@inproceedings{scholars17245,
             doi = {10.1109/ICFTSC57269.2022.10040061},
            year = {2022},
            note = {cited By 1; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671},
           pages = {82--86},
           title = {Explainable Artificial Intelligence Applied to Deep Reinforcement Learning Controllers for Photovoltaic Maximum Power Point Tracking},
         journal = {2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
        keywords = {Controllers; Decision making; Deep learning; Learning algorithms; Learning systems; Lime; Maximum power point trackers; Solar panels, Deep reinforcement learning; Duty-cycle; Explainable artificial intelligence; Interpretable machine learning; Machine-learning; Maximum Power Point Tracking; Photovoltaic systems; Reinforcement learning agent; Reinforcement learnings; Tracking controller, Reinforcement learning},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149135959&doi=10.1109\%2fICFTSC57269.2022.10040061&partnerID=40&md5=1b0c09d3cfc3b18639a92f81defe4469},
        abstract = {Deep Reinforcement Learning (DRL) algorithms have been applied to extract maximum power from photovoltaic (PV) modules under a variety of environmental conditions. However, it is difficult for a human to explain how a DRL-based maximum power point tracking (MPPT) controller works as it consists of Neural Networks (NNs) that are generally complex and non-linear. Various Explainable Artificial Intelligence (XAI) techniques have been proposed to interpret NNs in power system applications, but MPPT controllers have yet to be analyzed. This paper presents the application of XAI techniques to the DRL agents for MPPT. Two distinct DRL agents were developed, one with and one without the information of the converter's duty cycle, using the Deep Deterministic Policy Gradient (DDPG) algorithm and analyzed using XAI techniques, namely Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). The results reveal that the converter's input power is the most crucial information for the DRL agents when the converter is operating away from the maximum power point. When the converter approaches operation at the maximum power point, the DRL agents are significantly dependent on the power differential of the converter across time. If the information about the converter's duty cycle is available, the DRL agents are significantly reliant on the converter's duty cycle and disregard other observations for decision-making. {\^A}{\copyright} 2022 IEEE.},
          author = {Tan, P. S. and Tang, T. B. and Ho, E. T. W.},
            isbn = {9798350334548}
}