Kumar, Nishant

Each electric vehicle (EV) has a finite amount of energy storage capacity. The EV batteries need to be recharged after a certain amount of driving, and cities make good locations for charging facilities. However, finding a charging infrastructure can be difficult in isolated areas. Large countries including Canada, China, the US, India, Australia, Russia, and a few Arab states are proposing to install pillar top solar panels in remote places for emergency EV charging. This condition requires a specific charging adaptor to monitor the charging current, securely conclude the charging process, and extract the maximum power from the panel utilising MPPT (Maximum Power Point Tracking). This EV study proposes a sensor-based low-cost charging adapter. MPPT operation and battery charge monitoring should be completed using SIAFL-DO. Only one current sensor-based charging adapters are inexpensive and fast. Furthermore, the SIAFL-DO algorithm accurately executes MPPT and charging management thanks to its excellent condition estimation and decision-making capabilities. This work uses a hardware prototype to assess the adapter's compatibility with the SIAFL-DO algorithm. Additionally, comparison investigations using cutting-edge methods are carried out. The created technique is put to the test against the European Standard EN50530 in order to determine its applicability to the sector.

The dynamic environment conditions entail the need for the MPPT technique for the Solar Photovoltaic (SPV) System, which ensures optimal utilization of available solar energy. In this article, Rayleigh distribution-based Particle Swarm Optimization (RDPSO) method has been proposed for maximum power extraction from PV panels. The proposed approach has been developed to enhance the exploration and exploitation characteristics of PSO for the quick detection of GMPP with high efficacy. Teaser-Kaiser energy Operator (TKEO) has been used to mitigate redundancy in search space, improving convergence speed. In addition, a novel technique has been proposed to identify PV system dynamics caused by variations in irradiation level, shading conditions, and load fluctuations. In rooftop PV systems fluctuations in consumers' loads are frequent events. The initialization of the heuristic method owing to load variations creates additional stress on the system and impacts the overall performance and life span of the connected equipment. Therefore, the proposed method has the proficiency to respond quickly to load variations without re-initializing the heuristic method. The performance of the proposed method has been validated for different dynamic conditions. The outcomes proved that the presented method is having high convergence speed for different dynamic conditions along with minimal fluctuation in power during the search process.

This review article provides a qualitative description of health monitoring for the DC-DC converter using the digital twin concept. A Digital Twin is a virtual clone of a physical system that operates simultaneously with a physical DC-DC converter. It provides data for monitoring and controlling the converter by using measurement data from existing sensors in the hardware. A digital twin might be either model-based, data-driven, or a hybrid of both approaches. This work presents the creation, advancement, and experimental confirmation of a digital twin method for monitoring the health of the DC-DC converter. Parameter deterioration and Fault Diagnosis are the primary factors that determine the health of the DC-DC converter. Determining degradation characteristics and identifying faults in the physical system relies on analyzing data by comparing sensor measurements with data from the digital model. This presentation discusses and compares several methods for assessing the status of the DC-DC converter.

This article summarizes the state of digital twin-based health monitoring for DC-AC inverters. When used in conjunction with a real-life DC-AC inverter, a digital twin creates an exact replica of the system in question. It helps keep an eye on the converter and adjust its settings by analyzing readings from the hardware's current sensors. There are a variety of ways to create a digital twin; some are data-driven, while others rely on models. Developing and testing a digital twin approach to DC-AC inverter health monitoring is detailed in this paper. There are two main ways to tell whether a DC-AC inverter is healthy: parameter degradation and fault diagnosis. Data analysis involves comparing sensor readings with data from the digital model in order to determine deterioration characteristics and find defects in the physical system. This presentation will go over several ways to check the DC-AC inverter's health.

