Dirt sensor for photovoltaic surfaces
A monitoring device measures insulation resistance and environmental data to accurately assess soiling on photovoltaic surfaces, reducing unnecessary cleaning interventions and costs by distinguishing soiling from natural aging.
Patent Information
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2023-12-20
- Publication Date
- 2026-03-13
AI Technical Summary
Existing methods for assessing soiling on photovoltaic surfaces fail to distinguish between soiling and natural aging, leading to unnecessary frequent cleaning interventions and high maintenance costs.
A monitoring device that continuously measures the insulation resistance of a surface to determine soiling levels relative to an initial clean state, using a power supply, voltage and current measurement system, and environmental data to calculate soiling levels.
Provides continuous, low-cost, and human-intervention-free monitoring of surface cleanliness, accurately distinguishing soiling from natural aging, and triggering maintenance only when necessary.
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Abstract
Description
Title of the invention: Soil sensor for photovoltaic surfaces technical field
[0001] The present exposition relates to a soiling sensor establishing a level of soiling of a surface relative to an initial state of the surface and a method for calibrating such a sensor. STATE OF THE ART
[0002] It is common to have to assess the cleanliness of a surface in order to plan its maintenance, particularly in the case of solar power plants. These installations, which can be classified as photovoltaic or thermal, are indeed particularly sensitive to problems of soiling or dirt on their surfaces. For example, a so-called "tower" solar power plant, in which the radiation is concentrated towards a fixed focal point placed at a height, generally uses a minimum of several hundred heliostats, representing tens of thousands of square meters of mirrors whose soiling, by reducing their reflective capacity, directly impacts the overall efficiency of the installation. In a power plant using photovoltaic panels, the problem is the same and is even more pronounced when the plant is located in a desert region.Dust is then responsible for losses ranging from 2 to 10% of the total yield.
[0003] It is therefore necessary to regularly clean a large number of surfaces, which entails significant additional costs that must be controlled. To plan cleaning operations, it is necessary to measure the soiling rate. For this purpose, it is common practice to measure the short-circuit current of a clean cell and that of a dirty cell and compare the two after correcting for temperature and illumination; this ratio gives the soiling rate.
[0004] This measurement consists of physically bringing the positive and negative polarities or terminals, i.e., an anode and a cathode of a reference module or cell into contact, and measuring the current at these terminals, which is directly proportional to the irradiance. By calculating the ratio between the short-circuit current values of a clean reference module and the short-circuit current of a dirty module, a soiling rate can be deduced, since soiling generates a loss of irradiance received by the solar cells and therefore a proportional associated short-circuit current loss.
[0005] However, this measure does not distinguish between losses due to natural aging and the cell, unlike those caused by soiling, requires frequent intervention and sometimes even cleaning of all cells.
[0006] There is therefore a need for a sensor that continuously calculates a soiling level, expressing the loss of efficiency compared to a clean initial state of a surface. Ideally, this sensor is low-cost, simple, and requires no human intervention. It provides qualitative information on soiling, that is, whether the module becomes dirty or not, and, after calibration and the application of a model, provides quantitative information. EXPOSED
[0007] One object of the invention is to remedy the aforementioned drawbacks, by proposing a monitoring device that continuously measures the insulation resistance of the monitored surface in order to deduce a level of soiling defined in reference to an initial "clean" state and the state at the time of measurement based on the fact that a dirty surface conducts electricity better on the surface than a clean surface.
[0008] To this end, according to a first aspect of the invention, a device for monitoring the cleanliness of a surface is proposed, comprising a power supply configured to be connected to the surface in order to energize the surface at two or more points, a system for measuring the voltage and current resulting from the energizing of the surface, a weather station configured to acquire environmental data of the surface, the monitoring device comprising a control unit configured to
[0009] - control the power supply according to a power profile,
[0010] - to determine an insulation impedance from the measured current and voltage induced by the surface,
[0011] - determine a level of surface soiling relative to an initial state "clean" of the surface from impedance measurement and environmental data from a weather station.
[0012] The device according to the invention is advantageously complemented by the following features, taken independently or in one of their technically possible combinations:
[0013] - the control unit and the voltage and current measurement system are configured to determine the surface soiling level from measurements of alternating voltage and current at varying frequencies, deducing from these measurements an electrical capacitance induced by the surface, or from measurements of direct voltage and current, deducing from these measurements an electrical resistance induced by the surface condition,
[0014] - a conductive frame surrounds the monitored surface, the power source being adapted to be connected to the frame on one side and to positive and negative polarities of the surface of another, the voltage and current measurement system measuring the voltage and current flowing within the frame;
[0015] - the power supply is configured to be connected to several of the points of the monitored surface, and the voltage and current measurement system measures the voltage and current flowing within the frame;
[0016] - the environmental data measured by the meteorological station com take a quantity of particles, the mass of the particles, a humidity, a level of illumination, an ambient temperature, a surface temperature and a dew point, rain gauge, wind speed, spectrometer, date and time;
[0017] - the voltage and current measurement system includes a voltage divider bridge voltage and / or a current measurement resistance;
[0018] - the control unit includes a communication system configured to transmit a warning message when the level of soiling exceeds a predetermined threshold;
[0019] - the voltage applied to the surface by the power supply is between 1000 volts and 10,000 volts.
