This paper shows performance investigation on enhance efficiency of standalone solar energy conversion system using ANFIS based MPPT controller. Now a days many researches are going in the field of renewable energy technologies to bridge the gap between supply and demand. Due to the Intermittent nature of renewable sources, operating standalone operation of them is highly unreliable causes interruption in power supply to the load. A solar photovoltaic system with MPPT based on Adaptive-Network-Based Fuzzy Inference System (ANFIS) is used for stable operation and to meet the supply with the load demand. The application of ANFIS based MPPT technique requires detailed study and analysis of the Solar Photovoltaic (SPV) system. Adaptive-Network-Based Fuzzy Inference System based MPPT technique is used along with Solar PV system as they can provide fast and accurate response to various environmental conditions. The Levenberg-Marquardt algorithm is used to train the neural network for MPPT in the photovoltaic system. This developed control strategy is able to control the devices and various power interface circuitry used therein. The main aim of this paper is to ensure a maximum power output coordinating appropriate control strategy with sources & compare the performance analysis of the ANFIS based MPPT with conventional incremental conductance based MPPT for the SPV system. The simulation studies have been carried out to find out the SPV system performance with different input conditions such as typical solar radiation and temperature. The Simulation test results show variable power generation and verifies that the performance of the integrated system with this control strategy is effective for the real-time installation. The simulation results shows that the performance of this developed control strategy for receiving maximum power output from the standalone SPV system is farfetched.

Simulation test results show variable power generation and verify the performance of the integrated system with control strategy is overall effective for real-time installation. The developed system is essential in an isolated region where an existing grid is unable to supply secure power generation and system is beneficial with smartly feed ultimate power generation sources.

The study is investigating the volatility behaviour of short Treasury bill yields of India by applying GARCH (1, 1) model. The frequency of data used in the study is weekly from 2000 to 2021. The results suggest that Treasury bill yields of 3 months, 6 months and one year in India are highly volatile. The findings provide sufficient evidence that Indian Treasury bill yields are significantly volatile during entire study period. The results also show that corresponding probability value is significant at 1%. That reveals that Treasury bill yields of India are highly volatile at confidence level of 99% during study period i.e. 2000 to 2021.