With the continuous expansion of photovoltaic installed capacity, the traditional model of "heavy construction, light operation and maintenance" is being reshaped by AI technology. The operation and maintenance of photovoltaic power plants are moving from manual inspection, regular cleaning, and post maintenance to component level intelligent diagnosis, predictive maintenance, and accurate prediction of power generation.
Component level diagnosis: Let each photovoltaic panel speak up
Traditionally, the thermal spots, hidden cracks, and PID attenuation of a photovoltaic panel need to be inspected piece by piece by inspection personnel holding infrared thermal imagers. A hundred megawatt power station has tens of thousands of components, with long manual inspection cycles and high missed detection rates. Nowadays, drones are equipped with infrared and visible light dual light cameras, which, combined with visual models, automatically recognize the distribution of hot spots, occlusion, and dust. With a single flight, they can complete a full station scan, and the defect localization accuracy can reach component level. Some schemes also combine EL (electroluminescence) detection and IV curve data to achieve early detection of hidden defects such as cracks during nighttime or low irradiation periods.
Predictive maintenance: from 'fix when it breaks' to' intervene in advance '
The failure of key equipment such as inverters and transformer boxes is often preceded by minor abnormalities in temperature, voltage, and current. AI can predict typical faults such as inverter module aging and capacitor bulging several days in advance by jointly modeling historical operating data and meteorological data of devices, turning passive repairs into planned maintenance and reducing power generation losses. The decision-making process for component cleaning is also becoming intelligent: by combining dust deposition models, rainfall forecasts, and electricity price curves, AI can calculate the optimal solution for "when to clean and where to clean", avoiding cleaning costs exceeding the revenue from additional electricity generation.
Power generation prediction: from empirical estimation to meteorological large-scale models
The accuracy of photovoltaic output prediction directly affects power trading and grid scheduling. The new generation of solutions combines numerical weather forecasting with large-scale meteorological models, significantly improving the accuracy of short-term forecasts for cloud cover, aerosols, and irradiance; The ultra short term power prediction model trained with historical power generation data from the station has significantly improved the accuracy of recent predictions compared to traditional methods, providing a basis for spot market quotations and energy storage charging and discharging strategies.
From Single Station to Cluster: The Scale Value of AI Operations Platform
The top operation and maintenance service providers have accumulated AI capabilities into a group level platform, which integrates dozens of power stations distributed across the country to achieve a digital closed loop of equipment ledger, alarm, and work order. For power plant investors, the increase in power generation and the decrease in operation and maintenance costs brought about by AI operation and maintenance are becoming important variables in the valuation of power plant assets.
The operation and maintenance of photovoltaic AI still face challenges such as data silos and missing standards, but the direction is clear: the future power station will be an algorithm driven "self diagnostic power generation network".
[Reference source] This article comprehensively compiles technical data and operational service provider information publicly released in the photovoltaic industry.