This paper provides a novel implementation of the intelligent water drops (IWD) method for resolving data aggregation issues in heterogeneous wireless sensor networks (WSN). When the aggregating node is utilized to transmit the data to the base station, the research attempts to show that the traffic situations of WSN may be modified appropriately by parameter tuning and algorithm modification. IWD is used to generate an optimum data aggregation tree in WSN as one of its applications. IWD assumes that all nodes in the environment are identical, resulting in identical parameter updates for all nodes. In practical scenarios, however, diverse nodes with variable beginning energy, communication range, and sensing range characteristics are deployed. In order to replicate the influence of heterogeneity in the environment, improved IID (IIWD) is offered as an enhancement to the original IID. The suggested enhancement is appropriate for scenarios in which the aggregation node is utilized to transmit data to the base station in heterogeneous configurations. In terms of residual energy, dead nodes, payload, and network lifespan, a series of simulation results demonstrates that the proposed IIWD significantly improves the accuracy and effectiveness of the IWD method in comparison.

The power electronic interface is critical in matching a distributed generation (DG) unit’s characteristics to grid requirements as most DG technologies rely on renewable energy. Increased adoption of electric vehicles (EV) is seen as a positive step toward minimizing air pollution as well as carbon emissions. Rapid proliferation of electric vehicles as well as charging stations has exacerbated voltage quality as well as harmonic distortion difficulties, which harm the efficiency of combined renewable energy. This research proposes novel hybrid design techniques in control systems that enhance the energy efficiency of electronic converters for power electronics. The control system enhancement has been carried out using a hybrid energy storage electric convertor, and energy efficiency is improved using a synergetic battery reference adaptive controller. A plug-in hybrid electric vehicle (PHEV)’s internal combustion engine with a small photovoltaic (PV) module is utilised to assess a proposed control method which effectively regulates electric power on-grid by draining electricity from batteries during peak hours as well as then charging them during off-peak times, lowering the load on the converter as well as allowing electric vehicles to charge faster. Experimental results show the constant acceleration case obtained battery current of 92 Amps, ultracapacitor current of 89 Amps, charging voltage of 88 V, DC load current of 85 Amps, battery SOC of 72%, and the time-varying acceleration proposed technique obtained current of 94 Amps, and ultracapacitor current of 90 Amps, charging voltage of 90 V, DC load current of 82 Amps, battery SOC of 79%.

Optimal Energy Management for Hybrid PV-Wind-Battery Microgrids through Markov Decision Processes Technique

As per the Niti Aayog report, the Government of India is planning to double the income of farmers by 2022. In another report Niti Aayog depicted that, there is a 3.6% decrease in total engagement in agriculture from 2001 to 2022. One of the reasons will be so many people are leaving farming is that soil fertility rate, water level and climate are changing in India and its subcontinent. Due to this, crops grown by the farmers don’t get a surplus outcome after harvesting. The model will use Naïve Bayes algorithm, which comes under supervised learning algorithm for the prediction for most suitable crop. The seed data of the crops will be collected along with relevant parameters such as humidity, moisture, and temperature, which will aid the crops for effective and efficient growth. In the web application, the users will enter the parameter like temperature, location etc. and then the application will predict the most likable crop for better farming.

Increased usage of renewable energy sources such as solar photovoltaics and wind power might help lower carbon dioxide and other emissions. However, integrated PV-Wind RES and smart-grid electrical transmission systems have become less effective as non-linear loads have quickly expanded, causing voltage integrity and harmonic distortions concerns. This research shows that a microgrid might benefit from an active Shunt power filter by improving energy quality at the distribution level. The primary goal of this piece is to identify an appropriate controller technique for improving the shunt active power filter’s compensating capability. This research uses simulation to examine the performance of a PV-Wind hybrid renewable energy system under variable wind and solar conditions. The ultimate aim is to allow the construction of an electromagnetic method of control that can provide the required duty ratio. It will aid voltage control and stability in DC energy conversion plants. Utilizing simulation, the capability of the proposed control scheme to enhance power quality is assessed. Its capacity to compensate is heavily dependent on the voltage control provided by the DC link capacitor. In the past, a closed-loop proportional plus integral controller was used for this voltage regulation. To boost an active shunt power filter’s functionality, the modified fuzzy logic approach has been proposed. The SIMULINK and MATLAB model environments validated the efficacy of the proposed methods and allowed for a comparison of other pulse generation approaches. The suggested method successfully increases voltage regulation on the grid and maintains a consistent grid voltage despite variations in RE output and load

Two topics of data science are big data analytics and deep learning. Big data has become important as many private and public organizations collect massive quantities of domain-specific information that may include useful data on problems such as national intelligence, cyber security, detection of fraud, marketing and medical computing. Corporations such as Google and Microsoft analyze large volumes and impact existing and future technology for business analysis and decision-making. High-level, complex abstractions are extracted from deep learning algorithms through the hierarchical process. As data representations, complex abstractions are learned at a particular level on the basis of comparatively simple abstractions formulated at the previous level. The analysis and learning of large volumes of unattended data is a key advantage of deep learning and makes this an invaluable tool for big data analytics, with raw data largely unlabeled and unclassified. We are investigating how deep learning can be used in this research to deal with some major problems in big data analytics, such as extracting complex patterns from massive data volumes, semantic indexing, data tagging, quick recovery of information and simplifying discriminative tasks. We are examining several aspects of deep learning research, including streaming data, high-dimensional data, model scalability and distributed computers, which requires further exploration to incorporate specific challenges that have been introduced by large data analytics. In conclusion, we provide insights into relevant future works with questions such as the definition of data sampling criteria, domain adaptation modeling, the definition of criteria for useful data abstractions, better semantic indexing, semi-supervised study and active learning.