Search

Electricity demand is rising in lockstep with global population growth. The present power system, which is almost a century old, faces numerous issues in maintaining a steady supply of electricity from huge power plants to customers. To meet these issues, the electricity industry has enthusiastically embraced the new smart grid concept proposed by engineers. If we can provide a secure smart grid, this movement will be more useful and sustainable. Machine learning, which is a relatively recent era of information technology, has the potential to make smart grids extremely safe. This paper is a literature survey of the application of machine learning in different areas of smartgrids. This paper concludes by mentioning the best machine learning algorithms that can be used in different aspects of the smart grid.

Digital twins are becoming an integral part of mechanical systems in industries due to the several advantages that they can provide such as performance analysis, predictive maintenance, and real-time monitoring. However, to allow their usage by non-experts and integration with digital twins of other interacting systems, the model behind the digital twin should be simple, physics based, and yet accurate enough to serve its purpose. This article presents such a model for counterbalance valves (CBVs) which are commonly found in a variety of hydraulic systems. The model consists of parameters that account for the internal geometry, inertia, and friction characteristics of the CBV. To illustrate the parameter identification strategy for this model, a test set up is designed, and four sets of experiments are conducted. A subset of the measured data is used in a systematic identification of the parameters. The rest of the data is used to validate the CBV model and the parameter identification strategy. The results indicate that the model is successfully able to predict the behavior of the CBV across different operating conditions, thereby, validating the model. The proposed CBV model satisfies key requirements of digital twins and thus, is a suitable candidate for the digital twin of CBVs and can easily be integrated in the digital twins of larger systems where the CBVs are installed. Version of record

The purpose of the present study was to investigate the predictive contribution of attitude to life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation. The current research is fundamental in terms of its purpose and descriptive of correlation type in terms of method. The statistical population consists of all cancer patients of Shahinshahr city. The sample size was 150 people based on Cochran's formula and the country's corona situation. The sampling method of the current research is available. This research used tools of attitude to life, belief system, self-resilience, psychological toughness, and emotion regulation, all of which had acceptable reliability and validity. The research data was done in two parts, descriptive and inferential, with the help of Excel, SPSS, and SmartPLS software. The results showed that the attitude towards life and the belief system with the role of mediating emotion regulation has a significant predictive ability on the self-resilience of cancer patients. Also, attitude towards life and belief systems with the role of mediating emotion regulation can predict the psychological toughness of cancer patients. In addition, emotional regulation has a significant relationship with self-resilience and psychological toughness. According to the research results, it can be concluded that the attitude towards life and the belief system mediating emotion regulation can predict cancer patients' resilience and psychological toughness. Also, the attitude toward life and the belief system directly impacts. In addition, attitude towards life has a direct positive relationship with psychological toughness.

Breast cancer is the second leading cause of death after lung cancer. The only possible way to save patients' lives is early diagnosis of the disease; Because if this disease is diagnosed in the early stages and with a high level of accuracy, the chance of survival increases. Different fuzzy-based soft computing techniques have been proposed. In this research, the proposed fuzzy hybrid algorithm - particle swarm has been used to detect the type of breast tumors based on the analysis of features in mammography images. The proposed method in this study, the fuzzy hybrid algorithm - the proposed particle swarm algorithm, has a remarkable performance of 94.58% in breast cancer diagnosis. The results obtained from this study can be used for timely diagnosis and providing effective treatments for breast cancer

Fluid power: digital, reliable, sustainable : 13th International Fluid Power Conference : 13th-15th June 2022 : proceedings — Open Access version / Editor: Katharina Schmitz 13. International Fluid Power Conference, IFK 2022, Aachen, Germany, 13 Jun 2022 - 15 Jun 2022; Aachen : RWTH Aachen University 148-162 (2023)., Published by RWTH Aachen University, Aachen

This study proposes a data-driven methodology for modeling power and hydrogen generation of a sustainable energy converter. The wave and hydrogen production at different wave heights and wind speeds are predicted. Furthermore, this research emphasizes and encourages the possibility of extracting hydrogen from ocean waves. By using the extracted data from FLOW-3D software simulation and the experimental data from the special test in the ocean, the comparison analysis of two data-driven learning methods is conducted. The results show that the amount of hydrogen production is propor-tional to the amount of generated electrical power. The reliability of the proposed renewable energy converter is further discussed as a sustainable smart grid application.

