Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Superhalogen-Based Li-Rich Anti-Perovskite Superionic Conductors

Solid-state batteries are being widely explored to meet next-generation energy storage demand with a great potentiality of achieving high energy and power densities at All-solidstate Lithium-ion batteries (LIBs). In recent years, electronically inverted lithium-rich antiperovskite (LiRAP) solid electrolytes with the formula Li3OX, where X is a halogen or mixture of halogens have appeared as a prospective alternative of the commercially available flammable and corrosive organic liquid electrolytes because of their high ionic conductivity, structural variety, and wide electrochemical window. Here, For the first time, we have successfully formulated and synthesized a completely new class of super halogen based double anti-perovskite named Li6OS(BH4)2 using thin film methodology. As the earliest step, using density functional theory (DFT), the formation energy approach has been employed to determine the thermodynamically stability of Li6OS(BH4)2. The objective of this research is to find stable solid electrolyte that would be compatible for new lithium-ion battery. Experimental characterization supported the theoretical prediction that super halogen substitution of X (X= Cl, Br etc.) leads to stabilization of the double anti-perovskite structure with a low activation barrier for Li+ diffusion

The Satisfaction of Bangladeshi Pilgrims: Service gaps in spiritual tourism based on gender and expenditure

This study sheds light on the satisfaction of pilgrims and service gaps of tour operators. Data were gathered from 236 Bangladeshi pilgrims in the Kingdom of Saudi Arabia at Mecca and Madina and in Bangladesh in 2019. The results reveal that 94.9% of the tourists were satisfied with air services, followed by food, accommodation, Hajj- training, sightseeing, stone-throwing, Arafa, Mujdalifa, Meena and transportation services (75.8%, 61.9%, 56.8%, 54.3%, 54.3%, 53.4%, 52.5%, 51.3%, and 43.2% respectively. Under the Mann-Whitney U test, pilgrims’ perceptions of tour operators’ services significantly differed based on gender and expenditure of respondents. The results show that satisfaction with accommodation, food, Meena, Arafa, Mujdalifa, Hajj training, sightseeing, stonethrowing, and transportation services significantly varied. This suggests that tour operators need to offer the expected services to the spiritual tourists and minimise the service gaps. The entire hajj journey is full of rituals that require specific rules and Shari’ah knowledge to perform. The pilgrims need profound knowledge, but, they lack proper religious learning. Therefore, tour operators must appoint an Islamic scholar who needs to guide the pilgrims before and during the Hajj journey so that the pilgrims perform each ritual accurately. Spiritual tour operators have to attend to the tourists to make improve their satisfaction as the pilgrims are not simple tourists, rather, they are spiritual tourists, having a good relationship with their Creator, Allah the Almighty

Cryptocurrencies’ Prices Discovery Through Machine Learning Algorithms : Bitcoin and Beyond

The evolution of cryptocurrencies has emerged as a fundamental shift in the financial landscape, with price discovery being an area of intense interest and complexity. The thesis titled “Cryptocurrencies’ price discovery through machine learning algorithms: Bitcoin and beyond” aims to investigate and unravel this complexity through the lens of machine learning. In this comprehensive study, four major machine learning algorithms - Logistic Regression (LR), Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to forecast the daily prices of four leading cryptocurrencies: Bitcoin, Ethereum, Cardano, and Solana, alongside an analysis of hourly Bitcoin price prediction. The findings reveal distinct performance characteristics for each algorithm. Logistic Regression exhibited high accuracies for Bitcoin and Ethereum daily predictions at 0.86 and 0.85, respectively. Support Vector Machine proved particularly effective for Cardano and Solana with accuracies of 0.90 and 0.97. Conversely, the Decision Tree and RF algorithms demonstrated more modest performance across the examined cryptocurrencies. Besides, a specialized investigation into Bitcoin’s hourly price prediction, employing the same set of algorithms, yielded varying results, with LR showing a standout accuracy of 0.98. This research encompasses a journey from the foundational principles of cryptocurrency to the advanced techniques of machine learning, highlighting both the opportunities and challenges inherent in this rapidly evolving field. It acts as a roadmap for future investigations, offering the potential to deepen our understanding of cryptocurrencies’ impact on the global financial landscape and to extend the boundaries of knowledge in the area of price discovery through machine learning

CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets

Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relies on the input data. In this research, we propose a novel filtering algorithm called Cosine Similarity-based Image Filtering (CosSIF). We leverage CosSIF to develop two distinct filtering methods: Filtering Before GAN Training (FBGT) and Filtering After GAN Training (FAGT). FBGT involves the removal of real images that exhibit similarities to images of other classes before utilizing them as the training dataset for a GAN. On the other hand, FAGT focuses on eliminating synthetic images with less discriminative features compared to real images used for training the GAN. Experimental results reveal that employing either the FAGT or FBGT method with modern transformer and convolutional-based networks leads to substantial performance gains in various evaluation metrics. FAGT implementation on the ISIC-2016 dataset surpasses the baseline method in terms of sensitivity by 1.59% and AUC by 1.88%. Furthermore, for the HAM10000 dataset, applying FABT outperforms the baseline approach in terms of recall by 13.75%, and with the sole implementation of FAGT, achieves a maximum accuracy of 94.44%.Comment: 18 pages, 20 figure