France is one of the top-growing countries in the world and has a large amount of energy consumption. Nuclear energy is the major source of electricity production in France. Over the last two decades the contribution of renewable energy resources in electricity production has increased. Solar, wind and hydro are the major source of types of renewable electrical energy. The exponential growth of solar and wind energy over the past decade, contribution of hydroenergy in electricty production and other form renewable electrical energy like bioenergy, tidal energy and geothermal energy are discussed here. Statistical data of power generation capacity and electrical energy consumption, and description of some power plants are presented.

Threat detection systems play a pivotal role in safeguarding individuals and nations against various risks. In this paper, we present a novel approach to detect threats using speech input, where spoken content is analyzed for potential threats to individuals or national security. Our system leverages the Google API for speech-to-text conversion, enabling seamless integration of spoken input into the threat detection pipeline. The converted text undergoes preprocessing using advanced Natural Language Processing (NLP) techniques to transform it into numerical vectors, which capture semantic and contextual information essential for threat analysis. We then employ a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model trained on a diverse dataset comprising numerous speech samples containing threats. The LSTM-RNN model effectively learns the temporal dependencies within the speech data, enabling it to accurately predict and classify potential threats. Our test findings indicate how well the suggested method works in real-time danger identification from speech input, highlighting its potential uses in law enforcement, security, and other fields.

Multistart techniques are very useful in solving global optimization problems. Different algorithms and tools have evolved from the multistart approach to find optimal solutions to non-linear programming (NLP) problems. A modified version of the global search (MGS) toolbox is proposed here. MGS is applied to optimally reduce the capacitance of the transmission line. The algorithm is tested with three-phase double-circuit transmission lines with different voltage ratings, like 66 k V, 110 k V, etc.

This paper introduce a simple electric fence energizer modelling in MATLAB, currently most research is lined with the modelling, simulation, and its implementation using a conventional method i.e., by employing a battery backup system or online power system, but no one has so far established the use of renewable energy and battery back up as a simultaneous source of power for the operation of the electric fence energizer, by using electric energy harvested through small wind turbine, it is show that how battery can charged and simultaneously provide power to the electric fence energizer. The design and topology for the Control circuit at the interference of battery connection, small wind turbine - power transfer topology is being carried out. Standards for electric fence energizer and high voltage pulse application areas are being discussed.

High-frequency unipolar High Voltage (HV) pulses find diverse applications, serving as a vigilant shield for safe-guarding a range of sites from buildings and livestock enclosures to vast agricultural expanses and international borders. Developing a Power Energizer circuit (PEC) holds great importance for a wide range of protective applications. This is powered by solar PV that is connected to a DC-DC boost converter, also has a battery backup that is connected to a bi-directional DC-DC boost converter. The performance of the PEC is proposed under various solar irradiance conditions and also under the safe limits of the energizer provided by IEC 60335-2-76:2002 standard [1]. The key feature distinguishing the PEC is its capability to generate HVP, exceeding 15 kV in magnitude, sustained for a microsecond. This is achieved while maintaining a continuous supply of nearly 48 volts for the input DC source, with the discharged pulse conforming to power energizer standards, yielding an energy level of approximately 5 joules.

Though battery-less solar-plant integrated ultra-fast charging station (EV-UFCS) solutions are theoretically preferred, there is no existing control method that simultaneously ensures solar-plant's MPPT and maximum charging energy delivery to plug-in EVs (PEVs), while adhering to instantaneous grid-side power-ramp-rate and each PEV's charging current limits. A novel coordinated-control technique is proposed in this paper to meet these critical objectives, while being constrained by the instantaneous grid-side and PEV-side limits. The theoretical modelling and an implementable algorithm for the proposed control technique are elaborately explained. Experimental validation of the proposed technique is executed on a laboratory-scale 18 kVA solid-state-transformer (SST)-based solar-aided universal EV-UFCS testbed. The experimental results clearly demonstrate that (i) solar MPPT is achieved with >99% accuracy, (ii) 100% of maximum charging energies are delivered to all categories of connected PEVs, and (iii) instantaneous grid power-ramp-rate and PEV-BMS constraints are strictly adhered to, which highlight the proposed coordinated-control technique's advantages.