Search

The machine learning (ML) model provides an alternative method for estimating inherent optical properties (IOPs) in clear and coastal waters. This study introduces an effective approach by employing ensemble machine learning techniques, such as random forest, gradient boosting, extra tree, adaboost, bagging, and voting model, to predict phytoplankton absorption coefficient ( $\text{a}_{\mathrm {ph}}(\lambda)$ , $\text{m}^{-1}$ ) at selected key wavebands of 443, 489, 510, 555, and 670 nm in clear and coastal waters. The optimization of the hyperparameters of these models through Bayesian techniques ensured high predictive accuracy. Furthermore, this research highlights the critical importance of wavelengths 670, 489, and 510 nm through feature importance analysis. The models exhibit excellent performance in terms of the coefficient of determination (R2) value when predicting phytoplankton at various wavelengths (e.g., 443, 489, 510, 555, and 670 nm). The R2 value of around 0.9033 is obtained for the absorption coefficient of phytoplankton aph at the wavelength 510 nm. The lowest mean squared error (MSE) of 0.0001 was achieved at the green waveband (i.e., 555 nm). Other statistical matrices, such as mean absolute percentage error (MAPE) and mean absolute error (MAE), have shown a low error across the selected wavelengths. It is found that the predicted phytoplankton absorption coefficients are in close agreement with actual values. This study shows the success of optimized ensemble models for both global and selected regional datasets that can accurately derive $\text{a}_{\mathrm {ph}}(\lambda)$ , which will contribute to the improvement of ocean primary productivity modelling and understanding the distribution of phytoplankton blooms.

The increase in renewable energy resources penetration into microgrids weakens the inertia of microgrids and negatively affects their frequency stability. This reduction in inertia reduces the microgrid’s ability to handle disturbances and increases frequency instability risk. The purpose of this paper is to present a virtual inertia (VI) control strategy for an island microgrid with the use of an energy storage system (ESS) to improve inertia and frequency stability of the system. The VI controller parameters are optimized using the differential evolution (DE) algorithm. The proposed VI control strategy can effectively improve microgrid frequency stability and reduce the risk of frequency instability. The results demonstrate that the proposed strategy is effective and can provide fast and reliable virtual inertia support for the microgrid. The proposed VI control approach is verified through simulation results and performs better than the conventional inertial response for frequency stability in islanded microgrids. Virtual inertia control injects/absorbs active power into/from the microgrid during frequency deviations. Different disturbances are applied to demonstrate uncertain load and RES generation behavior. The proposed VI control method is compared with the available literature. In the proposed method, the frequency deviation is better controlled. Furthermore, the results demonstrate a more stable and efficient system operation with the developed VI controller than the existing methods.

Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3- $\phi $ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3- $\phi $ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.

The electrical energy demand of the Kingdom of Saudi Arabia (KSA) has been observing a surging growth due to high population growth and urbanization rate. The generation mostly comes from the burning of petroleum products. The electricity sector emits more than one-third of the national greenhouse gas (GHG) of the country. Thus, it is essential to reduce the GHG from the electricity generation sector to maintain sustainability. This article mainly concentrates on the existing electricity generation and focuses on the Kingdom’s GHG emissions mitigation initiatives. Although the use of natural gas and the number of cogeneration plants have been increased in electricity generation, the country has also been focusing on other initiatives, including the adaptation of combined cycle power plants, carbon capture, utilization, and sequestration (CCUS), renewable and alternative energy sources, and various utility-scale energy efficiency measures. The ambitious and inspirational renewable energy (RE) targets of the Saudi Vision 2030 will significantly facilitate GHG emission avoidance, which will support the Kingdom to meet its Nationally Determined Contributions. This article recommends various mitigation measures, including the usage of the efficient internal combustion engine, poly-generation, combined heat and power (CHP) systems, deployment of renewable resources in the distribution and microgrid systems, bio-mass engine, and ocean thermal energy conversion plants. The decision-makers and the researchers will find the discussions and recommendations helpful in determining suitable and appropriate mitigation initiatives and technologies.

The utilization of renewable energy sources (RESs) has become significant throughout the world, especially over the last two decades. Although high-level RESs penetration reduces negative environmental impact compared to conventional fossil fuel-based energy generation, control issues become more complex as the system inertia is significantly decreased due to the absence of conventional synchronous generators. Some other technical issues, high uncertainties, low fault ride through capability, high fault current, low generation reserve, and low power quality, arise due to RESs integration. Renewable energy like solar and wind are highly uncertain due to the intermittent nature of wind and sunlight. Cutting edge technologies including different control strategies, optimization techniques, energy storage devices, and fault current limiters are employed to handle those issues. This paper summarizes several challenges in the integration process of high-level RESs to the existing grid. The respective solutions to each challenge are presented and discussed. A comprehensive list of challenges and solutions, for both wind and solar energy integration cases, are well documented. Finally, the future recommendations are provided to solve the several problems of renewable energy integration which could be key research areas for the industry personnel and researchers.