Volume 25, Issue 5

Battery electric vehicles have been developing rapidly in recent years due to their advantages of energy saving and emission reduction. However, the development of battery electric vehicles is still in its infancy due to the relatively immature charging technology and the lack of infrastructure development. In many researches, there have been many solutions to adopt remote monitoring systems in battery electric vehicles to monitor and collect them in real time for statistical analysis and fault diagnosis. However, the current monitoring systems are carried out statistically and it is difficult to extract much valuable information from the data. In this paper, after analyzing the construction mode of battery electric vehicle charging facilities, the battery electric vehicle charging facility system platform using embedded system was analyzed. Several functions were simulated to meet the problems encountered by users in using it, studying the battery electric vehicle charging facility layout optimization model based on data mining algorithm. Focusing on the coverage problem in the siting problem, the State-of-Charge (SOC) of the battery charge state was analyzed. Taking residential areas as an example and 4 weeks as a cycle, it was found that during the first to fourth weeks, the negative power at 1:00, 20:00 and 24:00 was higher. At the same type of time point, different charging thresholds had a significant impact on the charging load demand of users. The larger the charging threshold of battery electric vehicles appeared, the earlier the peak moment and the higher the peak load. The battery state of the battery electric vehicle in the work area is 60 % at 1:00, and it reaches a relatively full state of 90 % at 8:00. In different regions, the allocation of charging thresholds for battery electric vehicles was different, and the Markov theory was derived from the analysis of the coverage problem to enable fast and effective siting of charging piles while satisfying charging convenience and high utilization of charging facilities to complement the research on the optimization model of battery electric vehicle charging facility layout.

In order to avoid the occurrence of unstable phenomena in the Power Grid (PG), it is necessary to have a thorough understanding of the current operating status of the PG. Research on voltage stability evaluation indicators and algorithms in the power system can assist power system operators in making effective decisions. In this paper, an improved continuous power flow scheme based on Lagrange quadratic interpolation was adopted. Secant method and Lagrange quadratic interpolation method were used for prediction comparison, and Newton method was used to correct the predicted results. This article utilized an online voltage safety assessment and warning model to ensure the safe operation of the PG and reduce the occurrence of faults. To ensure the integrity and accuracy of network dynamic information, a least squares fitting method based on orthogonal functions was adopted to improve the fitting accuracy without increasing communication volume. As the step size increases from 2 to 3, the number of power flow solutions decreased and the solution time increased. The effectiveness of the voltage stability evaluation algorithm in this paper was verified through simulation of two node systems: New England 39 and IEEE118.

With the development of the economy, many regions have experienced a slowdown in economic growth. In order to promote the development of the electric vehicle (EV) industry, the country has also begun to introduce various policies to encourage the development of the EV industry. In this context, many local governments have begun to introduce policies and measures related to the development of the EV industry, such as increasing land use for the development of the EV industry and increasing support for the new energy automobile industry. These policy measures have played a positive role in promoting the development of the EV industry, but there are also some problems. For example, when many local governments introduce policies to support the development of the new energy automobile industry, their support for the EV industry is not significant. This article studied the collaborative optimization of the EV industry chain in response to issues such as insufficient technical strength, imbalanced supply-demand relationship, and insufficient downstream service chain capabilities in the EV industry chain. This article analyzed the composition of the EV industry chain and established an EV industry chain model to address these issues. This article used collaborative optimization algorithms to analyze the production volume of EVs in the EV industry chain, as well as the comprehensive efficiency, pure technical efficiency, and scale efficiency values of upstream, midstream, and downstream. Through experimental analysis, it was found that the comprehensive efficiency value of the upstream of the EV industry chain after using the collaborative optimization algorithm was 0.0792 higher than before. The research results of this article have provided reference significance for the analysis of collaborative optimization algorithms in other fields.

