Power Transfer Capability Recognition in Deregulated System under Line Outage Condition Using Power World Simulator

The major challenges in deregulated system are determination of available transfer capability on the interconnected transmission lines. Electricity industry deregulation is the required for creating a competitive market throughout the world, which instigate new technical issues to market participants and Power System Operators (PSO). Power transfer capability is a crucial parameter to decide the power flow in the lines for further transactions and the estimation of Transfer Capability decides the power transactions based on the safety and ability of the system. This parameter will decide if an interconnected network could be reliable for the transfer of bulk power between two different areas of the network without causing risk to system consistency. The Power Transfer Distribution Factor (PTDF) is the sensitivity index, which decides the transfer capability in the interconnected network under deregulated power systems. This experiment is conducted on IEEE-6 bus system using Power World Simulator to determine the transfer capability in deregulated system under line outage condition.

Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model

Modelling systems are a new sort of electrical network that can be easily adapted. Dispersed generators are linked to a microgrid using voltage source inverters. Nonlinear modelling systems are used in this study to create an inverter voltage mode controller for power systems to control power supply volatility. Controller for a nonlinear inverter that operates in voltage control mode is proposed. The primary goal is to ensure that the output voltage of the system matches a predetermined standard. Once the system model is completed, the controller is constructed using the backstepping method. After the control law is developed, several simulations are run to test the proposed controller's performance. According to simulation findings and formal analysis, the output voltage matches the reference voltage with global asymptotic stability. The accomplishment of this work is that the controller built, works in both grid-connected and inverter voltage modes of microgrid operation.

Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure

It is possible to transmit electricity wirelessly without the need for cables. Wireless power transmission makes it possible to link remote places that would otherwise be cut off from access to reliable electricity. A wireless connection to the power supply is expected in the future. This study describes the experimental results of Wireless Power Transfer (WPT) utilizing a transformer coupling approach and its future potential. This WPT device (WPTD) is used to transmit power using two procedures of energy transfer: radiofrequency coupling and transformer coupling, both of which are magnetic based, in principle. The distance between the transmitter and receiver of the system affects the amount of power that can be sent. Research is performed to establish how far apart the system's transmitter and receiver should be. Magnetic fields may transmit energy between two coils, but the distance between the two coils must be too close for this approach to work. Aside from that, it assesses the setting parameter of a value that has been tabulated using a certain application, in the findings and discussion parts.

A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions

The paper aims at the incompatibility between the speed and stability of the traditional MPPT algorithm and the imprecise search of the fuzzy control algorithm. An improved photovoltaic adaptive fuzzy control MPPT algorithm is proposed in this thesis. The solar irradiance changes dramatically and hence four kinds of fuzzy control algorithms with different input are modeled and simulated. The results indicate that the proposed fuzzy control algorithm using slope and slope change rate of P-U curve as input is the best. On this basis, dP/dU and duty cycle D(n-1) at n-1 moment are used as input to improve the tracking speed and optimal range. At the same time using shrinkage factor 1/I*|dP/dU| real-time adjustment of D(n-1) further shortens the optimal time of the algorithm. The algorithm is simulated and applied in a block. Simulation results show that the proposed algorithm is superior to the fuzzy control algorithm in steady-state oscillation rate, tracking speed and efficiency, and the algorithm is simple and easy to implement.

Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder

Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.

Intelligent Automation System for Smart Grid Renewable Energy Generation on Climatic Changes

Nature oriented power generation systems are considered as renewable energy sources. Renewable energy generations are safe to the environment and nature, in terms of minimal radiation and pollution. The space requirement, operational and maintenance cost of renewable energy generation stations are also comparatively lesser than the conventional generating stations. The new form of micro grid energy stations of 230Volt supply attract the small commercial users and the domestic users. The smart grid energy generation is widely employed in the place where the conventional energy supply is not available. Due to its simple construction process, the smart grid renewable energy stations are employed on certain national highways as charging stations for electric vehicles and as a maintenance centre. The motive of the proposed work is to alert the smart grid system with an intelligent algorithm for making an efficient energy generation process on various climatic changes. This reduces the energy wastage in the primary smart grid station and makes the system more reliable on all conditions. The performance of the proposed approach is compared with a traditional smart grid system which yielded a satisfactory outcome.

Construction of Hybrid Model for English News Headline Sarcasm Detection by Word Embedding Technique

People often use sarcasm to taunt, anger, or amuse one another. Scathing undertones can't be missed, even when using a simple sentiment analysis tool. Sarcasm may be detected using a variety of machine learning techniques, including rule-based approaches, statistical approaches, and classifiers. Since English is a widely used language on the internet, most of these terms were created to help people recognize sarcasm in written material. Convolutional Neural Networks (CNNs) are used to extract features, and Naive Bayes (NBs) are trained and evaluated on those features using a probability function. This suggested approach gives a more accurate forecast of sarcasm detection based on probability prediction. This hybrid machine learning technique is evaluated according to the stretching component in frequency inverse domain, the cluster of the words and word vectors with embedding. Based on the findings, the proposed model surpasses many advanced algorithms for sarcasm detection, including accuracy, recall, and F1 scores. It is possible to identify sarcasm in a multi-domain dataset using the suggested model, which is accurate and resilient.

An efficient Capacitor Bank Operating System for Single Phase Power Factor Correction using Neural Network Estimations

Wastage of electricity occurs in all places starting from a small house electrical loading to a heavy industrial electrical loading. KiloVolt-Ampere Reactive (KVAR) power metering devices are employed in industrial applications for measuring the energy utilization which measure the energy wastage along with it. This urges a consumer to pay for the unutilized or wasted energy as well. To avoid this, certain capacitor bank units are connected to the industrial application motor units. The right choice of capacitor rating are helpful in minimizing the wasted power observation in the KVAR meters. The selection of capacitor rating is analysed with respect to the power factor calculation. The power factor is a derivation of working power to the apparent power in an electrical system. An optimum power factor to be maintained in an electrical system is 1. The motive of the proposed work is to maintain the power factor by selecting an optimum capacitor bank on the operation of an electrical system at various load conditions. The requirement of capacitor bank values get changed with respect to the load given to an electrical system. A neural network based prediction model is employed in the work for estimating the right choice of capacitor bank. The efficiency of the proposed work is verified and found satisfied with a traditional capacitor bank operating system.