Abstract
Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3–50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the stock price volatility. This paper introduces two modifications in the standard BB method: the exponential moving average (EMA) instead of the simple moving average (SMA), and the size of the width of BB, set to three times the standard deviation instead of four times. Until the produced energy of each array is contained in the BB, a serious anomaly is not present. A case study based on a real operating 19.8 kWp PV plant is discussed.
Highlights
The energy performance of a photovoltaic (PV) generator is mainly affected by the irradiance intensity and air temperature
The proposed methodology is based on a combined evaluation of Bollinger bands (BB) and spread between the average energy produced by each array and average energy of the whole PV plant
The energy performance of the PV plant under examination has been analyzed by the BB and the energy spreads of Section 2
Summary
The energy performance of a photovoltaic (PV) generator is mainly affected by the irradiance intensity and air temperature. The methodology is based on a combined strategy, and isasbased on the energy spread each array and Bollinger bands (BB). PV plant is studied in this paper, by applying the proposed approach four times for (SMA),Aasreal usually occurs. A real PV plant is studied in this bynine applying the and proposed approach times for different allows extracting information about incoming criticalities or low-intensity anomalies. The statistical tools proposed here can be applied to any PV plant, which collects and stores at least the energy values of each array. After collecting the energy data, the energy performance monitoring and detection of low-intensity anomalies are based on some statistical tools used in the financial field. The proposed methodology is based on a combined evaluation of BB and spread between the average energy produced by each array and average energy of the whole PV plant.
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