Energy loss analysis

The loss analysis module, uses advanced machine learning and data analytics techniques to categorize energy losses in solar power plants into 15 distinct loss buckets. This classification enables plant operators to detect inefficiencies, prioritize corrective actions, and improve overall performance.

Important: This analytics feature is only for solar assets.

In Maximo® Renewables, the loss analytics module uses a machine learning algorithm that combines pattern recognition and regression analysis to identify and categorize energy losses. By analyzing correlations among various plant parameters, the system creates a digital twin for each inverter, enabling precise detection of even minor performance deviations (as small as 1–2%). This structured approach helps operators quickly locate and address underperforming assets.

Losses are categorized into three main types:
  • Controllable losses
  • Partially, controllable losses
  • Uncontrollable losses

Controllable losses

Controllable losses can be directly addressed and potentially eliminated through operational or maintenance actions.

Types of controllable losses:

  • Downtime loss
    Energy loss due unexpected outages or downtime of any component during sufficient irradiance
  • Inverter efficiency
    Loss in energy due to inverter efficiency. Inverter efficiency varies depending on input energy (DC) and output energy (AC) and might be less than usual depending on the DC power and voltage.
  • Tracker loss
    Loss in energy due to tracker malfunction. This bucket represents only the losses during the controllable modes like cleaning mode, auto mode, and manual mode.
  • Inverter late start
    Energy loss due to delayed inverter wake-up, which is when the irradiance is greater than threshold but the inverter active power is not greater than zero.

Partially controllable losses

These losses can be influenced to some extent but not eliminated. They often depend on environmental and operational factors.

Types of partially controllable losses:

  • Cleaning loss
    Caused by dust, dirt, or inadequate cleaning cycles.
  • Snow loss
    Caused by snow accumulation on solar panels, which blocks sunlight.
  • Shadow loss
    Loss due to inter array shading from nearby table, structures, or solar array.
  • Clipping loss
    Energy is lost when inverters limit output to their maximum capacity.
  • Curtailment (external)
    Grid operators or SLDCs impose a mandatory reduction in output. The loss is calculated as the curtailment percentage multiplied by the plant’s estimated generation.
  • AC loss
    Losses during AC transmission and transformer inefficiencies.
  • Systemic loss
    Losses due to inverter derating, equipment degradation, module damage, and array downtimes.

Uncontrollable losses

These losses result from external or natural factors and cannot be controlled.

Types of uncontrollable losses:

  • Grid outages
    Losses that occur when the external grid fails or disconnects. During an outage the entire plant is treated as nonoperational, and the corresponding loss is estimated.
  • Temperature loss
    Loss in output due to high temperatures compared to standard test conditions.
  • Radiation loss
    Loss in output due to variation in solar irradiation compared to design specifications of the plant.
  • Extreme weather loss
    Loss due to solar tracker outage caused by the events beyond human control, such as natural disasters or severe weather, that prevent the power plant from generating electricity.