With the continuous expansion of the scale of photovoltaic power plants and the widespread popularization of distributed photovoltaics, the traditional operation and maintenance mode relying on manual inspection can no longer meet the needs of refined management. By integrating digital technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI), intelligent operation and maintenance technology realizes real-time monitoring, fault early warning, accurate diagnosis, and optimal control of photovoltaic systems. It has become a core means to improve the power generation efficiency of power plants and reduce operation and maintenance costs, maximizing the full-life-cycle value of photovoltaic power plants.

Data collection is the foundation of intelligent operation and maintenance. The deployment of a distributed sensor network realizes comprehensive perception of the state of the photovoltaic system. At the module level, equipped with micro-power sensors and temperature sensors, it can collect real-time parameters such as the power generation power and surface temperature of each module with an accuracy of ±0.5%; at the inverter level, the power electronic monitoring module realizes high-frequency collection of electrical parameters such as input and output voltage, current, and power factor; at the environmental level, solar irradiance sensors, wind speed and direction sensors are deployed to obtain the external environmental data of the photovoltaic system, providing a basis for power generation efficiency analysis. These data are transmitted to the cloud platform in real-time through 5G or industrial Ethernet, forming a massive operation and maintenance database.

The application of artificial intelligence algorithms realizes the transformation from data to decision-making. The fault diagnosis model based on machine learning can accurately identify common faults such as hidden cracks, hot spots, and shading by analyzing the deviation between the module power curve and the normal curve, with an accuracy rate of more than 95%, and can predict potential fault risks 3-7 days in advance. For string inverters, the current harmonic analysis algorithm can quickly locate the faulty string, reducing the fault detection time from several hours to a few minutes.

Optimizing power generation efficiency is the core goal of intelligent operation and maintenance. Combining historical power generation data and weather forecast information, the power prediction model can realize accurate prediction of power generation power in the next 24 hours with an error rate of less than 8%, providing a reliable basis for power grid dispatching and power trading. Aiming at the shadow shading problem of distributed photovoltaics, the intelligent light-tracking system adjusts the module inclination and azimuth angle in real-time to track the sun's trajectory. Combined with multi-branch MPPT (Maximum Power Point Tracking) technology, the power generation efficiency under shading conditions is increased by 10%-15%. In large-scale ground power stations, UAV inspection combined with thermal imaging technology can quickly complete the inspection of power stations covering tens of square kilometers, and the efficiency of detecting problems such as module hot spots is 50 times higher than that of manual inspection.

The intelligent operation and maintenance platform has also realized the digital upgrading of operation and maintenance management. Through the work order system, it automatically dispatches fault maintenance tasks, and optimizes the allocation of maintenance resources by combining personnel positioning and equipment ledger management, reducing the fault repair time by more than 30%. The data analysis module regularly generates power station operation and maintenance reports, providing data support for decisions such as module replacement and system upgrading, which increases the average annual power generation efficiency of the power station by 5%-8% and optimizes the operation and maintenance costs effectively.

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