Turkish researchers have developed an algorithm to distinguish faults and changes in PV plant output. The sensorless method only uses electrical data gathered from the DC inputs of inverters. They tested it on 8.5 MW of ground-mount PV at nine sites, as well as 8.2 MW across 18 rooftops.
Scientists from Turkey’s Dokuz Eylul University and Turkish EPC contractor Entegro Enerji Sistemleri have developed a new fault-detection method for large-scale PV plants.
They said it works “smoothly” for both small and large PV systems. They also claimed that it improved operations and maintenance by distinguishing between faults and changes in weather conditions in relation to project output.
“We have developed the sensorless fault-detection method based on long-term observations on nine ground-mounted PV plants with a total connected capacity of 8.5 MW and 18 rooftop PV plants with a total connected capacity of 8.2 MW,” Entegro’s founder, Esref Deniz, told pv magazine. “These PV plants have a variety of geographical features and environmental conditions, which helped us understand thoroughly the inherent characteristics of PV plants.”
The fault-detection technique is not based on the use of irradiance and temperature sensors, but on the DC input data received from the inverters.
“Detailed information of orientations of the modules, inverter size, the peak power of the PV plant, etc., are not required to provide residential and commercial users with an advanced and cost-effective solution for monitoring and controlling their photovoltaic system under cloudy and clear sky conditions,” the researchers said.
The DC power values at the inverter level are considered regardless of cloud conditions, with the data collected via a conventional monitoring system provided by the manufacturer. DC power values are only used during the mapping procedure and the collected information undergoes two pre-processing stages. First, the maximum DC power in every time step is determined and then the difference between all DC powers and the corresponding maximum power in each time step of the available data set is calculated.
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The researchers claimed that the algorithm they used to generates the fault condition results of each DC inputs individually. Each DC input is then benchmarked against its own inherent working potential, which is given by the PV plant site’s characteristics.
“We have developed a promising method that rationalizes only the electrical data already gathered from the DC inputs of the inverters,” Deniz explained. “This method has a holistic approach such that every DC side is utilized altogether simultaneously to extract the inherent working potentials of those DC sides individually.”
“Acceleration,” which refers to the change in the distance curve, is introduced to map the inherent characteristics of each PV array in the PV plant, instead of using additional sensors. “It allows us to overcome the misleading effect of weather and environmental conditions on the fault detection,” Deniz said.
It can be economically and technically challenging if a high number of installed irradiance and temperature sensors help to improve measurement accuracy, Deniz said. “On the other hand, our fault-detection method uses the exact electrical outputs instead of the additional sensor-based model. This is an advantage in terms of zero false-positives and, as our fault- detection method requires no additional hardware, any number of sensor usage results in a more expensive solution,” he added.
The researchers described the technique in “Sensorless fault detection method for photovoltaic systems through mapping the inherent characteristics of PV plant site: Simple and practical,” which was recently published in Solar Energy.
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Source: pv magazine
