Closed-Loop Control and Performance Evaluation of Reduced Part Count Multilevel Inverter Interfacing Grid-Connected PV System

Multilevel inverters (MLIs) have drawn a tremendous attention in power sector. Application of MLI has grown extensively to improve the power quality and efficiency of the photovoltaic (PV) system. For an MLI interfacing PV system, the size, cost and voltage stress are the key constraints of the MLI that need to be minimized. This paper presents a novel reduced part count MLI interfacing single-stage grid-tied PV system along with a closed-loop control strategy. The proposed MLI consists of n repeating units and a level boosting circuit (LBC) that assists to generate 4n+7 voltage levels instead of 2n+3 levels. Three different algorithms are proposed for suitable selection of dc-link voltages to further enhance the levels. Comparative analysis is carried out to confirm the superiority of developed MLI. The workability of the proposed MLI is investigated with a 1.3 kW PV system. The closed-loop control strategy ensures the maximum power tracking, dc-link voltage balancing, satisfactory operation of the MLI and injection of clean sinusoidal grid current under any dynamic changes. Comprehensive simulation analysis is carried out considering a 15-level MLI structure. The practicality of the topological advancement for PV system is further confirmed by experimental tests under different dynamic conditions.publishedVersio

Plasmid-Mediated Quinolone Resistance Genes and Antibiotic Residues in Wastewater and Soil Adjacent to Swine Feedlots: Potential Transfer to Agricultural Lands

Background: Inappropriate use of antibiotics in swine feed could cause accelerated emergence of antibiotic resistance genes, and agricultural application of swine waste could spread antibiotic resistance genes to the surrounding environment. Objectives: We investigated the distribution of plasmid-mediated quinolone resistance (PMQR) genes from swine feedlots and their surrounding environment. Methods: We used a culture-independent method to identify PMQR genes and estimate their levels in wastewater from seven swine feedlot operations and corresponding wastewater-irrigated farm fields. Concentrations of (fluoro)quinolones in wastewater and soil samples were determined by ultra-performance liquid chromatography–electrospray tandem mass spectrometry. Results: The predominant PMQR genes in both the wastewater and soil samples were qnrD, qepA, and oqxB, whereas qnrS and oqxA were present only in wastewater samples. Absolute concentrations of all PMQR genes combined ranged from 1.66 × 10(7) to 4.06 × 10(8) copies/mL in wastewater and 4.06 × 10(6) to 9.52 × 10(7) copies/g in soil. Concentrations of (fluoro)quinolones ranged from 4.57 to 321 ng/mL in wastewater and below detection limit to 23.4 ng/g in soil. Significant correlations were found between the relative abundance of PMQR genes and (fluoro)quinolone concentrations (r = 0.71, p = 0.005) and the relative abundance of PMQR genes in paired wastewater and agricultural soil samples (r = 0.91, p = 0.005). Conclusions: Swine feedlot wastewater may be a source of PMQR genes that could facilitate the spread of antibiotic resistance. To our knowledge, this is the first study to examine the occurrence of PMQR genes in animal husbandry environments using a culture-independent method

Short-term wind power forecasting using wavelet-based neural network

Wind power generation highly depends on the atmospheric variables which itself depend on the time of the day, months and seasons. The intermittency of wind hinders the accuracy of wind forecasting, which is important for safe operation and reliability of future power grid. One way to address this problem is to consider all these atmospheric variables which can be obtained from Numerical Weather Prediction (NWP) models. However, using NWP parameters increases the complexity of the forecast model and it requires a large amount of historic data. Additionally, different models are required for different seasons or months. This paper presents a wavelet-based neural network (WNN) forecast model which is robust enough to predict the wind power generation in short-term with significant accuracy, and this model is applicable to all seasons of the year. With reduced complexity, the model requires less historic data as compared to that in available literaturesRishabh Abhinava ,Naran M .Pindoriya, Jianzhong Wu and Chao Long