Design of Solar System by Implementing ALO Optimized PID Based MPPT Controller

This paper is a strive approach to design offgrid solar system in association with DC-DC boost converter and MPPT. The tuned PID based MPPT technique is adopted to extract maximum power from the solar system under certain circumstances (temperature and irradiance). The design parameters of PID controller play an imperative aspect to enhance the performance of the system. Ant lion Optimizer (ALO) algorithm is adopted to optimize PID parameters to contribute relevant duty cycle for DC-DC boost converter to maximize output power and voltage. P and O based MPPT technique is implemented to validate the supremacy of PID based MPPT to enhance the response of the system. In this paper, the proposed ALO optimized PID controller based MPPT technique is performed better over conventional P & O technique by conceding the oscillation, time response, settling time and maximum values of voltage, current and power of the solar system.Citation: SAHU, R. K., and Shaw, B. (2018). Design of Solar System by Implementing ALO Optimized PID Based MPPT Controller. Trends in Renewable Energy, 4, 44-55. DOI: 10.17737/tre.2018.4.3.004

Isolation, identification and expression analysis of salt-induced genes in Suaeda maritima, a natural halophyte, using PCR-based suppression subtractive hybridization

<p>Abstract</p> <p>Background</p> <p>Despite wealth of information generated on salt tolerance mechanism, its basics still remain elusive. Thus, there is a need of continued effort to understand the salt tolerance mechanism using suitable biotechnological techniques and test plants (species) to enable development of salt tolerant cultivars of interest. Therefore, the present study was undertaken to generate information on salt stress responsive genes in a natural halophyte, <it>Suaeda maritima</it>, using PCR-based suppression subtractive hybridization (PCR-SSH) technique.</p> <p>Results</p> <p>Forward and reverse SSH cDNA libraries were constructed after exposing the young plants to 425 mM NaCl for 24 h. From the forward SSH cDNA library, 429 high quality ESTs were obtained. BLASTX search and TIGR assembler programme revealed overexpression of 167 unigenes comprising 89 singletons and 78 contigs with ESTs redundancy of 81.8%. Among the unigenes, 32.5% were found to be of special interest, indicating novel function of these genes with regard to salt tolerance. Literature search for the known unigenes revealed that only 17 of them were salt-inducible. A comparative analysis of the existing SSH cDNA libraries for NaCl stress in plants showed that only a few overexpressing unigenes were common in them. Moreover, the present study also showed increased expression of phosphoethanolamine N-methyltransferase gene, indicating the possible accumulation of a much studied osmoticum, glycinebetaine, in halophyte under salt stress. Functional categorization of the proteins as per the Munich database in general revealed that salt tolerance could be largely determined by the proteins involved in transcription, signal transduction, protein activity regulation and cell differentiation and organogenesis.</p> <p>Conclusion</p> <p>The study provided a clear indication of possible vital role of glycinebetaine in the salt tolerance process in <it>S. maritima</it>. However, the salt-induced expression of a large number of genes involved in a wide range of cellular functions was indicative of highly complex nature of the process as such. Most of the salt inducible genes, nonetheless, appeared to be species-specific. In light of the observations made, it is reasonable to emphasize that a comparative analysis of ESTs from SSH cDNA libraries generated systematically for a few halophytes with varying salt exposure time may clearly identify the key salt tolerance determinant genes to a minimum number, highly desirable for any genetic manipulation adventure.</p

Self-adaptive fuzzy-PID controller for AGC study in deregulated Power System

The aim of this paper elucidates the AGC issues in a large scale interconnected power system incorporating HVDC link under the deregulated environment. The performance of the system is degraded under the influence of abrupt load change, and parameter variation. To perceive a reliable and quality power supply, secondary robust controllers are essential. A novel self-adaptive Fuzzy-PID controller is proposed to ameliorate the dynamic performance of both the conventional PID and Fuzzy-PID controller, employed in the restructured power system. In self-adaptive Fuzzy-PID controller unlike the Fuzzy-PID controller, the output scaling factors are tuned dynamically while the controller is functioning. These three controllers are designed by enumerating different gains and scaling factors, applying a budding nature-inspired algorithm known as Wild Goat Algorithm (WGA). The superior dynamic performance of frequency and tie-line power deviation under self-adaptive Fuzzy-PID controller in comparison to its' counterparts is investigated by dispatching the scheduled and unscheduled power under different contracts such as poolco based transaction, bilateral transaction and contract violation based transaction through different tie-lines. The dynamic response under parameter variation and random load perturbation confers the robustness of the proposed controller

Selfish Herd Optimisation based fractional order cascaded controllers for AGC study

In a modern, and complex power system (PS), robust controller is obligatory to regulate the frequency under uncertain load/parameter change of the system. In addition to this, presence of nonlinearities, load frequency control (LFC) of a Power System becomes more challenging which necessitates a suitable, and robust controller. Single stage controller does not perform immensely against aforesaid changed conditions. So, a novel non-integer/fractional order (FO) based two-stage controller incorporated with 2-degrees of freedom (2-DOF), derivative filter (N), named as 2-DOF-FOPIDN-FOPDN controller, is adopted to improve the dynamic performance of a 3-area power system. Each area of the power system consists of both non-renewable and renewable generating units. Again, to support the superior performance of 2-DOF-FOPIDN-FOPDN controller, it is compared with the result produced by PID, FOPID, and 2-DOF-PIDN-PDN controllers. The optimal design of these controllers is done by applying Selfish Herd Optimisation (SHO) technique. Further, the robustness of the 2-DOF-FOPIDN-FOPDN controller is authenticated by evaluating the system performance under parameter variation. The work is further extended to prove the supremacy of SHO algorithm over a recently published article based on pathfinder algorithm (PFA)

