This paper focuses on the detection and classification of faults electrical power transmission line using artificial neural networks. The three phase currents voltages one end are taken as inputs in proposed scheme. feed forward network along with back propagation algorithm has been employed for fault analysis each phases involved process. A detailed varying number hidden layers performed to validate choice network. simulation results concluded that present method based is efficient...

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used IDS. a limited training dataset, an ML-based IDS generates higher false ratio encounters imbalance issues. To deal with data-imbalance issue, this...

In this proposed work a fuzzy logic based algorithm using discrete wavelet transform is developed for identifying the various faults in electrical distribution system an unbalanced power system. This technique capable to identify ten different types of with negligible effect variation fault inception angle, loading and other parameters The method tested on IEEE 13 bus Indian scenario current respective three phases used as input signal identification results obtained from are more than satisfactory.

The rising advancement of intrusion strategies has given the desperate imperative for designing and developing IDS with excellent efficiency. existing have been developed to utilize obsolete threat datasets, concentrating too much on accuracy rate less prediction. Machine learning potential deliver an efficient approach when it arrives at detection due high dimensionality eminent dynamic nature available data in such mechanisms. However, plenty health care either uses network performance...

A revolutionary technology well into the world of has been in modern Internet Things. Due to continuing increases as nothing more than a consequence either rapid development computing things-based applications implementations. Many technologies become increasingly embraced throughout compatible devices such home automation and also smart cities. These IoT operated on both Internet, whereby information becomes transported publicly between network next, therefore flowing requires great deal...

The maintenance of building facades has traditionally relied on manual labor, which is both timeconsuming and potentially hazardous. development automated systems for such tasks offers significant improvements in safety efficiency. This paper introduces an innovative Automated Facade Cleaning Robot that utilizes reinforcement learning to navigate clean vertical surfaces independently. Equipped with Arduino UNO microcontroller, ultrasonic sensors precise navigation, a L298N motor driver...

In the current era, a lot of research is being done in domain disease diagnosis using machine learning. recent times, one deadliest respiratory diseases, COVID-19, which causes serious damage to lungs has claimed lives globally. Machine learning-based systems can assist clinicians early disease, reduce deadly effects disease. For successful deployment these systems, hyperparameter-based optimization and feature selection are important issues. Motivated by above, this proposal, we design an...

The significant proliferation of renewable resources, primarily inverter interfaced distributed generation (IIDG) in the utility grid, leads to a dearth overall inertia. Subsequently, system illustrates more frequency nadir and steeper response. This may degrade dynamic stability system. Further, virtual inertia has been synthetically developed IIDG, which is known as synchronous generator (VSG). In this work, novel STO-STC-based controller developed, offers flexible following disturbance....

In the present paper, optimal synchronization of PSS and STATCOM based controllers has been carried out using differential evolution (DE) algorithm for enhancement in power system stability. The design issues are generally formulated as constrained parameters, simulation non-linear optimized problem. performance successfully tested on a single machine infinite bus (SMIB) well multi (MMPS). For system, simulations conceded Kundur's four-machine two-area transmission network. effectiveness is...

Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised with high classification and smaller trees, this study will pre-process the data. study, some tree components are addressed enhanced. The algorithms should produce precise ideal in increase performance. Additionally, it hopes create a algorithm tiny global footprint excellent forecast accuracy. typical tree-based technique was created purposes used various kinds...

Increasing penetration of inverter interfaced distributed generation (IIDG) in distribution networks has severely impacted protection coordination. False tripping, sympathetic blinding, failure reclosing, and increase short circuit levels are the critical complications consequently disturbed setting network. This paper presents a novel multiloop current control scheme based on single integrator backstepping with proportional resonant (PR) controller assisted by whale optimization algorithm...

In this study, impact of active and reactive power injected at the point common coupling (PCC) has been investigated under low voltage conditions. The prime objective work is to enhance FRT capability in solar inverters using super-twisting observer (STO). STO tracks all states system further adaptive assistance provided PCC. evaluates optimal reference a finite external disturbance, which fall This based on German grid code basic controller design fault characteristic thoroughly studied....

Modern society relies on networks, making cybersecurity a vital field of study. Intrusion detection systems (IDS) monitor network software and hardware for cyber security. Despite decades research, IDSs struggle to improve accuracy, reduce false alarms, detect novel assaults. Many academics have developed machine learning based address the above challenges. Machine can automatically accurately normal abnormal data. approaches also unknown assaults due their generalizability. A famous study...