Dong Ryeol Shin

Distribution networks have entered a new era with the broad adoption of the distributed generation (DG) allocation as a practical solution for addressing power losses, voltage variation, and voltage stability. The primary goal is to enhance techno-economic and environmental characteristics while meeting the limitations of the system. In order to allocate DGs in active distribution networks (ADNs) efficiently, this study demonstrates two optimization methods inspired by nature: ant lion optimization (ALO) and multiverse optimization (MVO). Various multi-criteria decision-making (MCDM) methods are used to find the best possible solution among the different alternatives. On the IEEE 33- and 69-bus active distribution networks, the proposed ALO was shown to be effective and produces the highest loss reduction in the IEEE 33- and 69-bus systems at 94.43% and 97.16%, respectively, and the maximum voltage stability index (VSI) was 0.9805 p.u and 0.9937 p.u, respectively; moreover, the minimum voltage deviation (V_D) and annual energy loss cost for the given test systems was 0.00019 p.u and 3353.3 PKR, which shows that the suggested method can produce higher quality results as compared to other methods presented in the literature. Therefore, the proposed ALO is a very efficient, effective, and appealing solution to the optimal allocation of the distributed generation (OADG) problem.

The concept of microgrids has emerged as an effective way to integrate distributed energy resources (DERs) into distribution networks. The presence of DERs in microgrids leads to challenges in the formulation of protection for microgrids. Protection problems arise in a microgrid due to varying fault current levels in different operating scenarios. In order to overcome the practical challenges arising from varying fault current levels leading to short-circuit faults in microgrids, this paper proposes a MagnetoResistive (MR) sensors-based protection scheme, with fault localization through SuperimposedReactiveEnergy (SRE). The process is initiated by employing highly sensitive non-intrusive magnetic sensors to detect the magnetic field at each end of the distribution line. The magnetic field is then used to calculate the total harmonic distortion and thus detect faults in microgrids. After detection of faults, the proposed scheme uses SRE to identify faulty zones in microgrids. Finally, SI components of the current are extracted for fault classification. Extensive simulations on the International Electro-technical Commission (IEC) microgrid are performed in MATLAB/Simulink to validate the efficacy of the proposed scheme. Simulation results show that the proposed scheme can effectively detect, classify and isolate different faults in microgrids, while operating under various modes with varying fault locations and resistances, with the efficiency of approximately 97–98%.

Energy planning has become more complicated in the 21st century of sustainable development due to the inclusion of numerous standards such as techno-economic, and environmental considerations. This paper proposes multi-criteria sustainable planning (MCSP) based optimization approach for identifying DGs’ optimal allocations and rating powers. The main objectives of this paper are the reduction of the network’s total power loss, voltage profile improvement, energy loss saving maximization, and curtailing environmental emissions and water consumption to achieve Sustainable Development Goals (SDGs 3, 6, 7, 13, and 15) by taking the constraints into consideration. Different alternatives are evaluated across four aspects of performance indices; technical, cost-economic, environmental, and social (TEES). In terms of TEES performance evaluations, various multi-criteria decision-making (MCDM) approaches are used to determine the optimal trade-off among the available solutions. These methods are gaining wide acceptance due to their flexibility while considering all criteria and objectives concurrently. Annual energy loss saving is increased by 97.13%, voltage profile is improved to 0.9943 (p.u), and emissions are reduced by 82.45% using the proposed technique. The numerical results of the proposed MCSP approach are compared to previously published works to validate and may be used by researchers and energy planners as a planning tool for ADN schemes.

Metaverse is a novel innovative platform that connects users worldwide in the distributed virtual environment. People share their interests, opinions, and resources on this virtual reality platform. With this, we come to know that besides other fundamental techniques, the language generation method is also a necessity to regulate the VR environment. There are several types of language generation methods in 3D, including neural learning, such as GRU, RNN, and GPT-3, and transfer learning. This paper proposes a recurrent voting generator (RVG) system that understands the 3D text of a book and performs emotional analytics within a metaverse space. The proposed model RVG evaluates emotions through three algorithms such as the first module is a recurrent sentiment generator (RSG) that analyzes emotions and calculates and generates the distributions. The second module is the sentiment decomposition (SD) that optimizes higher dimensions in Big Data. And, the third module is the compound voting learning (CVL) module that performs calculations with an emphasis on optimal performance. The dataset used to evaluate RVG is based on the content of movie reviews and books. The performance evaluation shows that the proposed approach outperforms better compared to the existing 2D RNN models in the metaverse.

Artificial intelligence, specifically machine learning, has been applied in a variety of methods by the research group to transform several data sources into valuable facts and understanding, allowing for superior pattern identification skills. Machine learning algorithms on huge and complicated data sets, computationally expensive on the other hand, processing requires hardware and logical resources, such as space, CPU, and memory. As the amount of data created daily reaches quintillion bytes, A complex big data infrastructure becomes more and more relevant. Apache Spark Machine learning library (ML-lib) is a famous platform used for big data analysis, it includes several useful features for machine learning applications, involving regression, classification, and dimension reduction, as well as clustering and features extraction. In this contribution, we consider Apache Spark ML-lib as a computationally independent machine learning library, which is open-source, distributed, scalable, and platform. We have evaluated and compared several ML algorithms to analyze the platform’s qualities, compared Apache Spark ML-lib against Rapid Miner and Sklearn, which are two additional Big data and machine learning processing platforms. Logistic Classifier (LC), Decision Tree Classifier (DTc), Random Forest Classifier (RFC), and Gradient Boosted Tree Classifier (GBTC) are four machine learning algorithms that are compared across platforms. In addition, we have tested general regression methods such as Linear Regressor (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Gradient Boosted Tree Regressor (GBTR) on SUSY and Higgs datasets. Moreover, We have evaluated the unsupervised learning methods like K-means and Gaussian Mixer Models on the data set SUSY and Hepmass to determine the robustness of PySpark, in comparison with the classification and regression models. We used ”SUSY,” ”HIGGS,” ”BANK,” and ”HEPMASS” dataset from the UCI data repository. We also talk about recent developments in the research into Big Data machines and provide future research directions.

Continuous increase in number of wireless devices and data rate demand has motivated the usage of 5G New Radio in Unlicensed band (NR-U). Coexistence between 5G NR-U with other unlicensed technologies like Wi-Fi is challenging owing to the reduced probability of channel availability. Waiting to get access to unlicensed channel results in more battery consumption of User Equipment (UE). Discontinuous Reception (DRX) mechanism in NR-U can reduce UE’s energy consumption. In this article, we introduce Machine Learning (ML) based DRX mechanisms. The DRX is modeled through three state semi-Markov model. We utilized the ML classifiers to get access to the unlicensed channel. The simulation results point out that the performance of LSTM is superior to the Support Vector Machine (SVM) and Naive Bayes (NB) classifiers, which achieves up to 3% improvement in Power-Saving Factor (PSF) and 50% reduction of average delay compared to conventional 5G-DRX.