[0020] The invention also relates to a method for calibrating a monitoring device comprising the following steps:
[0021] - to have a reference surface equipped with the monitoring device in an environment environment in which environmental data are controlled;
[0022] - acquire measurements of an insulation resistance of the reference surface and of a level of soiling between states of the reference surface at a succession of predetermined times and an initial "clean" state, with environmental data varying during the succession of times;
[0023] - to establish, from the acquired measurements, a model linking the level of soiling to isolation impedance and environmental data.
[0024] The steps of the process can be supplemented by the following features, taken independently or in one of their technically possible combinations: - the acquisition step includes the acquisition of measurements of volume, surface area covered and mass of the particles deposited on the reference surface during the succession of instants as well as the electrical impedance of the reference surface during the succession of instants, the model being established taking into account these measurements,
[0025] - the model is established by performing a machine learning method regression on acquired measurements. DESCRIPTION OF THE FIGURES
[0026] Other features, purposes and advantages will become apparent from the following description, which is purely illustrative and not exhaustive, and should be read in conjunction with the attached drawings on which:
[0027] Fig. 1 schematically represents a surface monitoring device according to one embodiment of the invention;
[0028] Figure 2 schematically represents a detail of one embodiment of the surface monitoring device;
[0029] Figure 3 is a flowchart illustrating the steps of a calibration process for a monitoring device according to an embodiment of the invention.
[0030] Throughout the figures, similar elements bear identical references. DETAILED DESCRIPTION
[0031] With reference to [Fig. 1], a monitoring device 1 according to the invention is installed to monitor the cleanliness of a surface 2, for example, the surface 2 of a solar module. The monitoring device 1 comprises an electrical power supply 3, connected to the surface 2 so as to energize it. For example, such a power supply 3 could be a battery connected to a voltage boost converter, the converter being connected by conductive cables at two points near the surface. Typically, the voltage is boosted from between 3.7V and 5V up to between 1500V and 20,000V. Such converters have the advantage of being very inexpensive. The surface 2 is also connected to a voltage and current measurement system 4, comprising, for example, a voltage divider and a current-measuring resistor, i.e., a current shunt. The high voltage applied to the surface facilitates current measurement.
[0032] A control unit 5 is configured to control the power supply 3 according to a specific power profile. The power profile applies, for example, an increasing and then decreasing voltage so that the voltage and current measurement system 4 accurately measures the current, which is particularly low. The voltage and current measurements allow the insulation impedance of the surface 2 to be calculated; that is, an insulation resistance of the surface 2 when the voltage of the power profile is direct current, or an electrical capacitance when the voltage of the power profile is alternating current. In the direct current case, the insulation resistance varies according to the amount of dirt present on the surface 2, with the resistance decreasing and the leakage current increasing when dirt particles are deposited on the surface 2.Throughout this description, soiling and fouling means the presence on contact with surface 2 of any particle deposited by the environment, whether it be pollen, soil, sand, etc... Conversely, the "clean" or initial state, which is the state of surface 2 used as a reference, means a state where surface 2 is new and has . a quantity of particles that is zero or negligible.
[0033] The control unit 5 uses an algorithm for determining the soiling of surface 2. Since many environmental factors affect the insulation resistance of surface 2, particularly humidity, the determination algorithm takes into account measurements from a weather station 6. The weather station 6 includes a set of environmental sensors, which may include a particle detector, a hygrometer, a light sensor, a thermometer measuring ambient temperature, a thermometer measuring the temperature of surface 2, a dew point sensor, a rain gauge, an anemometer, a spectrometer, and a clock. Advantageously, the weather station 6 includes several particle sensors configured to detect particles of different sizes and their mass.
[0034] Based on these measurements and the insulation resistance, a model determines a soiling level, which corresponds to a ratio between a current state of surface 2, i.e., at the time of calculation by the control unit 5, the time being defined by an internal clock of the control unit 5, and an initial state of surface 2, which preferably corresponds to the time of commissioning of the monitoring device 1, at which point surface 2 was clean, free of any particles, and was therefore, in the case of a solar module, at its maximum efficiency. When the soiling ratio exceeds a predetermined threshold value, a message can be sent by a communication unit 51 of the control unit 5 to a remote management platform, which can then initiate maintenance operations to restore the full efficiency of the solar modules.
