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As an open electromagnetic system, internal sensors of photovoltaic (PV) power plants not only withstand severe stresses from their own power circuits but also are long-term exposed to a complex electromagnetic environment composed of adjacent equipment, grid background, and natural environment. The coupling effect of this environmental field does not always manifest as violent faults, but more often as a slow and hidden “chronic poisoning”—causing unpredictable drift in sensor measurement accuracy and gradual attenuation of the long-term credibility of system observation data. This problem is particularly prominent in large-scale power plants, eroding the reliability foundation of data-driven advanced applications such as energy efficiency analysis, equipment health prediction, and power generation assessment. This paper will analyze the generation mechanism of this hidden distortion and explore engineering methods to construct an “environmental perception-data self-purification” closed loop for maintaining measurement credibility.
I. Sources of Hidden Distortion: Uncontrollable Environmental Field Coupling Paths
The electromagnetic environment of a power plant is intertwined with conducted interference and radiated interference, and its impact on sensors far exceeds the pulse and surge scenarios covered by standard EMC tests.
1. Conducted Coupling: “Slow Infiltration” of Grid Background Pollution
Long-term impact of background harmonics and interharmonics: The power grid is not a pure power frequency source. Especially in areas with high proportions of new energy integration, there are abundant background harmonics and interharmonics generated by wind turbines and frequency converters. These frequency components are directly conducted to the AC side of the inverter through the grid connection point. Current and voltage sensors, especially those optimized for control bandwidth, are not completely immune to these low-frequency interferences, and their output signals will be superimposed with a long-term, slowly fluctuating systematic bias related to grid quality.
Slow fluctuations of Earth Potential Gradient (EPG): The grounding grid of a power plant is not an ideal equipotential body. When adjacent large loads start/stop or a single-phase ground fault occurs, ground current will form a potential gradient on the grounding grid. For sensors referenced to the ground, their “reference zero point” itself drifts slowly with EPG, resulting in measurement values containing intractable common-mode errors.
2. Radiated Coupling: “Imperceptible Injection” of Spatial Stray Fields
Cross-modulation of operating states of adjacent equipment: Broadband electromagnetic radiation generated by box-type transformers, reactive power compensation devices, and even adjacent inverters in the power plant will couple to insufficiently shielded sensor signal lines or PCB traces. This coupling is not continuous interference but forms a complex modulation relationship with the operating state of the interfered equipment, leading to dynamic changes in the background noise and nonlinearity of the sensor.
“Catalytic” effect of natural environment: Humidity and contamination will change the insulation characteristics and dielectric constant of the sensor housing, terminals, and PCB surface, thereby altering their coupling efficiency to external radiation fields. The same sensor may have significant differences in its anti-radiation interference capability between dry and clean spring and humid, salt-laden summer. This time-varying characteristic further complicates the error model.
II. Consequences of Credibility Degradation: From Data Distortion to Decision-Making Errors
Errors caused by environmental field coupling are not constant. Their slow time-varying and nonlinear characteristics limit the effectiveness of traditional periodic calibration.
1. The “Fog” in System Efficiency Analysis
“False optimal” MPP tracking: If there is a slow, nonlinear environmental bias in the string current or voltage measurement values, the “maximum power point” tracked by the MPPT algorithm will be a false optimal point defined by contaminated data. This causes the PV array to operate in the suboptimal neighborhood of the true maximum power point for a long time, resulting in imperceptible cumulative power generation loss.
Shaken foundation of power plant Performance Ratio (PR) calculation: PR is a core indicator for measuring power plant health, and its calculation relies heavily on sensor data from various measurement points. When the measurement values at the combiner box and inverter AC side deviate systematically from the true values due to environmental field coupling, the calculated PR will lose accurate horizontal and vertical comparability, masking real performance degradation.
2. Misjudgment in Fault Early Warning and Health Management
Contamination of feature extraction: Algorithms for DC arc early warning based on current harmonic analysis and fault early warning based on temperature trend prediction rely on feature values extracted by sensors, such as energy in specific frequency bands and temperature rise slope. Environmental interference will directly contaminate these features, leading to increased false alarm rates or missed alarm rates.
