Harmonic amplification is one of the primary issues in power system networks. The objective of this study is to manage the harmonic event and its significant effects on power quality. A new control approach that uses artificial intelligence (AI) is proposed and applied to a distribution static synchronous compensator (DSTATCOM). DSTATCOM is a FACTS device that can achieve highly effective reactive power compensation to reduce and/or damp the harmonic amplification in power system networks. Simulation results are obtained using the MATLAB/Simulink package.

Keywords : DSTATCOM, AI, Neural Network, Harmonics, Power Quality

Author : K. Krishna Reddy , N V Kishore Kumar

Title : DSTATCOM-based Artificial Neural Network Controller for Harmonic Reduction

Volume/Issue : 2024;1(1)

Page No : 1 - 6

The battery life time of an electric vehicle (EV) has significant impact on the development of EV. The proposed converter can operate in a step-up mode and a step-down both with bidirectional power flow control. In addition, the model can independently control power flow between any two low-voltage sources. Artificial Neural Networks (ANN) were used as a closed-loop control structure to control the DC/DC converters in the topology, whilst a rule-based control strategy was used to control the operating states of the hybrid energy storage system. The performance of the ANN controller was also experimentally found to be sufficient when used in conjunction with the rule-based control strategy. In extension we are using ANN controller to generate the triggering pulses. The ANN circuit will be controlling the input and outputs. The system allows one to utilize batteries that are optimized for energy density seeing that the system was able to actively limit the power drawn from the battery, whilst the circuit configuration, operation, steady-state analysis, and closed-loop control of the proposed BDC are discussed according to its three modes of power transfer.

Keywords : Electric Vehicle, Energy storage systems, ANN, Smart Grid, DC-DC converter

Author : M. Rana Prathap , P. Reddy Sekhar

Title : Artificial Neural Networks based SMES Implanted Smart Grid for EV Charging Station

Volume/Issue : 2024;1(1)

Page No : 7-13

Effective SARS- CoV- 2 webbing allows for a speedy and accurate opinion of COVID- 19, reducing the cargo on healthcare systems. In order to estimate the threat of infection, vaticination models that integrate numerous variables have been developed. These are intended to prop medical help around the world in triaging patients, particularly in areas where healthcare coffers are scarce. We developed a machine-learning algorithm that was trained on the records of 51,831 people who had been tested( of whom 4769 were verified to have COVID- 19). The data in the test set came from the coming week( tested individualities of whom 3624 were verified to have COVID- 19). Overall, we created a model that detects COVID- 19 cases using simple variables available by asking introductory questions grounded on civil data intimately released by the Israeli Ministry of Health. When testing coffers are limited, our approach can be used to precedence testing for COVID- 19, among other effects. In this design, we proposed the CNN grounded x-ray image for the discovery of covid and xgboost for the discovery of symptoms.

Keywords : CNN, Machine Learning, X-ray image, Gradient Boost Algorithm, Python

Author : Dekka Satish , K. Narasimha Raju

Title : Covid-19 Identification and Surveillance System using AI

Volume/Issue : 2024;1(1)

Page No : 14-20

This paper proposes a new technique for energy managing in hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point tracking (MPPT) for different types of sources like Photovoltaic(PV), wind energy and Fuel cell at DC-link.. The developed fuzzy logic controller (FLC)-based MPPT method and ANN based method was assessed using a hybrid system comprising PV panels, wind turbine (WT) and Fuel Cell with DC–DC converters. The dynamic behaviour of the proposed model is examined under different operating conditions. The analysis of the proposed hybrid system provides higher power as compared to PV, WT and FC systems at different loads. This research is utilized for the stand-alone system as well as a grid. ANN gives a better performs compared to the Fuzzy in the MPPT method for the PV panels, wind turbine (WT) and Fuel Cell with DC–DC converters for the DC loads. The result analysis of the Fuzzy and the ANN are simulated in the MATLAB/Simulink.

Keywords : Hybrid Power Supply, ANN, ML, Python, Non-Conventional Energy Sources

Author : K. Krishna Reddy , S. Hareesh

Title : An Integrated Hybrid Power Supply For Distributed Generation Applications Fed by Non Conventional Energy Sources

Volume/Issue : 2024;1(1)

Page No : 21 - 26