Ammar Ahmed Alkahtani

Bio: Ammar Ahmed Alkahtani is an academic researcher from Universiti Tenaga Nasional. The author has contributed to research in topics: Photovoltaic system & Wind power. The author has an hindex of 6, co-authored 52 publications receiving 146 citations.

Power Quality Assessment of Grid-Connected PV System in Compliance with the Recent Integration Requirements

Abstract: The generation and integration of photovoltaic power plants (PVPPs) into the utility grid have increased dramatically over the past two decades. In this sense, and to ensure a high quality of the PVPPs generated power as well as a contribution on the power system security and stability, some of the new power quality requirements imposed by different grid codes and standards in order to regulate the installation of PVPPs and ensure the grid stability. This study aims to investigate the recent integration requirements including voltage sag, voltage flicker, harmonics, voltage unbalance, and frequency variation. Additionally, compliance controls and methods to fulfill these requirements are developed. In line with this, a large-scale three-phase grid-connected PVPP is designed. A modified inverter controller without the use of any extra device is designed to mitigate the sage incidence and achieve the low-voltage ride-through requirement. It can efficiently operate at normal conditions and once sag or faults are detected, it can change the mode of operation and inject a reactive current based on the sag depth. A dynamic voltage regulator and its controller are also designed to control the voltage flicker, fluctuation, and unbalance at the point of common coupling between the PVPP and the grid. The voltage and current harmonics are reduced below the specified limits using proper design and a RLC filter. The obtained results show that the proposed controller fulfilled the recent standard requirements in mitigating power quality (PQ) events. Thus, this study can increase the effort towards the development of smooth PVPP integration by optimizing the design, operation and control strategies towards high PQ and green electricity.

Power Quality in Microgrids Including Supraharmonics: Issues, Standards, and Mitigations

Abstract: A microgrid (MG) is a small-scale power system with a cluster of loads and distributed generators operating together through energy management software and devices that act as a single controllable entity with respect to the grid. MG has become a key research element in smart grid and distribution power systems. MG mainly contains different renewable energy sources (RESs) that use various technological advancements, such as power electronics-based technologies. However, it has an unstable output, thereby causing different types of power quality (PQ) events. As a result, standards and mitigation methods have been developed in recent years. To mitigate PQ issues due to MG integration, various methods and standards have been proposed over the last years. Although these individual methods are well documented, a comparative overview had not been introduced so far. Thus, this study aims to fill the gap by reviewing and comparing the prior-art PQ issues, solutions, and standards in MGs. We compare the main issues related to voltage sag, voltage swell, voltage and current harmonics, system unbalances, and fluctuations to ensure high-quality MG output power. The new technologies associated with MGs generate harmonics emission in the range of 2–150 kHz, thereby causing a new phenomenon, namely, supraharmonics (SH) emission, which is not sufficiently covered in the literature. Therefore, the characteristics, causes, consequences, and measurements of SH are highlighted and analyzed. The mitigation strategies, control, and devices of PQ issues are also discussed. Moreover, a comparison is conducted between the most popular devices used to mitigate the PQ issues in MG in terms of cost, rating, and different aspects of performance. This review study can strengthen the efforts toward the mitigation and standards development of PQ issues in MG applications, especially SH. Finally, some recommendations and suggestions to improve PQ of MG, including SH, are highlighted.

A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data

Abstract: Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.

Defect Study and Modelling of SnX3-Based Perovskite Solar Cells with SCAPS-1D

Abstract: Recent achievements, based on lead (Pb) halide perovskites, have prompted comprehensive research on low-cost photovoltaics, in order to avoid the major challenges that arise in this respect: Stability and toxicity. In this study, device modelling of lead (Pb)-free perovskite solar cells has been carried out considering methyl ammonium tin bromide (CH3NH3SnBr3) as perovskite absorber layer. The perovskite structure has been justified theoretically by Goldschmidt tolerance factor and the octahedral factor. Numerical modelling tools were used to investigate the effects of amphoteric defect and interface defect states on the photovoltaic parameters of CH3NH3SnBr3-based perovskite solar cell. The study identifies the density of defect tolerance in the absorber layer, and that both the interfaces are 1015 cm-3, and 1014 cm-3, respectively. Furthermore, the simulation evaluates the influences of metal work function, uniform donor density in the electron transport layer and the impact of series resistance on the photovoltaic parameters of proposed n-TiO2/i-CH3NH3SnBr3/p-NiO solar cell. Considering all the optimization parameters, CH3NH3SnBr3-based perovskite solar cell exhibits the highest efficiency of 21.66% with the Voc of 0.80 V, Jsc of 31.88 mA/cm2 and Fill Factor of 84.89%. These results divulge the development of environmentally friendly methyl ammonium tin bromide perovskite solar cell.

Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles

Abstract: With the rise in the demand for electric vehicles, the need for a reliable charging infrastructure increases to accommodate the rapid public adoption of this type of transportation. Simultaneously, local electricity grids are being under pressure and require support from naturally abundant and inexpensive alternative energy sources such as wind and solar. This is why the world has recently witnessed the emergence of renewable energy-based charging stations that have received great acclaim. In this paper, we review studies related to this type of alternative energy charging infrastructure. We provide comprehensive research covering essential aspects in this field, including resources, potentiality, planning, control, and pricing. The study also includes studying and clarifying challenges facing this type of electric charging station and proposing suitable solutions for those challenges. The paper aims to provide the reader with an overview of charging electric vehicles through renewable energy and establishing the ground for further research in this vital field.

Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems

Abstract: Traditionally, for the planning and assessment of solar energy systems, the amount of solar radiation (sunlight) incident on the Earth’s surface is assumed to be constant over the years. However, with changing climate and air pollution levels, solar resources may no longer be stable over time and undergo substantial decadal changes. Observational records covering the past decades confirm long-term changes in this quantity. Here we examine how the latest generation of climate models used for the 5th IPCC report projects potential changes in surface solar radiation over the coming decades, and how this may affect, in combination with the expected greenhouse warming, solar power output from photovoltaic (PV) systems. For this purpose, projections up to the mid 21st century from 39 state of the art climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are analysed globally and for selected key regions with major solar power production capacity. The large model ensemble allows to assess the degree of consistency of their projections. Models are largely consistent in the sign of the projected changes in solar radiation under cloud-free conditions as well as surface temperatures over most of the globe, while still reasonably consistent over a considerable part of the globe in the sign of changes in cloudiness and associated changes in solar radiation. A first order estimate of the impact of solar radiation and temperature changes on energy yields of PV systems under the RPC8.5 scenario indicates statistically significant decreases in PV outputs in large parts of the world, but notable exceptions with positive trends in large parts of Europe, South-East of North America and the South-East of China. Projected changes between 2006 and 2049 under the RCP8.5 scenario overall are on the order of 1%/decade for horizontal planes, but may be larger for tilted or tracked planes as well as on shorter (decadal) timescales.

Power Quality in Microgrids Including Supraharmonics: Issues, Standards, and Mitigations

Abstract: A microgrid (MG) is a small-scale power system with a cluster of loads and distributed generators operating together through energy management software and devices that act as a single controllable entity with respect to the grid. MG has become a key research element in smart grid and distribution power systems. MG mainly contains different renewable energy sources (RESs) that use various technological advancements, such as power electronics-based technologies. However, it has an unstable output, thereby causing different types of power quality (PQ) events. As a result, standards and mitigation methods have been developed in recent years. To mitigate PQ issues due to MG integration, various methods and standards have been proposed over the last years. Although these individual methods are well documented, a comparative overview had not been introduced so far. Thus, this study aims to fill the gap by reviewing and comparing the prior-art PQ issues, solutions, and standards in MGs. We compare the main issues related to voltage sag, voltage swell, voltage and current harmonics, system unbalances, and fluctuations to ensure high-quality MG output power. The new technologies associated with MGs generate harmonics emission in the range of 2–150 kHz, thereby causing a new phenomenon, namely, supraharmonics (SH) emission, which is not sufficiently covered in the literature. Therefore, the characteristics, causes, consequences, and measurements of SH are highlighted and analyzed. The mitigation strategies, control, and devices of PQ issues are also discussed. Moreover, a comparison is conducted between the most popular devices used to mitigate the PQ issues in MG in terms of cost, rating, and different aspects of performance. This review study can strengthen the efforts toward the mitigation and standards development of PQ issues in MG applications, especially SH. Finally, some recommendations and suggestions to improve PQ of MG, including SH, are highlighted.

Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm

Abstract: The development and deployment of an effective wind speed forecasting technology can improve the safety and stability of power systems with significant wind penetration. Due to the wind’s unpredictable and unstable qualities, accurate forecasting of wind speed and power is extremely challenging. Several algorithms were proposed for this purpose to improve the level of forecasting reliability. The Long Short-Term Memory (LSTM) network is a common method for making predictions based on time series data. This paper proposed a machine learning algorithm, called Adaptive Dynamic Particle Swarm Algorithm (AD-PSO) combined with Guided Whale Optimization Algorithm (Guided WOA), for wind speed ensemble forecasting. The AD-PSO-Guided WOA algorithm selects the optimal hyperparameters value of the LSTM deep learning model for forecasting of wind speed. In experiments, a wind power forecasting dataset is employed to predict hourly power generation up to forty-eight hours ahead at seven wind farms. This case study is taken from the Kaggle Global Energy Forecasting Competition 2012 in wind forecasting. The results demonstrated that the AD-PSO-Guided WOA algorithm provides high accuracy and outperforms several comparative optimization and deep learning algorithms. Different tests’ statistical analysis, including Wilcoxon’s rank-sum and one-way analysis of variance (ANOVA), confirms the accuracy of the presented algorithm.

Two-Dimensional photonic crystal Biosensors: A review

Abstract: Photonic crystals are nanoscale structures that affect the motion of photons. The strong light limitation in photonic crystals and the adjustment of its structural parameters have led to the emergence of photonic crystal biosensors. Moreover, the use of holes as a feature of photonic crystals has resulted in sensors that are very sensitive to low refractive index changes with a small sensing area, which offers flexibility and integration on single-chip systems. Using emerging optofluidic technology, label-free biosensors are on the rise. In this review, we examine various types of photonic crystal sensors, such as waveguides, nanoresonators, LX resonators, holes, multi-channel resonators, nano RINGS resonators, and fibers. These sensors are based on the measurement of biomolecules and the refractive index properties that have been identified. Finally, a variety of challenges and guidelines for the construction of label-free diagnostic biosensors are examined.