# Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications
## 1 Introduction  
The development of smart cities leads to an increase in the complexity of electrical grids, and new challenges need to be addressed, such as the spread of electric vehicles and the management of renewable energy systems [1, 2]. In this sense, new devices, control techniques and monitoring methods are needed for proper energy management [3, 4, 5, 6, 7]. The technical optimization of the new electrical generators allows an increase in efficiency for renewable systems, but it is not sufficient for the correct distribution of this energy, which is difficult to predict and highly variable [8, 9]. For this reason, the development of new algorithms capable of predicting production from renewable sources and managing electrical loads will be a very important field of interest for many researchers [10, 11, 12]. Furthermore, the development and control of devices used as an interface between generators and the grid, or generators and other devices, play a fundamental role in the correct distribution of energy [13, 14, 15, 16, 17]. In this sense, the control of DC-DC converters represents a very important aspect because they can be used as an interface with renewable energy systems producing a Direct Current (DC) and are essential for all those systems powered by batteries, such as electric vehicles. In addition to controlling these devices, it is very important to monitor their state of health during the operation of the electrical system. In traditional diagnostic systems, the main objective is to identify and localize faults, which leads to a complete loss of functionality. On the other hand, in prognostic systems, the subject of the analysis is the malfunction condition. This means that the prognostic system focuses on slight variations from the nominal point of work, identifying partial losses of functionality that precede catastrophic failures. In this way, it is possible to organize maintenance operations, increasing the reliability of the electrical system and reducing recovery times. In this paper, the definition of a prognostic analysis for DC-DC converters is carried out and verified through a simulation procedure in a Matlab-Simulink environment. The converter taken into consideration is a Zeta converter, which allows for a high voltage gain and low ripple in the output current using four passive components [18; 19; 20]. These components are the main subjects of the prognostic analysis, and their variations with respect to the nominal values are used as indexes of the state converter of health. In fact, when a malfunction occurs on a passive component, its value changes; this introduces a variation of the working point [21; 22] and could produce catastrophic consequences. To make the simulations as close as possible to the real functioning of the converter, the parasites of the real active and passive components are considered in Simulink. The specific case of prognostics addressed in this paper involves the DC-DC converter featuring a photovoltaic (PV) input. This introduces two additional challenges to the prognostic problem. The first is the non-linear current-voltage characteristic of the source, which can result in irregular trends (if compared with ideal voltage and current sources, often used in diagnostics and prognostics problems) of the converter current and voltages. The second is the functional relationship between the characteristics and the environmental quantities of temperature and irradiance. Both difficulties might lead to erroneous classifications of the working condition of the converter. To address this problem, a specific normalization approach is used to decouple the prognostic-sensitive quantities from the environmental-dependent nature of the source. Prognosis is performed by means of a supervised machine-learning approach. Several sensitive electrical quantities are measured on the passive elements of the operating circuit in the time domain and are processed by a Multilayer neural network with Multi-Valued Neurons (MLMVN).