[0035] The supply profile applied by the supply source 3 to the surface 2 can be modified by the control unit 5 according to the weather conditions determined by the measurements of the weather station 6 in order to accurately extract the insulation parameters, in particular the insulation impedance.
[0036] The accuracy of the soiling level determined by the model can be further improved when the control unit 5 commands a power supply profile in which the applied voltage is alternating. Such an alternating power supply profile can be used in succession with the direct current power supply profile described previously. The insulation impedance of surface 2 is then deduced from the alternating voltage and current measurements provided by the measuring system 4 and represents an electrical capacitance of surface 2. In this case, the determination algorithm uses a model that determines, from the electrical capacitance and the data from the weather station 6, a soiling level relative to the initial state. Indeed, the particles on surface 2 form a "parasitic" capacitance, the value of which varies according to the particle size of the particles present.
[0037] Preferably, when using measurements based on alternating current and voltage, the frequency of the supply profile is around 1 Hz to 100 kHz. This allows for greater accuracy in the measurements because the dirt particles induce a different parasitic capacitance depending on their type, a capacitance which is measured within this range.
[0038] The two voltage supply profiles, direct or alternating, can be used independently or one after the other.
[0039] Advantageously, the surface 2 is framed by a conductive frame 7, for example made of aluminum, which is in contact with the entire perimeter of the surface 2. The power supply 3 and the voltage and current measurement system 4 are also connected to the frame 7 on one side and to the positive and negative polarities on the other.
[0040] Insulation resistance is defined as the resistance value between the module frame and the polarities. This depends on material constants (e.g., glass resistivity), the size of the module, and also the surface condition of the module. Insulation resistance can also be the surface resistance, which is measured by measuring probes, typically two or more probes placed on the surface 2. These surface probes can be of any type, for example, electrically conductive aluminum tape from the RAJA™ brand or copper tape from the MANUTAN™ brand, a copper plate, etc.
[0041] The same definition can be applied to the insulation capacity which is measured in the case of alternating currents and voltages.
[0042] The surface probes can be placed or glued onto the surface 2 and can be positioned equidistant from each other on the frame 7. Preferably, their positions are not changed during measurements, in order to avoid parasitic effects, particularly those created by resistances between the edge of the surface 2 and the frame 7.
[0043] The distance between the probes can be substantially less than the distance to frame 7.
[0044] The invention also relates, with reference to [Fig. 2], to a calibration method of a monitoring device 1 according to one of the embodiments above. A first step (SI) consists of placing a reference surface 2, for example a closed volume in a laboratory, in a controlled environment. By reference surface 2, we mean that this surface 2 has area, thickness, and material characteristics similar to the surface 2 that we wish to monitor subsequently under real operating conditions. The controlled environment is preferably enclosed and has continuously measured humidity, illumination, and temperature characteristics. Advantageously, systems allow these characteristics to be varied precisely, in order to subject the surface 2 to the widest possible range of characteristics during an acquisition step (S2). During the acquisition, over a succession of instants constituting the measurement intervals, the impedance The insulation resistance or capacitance of surface 2 is therefore measured for different values of humidity, temperature, and illumination, preferably varying the values one by one so that the measurements cover as many situations as possible. At each time the insulation impedance is measured, the parameters necessary for calculating the soiling ratio are also measured, which can be defined, for example, as follows in the case where surface 2 is a photovoltaic module:
[0045] SRRiso = = = 2.5+b*PM2 W+c*PM225M*PM25tee*PM2 100)
[0046] Where is the soiling ratio;
[0047] The leakage current between the two points of application of the voltage on the dirty module;
[0048] Dref the reference leakage current of the photovoltaic module between the two points ^isoi application of voltage on the reference module, in amperes, measured under controlled conditions;
[0049] RH2 is the humidity at the surface 2 of the reference module, which can be expressed from the ambient humidity and temperature of a module;
[0050] RH1 is the humidity on the surface 2 of the dirty module, which can be expressed from the ambient humidity and the temperature of a module, expressed without units as a percentage;
[0051] T2 clean module temperature in degrees Celsius (°C);
[0052] Tl temperature of the dirty module in degrees Celsius (°C);
[0053] V2 voltage applied between the two points of the self-module in volts (V);
[0054] V1 voltage applied between the two points of the dirty module in volts (V);
[0055] VI and V2 can be used as nonlinear parameters of Rjso;
[0056] A, B, C, D are empirical coefficients estimated in the laboratory, without units;
[0057] PM particle size value that can be classified into categories, for example less than 2.5 pm, between 2.5-10 pm, etc. and
[0058] a, b, c, d, e) laboratory-estimated regression coefficients, unitless.
