Search

The power transformer is an essential component of the electrical network that can be used to step up and step down voltage. Dissolved gas analysis (DGA) is the most reliable method for the identification of incipient faults in power transformers. Various DGA methods are used to observe the generated key gases after oil decomposition. The main gases included are hydrogen (H2), ethylene (C2H4), acetylene (C2H2), methane (CH4), and ethane (C2H6). There is a lack of research that can compare the performance of various DGA methods in identification of faults in power transformer. In addition, it is also not clear which DGA method is optimal for identification of faults in power transformer. In this paper, the comparative performance study of seven DGA methods such as Roger's ratio, key gas, IEC ratio, the Doernenburg ratio, the Duval triangle, three-ratio method, and the relative percentage of four gases is carried out in order to identify the optimal technique for fault identification in transformer. The data of various power transformers installed in "RAWAT" NTDC grid station, Islamabad, and "UCH-II" power station, Balochistan, are considered for the comparative analysis. This analysis shows that the three-ratio method provides better performance than other DGA methods in accurately identifying the faults in power transformers. The three-ratio method has 90% accuracy in identifying the faults in power transformer. [ABSTRACT FROM AUTHOR]

The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system's ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS's efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model of 255 kW grid-connected solar photovoltaic (SPV) system. To study the performance characteristics of the grid-connected SPV system, a new hybrid adaptive grasshopper optimization algorithm with the recurrent neural network (AGO-RNN) control technique was implemented. Furthermore, the power quality at the point of common coupling (PCC) has been studied using the conventional (PSO) and proposed AGO-RNN controllers. The characteristics of the PV system were analyzed under varying environmental (variable irradiance and temperature) conditions considering 3 different cases such as (i) standard test conditions (STC), (ii) variable radiation with constant temperature, and (iii) variable radiation with variable temperature. For each case, the total harmonic distortion (THD) has been calculated using the proposed AGO-RNN control technique, and the results were compared with particle swarm optimization (PSO) technique. The 255 kW PV model is initially developed and connected to a three-level NPC inverter, an MPPT-based perturbation and observation algorithm. Later, the PV model is controlled by an AGO-RNN pulse width modulation (PWM) controller and is then integrated to the main grid at PCC. The main advantage of this technique is exploiting the separate DC-DC converter between the SPV module and the inverter. Finally, the proposed grid-connected SPV system was simulated on MATLAB for analyzing the performance of the system based on its I-V and P-V characteristics, inverter voltage, grid power, gird voltage, grid current, power factor, and THD under different environmental conditions. The simulation results demonstrate that the current magnitude and THD of the SPVGC system are improved with the cutting-edge AGO-RNN controller compared to PSO in all three different scenarios, and this value is less than 1.6%, which is within the permitted limits of IEC 61727 standards. [ABSTRACT FROM AUTHOR]

Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers' different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy. [ABSTRACT FROM AUTHOR]

The major setback faced by any of the production industry was maintaining the surface quality and dimensional accuracy of parts manufactured. Higher productivity is expected from industrial point of view, but resulting in poor surface texture. This work aims at addressing this problem by developing a suitable damping system for increased production with high precision products. In conventional method, dampers used is assembled of many parts, whereas the innovation in this paper is that the damper structure is monolithic in nature. Hence, manufacture time and cost of new innovative compliant damper is reduced. The work addressed the problem of damping by the use of compliant mechanism developed through building blocks of planar compliant mechanisms synthesis. Finite element analysis (FEA) was carried out in deciding the final form of the damping system. The proposed design was created by following fused deposition modelling (FDM 3D printing technique) using acrylonitrile butadiene styrene (ABS) material. Based on the results obtained from the experiments, the damping system has an impact on the surface quality of the product. The cutting force produced between the cutting edge and work surface could be improved by providing continuous contact (i.e., higher tool stability). The tool stability could be improved by using the compliant damping design and 3D printing technology for developing the complex designs into real products. The major limitation of the proposed work is the complexity in analysing compliant models with all the boundary conditions prevailing in the real time environment. The major influencing boundary conditions could be applied in neglecting insignificant factors. This work is a novel approach for developing a compliant mechanism-based damper that could restrict the effects of chatter in machining operation. The building blocks-based design was produced using 3D printing of the ABS material. Turning of aluminium was analysed for surface improvement by the tool stability improvement. Results revealed the impact of the ABS compliant damper. On an average, surface roughness of the products was reduced by 27.61%. [ABSTRACT FROM AUTHOR]