Sarat Kumar Sahoo

Bio: Sarat Kumar Sahoo is an academic researcher from VIT University. The author has contributed to research in topics: Photovoltaic system & Maximum power point tracking. The author has an hindex of 12, co-authored 70 publications receiving 854 citations.

Renewable and sustainable energy reviews solar photovoltaic energy progress in India: A review

Abstract: The mitigation of global energy demands and climate change are the most important factors in the modern days. Development and application of solar energy have been regarded by the government of India and common people, and they thought that solar photo voltaic energy can provide more energy in future compare to other renewable energies. In the last decade, solar photovoltaic energy research and development has supported by the central government and state governments. This paper discusses the progress of current solar photovoltaic energy in India. It highlights the renewable energy trend in India with major achievements, state wise analysis of solar parks and industrial applications. Finally, it discusses the Indian government policies and initiatives to promote solar energy in India. This review on solar photovoltaic energy will help decision makers and various stakeholders to understand the current status, barriers and challenges for better planning and management in this field.

Review: Pulse coupled neural networks and its applications

Abstract: This paper surveys the extensive usage of pulse coupled neural networks. The visual cortex system of mammalians was the backbone for the development of pulse coupled neural network. PCNN (Pulse Coupled Neural Networks) is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. is Hence the uniqueness of this network utilized in the fields of image processing. The basic model of PCNN and the consecutive changes implemented, to strengthen the pulse coupled neural network are discussed initially. Then the applications of PCNN are broadly discussed. The other miscellaneous applications utilizing pulse coupled neural networks are thrown light in the last section.

A review on state of art development of model predictive control for renewable energy applications

Abstract: Renewable energy sector is undergoing rapid expansion as the global focus is shifting towards cleaner, reliable and sustainable resources. As the new installation of these resources are well underway, there is tremendous potential for exploring these to more advanced control algorithms. Model predictive control is gaining immense popularity because of its flexible controllability, its ability to be used in any of application irrespective of its field as well as the availability of fast processors. This paper presents a systematic review on Photo-voltaic (PV) and wind energy systems controlled by Model predictive control approach. The work presented here will help the researchers to further explore the flexibility of this controller for design, analysis and implementation in renewable energy systems.

A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques

Abstract: To suggest a non-invasive method for classification of Astrocytoma through rigorous training and testing.To compare and conclude the best medical image segmentation technique.To develop an efficient automatic feature selection technique for grade identification.To analyze and quantify the performance of classifiers constructed for the grade identification of Astrocytoma (tumor). Brain tumor grade identification is an invasive technique and clinicians rely on biopsy and spinal tap method. The proposed method takes an effort to develop a non-invasive method for the tumor grade (Low/High) identification using magnetic resonant images. The process involves preprocessing, image segmentation, tumor isolation, feature extraction, feature selection and classification. An analysis on the performance of the segmentation techniques, feature extraction methods, automatic feature selection (SFLA) and constructed classifiers (support vector machines, learning vector quantization and Naives Bayes) is done on the basis of accuracy, efficiency and elapsed time. This analysis motivates towards the accurate determination of tumor grade from MR images instead of depending on magnetic resonant spectroscopy and biopsy. Fuzzy c-means segmentation outperformed other segmentation techniques, shape and size based textural feature promoted the demarcation of tumor grades, Naive Bayes classifier succeeded in terms of efficiency, error and elapse time when compared with SVM and LVQ. The study was carried out with 200 images consisting training set (164 images) and testing set (36 images). The results revealed that the system is robust and accurate (91%), consumed less time in grade identification, an alternative for biopsy and MRS in the brain tumor grade identification diagnosis procedure.

Application of soft computing methods for grid connected PV system: A technological and status review

Abstract: Soft Computing (SC) methods are emerged as an alternative approach to conventional techniques for Grid connected Photovoltaic (PV) system because of their ability to solve the complex non-linearity problems. Soft Computing (SC) techniques are developed to handle the adverse environmental conditions such as sudden change in temperature and irradiation which are not addressed by conventional methods. The main focus of this paper is to summarize the application of various SC techniques which apply to different stages of a grid connected PV system such as panel reconfiguration, Maximum Power Point Tracking (MPPT) technique in the converter, Harmonic elimination in inverter and islanding detections. Furthermore, the comparison is made on the performance of SC methods based on several conditions, namely convergence, algorithm complexity, real time implementation, System dependency, Periodic tuning and Oscillations around the operating point. It is envisioned that the information provided in this paper is intended to serve as a valuable reference for future research in grid connected PV system.

The impact of occupants’ behaviours on building energy analysis: A research review

Abstract: Over the past 15 years, the evaluation of energy demand and use in buildings has become increasingly acute due to growing scientific and political pressure around the world in response to climate change. The estimation of the use of energy in buildings is therefore a critical process during the design stage. This paper presents a review of the literature published in leading journals through Science Direct and Scopus databases within this research domain to establish research trends, and importantly, to identify research gaps for future investigation. It has been widely acknowledged in the literature that there is an alarming performance gap between the predicted and actual energy consumption of buildings (sometimes this has been up to 300% difference). Analysis of the impact of occupants’ behaviour has been largely overlooked in building energy performance analysis. In short, energy simulation tools utilise climatic data and physical/ thermal properties of building elements in their calculations, and the impact of occupants is only considered through means of fixed and scheduled patterns of behaviour. This research review identified a number of areas for future research including: larger scale analysis (e.g. urban analysis); interior design, in terms of space layout, and fixtures and fittings on occupants’ behaviour; psychological cognitive behavioural methods; and the integration of quantitative and qualitative research findings in energy simulation tools to name but a few.

Renewable energy and biodiversity: Implications for transitioning to a Green Economy

Abstract: This literature review identifies the impacts of different renewable energy pathways on ecosystems and biodiversity, and the implications of these impacts for transitioning to a Green Economy. While the higher penetration of renewable energy is currently the backbone of Green Economy efforts, an emerging body of literature demonstrates that the renewable energy sector can affect ecosystems and biodiversity. The current review synthesizes the existing knowledge at the interface of renewable energy and biodiversity accross the five drivers of ecosystem change and biodiversity loss of the Millennium Ecosystem Assessment (MA) framework (i.e. habitat loss/change, pollution, overexploitation, climate change and introduction of invasive species). It identifies the main impact mechanisms for different renewable energy pathways, including solar, wind, hydro, ocean, geothermal and bioenergy. Our review demonstrates that while all reviewed renewable energy pathways are associated (directly or indirectly) with each of the five MA drivers of ecosystem change and biodiversity loss, the actual impact mechanisms depend significantly between the different pathways, specific technologies and the environmental contexts within which they operate. With this review we do not question the fundamental logic of renewable energy expansion as it has been shown to have high environmental and socio-economic benefits. However, we want to make the point that some negative impacts on biodiversity do exist, and need to be considered when developing renewable energy policies. We put these findings into perspective by illustrating the major knowledge/practices gaps and policy implications at the interface of renewable energy, biodiversity conservation and the Green Economy.