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The hot spot effect of photovoltaic (PV) modules is a critical factor leading to power degradation, accelerated aging, and even fires. Traditional operation and maintenance rely on periodic infrared inspections or simplified module-level temperature measurement, which suffer from fundamental flaws such as delayed response, ambiguous positioning, and inability to distinguish the causes of hot spots. With the continuous increase in module power and system voltage levels, the risk and destructiveness of hot spots induced by local shading, cell cracks, bypass diode failure, etc., have grown exponentially. Beyond the traditional “temperature alarm” paradigm, this paper aims to explore the construction of an online monitoring network based on distributed, high-precision temperature sensing nodes, starting from the multi-physics coupling mechanism of hot spot generation. Combined with electrical performance data, it realizes early warning, precise localization, and intelligent root cause diagnosis of hot spots, thereby transforming hot spot management from post-event disposal to pre-event prevention and precise intervention.
I. Multi-source Mechanism of Hot Spot Effect and Evolution Characteristics of Temperature Field
A hot spot is essentially a phenomenon where local cells in a module generate concentrated heat due to power dissipation under reverse bias. Its severity and temperature field distribution characteristics are highly dependent on the inducing mechanism.
1.1 Differences in Heat Generation and Heat Transfer Paths Under Different Failure Modes
Local Shading (e.g., leaves, bird droppings): Shaded cells cannot generate photocurrent and become loads consuming current from the series circuit. The heat source is relatively uniform, the temperature rise rate is moderate, and the temperature field range is strongly correlated with the shape of the shade.
Cell Microcracks (hidden cracks): Cracks block current transmission paths in partial cell areas, forming local high-resistance regions. Heat generation is concentrated and intense, with the heat source being linear or point-like and an extremely fast temperature rise rate. Heat is conducted from the silicon wafer and EVA film to the glass and backsheet, forming a characteristic radial temperature gradient.
Bypass Diode Failure: When a diode fails open-circuited, shading of any cell in the substring it protects triggers severe reverse bias of the entire substring, expanding the heat source from a single cell to the entire substring. The heating area is large, but the peak temperature may be lower than the extreme hot spot of a single cell. A short-circuited diode failure causes the substring to be permanently bypassed, resulting in no hot spot but continuous power loss.
1.2 Limitations of Traditional Temperature Sensing Schemes: Sparse, Low-Dimensional, and Asynchronous
The current mainstream scheme is to place 1-2 temperature sensors in the junction box or at the center of the module backsheet. This “single-point sparse sampling” method has fatal flaws:
Insufficient Spatial Resolution: Unable to capture the heterogeneity of temperature distribution on the module surface, making it highly likely to miss local hot spots far from the sensors.
Delayed Temporal Response: Changes in backsheet temperature lag behind cell heating by minutes and are greatly affected by ambient wind speed, failing to reflect rapid temperature rise processes.
Single-Dimensional Information: Isolated temperature points cannot distinguish the aforementioned different failure modes or provide key diagnostic features such as temperature field morphology and gradient.
II. Construction of a Distributed High-Precision Temperature Sensing Network
Achieving early and precise detection of hot spots requires systematic upgrades in three dimensions of the sensing network: “spatial density,” “measurement accuracy,” and “temporal synchronization.”
2.1 Deployment Strategy for High Spatial Resolution Nodes
Matrix Sensors Based on Printed or Flexible Circuits: Low-temperature-drift, high-linearity thin-film platinum resistors or digital temperature sensors are integrated in a grid array on the module backsheet or a dedicated flexible substrate, attached to the inner side of the backsheet. Spatial resolution can be improved from the traditional single point to dozens of points, sufficient to generate a 2D temperature cloud map of the module.
Focused Monitoring of Key Areas: Based on thermodynamic simulations and historical fault data, identify high-risk hot spot areas such as those near the frame, under the junction box, and dust-prone central areas of the module, and increase the density of sensor nodes in these regions.
Wireless Ad Hoc Network and Energy Harvesting: To reduce wiring complexity, low-power wireless sensing nodes can be adopted, with energy self-sufficiency achieved through temperature differences between the backsheet and the environment or PV micro-power generation under low-light conditions, forming an scalable distributed monitoring network.
2.2 Key Performance Requirements of Sensing Nodes
Absolute Accuracy and Long-Term Stability: Hot spot early warning requires judging absolute temperature rise thresholds, demanding sensors with high absolute accuracy (±0.5°C) and extremely low annual drift rate (<0.1°C/year) over the entire operating temperature range (-40°C to +100°C).
Fast Response Time: To capture rapid temperature rises caused by hidden cracks, the sensor’s thermal response time constant should be much shorter than the hot spot formation time, targeting the second level. This requires sensors with low packaging thermal mass and good thermal contact with the backsheet.
Synchronous Sampling Capability: All nodes in the network must achieve millisecond-level time-synchronized sampling via wired or wireless means to obtain a global temperature “snapshot” at the same moment, avoiding misjudgment of temperature field distortion due to asynchronous sampling.
III. Intelligent Diagnosis Algorithm Based on Fusion of Temperature Field and Electrical Data
Multi-dimensional sensing data itself does not generate value; intelligent algorithms are required to extract state features and implement diagnosis.
3.1 Feature Extraction: From Temperature Data to Field and Sequence Features
Spatial Domain Features: Calculate temperature field statistics, temperature gradients in specific directions, and identify the morphology of high-temperature regions.
Temporal Domain Features: Analyze the temperature rise rate, time to reach steady state, and temperature fluctuation frequency of specific points after changes in irradiance or the appearance of shading.
Electro-thermal Correlation Features: Synchronously acquire string current, voltage, and approximate operating points of each module or substring, and calculate the deviation between “measured temperature and expected temperature.” The expected temperature can be estimated using ambient temperature, irradiance, wind speed, and the module’s nominal thermal characteristic model.
3.2 Diagnosis and Early Warning Decision Tree Model
Combining the above features, a hierarchical decision model is constructed:
Level-1 Warning: Trigger a primary alarm if the temperature of any sensor node exceeds the ambient temperature + ΔT1 (e.g., 15°C) threshold or the temperature field standard deviation exceeds the threshold.
Level-2 Diagnosis: Analyze the spatial morphology and temperature rise sequence of high-temperature regions. Rapid point-like temperature rise → suspected hidden crack; slow large-area temperature rise synchronized with irradiance changes → suspected shading; high-temperature area covering the entire substring with abnormal substring voltage → suspected diode failure.
Level-3 Evaluation: Based on the temperature rise rate, absolute temperature, and the module’s position in the string (affecting reverse bias magnitude), use empirical or physical models to estimate the accelerating effect of thermal stress on EVA delamination and backsheet carbonization, predict the remaining safe operating time or lifespan loss, and dynamically adjust maintenance priorities.
IV. Conclusion
The management of PV module hot spots is evolving from a “discrete snapshot” mode relying on periodic manual inspections to a “holographic dynamic video” mode based on continuous online sensing. The core driver of this transformation is the construction of a “digital twin” that takes a distributed, high-precision, high-response-speed temperature sensing network as the physical foundation and integrates electro-thermal multi-physics data with intelligent diagnosis algorithms as the decision-making core. By capturing subtle spatiotemporal evolution features of the temperature field and deeply correlating them with electrical states, the system can not only issue early warnings hours or even days before a hot spot reaches destructive temperatures but also accurately locate specific cells and determine the root cause of failure, guiding maintenance personnel to implement the most effective interventions. This is not only an upgrade of sensing technology but also an inevitable path for refined and intelligent operation of PV assets, providing a crucial technical foundation for the long-term safe, efficient operation and asset value protection of power plants.
