What are KPIs in the context of photovoltaic farms?
KPI indicators in the renewable energy sector are quantitative and qualitative measures used to assess the efficiency of a photovoltaic farm’s operation. They facilitate management of both technical operations (O&M) and investment profitability. In the PV context, KPIs are used, among other things, to measure whether the system produces as much energy as it should under given conditions, whether components operate according to manufacturer specifications, and where losses occur.
Interpreting KPIs requires understanding the context in which they are measured. Data without comparison to reference values or forecasts (e.g., weather data, design characteristics) can lead to incorrect conclusions. Therefore, it is important to analyze not only absolute values but also their relationship to time, weather conditions, and installation configuration.
The most important KPIs – what’s really worth measuring?
Among the many available indicators, there are several that directly affect the technical and economic performance of the farm. The most commonly monitored include:
- PR (Performance Ratio) – the ratio of actual energy production to theoretical production, calculated based on irradiation and installed capacity. A low PR may indicate conversion issues, module soiling, or design errors.
- Technical availability – the percentage of time during which the installation is ready to produce energy. It includes failures as well as downtimes related to service and maintenance.
- Energy production (AC/DC) – separate analysis of energy on the DC and AC sides helps detect losses related to inverters or transformers.
- AC/DC losses – comparing gross (DC) energy with net (AC) energy allows identification of inefficiencies in energy conversion and transmission.
- Module degradation – the systematic decrease in PV panel performance, which should be monitored over time to plan replacements or warranty claims.
Measuring these indicators requires access to data from various sources, including SCADA, weather stations, inverters, or energy meters. Their analysis allows not only for the detection of ongoing problems but also for forecasting future performance and planning preventive actions.
The role of SCADA systems
The SCADA (Supervisory Control and Data Acquisition) system is a basic monitoring tool for photovoltaic farms. It provides real-time data on inverter operation, voltages, currents, device statuses, and technical alarms. It allows managers to respond to failures and perform basic technical analyses. However, from the perspective of farm optimization and long-term performance analysis, SCADA does not provide a sufficiently broad view of the situation.
SCADA’s limitations stem from its architecture. These systems are primarily designed for automation and technical supervision, not for advanced data analytics. They often lack the ability to flexibly process historical data, compare KPIs over months, or integrate with external data such as weather forecasts or past failure reports. Moreover, SCADA’s data presentation – in the form of charts or alarms – can be unintuitive for non-technical users. As a result, investors and farm owners do not always have a real insight into how their power plant operates and whether the obtained results align with project assumptions or PPA agreements.
Advanced analytics and KPI reporting
In response to these limitations, the importance of specialized analytical platforms that can collect and process SCADA data – but also combine them with other information sources – is growing. For example, integrating data from meteorological systems, energy consumption meters, thermal imaging cameras, or service reports allows for a more accurate assessment of the farm’s technical condition.
Such platforms make it possible to create KPI reports tailored to different user needs – from O&M technicians to operational managers and investors. Data are transformed into clear indicators and visualizations that help answer questions such as: “When to schedule maintenance?”, “Which components need replacement?”, “Where do recurring problems occur?” or “Which inverters consistently produce less energy?”
Advanced analytics also enables farm-to-farm comparisons, making it easier to identify underperforming units. This allows owners of multiple installations to manage them more efficiently and consistently improve their profitability.
How to interpret KPIs without drawing incorrect conclusions?
Analyzing KPIs requires the right tools but also an understanding that data do not exist in isolation from the technical and environmental context. Misinterpretations most often occur when KPIs are analyzed statically, without considering seasonality, weather changes, or the specifics of a given component. For example, a drop in energy production does not always mean a failure. It can result from cloudy days, shading, or module soiling, which in itself may not require immediate action. Similarly, lower PR is not always a sign of inefficiency. If input data (e.g., irradiance measurements) are incorrect or poorly calibrated, the result will be misleading.
In practice, this means that KPI indicators should be analyzed in combination with external data and by teams experienced in interpreting PV parameters. Calibration and standardization of data are also important. Different systems may use different definitions of the same indicators, which makes comparisons difficult and may lead to conflicting conclusions.
SCADA system data are an important source of information, but only the analysis of the right KPIs allows for a full assessment of PV farm performance and accurate operational decisions. At Nomad Electric, we combine analytical expertise with experience in O&M management, transforming data into concrete actions that increase the profitability of photovoltaic installations.
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