Simulation methods for smart alarms

You can choose from the following simulation methods for both misproduction and total outage alarms. The method you choose depends on your system setup and preferences. 

Note

If you have selected a simulation method that does not produce a value, and all your inverters fail simultaneously, a basic default calculation based on the sun's position will be performed. This calculation simply checks if power should be available, without considering the configured nominal power value.

To activate a simulation method, go to the System configuration (Wrench icon) > Monitoring > Alarms >  open Misproduction alarm (or Total outage alarm) > Trigger criteria > Select the desired simulation method.

Inverter comparison

For this method, the target value source is determined by comparing the inverters with each other. The respective input configuration is automatically considered. For this, all inverters are grouped based on their input ratios. Then, the best-performing inverter (the one with the highest normalized power) is established as a reference for other inverters in that group. In the next step, the normalized power is considered a target value for the inverters being compared.

Prerequisites

• All inverters are assigned to a subsystem.

Use case 1: Matching configuration

In this scenario, the VCOM Cloud would form two groups:

• Group 1: Inverter 1 and Inverter 2 (1:1 ratio of 95°/20° and 275°/20°)

• Group 2: Inverter 3 and Inverter 4 (1:1 ratio of 95°/30° and 275°/30°)

Use case 2: Multiple configurations that do not match another configuration

In this scenario, the VCOM Cloud would form three groups: 

• Group 1: Inverter 1 and Inverter 2 (1:1 ratio of 95°/20° and 275°/20°)

• Group 2: Inverter 3 and Inverter 4 (1:1 ratio of 95°/30° and 275°/30°)

• Group 3: Inverter 5 and Inverter 6 (rest)

Use case 3: Single configuration that does not match any other configuration

In this scenario, the VCOM Cloud would form only one group containing all the inverters. Otherwise, there would not be any reference for Inverter 3.

Physical simulation 

The physical simulation uses irradiance values. To gather the necessary data from the system configuration (the orientation and tilts of the system), the specific power (module and inverter) is first calculated for each orientation subsystem. The simulation is then generated based on the irradiance values for each orientation subsystem. Every subsystem is simulated with the irradiation from the sensor that is most like it. Similarity between subsystems and sensors is determined by calculating the correlation between the subsystem’s power measurements and the sensor radiation value.

Prerequisites

• All inverters are assigned to a subsystem

• At least one working irradiance sensor

	System configuration is retrieved, and the orientation subsystem is defined
	Installed sensors in the PV system are mapped to the respective orientation subsystem. Temperature values are taken into consideration
	Specific power for each orientation subsystem is calculated
	The values from the inverters and the installed power of the modules are used to calculate the specific yield for each orientation subsystem
	The target value per interval is calculated at the system level as a specific value
	The target value per interval is calculated at the system level as an absolute value

Machine learning simulation

The machine learning simulation is like the physical simulation, but uses historical data for more accurate simulations. Machine learning algorithms analyze the historical measured data of the PV system and optimize the physical simulation. This reduces the deviation between the measured power and the target power to achieve the most accurate target value. The machine learning simulation method can help you to achieve the most accurate results. To apply machine learning, the system must meet the following criteria:

 Prerequisites

• The number of inverters assigned to each subsystem has to match the number of inverters configured in that subsystem

• There are no "unknown" panels configured in any subsystem

• Manual correction of subsystem configuration is not taken into consideration

• 70% or more of the daytime data points are valid

• At least two weeks’ worth of valid training data within the last 30 days is available

If valid training data is missing, the machine learning-optimized simulation will not be available, and a message is displayed on the simulation configuration page.

Note

The physical and machine learning simulations are also relevant to the solar power chart simulation.