Grid impact of wind energy on isolated and remote power system

In this thesis, the impact of wind energy on isolated wind-diesel hybrid grid system and on remote grid connected wind farm were studied. In the case of the wind-diesel system a detailed mathematical model of Cartwright grid system is presented. The impact of a grid connected wind farm near St. Anthony, Newfoundland was also studied. Initially a pre-feasibility study was conducted to size the wind farm depending on wind data and local load data. In both cases voltage fluctuation and frequency variation in the grid power system due to the addition of wind energy were studied. Optimization software tool HOMER was used for pre-feasibility study for wind farm in St. Anthony. Detailed dynamic models of all system components and system parameters are provided in the thesis. MATLAB-S1MULINK was used for both cases for the dynamic modeling and system analysis. Finally, results are presented in both cases and future studies are proposed

PRIKAZ DOMAĆIH I STRANIH KULTIVARA LUKA (Allium cepa L.) ZA POTENCIJAL PROIZVODNJE SJEMENA

An experiment was conducted aiming to find out the seed production potentiality of 19 local and exotic onion cultivars. The analysis of variance showed significant differences among the genotypes for all characters except sprouting percentage, number of flowers per umbel and number of umbel per bulb. Maximum number of days to 50% bolting (52.67) was exhibited by the genotype G2 and minimum by G6 (27.00 days). The larger bulb size after harvest was obtained from G14 and G19 (18.11 g). Genotypes G4 and G11 required the maximum (16.66) and the minimum (9.00) days for 100% sprouting, respectively. The highest stalk length was found in the genotype G1 (67.23 cm) and the lowest in G8 (38.47 cm). Maximum number (5.75) of stalk was produced by the genotype G7 and minimum number (2.09) of stalk by the genotype G11. The genotype G1 produced the highest number of seeds per umbel (1395.92) and seed yield per plant (4.29 g). The lowest (0.45 g) seed yield per plant and maximum bulb weight was obtained by the genotype G8.Istraživanje je provedeno s ciljem utvrđivanja potencijala sjemenske proizvodnje 19 domaćih i stranih kultivara luka. Analiza varijance pokazala je signifikantne razlike između genotipova za sva svojstva, osim postotka naklijavanja, broja cvjetova po štitastom cvatu i broja štitastog cvata po lukovici. Maksimalan broj dana do 50% naklijavanja (52,67) je svojstvo genotipa G2, a minimalan (27,00 dana) je svojstvo genotipa G6. Nakon vađenja utvrđena je veća lukovica kod genotipova 14 i 19 (18,11 g). Genotipovi G4 i G11 trebali su maksimalno 16,66 dana, odnosno minimalno 9,00 dana za 100% naklijavanja, redosljedom. Genotip G1 imao je najdulju (67,23 cm), a G8 najkraću (38,47 cm) stabljiku. Maksimalan broj stabljika (5,75) ustanovljen je kod genotipa G7, a minimalan (2,09) kod genotipa G11. Ustanovljeno je da G1 ima najveći broj sjemena po štitastom cvatu (1395,92) i prinosa sjemena po biljci (4,29 g). Najniži prinos sjemena (0,45 g), kao i najteža lukovica po biljci dobiveni su od genotipa G8

Sustainable business development and its challenges : a study on Scandic hotels

This study analyses the housekeeping service performance of two Scandic hotels and perceptions of the hotel guests about the towel re-use and food waste issues, which make both environmental and business sense. The cleaning service in the hotel rooms is analyzed by the direct observation of 50 hotel rooms contributing to guests’ satisfaction. The present study has followed qualitative research approach to perceive the service gaps of the housekeeping employees and perceived gaps of the guests as to sustainability concerned with the towel re-use and food waste issues. Notably, the study presents that hotels face the challenges of offering standard and constant housekeeping service. Furthermore, factors contributing to guests’ satisfaction on the cleaning service were tested using the Wilcoxon test. In addition, guests’ perceptions regarding the cleaning service and food waste were tested. The relevant data were collected through the questionnaire survey, observation and interview methods. The complete sample data in the questionnaire consist of 132 observations, 130 were used in the analysis. The results of the study view that satisfaction of the guests on the cleaning service differed due to the service gaps of the housekeeping personnel. Additionally, perceived gaps of the guests and their demographics made a variation on their satisfaction. Besides, the study identified guests’ satisfaction as an important factor for the hotel sustainability. Moreover, towel re-use is an important issue since the guests perceive the economic aspect along with its environmental effect. Although hotels use small plates and communication message to reduce food waste, the guests wasted food due to the poor food taste. Habitual facts of the guests were also the causes of the food waste. Food waste and towel re-use are the concerns for the hotels. However, the hotel guests perceived these two issues differently. They perceived the impact of towel re-use on the environment more important than food waste. Increasing guests’ attention as to the towel re-use and food waste issues seems to be a challenge

Industry-Level Disparities in Antitrust Enforcement

Purpose- The purpose of this study is to analyze whether an increase in the concentration of industry causes an increase in the level of the Department of Justice Antitrust Division (DoJ)’s antitrust enforcement within that industry. Design/Methodology- The study employed secondary data and quantitative research method was also utilized to achieve the objectives of the study. Multiple regression analysis techniques were used to analyze the data. Findings- The results support the hypothesis that an increase in the concentration of industry causes an increase in the level of Department of Justice Antitrust Division (DoJ)’s antitrust enforcement within that industry. It appears that industry-level revenue from exports is highly correlated with the size of that industry and its lobbying activity. Practical Implications- These results have practical relevance which helps to predict the intensity of antitrust activity in future years. Its practical implication is that there are disparities in antitrust enforcement that are influenced by factors other than concentration. By creating a benchmark that takes into account components such as this, the Department of Justice Antitrust Division (DoJ) can identify those companies who are likely to be engaging in anticompetitive behavior