An intelligent real-time power management system with active learning prediction engine for PV grid-tied systems

• Published in: Journal of cleaner production Vol. 205; pp. 252 - 265
• Main Authors: Kow, Ken Weng, Wong, Yee Wan, Rajkumar, Rajprasad, Isa, Dino
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 20.12.2018
• Subjects: Active learning; Artificial intelligence; BESS; Micro-grid; Power fluctuation; Prediction; Micro-grid; Power fluctuation; BESS; Active learning; Artificial intelligence; Prediction
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2018.09.084

In this paper, an incremental unsupervised neural network algorithm namely memory self-organizing incremental neural network (M-SOINN) is proposed to first predict the output power and subsequently detect the occurrence of power fluctuation events in a photovoltaic microgrid system. The M-SOINN uses clustering technique to form the data map, identifies the most similar patterns to predict the photovoltaic output power and then detects power fluctuation events. A novel memory layer is incorporated to establish the time-series learning. By using real life environment data, the proposed M-SOINN based real-time prediction engine detects power fluctuation events with a high detection rate of 92.69% and it outperforms the conventional self-organizing map (SOM), k-nearest neighbour (KNN), focused time delay neural network (FTDNN), and nonlinear autoregressive with exogenous input (NARX) networks. The system is simulated in the PSCAD environment and later experimentally. The proposed M-SOINN is then integrated into a power management system to mitigate power fluctuation events of the photovoltaic grid-tied system. Results show that the proposed power management system reduces 79.62% of power fluctuation events with an energy loss of 2.16% and battery state-of-charge maintains within 30%–100%. The proposed system outperforms hourly rule-based controller and the ramp rate controller by 44.02% and 27.57%, respectively in terms of the mitigated power fluctuation events. •Use a novel memory layer to establish time-series learning.•Requires no historical data to train the AI and it is life-long learning.•The dynamic stress of the battery are greatly reduced.•Detects 92.69% of power fluctuation events in advanced.•Mitigates 79.60% power fluctuation events with energy loss of 2.16%.