Showing papers by "Umashankar Subramaniam published in 2023"

A MIMO–ANFIS-Controlled Solar-Fuel-Cell-Based Switched Capacitor Z-Source Converter for an Off-Board EV Charger

Abstract: The efficiency of a nation’s progress is determined by a variety of factors; however, transportation plays a critical role in boosting progress because it facilitates trade and communication between countries. The majority of transportation is powered by fossil fuels such as gasoline or diesel, which will be depleted in less than 50 years. Another option is to operate transportation systems after replacing conventional vehicles with electric vehicles (EV). Powering these vehicles with green electricity contributes zero carbon emissions from production to the final product. Together with the controller, an efficient charger ensures that the entire system is reliable and stable. The current work focuses on charging an off-board EV from greener energy sources (both a fuel cell and PV array forming a micro-grid) based on their availability via an efficient converter controlled by an adaptive multi-objective controller. A novel multi-output-based adaptive neuro fuzzy inference system (ANFIS) controller for charging the off-board EV at a constant current and voltage for both line and load regulations is proposed, in the current work. A comparison study of grid partitioning and subtractive clustering was conducted in order to select an optimized algorithm for generating FIS. Novelty is achieved by ensuring closed-loop stability is the main aim of the work. The entire work was created with the MATLAB/Simulink software.

Comparative Review of Energy, Crude Oil, and Natural Gas for Exchange Markets in Nigeria, India and Bangladesh

Abstract: In 2021, there was a global energy crisis that affected different parts of the world. In most countries, energy heavily relies on natural gas, including Nigeria, India, and Bangladesh. Several studies have explored the differences in the energy of crude oil and natural gas. Nonetheless, little effort has been made toward exploring the exportation of energy for exchange markets in Nigeria, India, and Bangladesh. This paper primarily aims at comparatively exploring the energy of crude oil and natural gas for exchange markets in the aforementioned countries. The methodology used in this paper is qualitative content analysis (QCA) and a systematical literature review (SLR) which includes various sources such as journals, the core collection of the Web of Science (WOS), oil peer review resources, and library sources. The study systematically mapped out different bibliographic materials whereby the visualization of similarities (VOS) was used to explore exchange markets for energy, crude oil, and gas in Nigeria, India, and Bangladesh. The results of the analysis indicated that, in Africa, Nigeria is regarded as the largest producer of natural gas and crude oil, with an approximation of 1.2 million barrels per day. Concerning oil and gas reserves, the country is considered the 10th and 8th largest producer in the world, having 37 billion barrels and roughly 206 trillion cubic feet, respectively. Thus, the exportation of energy is considered a central pillar of the country’s economy. In addition, India is regarded as the second largest producer of crude oil with 4.972 million barrels per day, which is approximately 5.1% of the entire world’s capacity for refining crude oil. Similarly, at the global level, India is considered the topmost consumer of crude oil, accounting for 4.8% of the world’s consumption. In the context of Bangladesh, their gas reserves account for 39.4 trillion cubic feet, and they are considered to be 70% of the commercial energy supply in the country. In conclusion, the importance of energy, crude oil, and natural gas cannot be underestimated, specifically, for the exchange import markets in the current context of the aforementioned countries. It is, therefore, suggested that the governments of Nigeria, India, and Bangladesh should strengthen their national policies on energy in order to be responsive to the global energy crisis as well as boost the exchange market in the energy sector.

Avant-Garde Solar Plants with Artificial Intelligence and Moonlighting Capabilities as Smart Inverters in a Smart Grid

Abstract: Intelligent inverters have the capability to interact with the grid and supply supplemental services. Solar inverters designed for the future will have the ability to self-govern, self-adapt, self-secure, and self-heal themselves. Based on the available capacity, the ancillary service rendered by a solar inverter is referred to as moonlighting. Inverters that communicate with the grid but are autonomous can switch between the grid forming mode and the grid following control mode as well. Self-adaptive grid-interactive inverters can keep their dynamics stable with the assistance of adaptive controllers. Inverters that interact with the grid are also capable of self-adaptation Grid-interactive inverters may be vulnerable to hacking in situations in which they are forced to rely on their own self-security to determine whether malicious setpoints have been entered. To restate, an inverter can be referred to as a “smart inverter” when it is self-tolerant, self-healing, and provides ancillary services. The use of artificial intelligence in solar plants in addition to moon-lighting capabilities further paves the way for its flexibility in an environment containing a smart grid. This perspective paper presents the present as well as a more futuristic outlook of solar plants that utilize artificial intelligence while moonlighting advanced capabilities as smart inverters to form the core of a smart grid. For the first time, this perspective paper presents all the novel ancillary applications of a smart inverter while employing Artificial intelligence on smart inverters. The paper’s emphasis on the Artificial Intelligence associated with PV inverters further makes them smarter in addition to ancillary services.

Wireless Power Transfer in Electric Vehicles: A Review on Compensation Topologies, Coil Structures, and Safety Aspects

Abstract: The scarce availability of non-renewable sources and the staggering amount of pollution have inevitably provoked many countries to opt for renewable sources. Thence, invariably, more renewable energy-based applications are hoarded by market stakeholders. Compared to all spheres of renewable energy applications, a considerable part of the energy is pulled into transportation. Wireless power transfer techniques play a significant role in charging infrastructure, considering the current development and advancement in the automotive industry. It will promise to overcome the widely known drawbacks of wired charging in electric vehicles. The effectiveness of wireless charging depends on coil design, compensation techniques, and the airgap between the coils. However, coil misalignment, improper compensation topologies, and magnetic materials reduce the efficacy. We can improve efficacy by overcoming the problems mentioned above and optimizing charging distance, time, and battery size. This paper comprehensively discussed the various electric vehicle charging technologies in conjunction with common charging standards, a list of factors affecting the charging environment, and the significance of misalignment problems. Furthermore, this review paper has explored the suitable coil design structure and different compensation techniques for an efficient wireless charging network.

Life Cycle Cost Analysis of Pumping System through Machine Learning and Hidden Markov Model

Abstract: The pumping system is a critical component in various industries and consumes 20% of the world’s energy demand, with 25–50% of that energy used in industrial operations. The primary goal for users of pumping systems is to minimise maintenance costs and energy consumption. Life cycle cost (LCC) analysis is a valuable tool for achieving this goal while improving energy efficiency and minimising waste. This paper aims to compare the LCC of pumping systems in both healthy and faulty conditions at different flow rates, and to determine the best AI-based machine learning algorithm for minimising costs after fault detection. The novelty of this research is that it will evaluate the performance of different machine learning algorithms, such as the hybrid model support vector machine (SVM) and the hidden Markov model (HMM), based on prediction speed, training time, and accuracy rate. The results of the study indicate that the hybrid SVM-HMM model can predict faults in the early stages more effectively than other algorithms, leading to significant reductions in energy costs.