Expanding the number of photovoltaic (PV) systems integrated into a grid raises many concerns regarding protection, system safety, and power quality. In order to monitor the effects of the current harmonics generated by PV systems, this paper presents long-term current harmonic distortion prediction models. The proposed models use a multilayer perceptron neural network, a type of artificial neural network (ANN), with input parameters that are easy to measure in order to predict current harmonics. The models were trained with one-year worth of measurements of power quality at the point of common coupling of the PV system with the distribution network and the meteorological parameters measured at the test site. A total of six different models were developed, tested, and validated regarding a number of hidden layers and input parameters. The results show that the model with three input parameters and two hidden layers generates the best prediction performance.

Infrared thermography, in the analysis of photovoltaic (PV) power plants, is a mature technical discipline. In the event of a hailstorm that leaves the PV system without the support of the power grid (and a significant portion of the generation potential), thermography is the easiest way to determine the condition of the modules and revive the existing system with the available resources. This paper presents research conducted on a 30 kW part of a 420 kW PV power plant, and demonstrates the procedure for inspecting visually correct modules that have suffered from a major natural disaster. The severity of the disaster is shown by the fact that only 14% of the PV modules at the test site remained intact. Following the recommendations of the standard IEC TS 62446-3, a thermographic analysis was performed. The thermographic analysis was preceded by an analysis of the I-V curve, which was presented in detail using two characteristic modules as examples. I-V curve measurements are necessary to relate the measured values of the radiation and the measured contact temperature of the module to the thermal patterns. The analysis concluded that soiled modules must be cleaned, regardless of the degree of soiling. The test results clearly indicated defective module elements that would result in a safety violation if reused. The research shows that the validity criterion defined on the basis of the analysis of the reference module can be supplemented, but can also be replaced by a statistical analysis of several modules. The comparison between the thermographic analysis and the visual inspection clearly confirmed thermography as a complementary method for testing PV-s.

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This paper gives a comprehensive approach to the emulation of photovoltaic (PV) plants made of different module technologies as well as varying peak power through the advanced fast PV power emulation technique. Even though PVs are recognized as a technology for CO2 emissions mitigation, the proposed emulation technique provides the opportunity to replicate PV plant operation without a carbon footprint because of its working principle. The process of PV power plant emulation consists of several stages which are described in detail. An algorithm for determining PV power plant configuration based on the technical characteristics of the PV emulation system equipment is developed and presented, as well as an algorithm for preparing data on the current–voltage (i–v) characteristics used as input data into programmable sources that mimic the power plant PV array. A case study of a single day operation of PV power plants made of two different topologies and technologies was carried out with the fast PV power emulation approach and the results are evaluated and presented.

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