Sequence based polymorphic (SBP) marker technology for targeted genomic regions: its application in generating a molecular map of the Arabidopsis thaliana genome

<p>Abstract</p> <p>Background</p> <p>Molecular markers facilitate both genotype identification, essential for modern animal and plant breeding, and the isolation of genes based on their map positions. Advancements in sequencing technology have made possible the identification of single nucleotide polymorphisms (SNPs) for any genomic regions. Here a sequence based polymorphic (SBP) marker technology for generating molecular markers for targeted genomic regions in Arabidopsis is described.</p> <p>Results</p> <p>A ~3X genome coverage sequence of the <it>Arabidopsis thaliana </it>ecotype, Niederzenz (Nd-0) was obtained by applying Illumina's sequencing by synthesis (Solexa) technology. Comparison of the Nd-0 genome sequence with the assembled Columbia-0 (Col-0) genome sequence identified putative single nucleotide polymorphisms (SNPs) throughout the entire genome. Multiple 75 base pair Nd-0 sequence reads containing SNPs and originating from individual genomic DNA molecules were the basis for developing co-dominant SBP markers. SNPs containing Col-0 sequences, supported by transcript sequences or sequences from multiple BAC clones, were compared to the respective Nd-0 sequences to identify possible restriction endonuclease enzyme site variations. Small amplicons, PCR amplified from both ecotypes, were digested with suitable restriction enzymes and resolved on a gel to reveal the sequence based polymorphisms. By applying this technology, 21 SBP markers for the marker poor regions of the Arabidopsis map representing polymorphisms between Col-0 and Nd-0 ecotypes were generated.</p> <p>Conclusions</p> <p>The SBP marker technology described here allowed the development of molecular markers for targeted genomic regions of Arabidopsis. It should facilitate isolation of co-dominant molecular markers for targeted genomic regions of any animal or plant species, whose genomic sequences have been assembled. This technology will particularly facilitate the development of high density molecular marker maps, essential for cloning genes based on their genetic map positions and identifying tightly linked molecular markers for selecting desirable genotypes in animal and plant breeding experiments.</p

FOHC: Firefly Optimizer Enabled Hybrid approach for Cancer Classification

Early detection and prediction of cancer, a group of chronic diseases responsible for a large number of deaths each year and a serious public health hazard, can lead to more effective treatment at an earlier stage in the disease's progression. In the current era, machine learning (ML) has widely been used to develop predictive models for incurable diseases such as cancer, heart disease, and diabetes, among others, taking into account both existing datasets and personally collected datasets, more research is still being conducted in this area. Using recursive feature elimination (RFE), principal component analysis (PCA), the Firefly Algorithm (FA), and a support vector machine (SVM) classifier, this study proposed a Firefly Optimizer-enabled Hybrid approach for Cancer classification (FOHC). This study considers feature selection and dimensionality reduction techniques RFE and PCA, and FA is used as the optimization algorithm. In the last stage, the SVM is applied to the pre-processed dataset as the classifier. To evaluate the proposed model, empirical analysis has been carried out on three different kinds of cancer disease datasets including Brain, Breast, and Lung cancer obtained from the UCI-ML warehouse. Based on the various performance parameters like accuracy, error rate, precision, recall, f-measure, etc., some experiments are carried out on the Jupyter platform using Python codes. This proposed model, FOHC, surpasses previous methods and other considered state-of-the-art works, with 98.94% accuracy for Breast cancer, 95.58% accuracy for Lung cancer, and 96.34% accuracy for Brain cancer. The outcomes of these experiments represent the effectiveness of the proposed work

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Tanscriptomic Study of the Soybean-Fusarium virguliforme Interaction Revealed a Novel Ankyrin-Repeat Containing Defense Gene, Expression of Whose during Infection Led to Enhanced Resistance to the Fungal Pathogen in Transgenic Soybean Plants

Fusarium virguliforme causes the serious disease sudden death syndrome (SDS) in soybean. Host resistance to this pathogen is partial and is encoded by a large number of quantitative trait loci, each conditioning small effects. Breeding SDS resistance is therefore challenging and identification of single-gene encoded novel resistance mechanisms is becoming a priority to fight this devastating this fungal pathogen. In this transcriptomic study we identified a few putative soybean defense genes, expression of which is suppressed during F. virguliformeinfection. The F. virguliforme infection-suppressed genes were broadly classified into four major classes. The steady state transcript levels of many of these genes were suppressed to undetectable levels immediately following F. virguliforme infection. One of these classes contains two novel genes encoding ankyrin repeat-containing proteins. Expression of one of these genes, GmARP1, during F. virguliforme infection enhances SDS resistance among the transgenic soybean plants. Our data suggest that GmARP1 is a novel defense gene and the pathogen presumably suppress its expression to establish compatible interaction