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

Abstract

Reconfiguring photovoltaic (PV) array connections among different topologies such as seriesparallel, 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.

A review of optimization modeling and solution methods in renewable energy systems

“…For instance, Xiong et al (2018) utilized a single hidden layer neural network to fit the investment-benefit relationship of distribution networks, resolving an investment decision model. Narayanaswamy et al (2023) introduced regularized deep neural network algorithms to optimize PV topology reconfiguration. Wang et al (2020b) employed a stacked autoencoder (SAE) to evaluate the efficiency of distribution network asset utilization, solving a distribution network expansion planning model across multiple years.…”