Compared to electromechanical drives, hydraulic drives can be a good actuation alternative for exoskeleton devices due to their advantages in terms of high force density and capabilities of energy recuperation, motion damping and locking. However, to promote a wider adoption of hydraulically driven exoskeleton devices, key challenges related to the compact lightweight design and energy efficient operation and control are needed to be addressed. To tackle these challenges, the authors have recently developed a novel design of digital hydraulically driven knee exoskeleton. In this work, the authors address the control challenges during the operation of the knee exoskeleton over multiple gait cycles. Proper control methods for different phases of the gait cycle are adopted. Furthermore, these methods are augmented with strategies to accomplish knee motion repeatability across gait cycles. These strategies involve steps to ensure that at the beginning of every gait cycle: (a) the knee angle returns to the same position, and (b) the pressure in the hydraulic chambers return to the same levels. These control strategies are investigated via a numerical model of the knee exoskeleton. The simulation results indicate that the device is able to repetitively track the motion of the knee over multiple gait cycles while delivering the required torque.

The Convolutional Neural Network (CNN) methodologies have been a fundamental deep learning solution to smart grid applications. It is essential to investigate and evaluate the progress of this method in the smart grid. Consequently, a comprehensive investigation with the aid of PRISMA had been conducted. The PRISMA standard queries including the CNNs and its abbreviation forms of ConvNet or CNN reveal a significant increase in the popularity of this deep learning method in smart grid applications. This research identifies 2200 pieces of literature in the field. After considering the PRISMA guideline the most relevant and fundamental application had been reduced to 46 documents where the single and hybrid methods had been identified. The investigation showed that hybrid methods delivered a better performance with higher accuracy. It is expected that more hybrid methods will have emerged in the smart grid application.

The optimal dispatch of a microgrid is of great significance to reduce energy consumption, user’s electricity costs, and environmentalpollution. The microgrid models are not only able to meet the power requirements but also improve the system economically efficient andenvironmentally friendly. In this regard, this paper proposes a new off-grid microgrid model to meet the energy requirement of a small touristicplace in India. This paper proposed a time series analysis of a microgrid that helps to see how the given system’s environmental and economicvariable changes over time. The proposed model optimizes the value of the hybrid power system from utility-scale and distributed generationto standalone microgrid. The paper also discussed the market value of microgrid systems around the globe. The paper used the HOMER gridsoftware simulation tool to analyze the microgrid. It helps to determine the use of the system’s components for demand change reduction whileserving the electric loads. This paper also explains the SARIMA model for forecasting the economical behavior of the microgrid. For the analysisand prediction, only the economic factors are taken into consideration. The analysis compares the performance of the system and shows thatthe system is economically viable concerning the present grid system. To improve the time series analysis information and efficiency of theproposed model, this paper proposes the integration of SARIMA with the HOMER grid software solution. The results show that the effectivenessand superiority of the proposed model. This can reduce the electricity’s costs and pollution of the system.

The manuscript represents the state-of-the-artreview of the deep learning methods for smart grid applications.This paper reviews novel applications of deep learningalgorithms in smart grid. The deep learning based threealgorithms i.e., Long-Short Term Memory, recurrent neuralnetwork, convolution neural network found to be most useful insmart grid application. These algorithms are found to be mostuseful for forecasting, cyber-attack, anomaly detection andelectricity theft in smart grid. This paper briefly surveys themost usable deep learning algorithms for making the smart isresilience, accurate, and safe. The review result shows that thementioned deep learning algorithms give an excellent resultsover other deep learning algorithm. Therefore, these threealgorithms are widely acceptable for the evaluation of smartgrids.

Hydraulic actuation of exoskeletons has gained interest among researchers due the potentials of high power density and energy recuperation allowing the reduction of mass and space used by the device (when compared to the traditional electrically actuated exoskeletons). However, developing a light and cost-effective design of such exoskeleton remains a key challenge. In this work, a novel design of digitally driven knee exoskeleton is presented. The design uses simple hydraulic cylinders instead of multi-chamber cylinders (which are typically used in digital actuations and are expensive). The design also includes a unique mechanism that is able to satisfy the peak torque requirements during a typical gait cycle with a smaller hydraulic force. This ensures small size of hydraulic components and a moderate power demand from the energy source. To study this exoskeleton design, a numerical model of the exoskeleton and the lower limb is developed in this work. The simulation results show that the design is able to track the motion of the knee in a typical gait cycle as well as satisfy the necessary torque requirements.