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Renewable energy is now in high demand due to the deterioration of the global climate and the depletion of conventional sources. Renewable energy sources (RES) such as wind and solar are extremely intermittent, making it impossible to sustain system reliability with an unacceptably high proportion of renewable energy injection. An intrinsic attribute common to all renewable power plants is that the production of energy relies on environmental conditions such as temperature, pressure, wind speed, humidity, clouds, etc. Therefore, the power from RES cannot be completely regulated or pre-planned. It is important to forecast the amount of electricity that can be produced in a power grid for future demand. Machine learning (ML) is an emerging technology and used in all fields nowadays to perform different tasks. In this paper, the applications of machine learning in renewable energy sources are discussed. These ML techniques are mainly used to predict the power from renewable energy sources like wind, solar, hydro, biomass, tidal, and geothermal. Fault detection is a significant aspect in renewable energy systems to reduce the operation and maintenance cost and to deliver the continuous power to the loads. This paper also focusses on the use of ML techniques in predicting the faults before it occurs, early detection of faults and also to diagnose the faults in renewable energy systems. Along with the above-mentioned applications, these ML techniques used in RES for different purposes are also discussed. [ABSTRACT FROM AUTHOR]

Grid integration of solar photovoltaic (PV) systems is becoming popular recently due to the merits of stable support to conventional grid, limiting global warming and reduced emissions. However, maintaining capacitor voltage balancing, unity power factor, sinusoidal grid current and harmonic profile improvement are the challenging tasks owing to change in irradiation conditions. Multi-level inverters in grid connected systems will enhance the power quality of the system in terms of voltage and current total harmonic distortion. This paper proposes a highly reliable, robust and modular single phase solar PV fed fault tolerant hybrid cascaded H-bridge inverter with individual voltage/current control strategy for grid integration. Higher reliability and modularity, power factor correction, able to with stand without degrading the performance of the topology even under faulted conditions and harmonic profile improvement are the main objectives of the proposed system. The proposed system has been simulated and validated with experimental results on the developed prototype for different irradiation conditions and tested the performance of the topology with creation of faults on different modules at different times. [ABSTRACT FROM AUTHOR]

The cascaded H-bridge (CHB) inverters are the prominent inverters used for renewable energy applications. Fault-tolerant operation is one of the foremost concerns to safeguard the continuous operation of the system under a fault on semiconductor switches and DC power supplies. A novel hybrid CHB inverter is proposed by adding a seven-level boost SLB module in cascade with the H-bridge modules. The proposed topology ensures the reliable and dynamic operation of the inverter during the occurrence of a fault. In addition to the fault-tolerant capability of the SLB inverter, the proposed design provides the voltage boosting and self-balancing of the switched capacitors. Extensive simulation results at different operating conditions substantiate the concept of the proposed novel fault-tolerant inverter structure. The prototype system is developed in the laboratory and the experimental results validate the proposed system configuration. [ABSTRACT FROM AUTHOR]