[0059] The set of measurements acquired is then used to establish (S3) a model or an approximation of the law relating the level of soiling to the insulation impedance and environmental data.
[0060] The model's accuracy is considerably improved when the acquisition step (S2) also includes the acquisition of measurements of the volume, surface area covered, and mass of particles deposited on the reference surface 2 during the succession of time intervals. Even more advantageously, the particles are chosen to be similar or identical to those that will be deposited under real-world conditions on the surface 2. The electrical insulation capacitance of the reference surface 2, which is modified by the particles present, is also measured during the succession of instants and at a frequency of voltage and currents defined in the given range of values and which can be modified, the established model then relating the measurement of capacitance, the measurement of insulation resistance and the measurements of environmental data at a precise instant to the soiling ratio between the precise instant and a reference instant, typically an initial state which can be established in the controlled environment from the reference surface 2.
[0061] Of course, the methods described above are also applicable to the case where the reference surface 2 is framed by a conductive frame 7.
[0062] The model can be established by regression, using all suitable methods, particularly machine learning methods, for example using an artificial neural network.
[0063] The device according to the invention is simple, inexpensive, and allows for continuous monitoring of surface cleanliness without requiring human intervention. The detection method using insulation impedance is particularly well-suited to monitoring the cleanliness of photovoltaic cells, since the measurement is independent of losses due to the natural aging of the module.
Claims
Demands
1. A surface (2) cleanliness monitoring device (1), comprising an electrical power supply (3) configured to be connected to the surface (2) to energize the surface (2) at two or more points, a voltage and current measurement system (4) resulting from the energizing of the surface (2), a weather station (6) configured to acquire environmental data of the surface (2), the monitoring device (1) comprising a control unit (5) configured to: - control the power supply (3) according to a supply profile, - determine from the measured current and voltage an insulation impedance induced by the surface (2), - determine a level of soiling of the surface (2) relative to an initial "clean" state of the surface (2) from the impedance measurement and environmental data from a weather station (6).
2. A monitoring device according to claim 1, wherein the control unit (5) and the voltage and current measurement system (4) are configured to determine the level of soiling of the surface (2) from measurements of alternating voltage and current at varying frequencies by deducing from these measurements an electrical capacitance induced by the surface (2), or from measurements of direct voltage and current, by deducing from these measurements an electrical resistance induced by the state of the surface (2).
3. A monitoring device according to any one of the preceding claims, a conductive frame (7) framing the monitored surface (2), the power supply (3) being adapted to be connected to the frame (7) on one side and to positive and negative polarities of the surface (2) on the other, the voltage and current measuring system (4) measuring the voltage and current flowing in the frame (7).
4. Monitoring device according to claim 3, the power supply (3) being configured to be connected to several of the points of the surface (2) being monitored, and the voltage and current measurement system (4) measuring the voltage and current flowing in the frame (7).
5. A monitoring device according to any one of claims 1 to 4, wherein the environmental data measured by the weather station (6) include a quantity of particles, the mass of particles, humidity, illumination level, ambient temperature, surface temperature (2) and dew point, rain gauge, wind speed, spectrometer, date and time.
6. Monitoring device according to any one of claims 1 to 5, wherein the voltage and current measurement system (4) comprises a voltage divider bridge and / or a current measurement resistor.
7. Monitoring device according to any one of claims 1 to 6, wherein the control unit (5) includes a communication system (51) configured to emit a warning message when the soiling level exceeds a predetermined threshold.
8. Monitoring device according to any one of claims 1 to 7, wherein the voltage applied to the surface (2) by the power supply (3) is between 1000 volts and 10,000 volts.
9. A method for calibrating a monitoring device according to any one of claims 1 to 8, comprising the following steps: - placing (S1) a reference surface (2) equipped with the monitoring device (1) in an environment in which environmental data are controlled; - acquiring (S2) measurements of an insulation resistance of the reference surface (2) and a level of soiling between states of the reference surface (2) at a succession of predetermined times and an initial "clean" state, the environmental data varying during the succession of times; - establishing (S3), from the acquired measurements, a model relating the level of soiling to the insulation impedance and the environmental data.
10. Calibration method according to claim 9, wherein the acquisition step (S2) includes the acquisition of measurements of volume, area covered and mass of the particles deposited on the reference surface (2) during the succession of instants as well as of the electrical impedance of the reference surface (2) during the succession of instants, the model being established (S3) taking into account these measurements.
11. Calibration method according to any one of claims 8 or 10, wherein the model is established (S3) by performing a regression on the acquired measurements using a machine learning method.