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For making the grid to be more intelligent, a unique design is required in this changing sphere. A better-automated grid is more important for controlling the variations in the entire system. This is possible by introducing secondary controllers where both power and voltage are shifted at necessary stages. Therefore, in this article, a Neural Fizzy Identification System with Partial Integral Controller (NFISPID) is introduced. Also, a secondary controller named Selective Voltage Pulse Width Modulation with Hybrid Artificial Bee Colony-Particle Swarm Optimization technique is incorporated for handling all false situations in the grid. The efficiency of the proposed system is checked with MATLAB environment where the results prove to be much efficient in terms of all parametric values, and the grid becomes stable within a short span of time than other existing controllers. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

The run-on flat tire is an important technology in the area of vehicle safety technology. Nowadays, traveling by a personal vehicle from one place to another has increased due to the increase in the global economy. The main problem that is faced while traveling is tire puncture. The tire may get punctured by any sharp objects on the road such as screws and iron pins that traps the tire surface. A situation like this may be overcome by the usage of the drive on a puncher tire device which will be used to reach the destination without any need for a puncher shop. Using this device will reduce the damage of the tire and tube. This device is portable and can be placed in a vehicle itself. For this reason, a design has been developed by the CATIA 3D experience. The drive on a puncher tire device was designed using the software developed by the Dassault systems named CATIA 3D experience. Aluminum alloy was chosen as the base material to design and simulate this product. Also, the network distribution simulations were used to develop the mathematical equations to check the adequacy of the model. This device is very useful while traveling long distances if any puncher will occur. The sensors were attached to the product to receive the signal from the vehicle, so that the vehicle will run smoothly for a short distance. Drive on a puncher tire will be suitable for both two- and four-wheeler vehicles and also useful for all the areas like villages, towns, panchayats, and cities. If the tire is puncher, we cannot drive the vehicle; hence, a skating device is fixed at a tired bottom, which is used to move the vehicle. The proposal will be beneficial for a huge number of people who own two and four wheelers. This design and simulation will address the problem of the two- and four-wheeler owners traveling long distances., Author(s): V Sivamaran [1]; Hariprasath Manoharan [2]; Yuvaraja Teekaraman (corresponding author) [3]; Ramya Kuppusamy [4]; Arun Radhakrishnan (corresponding author) [5] 1. Introduction The safety of the automobile vehicles was highly [...]

Unmanned aerial vehicles (UAVs) also called as a drone comprises of a controller from the base station along with a communications system with the UAV. The UAV plane can be precisely controlled by a machine operator, similar to remotely directed aircraft, or with increasing grades of autonomy, as like autopilot assistance, up to completely self-directed aircraft that require no human input. Obstacle detection and avoidance is important for UAVs, particularly lightweight micro aerial vehicles, but it is a difficult problem to solve because pay load restrictions limit the number of sensors that can be mounted onto the vehicle. Lidar uses Laser for finding the distance between objects and vehicle. The speed and direction of the moving objects are detected and tracked with the help of radar. When many sensors are deployed, both thermal and electro-optro cameras have great clustering capabilities as well as accurate localization and ranging. The purpose of the proposed architecture is to create a fusion system that is cost-effective, lightweight, modular, and robust as well. Also, for tiny object detection, we recommend a novel Perceptual Generative Adversarial Network method that bridges the representation gap between small and large objects. It employs the Generative Adversarial Networks (GAN) algorithm, which iimproves object detection accuracy above benchmark models at the same time maintaining real-time efficiency in an embedded computer for UAVs. Its generator, in particular, learns to turn unsatisfactory tiny object representations into super-resolved items that are similar to large objects to deceive a rival discriminator. At the same time, its discriminator contests with the generator to classify the engendered representation, imposing a perceptual restriction on the generator: created representations of tiny objects must be helpful for detection. With three different obstacles, we were able to successfully identify and determine the magnitude of the barriers in the first trial. The accuracy of proposed models is 83.65% and recall is 81% which is higher than the existing models., Author(s): Nageswara Guptha M [1]; Y. K. Guruprasad [1]; Yuvaraja Teekaraman (corresponding author) [2]; Ramya Kuppusamy [3]; Amruth Ramesh Thelkar (corresponding author) [4] 1. Introduction Object identification, as a central [...]

