Energy development improves quality of life for humans, but also incurs environmental consequences. A global energy transition from fossil fuels to renewable energy may mitigate climate change but may also undermine the capacity to achieve some or all 17 Sustainable Development Goals (SDGs). In this study, we use an innovation systems approach to construct a comprehensive roadmap for solar and wind energy to anticipate and improve impacts of a transition to a low carbon future in a manner ensuring climate goals and SDGs are mutually reinforcing. Our multidisciplinary approach began with an assessment of public investments in renewable energy followed by a 2-day research prioritization workshop. Fifty-eight expert workshop participants identified six research themes that proactively address the environmental sustainability of renewable energy. Next, we identified linkages between the six research themes and all 17 SDGs. Finally, we conducted a scientiometric analysis to analyze the research maturity of these themes. The results of these efforts elucidated the limits of existing knowledge of renewable energy-SDG interactions, informing the development of a research, development, demonstration, and deployment (RD3) roadmap to a renewable energy future aligned with both climate goals and SDGs. The RD3 roadmap has been designed to systematically develop solutions for diverse actors and organizations. Overall, our findings confer a broad vision for a sustainable transition to renewables to minimize unintended environmental consequences while supporting interoperability among actors particularly poised to influence its magnitude and direction.

With its enormous environmental and monetary benefits, the wind turbine has become an acceptable alternative to the generation of electricity by fossil fuel or nuclear power plants. Research remains focused on improving the performance of wind turbines with maximum flexibility and gains. The main objective of the paper is to simulate a low-voltage ridethrough (LVRT) control system that is convenient for the development of a controller that should have the ability to rectify fault signals. This paper proposes a novel method called grey wolf optimization with fuzzified error (GWFE) model to simulate the optimized control system. Further, it compares the GWFE-based LVRT system with the standard LVRT system, systems with minimum and maximum gain, and conventional methods like genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), ant bee colony (ABC), and grey wolf optimization (GWO) algorithms. Accordingly, it analyses the simulation results regarding qualitative analysis like active power, V dc comparison, gain, pitch degree, reactive power, rotor current, stator current, and V dc and V dqs measurements; and quantitative analysis like RMSE computation of V dc with varying speed. Hence, the proposed GWFE algorithm is beneficial for simulating the LVRT system compared to other conventional methods.

PurposeBy means of the massive environmental and financial reimbursements, wind turbine (WT) has turned out to be a satisfactory substitute for the production of electricity by nuclear or fossil power plants. Numerous research studies are nowadays concerning the scheme to develop the performance of the WT into a doubly fed induction generator-low voltage ride-through (DFIG-LVRT) system, with utmost gain and flexibility. To overcome the nonlinear characteristics of WT, a photovoltaic (PV) array is included along with the WT to enhance the system’s performance.Design/methodology/approachThis paper intends to simulate the control system (CS) for the DFIG-LVRT system with PV array operated by the MPPT algorithm and the WT that plays a major role in the simulation of controllers to rectify the error signals. This paper implements a novel method called self-adaptive whale with fuzzified error (SWFE) design to simulate the optimized CS. In addition, it distinguishes the SWFE-based LVRT system with standard LVRT system and the system with minimum and maximum constant gain.FindingsThrough the performance analysis, the value of gain with respect to the number of iterations, it was noted that at 20th iteration, the implemented method was 45.23% better than genetic algorithm (GA), 50% better than particle swarm optimization (PSO), 2.3% better than ant bee colony (ABC) and 28.5% better than gray wolf optimization (GWO) techniques. The investigational analysis has authenticated that the implemented SWFE-dependent CS was effectual for DFIG-LVRT, when distinguished with the aforementioned techniques.Originality/valueThis paper presents a technique for simulating the CS for DFIG-LVRT system using the SWFE algorithm. This is the first work that utilizes SWFE-based optimization for simulating the CS for the DFIG-LVRT system with PV array and WT.

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The inner padding layer of the motorcycle helmet is one of the important components that control the factors of comfort during wearing the helmet and provides a perfect fit and stability of the helmet on the head. The inner padding consists of a low-density flexible polyurethane layer attached to a soft fabric layer that is in direct contact with the head. The present study aims to replace foam in the comfort liner of a helmet with spacer fabrics to avoid the risk of heat stress during the use of the helmet. In this work, 9 warp-knitted spacer fabrics were produced by varying the course/cm (10, 12 & 14) and thickness (3, 3.5, and 4 mm). Air permeability, thermal, and water vapour resistance of the developed spacer fabrics were characterized along with compression properties and compared with commercially available standard liner. The results showed that warp-knitted spacer fabrics had better energy absorption compared to the standard liner. Also, the developed spacer fabrics had better breathability and evaporative heat transfer compared to the standard liner.