Against the backdrop of China’s implementation of the “dual carbon” target and carbon emissions trading policies, renewable energy generation technologies have matured and received support from related policies. Distributed power sources have played a crucial role in the power system, and aggregators have integrated a large number of distributed power sources with diverse characteristics, shielding the complex characteristics of the underlying distributed power sources from grid scheduling. This article introduces the revenue optimization of low-carbon integration to optimize the aggregator’s scheduling model, designs distributed renewable energy generation units, and studies the solution strategy based on quantum genetic algorithms for large-scale optimization scheduling problems. The aggregator’s optimization variables divide the entire optimization problem and consider low-carbon integration to achieve distributed management of green energy parks, providing a feasible theoretical framework for the further development of distributed power sources. It has important practical significance in energy conservation, emissions reduction, and ecological environmental protection.

Pollution and energy crisis are two major challenges facing society today, and electric vehicles are regarded as an effective way to solve these problems. Solving the heat dissipation problem of the motor controller can effectively solve the problem of small motion range of electric vehicles, which is an effective attempt for new energy vehicles. Optimizing the heat dissipation structure of the power system can improve the efficiency of electric vehicles, save energy, and truly achieve optimal energy management. Therefore, the establishment of a reasonable and complete power system cooling system is of great significance for improving the overall performance of the vehicle. This article aimed to explore the heat dissipation problem of electric vehicle motor controllers. This article proposed that the motor can be divided into DC and AC based on the power supply. The DC motor has the characteristics of complex structure, cumbersome production process, easy to wear during use, and troublesome repairs in the later period. Through CFD simulation analysis, the experimental temperature of 1.5 kw natural convection is 62.35 °C, the simulation temperature is 64.05 °C; the experimental temperature of 1.1 kw natural convection is 50.45 °C, and the simulation temperature is 66.67 °C.

The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution.

In order to achieve maximum carbon reduction during the operation of pure electric buses, the author proposes a re estimation of carbon emissions in international trade based on evolutionary algorithm analysis of electric vehicle green energy regeneration systems. On the basis of analyzing single line scheduling, the author focuses on studying the regional scheduling mode of pure electric buses, and establishes a multi-objective optimization model for pure electric bus regional scheduling considering carbon benefits with the goals of minimizing the number of operating vehicles, minimizing the empty distance, and maximizing carbon benefits. Based on the actual operation data of pure electric buses 146 and 149 in a certain city, the author used an improved particle swarm optimization algorithm to solve the regional scheduling problem of pure electric buses according to the characteristics of the model. The results indicate that assuming other conditions remain unchanged, when the price of diesel rises to around 7.9 yuan, its operating costs will exceed those of pure electric buses, and the cost advantage of diesel vehicles will gradually decrease. Other conditions remain unchanged, and when the battery price per vehicle drops to around 300,000 yuan, the operating cost of pure electric buses will be lower than that of diesel vehicles. Conclusion: Under the premise of considering carbon benefits, adopting regional dispatch mode for pure electric buses has better economic efficiency and is more conducive to the promotion of pure electric buses.

A problem of great interest for power distribution companies is ensuring uninterrupted service in extensive power distribution systems. Thus, the monitoring of networks and identification of system faults become essential. This work focuses on identifying a fault’s occurrence from a small number of low-cost measurements in a power distribution system. The determination of sensor locations is based on the recent feature selection approach LassoNet, where the measurement locations are ranked. It provides the most informative measures during a fault resulting in a shortening data set. It is used as input to a deep neural network without a significant loss in accuracy. We validate our method on the IEEE 13 and 34 node test feeders for distribution systems to conduct the suggested approach’s experimental studies.

