Maintenance and Operation Optimization Algorithm of PV Plants under Multiconstraint Conditions

Abstract

With the rapid increase in the photovoltaic (PV) plants, the real-time operation and maintenance of photovoltaic power generation equipment is very important. The maintenance and dispatching of decentralized power stations is still one of the key issues affecting the operation safety of photovoltaic power stations. However, most of the photovoltaic power stations in China fail to rationally optimize the utilization of resources and time. The current study puts forward effort implementation via genetic algorithm-based multiconstrained optimization methodology. The proposed study optimally overrides the traditional PV plant operation and maintenance dispatching operations with automation and reliability. The proposed study is also applicable to multiple PV plants, multiple maintainers, multipoint departure, different dispatching conditions, and cost considerations. We propose an MOOA algorithm to solve this issue, and we strongly believe that, by defining a suitable fitness function, the convergence speed and optimization ability can be greatly improved, and this study puts a forward step.

Employment of Intelligent Predictive Maintenance on Thermal Power Plant Component Parts Taking Condenser Vacuum as a Case Study

“…In the literature abounds the applications of ML in facilities other than power systems (Bahri et al, 2009;Brahm de Jong, 2015;Bayoumi and McCaslin, 2017;Vuyyuru, 2018;Ahmad et al, 2018;Pai et al, 2019;Long et al, 2020;Niyonambaza et al, 2020;Girit et al, 2021;Miao, 2021;Li et al, 2021;Wei et al, 2021). In similarly vein, Hua et al (2020), Olesen and Shaker (2020) and Yang et al (2020) are instances of several cases of deploying ML in power systems infrastructures other than thermal generating plants. In Hua et al (2020), a maintenance and operation optimization method to tackle the challenges associated with traditional photovoltaic (PV) power plant maintenance and the convergence speed and optimization ability were considerably improved by defining an appropriate fitness function using the ML method.…”

“…In similarly vein, Hua et al (2020), Olesen and Shaker (2020) and Yang et al (2020) are instances of several cases of deploying ML in power systems infrastructures other than thermal generating plants. In Hua et al (2020), a maintenance and operation optimization method to tackle the challenges associated with traditional photovoltaic (PV) power plant maintenance and the convergence speed and optimization ability were considerably improved by defining an appropriate fitness function using the ML method. The technology optimally automated and reliably overridden typical PV plant operation and maintenance dispatching activities.…”

Bi-Objective Preventive Maintenance Scheduling Optimization of Photovoltaic System based on Availability

“…Villiani et al [2] performed RCM based PV PM based on Failure Mode and Effects Analysis (FMEA) on real life plants based on expert experiences. Machine learning approach was used by Hua et al [16] to develop a prototype maintenance policy under multiple constraints such as cost, multiple maintainers, multipoint departure, different dispatching conditions applicable to multiple PV systems.…”