This study presents an adaptive perturb and observe (P&O)‐fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc–dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB‐Simulink, respectively, together with PV module of Kyocera KD210GH‐2PU connected to PV boost dc–dc converter. For hardware implementation, the proposed adaptive P&O‐fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O‐fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.

One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

Agrivoltaic systems (AVS) offer a symbiotic strategy for co-location sustainable renewable energy and agricultural production. This is particularly important in densely populated developing and developed countries, where renewable energy development is becoming more important; however, profitable farmland must be preserved. As emphasized in the Food-Energy-Water (FEW) nexus, AVS advancements should not only focus on energy management, but also agronomic management (crop and water management). Thus, we critically review the important factors that influence the decision of energy management (solar PV architecture) and agronomic management in AV systems. The outcomes show that solar PV architecture and agronomic management advancements are reliant on (1) solar radiation qualities in term of light intensity and photosynthetically activate radiation (PAR), (2) AVS categories such as energy-centric, agricultural-centric, and agricultural-energy-centric, and (3) shareholder perspective (especially farmers). Next, several adjustments for crop selection and management are needed due to light limitation, microclimate condition beneath the solar structure, and solar structure constraints. More importantly, a systematic irrigation system is required to prevent damage to the solar panel structure. To summarize, AVS advancements should be carefully planned to ensure the goals of reducing reliance on non-renewable sources, mitigating global warming effects, and meeting the FEW initiatives.

Traditional transmission line ratings are limited by a set of fixed conservative weather assumptions that are also known as static thermal rating (STR). Owing to STR, new line corridors are continuously required to address increasing electricity demands while minimizing the curtailment of renewable energy sources (RES). However, the expansion of an electricity network is expansive, long, and limited due to the scarcity in land and space. To overcome this issue, researchers have proposed a dynamic thermal rating (DTR) system that can increase the capacity of existing transmission lines. Research has shown that actual line ratings are higher than STR most of the time. The potential of using the DTR system to increase the reliability of power systems is therefore significant. Almost every country has begun the process of increasing the integration of RES, and consequently, the DTR system has become increasingly important. Exploring and reviewing critical studies on the DTR system are thus beneficial for researchers who are interested in the developments of DTR technology. This review paper begins by comparing the two main DTR system standards. Then, monitoring technologies of the DTR system are reviewed. Notable research on the reliability impacts of the DTR system on electrical networks are surveyed. Interactions with wind power and other smart grid technologies are also examined, and the concept of power system reliability is briefly discussed.

Owing to the destructive impacts of harmonic currents, the topic of reducing their impacts on power system has attracted tremendous research interests. In this regard, a shunt active power filter (SAPF) is recognized to be the most reliable instrument. It performs by first detecting the harmonic currents that are present in a harmonic-contaminated power system, and subsequently generates and injects corrective mitigation current back into the power system to cancel out all the detected harmonic currents. This means that other than the ability to generate corrective mitigation current itself, it is actually more important to make sure that the SAPF is able to operate in phase with the operating power system, so that the mitigation current can correctly be injected. Hence, proper synchronization technique needs to be integrated when designing the control algorithms of SAPF. This paper critically discusses and analyzes various types of existing phase synchronization techniques which have been applied to manage operation of SAPF; in terms of features, working principle, implementation and performance. The analysis provided can potentially serve as a guideline and provision of information on selecting the most suitable technique for synchronizing SAPF with the connected power system.

Renewable energy (RE) has become a focal point of interest as an alternative source of energy to the traditional fossil fuel and other energy sources due to the fact that it is more environmentally friendly, abundant and economically feasible. Many countries aggressively promote feed-in tariff schemes and solar photovoltaic (PV) systems have become one of the fastest growing RE sources that can be integrated into the grid distribution network. This paper reviews the recent development of grid-connected PV (GPV) generation systems comprising of several sub-components such as PV modules, DC-DC converter, maximum power point tracking (MPPT) technique, and an inverter. In addition, various grid synchronization and islanding detection methods are elaborated. The future key challenges to build a smart and efficient GPV generation system were also presented.