Inaccurate equipment degradation models: Degradation models used to predict the lifespan of inverters and transformers require long-term, pure operating data for training. Data mixed with environmental coupling errors will guide the model to learn incorrect degradation trajectories, making prediction results deviate from reality and losing maintenance guidance value.
III. Constructing an “Environmental Perception-Data Self-Purification” Closed Loop
To resist the chronic erosion of the environmental field, the sensing system needs to shift from passive tolerance to active perception and dynamic purification.
1. Integrating “Environmental Sentinels” into Sensing Nodes
Multi-physical quantity in-situ monitoring: Install miniaturized broadband electric field probes, magnetic field probes, and temperature-humidity sensors next to key current/voltage sensor nodes. These “environmental sentinels” do not measure the main circuit but quantitatively monitor the electromagnetic field intensity spectrum and micro-environmental state at the sensor’s location in real time.
Establishing a “measurement error-environmental parameter” correlation database: Through long-term data accumulation and machine learning, offline or online learn the typical error vectors of specific sensors under combinations of specific environmental field intensity, spectrum, and temperature-humidity. This constitutes a “digital medical record” for hidden distortion.
2. Implementing Real-Time Self-Purification of Data Streams
Feedforward compensation based on environmental sensing: Before advanced applications such as power calculation and feature extraction, use real-time acquired environmental field data as input to query or calculate the preset error model, and perform feedforward compensation correction on the original measurement values. This is equivalent to providing the sensor with a dynamic “electromagnetic environment filter”.
Adaptive fusion and confidence weighting of multi-source data: For the same physical quantity, the system can obtain multiple estimated values from sensors at different locations and with different principles. Based on the current environmental field exposure level and self-health status of each sensor, the system dynamically assigns confidence weights to their data outputs, giving higher weights to data less affected by interference during fusion to achieve more robust state estimation.
3. System-Level Collaboration and Calibration Evolution
Online self-learning using grid “quiet periods”: During late-night periods when the grid background is purest and the power plant itself is not generating electricity, the system can actively run self-learning programs to fine-tune and update environmental error model parameters by injecting small test signals or comparing readings from different sensors, realizing slow self-evolution of the model.
Digital Twin-driven virtual calibration field: In the Digital Twin model of the power plant, not only electrical and thermal models are included, but also its electromagnetic environment model. When evaluating an operation and maintenance decision, its implementation effect under different electromagnetic environment scenarios can be simulated in the digital world to pre-evaluate the potential impact on sensor data credibility, thereby selecting the optimal plan.
IV. Conclusion: Paradigm Shift from “Measurement Tools” to “Environmental Interaction Agents”
The long-term reliable operation and value excavation of PV power plants are increasingly dependent on high-quality and high-credibility data streams. Attributing the challenges faced by sensing systems solely to their own static accuracy, bandwidth, or temperature drift is no longer sufficient to cope with the real, dynamic, and complex outdoor electromagnetic environment. As a chronic, time-varying, and nonlinear interference source, the coupling effect of the environmental field is silently eroding the credibility foundation of the entire power plant’s digital system.
Therefore, the design concept of the next generation of PV sensing systems must undergo a fundamental leap: sensors should no longer be isolated measurement tools encapsulated in housings and only connected to wires, but evolve into “environmental interaction agents” integrating main electrical quantity sensing, micro-environmental sensing, and local intelligent processing capabilities. They can not only report “what I measured” but also evaluate “under what interference environment I completed the measurement” and perform preliminary “environmental purification” processing on the original data.
This signifies that the digitalization process of PV power plants has entered the deep water zone—shifting from pursuing “volume and comprehensiveness” of data to pursuing “truth and credibility” of data. Only by establishing a perception system immune or adaptive to environmental interference can the power generation analysis, equipment health management, and asset value assessment of the power plant be truly built on a solid and reliable foundation. This allows data-driven intelligent decisions to penetrate the fog of complex environments and illuminate every corner of the full-lifecycle management of PV assets.
