Optimizing Solar Power Using Array Topology Reconfiguration With Regularized Deep Neural Networks

Vivek Narayanaswamy, Raja Ayyanar, Cihan Tepedelenlioglu, Devarajan Srinivasan, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Reconfiguring photovoltaic (PV) array connections among different topologies such as series-parallel, bridge-link, honeycomb, or total-cross-tied is a popular strategy to mitigate impediments in power production caused by partial shading. Conventional approaches rely on either by-passing or replacing shaded modules with auxiliary panels through complex control mechanisms, optimization strategies, or simulator driven approaches to obtain the best topology. However, these solutions are not scalable and incur significant installation costs and computational overhead, motivating the need to develop 'smart' methods for topology reconfiguration. To this end, we propose a regularized neural network based algorithm that leverages panel-level sensor data to reconfigure the array to the topology that maximizes power output under arbitrary shading conditions. Based on our simulations that include wiring losses in different configurations, we observe power improvement of up to 11% through reconfiguration. The proposed algorithm can be easily integrated in any cyber-physical PV system with reconfiguration capabilities and is scalable.

Original languageEnglish (US)
Pages (from-to)7461-7470
Number of pages10
JournalIEEE Access
StatePublished - 2023


  • Topology reconfiguration
  • deep neural networks
  • partial shading
  • photovoltaic arrays
  • solar energy

ASJC Scopus subject areas

  • Engineering(all)
  • Materials Science(all)
  • Computer Science(all)


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