Temperature and irradiance reset function

Hi, everyone,

I am working on a project that focuses on the maximum power point tracking of solar PV under partial shading condition using Deep Q-Network. The system input reset function randomizes the values of temperature and irradiance at each training time step. After training the algorithm, the deep learning agent is able to track the PV maximum power at standard testing conditions (1000W/m2 and 25 deg.). However, when the irradiance drops below 800W/m2, the power level falls below than expected. For example, if the pv power level is supposed to be 798W, it drops to 705W.

Answer 1

[Karan Singh]

Hi Sampson,

From what I understand, the project involves training the DQN algorithm to track the PV system's maximum power under standard testing conditions. However, when the irradiance drops below 800W/m2, the power level observed falls below the expected level.

When the irradiance drops below 800W/m2, it is expected that the power level will decrease. However, if the power level falls significantly lower than expected, there could be several factors contributing to this issue. 

There are a few approaches you can consider:

1. Insufficient training data: The DQN agent might not have enough training data for low irradiance conditions. Ensure that the training dataset includes enough samples covering a wide range of irradiance levels, including low irradiance scenarios.
2. Exploration-exploitation trade-off: The DQN agent might not be exploring enough during training to discover optimal policies for low irradiance conditions. Adjusting the exploration rate or using exploration strategies like epsilon-greedy or Boltzmann exploration.
3. Limited action space: The action space of the DQN agent may not be suitable for handling low irradiance conditions. Consider expanding the action space to allow for more fine-grained control over the PV system parameters, such as adjusting the voltage or current levels. 

Attached below are some documentation links that you may find helpful:

• Get Started with Reinforcement Learning Toolbox (mathworks.com)
• Deep Learning Toolbox Documentation (mathworks.com)

Hope this helps!

[Sampson Nwachukwu]

Hi, Karan,

Thank you for your detailed response. I appreciate!