A design methodology for minimum volume of LCL filters applied to grid connected converters is proposed. It combines the determination of filter parameter values (inductances and capacitances) to hardware design (component technology and inductor construction). Using the proposed strategy, different combinations of L-C-L that meet standard restrictions are determined. The influence of the harmonic content that results from filter design is considered in order to estimate component losses, and filter and bus capacitor lifetimes. Results are presented for filters that lead to the smallest volume, highest capacitor lifetime, or a compromise between both. A case study with three different magnetic material technologies and two types of capacitors is done. The design methodology is experimentally validated for a 1 kW converter. Step-by-step procedures for the determination of filter parameters and inductor hardware design are provided.

High impedance faults constitute one of the biggest challenges in electrical power systems. In overhead distribution systems, faults are caused by tree branches that touch the electrical grid or by the rupture of energized conductors on low conductivity soils. They are also known as low-current faults, which are not detected by conventional protection systems, compromising the quality of the power supply and causing hazardous risks to the electrical system. This paper aims to address the problem of high impedance faults detection using Artificial Neural Networks: two Multi Layer Perceptron networks, being one Neural Pattern Recognition and another Neural Fitting, and a Convolutional Neural Network. The neural networks are trained and analyzed in scenarios based on a medium-voltage distribution grid model, located in the Basque Country, Spain. The network topologies are implemented, repeatedly trained considering multiple architectures, and validated in other scenarios with different location, time, and duration of the fault using the Matlab software. After, the criteria of accuracy, reliability, security, safety and sensitivity are evaluated. At last, a comparative analysis between them is carried out, and from the results obtained, a superior performance of the Convolutional Neural Network in compared to the Multi Layer Perceptron networks is observed.

A lifetime analysis was performed on the DC-link capacitor of a photovoltaic power-processing system composed of a boost converter in the first stage. It was investigated how the switching frequency and ripple current design choice affect the capacitor lifetime. Through an electro-thermal modeling, the capacitor operating condition was obtained for various levels of power processing, based on a real photovoltaic mission profile built for ten years period of weather data. The lifetime of three different designs of electrolyte capacitor bank was analyzed and discussed. Analyzed results showed that the increase of the converter's current ripple diminishes much the of capacitor's lifetime. A discussion is presented about the tradeoff between choosing capacitors with low equivalent series resistance and increasing the number of capacitors on the DC-link.