Abstact The corona-generated electromagnetic field environment under and nearer to the extra high dc potential conductors generates an ill health environment which causes serious electrical threats to living organisms present in the conductor corridor. The ionized electromagnetic field environment is characterizing with ionized ionic current flow and enhanced electric field perturbation at ground levels. The primary factors which affect the safe and economical design dimensions of high potential dc conductors are conductor diameter, altitude, applied potential and atmospheric parameters such as temperature, humidity, pressure, and aerosol pollution, etc. This remains a challenging task to design the HVDC transmission system at extended potential levels, as HVDC system employed for a large amount of power transfer barrier in the electrical system to meet inevitable power thrust. To obtain a safe and economical design configuration, multiple experimental design measurements are essential at various climatic seasons which incorporates the tedious process and significant economical expenditure. To obtain the safe and cost-effective design dimension configuration, computational-based numerical analysis with final experimental validation offers substantial support. Using various computational methodologies, many researchers have attempted to guide the critical line design proportions to establish a safe and economical electrical environment beneath the lines at ground levels. All these computational techniques are emphasizing the algorithm iterative-based complex solutions claim the lack of computational accuracy. Hence, this work aims to provide a unique computational technique to analyse the ionised electromagnetic field environment of a unipolar UHV dc transmission system using modelling of the physics of the corona discharge procedure in the active high electrostatic field zone. Modelling of the corona discharge includes the estimation of the amount of new ionized ions generated in the ionization region dynamically claims complex less equations offer a unique solution. To validate the accuracy of the proposed computational technique, multiple outdoor experimental measurements are carried out at HV/EHV/UHV potential applications and discussed. Also, the computed results are compared with the experimental findings, from the comparison outstanding computational accuracy was marked.

Protection of critical components in the power sector especially the transmission line system is very important to minimize loss of revenue, resources, and productivity. Improvement of power transfer capacity and voltage profile in smart the power grid is achieved by Series Compensation (SC). However, it poses many serious issues in the protection of compensated transmission lines. The majority of faults occurring in transmission lines are transient in nature for which service continuity can be regained by an auto-reclosing scheme. An efficient auto-reclosing scheme that can provide adaptive protection with reliable operation is required to prevent transient and permanent faults in the series compensated transmission line (SCTL). This work highlights the emulation of an Internet of Things (IoT) aided adaptive auto-reclosing scheme in association with an ESP32 controller that provides adaptive dead time control. The reclosing instance has been identified by means of a synchro-check relay. A real-time IoT-aided auto-reclosing scheme has been realized using the ESP32 controller that can generate a trip/reclosing signal instantaneously using the concepts of edge computing. The projected system has been also corroborated by simulating the modeled smart power grid network with wide variations in fault context in PSCAD. The validation of the set of rules is done in MATLAB software for the proposed auto-reclosing scheme. The extracted test outcomes of the simulation and its laboratory hardware implementation indicate an appreciable reduction in dead time which reflects the efficacy of the developed protection scheme.

This paper proposes a novel zero-voltage zero-current transition (ZV-ZCT) DC/DC high gain coupled inductor based boost converter for applications in renewable energy systems. It employs an auxiliary resonant network to soft-switch transition of semiconductor switches and soft-recovery of power diodes over wide input voltage and output power range of the converter with high efficiency. The auxiliary cell of the proposed converter has negligible impact on the converter’s overall dynamics and it doesn’t contribute any extra current stress to the semiconductor switches compared to classical hard switched boost converters. A thorough theoretical analysis of each operating mode has been presented together with the converter’s steady-state performance. The mathematical equations illustrating the dynamic behaviour of the converter have been used to design the auxiliary cell components. The circulating current loss has been minimized and simultaneously soft-switched operation of the converter is maintained through optimal selection of auxiliary cell components. Additionally, a power loss study and the proposed converter’s advantages over existing traditional converters are discussed. The converter’s soft-switching behaviour is established by hardware testing on a 400 W, 100 kHz prototype model. The theoretically predicted behaviour of the converter has been well corroborated by experimental observations.

PMUs and micro PMUs are an emerging technology that is gaining increasing importance in the monitoring and control of electric distribution grids. Their ability to provide accurate, real-time information on power quality, along with their ability to quickly detect and isolate any problems on the grid, make them a key tool for increasing grid safety and reliability. In terms of research and development, there is a growing interest in developing more advanced and reliable Micro PMUs that can provide more detailed and accurate information about the grid. In addition, there is a growing focus on integrating Micro PMUs with other grid control and monitoring systems, such as frequency and voltage control systems, to achieve greater efficiency in grid management. In this research, a commercial PMU is tested during transient conditions typical of distribution grids such as load insertion and de-insertion and distributed generation. The system involves simulating a grid in a Simulink environment and transforming digital signals into analog signals to be supplied to the PMU. Some indices such as TVE, ROCOF are then used for performance analysis.