Development of rotating vortex rope (RVR) at part load (PL) operation is a source of pressure fluctuations in draft tube and power swings, which may lead to runner failure under extreme conditions. Fluid injection methodologies like air and water injection may be employed to mitigate the RVR and the pressure pulsations associated with it. A small turbine test rig is being developed at IIT Roorkee (India) to study the effect of the fluid injection measures (air/ water) in the Francis turbine. The present work summarizes a preliminary numerical investigation of the runner cone design and water jet injection to be used in the test rig. The runner cone is modified to incorporate provisions for axial water jet injection. Two different runner cone designs have been compared based on power output, efficiency, pressure recovery, and pressure pulsations in the draft tube for four different jet discharges. The water jet affects both the magnitude and the frequency of the pressure pulsations associated with RVR, and improves the overall efficiency. The results also indicate that the water jet injection may not always be effective and may increase pressure fluctuations in some cases.

Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion methodology that incorporates autoencoders (a deep neural network architecture) and support vector machines (SVM) (a machine learning algorithm) is proposed in the present study. To generate high-quality image datasets for training, unmanned aerial vehicles (UAVs) equipped with RGB cameras were employed to capture detailed images of PV modules. Burn marks, snail trails, discoloration, delamination, glass breakage and good panel were the conditions considered in the study. The experimental results demonstrate remarkable accuracy of 98.57% in diagnosing faults, marking a significant advancement in enhancing the reliability and performance of PV modules. This research contributes to the sustainability and efficiency of renewable energy systems, underlining its importance in the quest for a cleaner, greener future.

Renewable energy is found to be an effective alternative in the field of power production owing to the recent energy crises. Among the available renewable energy sources, solar energy is considered the front runner due to its ability to deliver clean energy, free availability and reduced cost. Photovoltaic (PV) modules are placed over large geographical regions for efficient solar energy harvesting, making it difficult to carry out maintenance and restoration works. Thermal stresses inherited by photovoltaic modules (PVM) under varying environmental conditions can lead to failure of internal components. Such failures when left undetected impart a number of complications in the system that will lead to unsafe operation and seizure. To avoid the aforementioned uncertainties, frequent monitoring of PVM is found necessary. The fault identification in PVM using essential features taken from aerial images is presented in this study. The feature extraction procedure was carried out using convolutional neural networks (CNN), while the feature selection process was carried out by the J48 decision tree method. Six test conditions were considered such as delamination, glass breakage, discoloration, burn marks, snail trail, and good panel. Bayes Net (BN) and Naïve Bayes (NB) classifiers were utilized as primary classifiers for all the test conditions. Results obtained from the classifiers were compared and the best classifier for fault detection in PVM is suggested.