This paper has developed a closed-loop control algorithm to operate the G2V/V2G action, tested under varying battery voltage conditions and load and source power differences. Under V2G action, to maintain total harmonic distortion under minimum level and grid frequency under the standard limit, a Model predictive controller (MPC) has been used to control the gate driver circuit of the inverter. The state space model of the plant has been created using the system identification toolbox, and the MPC Controller block has been designed using the Model Predictive Control Toolbox of MATLAB. The proposed methodology is tested using MATLAB/Simulink and OPAL-RT (OP4510) in a real-time environment. This methodology reduces %THD to less than 0.5%, improves waveform quality of grid voltage, inverter output voltage, grid current, and inverter output current to nearly 99%, and maintains the grid frequency in standard limit while in G2V/V2G action.
The basic configuration of H-Bridge multilevel inverter (HBMLI) is capable to produce desired number of voltage levels but suffers due to higher number of switches and thus deals with the higher complexity and cost. In this paper a new inverter configuration is proposed which is capable to produce 13/17 voltage level. The proposed inverter comprises of three modules namely Modified H-Bridge, DC link Capacitor unit and Step-up capacitor unit. This is a single stage extendable multilevel inverter and the voltage level of inverter will increase if number of step-up switched-capacitor unit and DC-link capacitors is increased. Step-up switched-capacitor/DC-link capacitor unit charged by using the both the binary asymmetrical algorithm and binary symmetrical algorithm. The proposed inverter configuration is simulated for 17 Level and the results has been verified using MATLAB/SIMULINK software.
Domain adaptation (DA) is an efficient approach that transfers the knowledge gained from the source domain to learn an accurate classifier for the target domain. But the majority of the current domain adaptation algorithms assume that both the training and testing data have similar feature space with unaligned data distribution. Such algorithms fail when the feature representations vary. Heterogeneous Domain Adaptation (HDA) effectively addresses multi-modal data analysis where the features and distribution of source and target domain data are different. But, some of the existing heterogeneous domain adaptation approaches utilize either statistical or geometrical data alignment. Hence, we propose Joint Cross-Domain Preserving and Distribution Adaptation (JCDDA) for semi-supervised HDA, which learns a new latent subspace where the data samples from the source and target domain are projected. JCDDA preserves the cross-domain structure of the data and minimizes the discrimination differences between the domains. As our approach is semi-supervised HDA, we exploit the selective pseudo-labeling method to utilize unlabelled target data. We did extensive experiments using two benchmark object recognition datasets, and from our results, it is evident that our algorithm outperforms all of them.
Today various technologies like artificial intelligence, edge computing, the internet of things, machine learning, blockchain platforms, and are all playing a very significant role as the internet evolves into a novel industrial science typology that is built on the foundation of smart systems. The combination of three technologies namely blockchain, internet of things (IoT), and edge computing has been an extremely essential aspect in creating revolutionary autonomous assistance and industrialized models with many anticipated qualities, such as self-verifying and execution, data integrity, and data dependability, along with secrecy. Critical concerns such as privacy, sustainability, and resource planning should be resolved in order to achieve efficient integration. In this study, edge computing is utilized to construct an Internet of Things (IoT) architecture that fulfills the confidentiality and scalability requirements necessary for integration, using peer-to-peer and blockchain technologies.
The major challenge is the scalability issue, which the second layer of blockchain must solve. But the transfer of micro-payment creates another problem: node idleness due to the insufficient channel balance. So that we aim to implement an atomic payment splitting network using the ethereum blockchain, which can transfer the micro-payments quickly in different paths from sender to receiver. It is the first Ethereum-based atomic payment splitting model. This model works faster than other Bitcoin-based models, and smart contracts make it easier with the help of random seeds. Again the payment splitting decreases the node idleness. The significant challenges in Bitcoin lightning-based networks are intermediary insecurity and flood, and loot attack is not happening in our system that of the random seed and smart contracts. Our resultant model guarantees that the honest intermediaries have not lost funds, and the payment split network is not a failure after the path confirmation. Using negative acknowledgment also ensures the payment’s failure in the model’s initial phase. The final payment confirmation happens only by receiving all the unique seeds within the ∆ time. The proposed model has been analyzed with different existing models, and the success ratio is better.
