M. MarsalineBeno

Bio: M. MarsalineBeno is an academic researcher. The author has contributed to research in topics: Renewable energy & Energy source. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Experimental investigation and comparative harmonic optimization of AMLI incorporate modified genetic algorithm using for power quality improvement

Abstract: A super-lift mechanism has made tremendous progress in DC/DC conversion technology. In comparison to the asymmetrical form of MLI, the novel Asymmetric Multilevel Inverter (AMLI) technology proposes a minimized number of components. The Fuzzy-PI (Proportional integral) and Modified Genetic Algorithm (MGA) utilizes to minimize the harmonic content considerably using a variety of modulation index and firing angle values in open-loop and closed-loop control. This architecture for designing single-phase 7-level AMLI with an intelligent algorithm proposed for Renewable Energy (RE) applications. This circuit uses a single MOSFET switch with less switching stress and a single DC source. The effectiveness of the proposed MGA optimization eliminates the lower-order harmonics. MGA and Fuzzy-PI based Distributed Power Flow Intelligent Control (DPFIC) algorithms are applied with multilevel structures while maintaining the fundamental frequency for both MATLAB platform and hardware implementation. During this analysis, the losses is also find to investigate the influence of modulation index and output power factor on inverter efficiency. Simulations and experimental findings confirm the proposed inverter capacity to create high-quality multilayer output voltage. However, the proposed closed loop simulation circuit gives 0.47% minimum THD level, and 10.4% in experimental results.

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Abstract: Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.

A Review of Control Techniques in Photovoltaic Systems

Abstract: Complex control structures are required for the operation of photovoltaic electrical energy systems. In this paper, a general review of the controllers used for photovoltaic systems is presented. This review is based on the most recent papers presented in the literature. The control architectures considered are complex hybrid systems that combine classical and modern techniques, such as artificial intelligence and statistical models. The main contribution of this paper is the synthesis of a generalized control structure and the identification of the latest trends. The main findings are summarized in the development of increasingly robust controllers for operation with improved efficiency, power quality, stability, safety, and economics.

Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review

Abstract: Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable generation, keeping energy production. In this work, an analysis of the most common faults that appear in wind and photovoltaic generation systems is presented. Moreover, the main techniques and strategies developed for the identification of such faults are discussed in order to address the advantages, drawbacks, and trends in the field of detection and classification of specific and combined faults. Due to the role played by wind and photovoltaic generation, this work aims to serve as a guide to properly select a monitoring strategy for a more reliable and efficient power grid. Additionally, this work will propose some prospective with views toward the existing areas of opportunity, e.g., system improvements, lacks in the fault detection, and tendency techniques that could be useful in solving them.