In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 10⁶ for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.

Permanent construction has evolved from the Palaeolithic age to today’s skyscrapers. Constructing the structure, which promises occupants safety, has become a concern because of the uncertainties in nature. Therefore in recent years, attention has been given to the development of structural protective devices that could take care of the external loads. Structural control against the wind and earthquake load has been seriously studied where the structure behaves differently for wind and earthquake load has been briefly discussed in this paper. Initially, paper discusses the history of the construction and the passive control system, which was used in structural control, is briefly discussed in this paper. Also, the implementation of active control has been discussed which was introduced later in the structural control for more effective control. But the limitations of the passive and active control system have introduced semi-active control and also the hybrid control strategy. The two mechanisms are put together in the semi-active and hybrid system to obtain all advantages of the algorithm along with overcoming their limitations. The review also briefs about stochastic vibrational control of the structure where randomness is considered in external loads, parameter of the system and also in the external devices which are implemented in the structural control. As construction sector is a complex system, big data analysis, a new field in structural control system is discussed and future scope is also mentioned.

Over the past decade, rehabilitative devices have undergone significant advancements. Orthoses, which are assistive limb devices, have transitioned from passive supportive devices to active or semiactive devices with adjustable damping. Similarly, prosthetic limb-replacement devices for amputees have shifted towards the integration of semiactive dampers instead of passive or bulky dampers. The implementation of semiactive damping is achieved through the use of smart magnetorheological (MR) fluids that can modify their viscosity based on the input current, resulting in reduced power consumption. This article focuses mainly on modelling, sensors and control strategies in MR damper-based rehabilitative devices since these areas contribute to the development of the overall end product. There have been notable improvements in the modelling of human knee joints and the damping system components, aiming to achieve more efficient damping control. Traditional mathematical equations, such as Lagrangian and Newtonian formulations, have been supplemented with machine learning algorithms. Additionally, the utilization of various sensor combinations to measure knee/ankle joint angles has advanced. These sensors range from basic mechanical sensors to wireless inertial and piezoelectric sensors, enabling faster and more diverse communication. Furthermore, control algorithms have also witnessed a progression from classical control approaches to more sophisticated strategies such as fuzzy control and neural network controllers. These advanced control algorithms enhance the overall performance and responsiveness of the rehabilitative devices. However, there are certain disadvantages found in MR fluids, sensors, modelling and control algorithms that are discussed further in this review article. This review explores the developments in different rehabilitative devices that integrate modelling, sensors, and control designs to achieve optimal and efficient outcomes.

Global warming and the energy crisis are two significant challenges in the world. The prime sources of greenhouse gas emissions are the transportation and power generation sectors because they rely on fossil fuels. To overcome these problems, the world needs to adopt electric vehicles (EVs) and renewable energy sources (RESs) as sustainable solutions. The rapid evolution of electric mobility is largely driven by the development of EV charging infrastructures (EVCIs), which provide the essential support for large-scale EV adoption. As the number of CIs grows, the utility grid faces more challenges, such as power quality issues, power demand, voltage instability, etc. These issues affect the grid performance, along with the battery lifecycle of the EVs and the charging system. A charging infrastructure integrated with the RES-based microgrid (MG) is an effective way to moderate the problem. Also, these methods are about reframing how smart sustainable cities generate, distribute, and consume energy. MG-based CI operates on-grid and off-grid based on the charging demand and trades electricity with the utility grid when required. This paper presents state-of-the-art transportation electrification, MG classification, and various energy sources in the DC MG. The grid-integrated DC MG, international standards for EV integration with the grid, impacts of CI on the electrical network, and potential methods to curtail the negative impact of EVs on the utility grid are explored comprehensively. The negative impact of EV load on the voltage profile and power loss of the IEEE 33 bus system is analysed in three diverse cases. This paper also provides directions for further research on grid-integrated DC MG-based charging infrastructure.

This study presents an observer-based anti-windup robust proportional–integral–derivative controller with state estimator method for damped outrigger structure using magneto-rheological damper to mitigate the seismic response. In this approach, full-order Kalman observer is designed for estimating the states of the damped outrigger system from the feedback of the system output with optimum observer gain. However, due to the computational complexity, the integral windup is observed in the loop; therefore, integral anti-windup is introduced for the internal stability in the loop to produce the desired output. The semi-active magneto-rheological damper is integrated with the proposed system, to produce the required force by the system that ranges between the maximum and minimum values as regulated by the voltages produced by the controller in action for every instant of the seismic energy. The proposed strategy is designed in MATLAB and Simulink to find the adequacy of the damped outrigger system in terms of mitigating the following seismic responses like displacement, velocity, and acceleration. The dynamic analysis of the damped outrigger structure with the proposed control strategy shows enhanced performance in reducing the response of the structure as observed in peak response values. The evaluation criteria show a significant reduction in the vibration of the structure.