A High Gain DC-DC Converter with Grey Wolf Optimizer Based MPPT Algorithm for PV Fed BLDC Motor Drive

Photovoltaic (PV) water pumping systems are becoming popular these days. In PV water pumping, the role of the converter is most important, especially in the renewable energy-based PV systems case. This study focuses on one such application. In this proposed work, direct current (DC) based intermediate DC-DC power converter, i.e., a modified LUO (M-LUO) converter is used to extricate the availability of power in the high range from the PV array. The M-LUO converter is controlled efficiently by utilizing the Grey Wolf Optimizer (GWO)-based maximum power point tracking algorithm, which aids the smooth starting of a brushless DC (BLDC) motor. The voltage source inverter’s (VSI) fundamental switching frequency is achieved in the BLDC motor by electronic commutation. Hence, the occurrence of VSI losses due to a high switching frequency is eliminated. The GWO optimized algorithm is compared with the perturb and observe (P&O) and fuzzy logic based maximum power point tracking (MPPT) algorithms. However, by sensing the position of the rotor and comparing the reference speed with the actual speed, the speed of the BLDC motor is controlled by the proportional-integral (PI) controller. The recent advancement in motor drives based on distributed sources generates more demand for highly efficient permanent magnet (PM) motor drives, and this was the beginning of interest in BLDC motors. Thus, in this paper, the design of a high-gain boost converter optimized by a GWO algorithm is proposed to drive the BLDC-based pumping motor. The proposed work is simulated in MATLAB-SIMULINK, and the experimental results are verified using the dsPIC30F2010 controller

Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

Effect of infill pattern, density and material type of 3D printed cubic structure under quasi-static loading

The present research work is aimed to investigate the effect of infill pattern, density and material types of 3D printed cubes under quasi-static axial compressive loading. The proposed samples were fabricated though 3D printing technique with two different materials, such as 100% polylactic acid (PLA) and 70% vol PLA mixed 30% vol carbon fiber (PLA/CF). Four infill pattern structures such as triangle, rectilinear, line and honeycomb with 20%, 40%, 60%, and 80% infill density were prepared. Subsequently, the quasi-static compression tests were performed on the fabricated 3D printed cubes to examine the effect of infill pattern, infill density and material types on crushing failure behaviour and energy-absorbing characteristics. The results revealed that the honeycomb infill pattern of 3D printed PLA cubic structure showed the best energy-absorbing characteristics compared to the other three infill patterns. From the present research study, it is highlighted that the proposed 3D printed structures with different material type, infill pattern and density have great potential to replace the conventional lightweight structures, which could provide better energy-absorbing characteristics

Improving Overall Equipment Effectiveness by Enabling Autonomous Maintenance Pillar for Integrated Work Systems

Integrated Work System (IWS) and Overall Equipment Effectiveness (OEE) are two popular approaches used by production firms to identify and eliminate production losses. In a highly competitive business environment, companies must increase their efficiency in the manufacturing process to support resilient business continuity. While OEE is widely used as a quantitative tool for measuring the performance of total productive maintenance (TPM), the IWS approach integrates equipment, processes, and involvement of people into a unified approach to reduce costs, improve quality, and increase productivity. Principally, there is an alignment between the two concepts. The IWS has the potential to maximize OEE to eliminate equipment failure and defects, minimize downtime, and maximize productivity with less time, effort, and waste. The purpose of this work is to compare the performance of the OEE with the implementation of the IWS pillar, i.e., autonomous maintenance (AM). The rollout of the AM pillar was carried out on the two identical packaging machines (HLP1) with a speed of 120 packets per minute. The data which is shown in this paper is for both machines during the operational hours. Finally, the analysis showed positive results for both machines within a five-month period, with an increase of 27% and 15% in OEE, respectively. Later in the discussion, the root cause and SWOT analysis were perused for OEE and TPM, respectively, in this paper