Given the various aspects of climate change and the growing demand for energy, energy efficiency and environmental protection have become major concerns worldwide. If not taken care of, energy demand will become unmanageable due to technological growth in cities and nations. The solution to the global energy crisis could be an advanced two-way digital power flow system that is capable of self-healing, interoperability, and predicting conditions under various uncertainties and is equipped with cyber protections against malicious attacks. The smart grid enables the integration of renewable energy sources such as solar, wind, and energy storage into the grid. Therefore, the perception of the smart grid and the weight given to it by researchers and policymakers are of utmost importance. In this paper, the studies of many researchers on smart grids are examined in detail. Based on the literature review, various principles of smart grids, the development of smart grids, functionality of smart grids, technologies of smart grids with their characteristics, communication of smart grids, problems in the implementation of smart grids, and possible future studies proposed by various researchers have been presented. [ABSTRACT FROM AUTHOR]

This research study examines the integration of wireless system and solar cells for detection and location of faults in power grids by implementing minimum number of Phasor Measurement Units (PMUs) after considering zero injection buses. A fault in the grid networks indicates a failure in the entire network that interfaces the normal flow of current which can be measured using wireless systems. The fault location in power grid is problematic because of high existence in several lateral tap-offs. In addition, the identification of exact location with presence of faults in the power system and severity of the faults are challenging tasks due to bulk and complex nature of the electrical power system state estimation. The major requirement of PMU placement approach is to find the minimum number of PMUs and their location with maximum wireless coverage under fault conditions with a prerequisite for the projected algorithm. The integrated algorithm is tested and validated for different fault conditions where the results are compared with different conventional methods using different optimization models such as heuristics, decision rules and mathematical programming where the outcomes proves to be much effective in terms of number of PMU installation for proper placements and detection, location in number of fault systems thus achieving 82% reliability using Ant Lion Optimizer (ALO). [ABSTRACT FROM AUTHOR]

Asynchronous machines are predominantly preferred in industrial sectors for its reliability. Power quality perturbations have a greater impact on industries; among the different power quality events, voltage fluctuations are the most common and that may cause adverse effect on machine’s operation since they are longer enduring. The article discusses a numerical technique for evaluating asynchronous motors while taking into account magnetic saturation, losses, leakage flux, and voltage drop. A 2D linear analysis involving a multi-slice time stepping finite element model is used to predict the end effects. As an outcome, the magnetic saturation and losses are estimated using a modified 2D nonlinear time-stepping finite element formulation. The method takes the electromagnetic fields at the ends of the motor into account using limited computer resources. The proposed method will greatly reduce computation time with limited computer resources for analyzing the machine’s performance with high precision. The analyzed findings assist in preventing voltage variance issues in the power network system and provide suggestions for developing a robust system. [ABSTRACT FROM AUTHOR]

In the digital age, cybercrime is spreading its root widely. Internet evolution has turned out to a boon as well as curse for those confronting the issues of privacy, national security, social decency, IP rights, child protection, fighting, detecting, and prosecuting cybercrime. Hence, there arises a need to detect the cybercriminal. Cybercrime identification utilizes dataset that is taken from CBS open dataset. For identifying the cybercriminal, support vector machine (SVM) in the C SVM classification and K -nearest neighbor (KNN) models is utilized for determining the cybercrime information. The evaluation of the performance is done taking the following metrics into consideration: true positive, false positive, true negative and false negative, false alarm rate, detection rate, accuracy, recall, precision, specificity, sensitivity, classification rate, and Fowlkes-Mallows Scores. Expectation maximization (EM) calculation is utilized for evaluating the presentation of the Gaussian mixture model. The performance of classifier's presentation is also done. Accuracy is accomplished in the event of grouping by means of SVM classifier as 89% in the supervised method. [ABSTRACT FROM AUTHOR]