The set-up for a new electrical system for variable renewable energy resources (RERs) will bring additional capital costs and therefore upgradation of the existing systems is by far the best option to ensure little to no greenhouse gases (GHGs) or harmful pollutants. This paper reports advanced power electronic converter-based devices (PECs) which play a crucial role in conversion and control activities in connecting RERs to power grids they cannot only bypass the associated challenges but can also be an efficient and potential equipment in power and energy sector. The interfacing of PECs with RERs is explored in this work is powered by an advanced framework in which smart inverters (SIs) are also utilized to maintain the regulated output voltage in line with the grid requirement and the effectiveness of the developed system is verified on MATLAB environment. The capabilities of SIs are emphasized which ranges from enhanced coordination between the devices, better efficiency and seamless integration of renewables with the utility grid to ensure holistic operation. A detailed analysis is performed and the outcomes reveal the suitability and applicability of the proposed work and in particular, interfacing PECs with the utility grid not only improves the performance of the system but also increases efficiency and limits the challenges associated with the integration of renewables in the mainstream grid to a considerable extent. The peak voltage of 300 V is produced by employing SIs from solar PV system which corresponds to an RMS voltage of 212.13 V within the permissible frequency range.

Maintaining worker safety on hazardous construction sites remains a serious challenge, especially due to the rampant shortage or misuse of Personal Protective Equipment (PPE), which is a major contributor to accidents in at-height operations. Conventional monitoring systems are usually not as adaptable or accurate as needed in real-world situations. To address this, a Discrete Dilated Cosine Causal Convolution Krawtchouk Orangutan Multi-Tchebichef Head Self-Attention Network (2D-3CKO-MTHSAN) is proposed, which incorporates Discrete Dilated Cosine Causal Convolution with Multi-Head Self-Attention (DCCMSA) and Discrete Cosine-Krawtchouk-Tchebichef Transform (DCKTKT) to enhance PPE detection accuracy and robustness. The approach uses real-time drone surveillance to continuously acquire visual data at actual construction sites and controlled lab settings, with emphasis on identifying the salient elements of the Personal Fall Arrest System (PFAS), such as helmets, harnesses, and lifelines. A two-stage preprocessing pipeline comprising entropy filtering and τ-Kendall correlation analysis improves image quality and feature prominence. Deep spatio-temporal features are extracted using Adaptive Causal Decision Transformers, and the DCCMSA-DCKTKT model parameters are adjusted using the Orangutan Optimization Algorithm (OOA) to achieve stable performance under changing environmental conditions. Experimental results confirm the system’s superiority over conventional deep learning models, achieving 99.9% detection accuracy. Compared to other fixed CCTV image-based or single-stage CNN-based PPE detection methods with limited scalability, the new hybrid 2D-3CKO-MTHSAN approach integrates adaptive optimization and multi-scale attention in an innovative manner to enable real-time, accurate drone-based detection. This is an example of our solution’s novelty in integrating discrete transforms with the Orangutan Optimization Algorithm (OOA) for efficient field deployment.

An Electrical vehicle can utilize a force move framework to charge the on-board Rechargeable Energy Storage System (RESS the battery) or to give the capacity to the electric engine. It is additionally conceivable to utilize remote force move to charge the batteries while fixed, using charging pads. Both of these arrangements are satisfactory for assessing at home or in-vehicle parking, yet at the same time, require the vehicle to stop in a proper area to charge the battery. We propose an eco-friendly wireless charging system for electric vehicles, which enables wireless charging of the vehicle while running on the highways. The proposed approach is also used for the charging of cars in the shopping malls and other parking places. Moreover, the proposed charging system may require electrical energy from the solar PV systems and wind turbines, installed along the highways. The solar PV system and wind turbines are connected to form a Microgrid system and feed generated electrical energy to the wireless charging system. The surplus electrical energy may be either fed back to the primary grid or used for highway illumination. The wireless charging system also consists of an air-core transformer with its primary winding below the highway surface and secondary mounted on the vehicle. The ac power from secondary passes through a rectifier and dc-dc conversion stage to produce suitable dc voltage and current for the charging of the battery unit.