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At present large rating solar photovoltaic (PV) plants are now being installed worldwide. As these plants face the challenge of non dispatchable power in comparison to conventional, researchers has focused on energy storage technology. An energy storage technology not only provides the uninterrupted power supply but also reduces the chance of disconnection from load thus better reliability and robustness in combination with these systems. In this Paper an attempt is made to mimic the entire operation from designing of 5.2 kWp DC to AC system without storage, which supplies the power to a 5KVA load. The PSIM model of this system compared ‘with’ and ‘without’ storage technology for 0.9 sun, 0.6 sun and 0.3 sun radiation. The observations are that at 0.9 sun, 3.97 kW power delivered to load and storage is not required .The PF and CUF in this situation are 78% and 18% respectively. While at 0.6 sun the power delivered to load is 3.68W, the PF and CUF in this situation are 72% and 16% respectively .i.e. partial storage of 460 Ah is required. At another radiation of 0.3 sun, the power delivered is 2.19 kW, the PF and CUF in this situation are 55% and 10% respectively so a key role is played by storage of 1183Ah. It is seen that Volatge and Current THD also increses in absence of storage while these values remains constant in presence of storage system. For the given DC/AC standalone system, the storage capacity proposed of 5228 Ah for complete 08 hours. Due to these extended operational hours performance and reliability is increased with robustness &nbsp

Background: Myocardial infarction, or heart attack, is caused by a blockage of a coronary artery, which prevents blood and oxygen from accessing the heart properly. Arrhythmias are a form of CVD that refers to irregular variations in the normal heart rhythm, such as the heart beating too quickly or too slowly. Arrhythmias include Atrial Fibrillation(AF),Premature Ventricular Contraction(PVC), Ventricular Fibrillation(VF), and Tachycardia are just a few examples of arrhythmias. It aggravates if not detected and treated on time i.e., on-time /proper diagnosis of arrhythmias may minimize the risk of death. It is very labor-intensive to externally evaluate ECG signals, due to their small amplitude. Furthermore, the analysis of ECG signals is arbitrary and can differ between experts. As a consequence, a computer-aided diagnostic device that is more objective and reliable is needed. Methods: In the recent era, Machine Learning based approaches to detect arrhythmias has been established proficiently. In this view, we proposed a hybrid Deep Learning-based model to detect three types of arrhythmias on MIT-BIH arrhythmia databases. In particular, this paper makes two-fold contributions. First, we translated 1D ECG signals into 2D Scalogram images. When one-dimensional ECG signals are turned into two-dimensional ECG images, noise filtering and feature extraction are no longer necessary. This is notable since certain ECG beats are ignored by noise filtering and feature extraction. Then, based on experimental evidence, we suggest combining two models, 2D-CNN-LSTM, to detect three forms of arrhythmias: Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). Results: The experimental findings indicate that the model attained 99\% accuracy for "normal sinus rhythm," 100\% accuracy for "cardiac arrhythmias," and 99\% accuracy for "congestive heart failures," with an overall classification accuracy of 98.6\%. The sensitivity and specificity were 98.33\% and 98.35\%, respectively. The proposed model, in particular, will aid doctors in correctly detecting arrhythmia during routine ECG screening. Conclusion: As compared to the other State-of-the-art methods our proposed model outperformed and will greatly minimise the amount of intervention required by doctors.