EP4158773A1 - System and method for monitoring solar energy systems - Google Patents

Abstract

The present description relates to a system for monitoring the operation of one or more solar panels (102, 104), the system comprising: a communication interface arranged to receive performance data indicating the power generated by the one or more solar panels (102, 104) during a plurality of energy recovery periods; and a processing device arranged to filter the performance data to classify at least one energy recovery period as a nominal period during which the solar radiation was stable, and to process the performance data to detect degraded operation of the one or more solar panels (102, 104) on the basis of the performance data during the at least one nominal period.

Description

DESCRIPTION

System and method for monitoring solar energy installations

The present patent application claims the priority of the French patent application FR2005564 filed on May 26, 2020, which will be considered as an integral part of the present description.

Technical area

The present description relates generally to the field of solar energy recovery, and in particular a system and a method for monitoring one or more solar panels.

Prior art

Solar panels for energy recovery include solar cells which produce a current by photovoltaic effect when exposed to solar energy. The efficiency of a solar panel can be measured as the amount of electrical energy produced for a given amount of solar energy available.

Solar farms having a high efficiency are often arranged in favorable locations for the recovery of solar energy, such as sparsely populated regions in the countryside or deserted relatively far from towns and cities. The performance of solar installations in dedicated solar farms will depend on factors such as the particular technology used for energy harvesting, the age of the solar panel, the state of maintenance of the solar panel, including the amount of dust covering the solar panels. solar cells in the panel, and the orientation of the panel in relation to solar radiation. In general, a relatively high efficiency can be obtained for this type. solar installation. However, solar farms located in sparsely populated areas require the addition of relatively expensive power grids in order to transport the electrical energy generated to the locations where the energy is demanded.

[0004] In recent years, we have seen a growth in photovoltaic installations integrated into buildings (BIPV). Such installations integrate photovoltaic technology into building construction materials in order to transform areas of roofs and / or facades of buildings into solar panels. An advantage of such installations over solar farms located in sparsely populated areas is that electrical energy can be produced close to the location where it is to be used, often at least part of the energy produced being used. in the building itself.

[0005] BIPVs are generally located in urban locations. Thus, the energy efficiency of such installations depends not only on the factors indicated above which affect the efficiency of solar farm installations, but also on other factors such as light reflected from surrounding buildings, shadows cast by surrounding buildings, effects premises caused by the building in which the solar panel is installed, etc. Ensuring near-optimal operating efficiency for a solar installation is important to achieving a reasonable return on investment.

[0006] For a given solar energy installation, on-site inspections can be carried out by a technician to verify that the installation is operating with a performance close to the optimum. However, there is a difficulty in that such on-site inspections are expensive, and that There may be a significant delay between the degradation of the performance of a solar installation and a subsequent scheduled on-site inspection which can identify the problem and correct it. However, there is a difficulty in obtaining a system capable of rapidly detecting a degradation in the efficiency of a solar energy installation, in particular for solar installations located in urban environments, where there are many factors which can. impact performance.

Summary of the invention

According to one aspect of the present description, there is provided a system for monitoring the operation of one or more solar panels, the system comprising: a communication interface arranged to receive performance data indicating the power generated by said one or more solar panels. multiple solar panels during a plurality of energy recovery periods; and a processing device arranged to filter the performance data to classify at least one energy recovery period as a nominal period during which the solar radiation was stable, and to process the performance data to detect degraded operation of said one or more solar panels based on performance data during said at least one nominal period.

[0008] According to one embodiment, the processing device is arranged to perform the classification on the basis of a comparison of at least one parameter with at least one threshold.

[0009] According to one embodiment, the processing device is arranged to select said at least one threshold such that at least 10%, and for example between 10% and 20%, of the energy recovery periods are classified as nominal. According to one embodiment, said at least one parameter comprises at least one element of the group consisting of: a clarity index indicating an intensity and / or a stability of solar radiation during each energy recovery period; a performance report indicating a ratio of electrical performance of said one or more solar panels to a simulated behavior of said one or more solar panels during each energy recovery period; and a data simulation report indicating a difference between a measured output power of said one or more solar panels and a predicted nominal thermal and electrical response of said one or more solar panels during each energy recovery period.

[0011] According to one embodiment, the processing device is arranged to perform the classification on the basis of the clarity index, the performance report, and the data simulation report.

[0012] According to one embodiment, the processing device is arranged to use a nominal sub-assembly, corresponding to the performance data during said at least one nominal period, to characterize the performance of the solar panels, and, by extrapolation and comparison with performance observed during one or more periods of questionable behavior, to assess the losses during these periods.

[0013] According to one embodiment, the processing device is further arranged to generate and transmit to said one or more solar panels a control signal based on the degraded operation detected.

According to one embodiment, the control signal is a deactivation signal for deactivating said one or more solar panels. According to another aspect, there is provided a method of monitoring the operation of one or more solar panels, the method comprising: receiving, through a communication interface, performance data indicating the power generated by said one or more panels solar during a plurality of energy recovery periods; filtering, by a processing device, the performance data to classify at least one energy recovery period as a nominal period during which the solar radiation was stable; and processing the performance data by the processing device to detect degraded operation of said one or more solar panels based on the performance data during said at least one nominal period.

According to one embodiment, the classification is based on a comparison of at least one parameter with at least one threshold.

According to one embodiment, at least 10%, and for example between 10% and 20%, of the energy recovery periods are classified as nominal.

According to one embodiment, said at least one parameter comprises at least one element of the group consisting of: a clarity index indicating a quantity of solar radiation; a performance report indicating a ratio of electrical performance of said one or more solar panels to simulated behavior of said one or more solar panels; and a data simulation report indicating a difference between a measured output power of said one or more solar panels and a predicted nominal thermal and electrical response of said one or more solar panels. According to one embodiment, the classification is based on the clarity index, the performance report and the data simulation report.

According to one embodiment, a nominal sub-assembly, corresponding to the performance data during said at least one nominal period, is used to characterize the performance of the solar panels, and by extrapolation and comparison with the performance observed during one or more periods of questionable behavior, to assess losses during these periods.

According to one embodiment, the method further comprises the generation and transmission to said one or more solar panels of a control signal based on the degraded operation detected.

According to one embodiment, the control signal is a deactivation signal for deactivating said one or more solar panels.

According to another aspect, there is provided a non-transient storage medium storing computer instructions which, when they are executed by a processing device, cause the implementation of the aforementioned method.

Brief description of the drawings

These characteristics and advantages, as well as others, will be explained in detail in the following description of particular embodiments given without limitation in relation to the accompanying figures, among which:

[0025] FIG. 1 schematically represents a solar energy system according to an exemplary embodiment of the present description; FIG. 2 schematically represents a monitoring and diagnostic system of FIG. 1 according to an exemplary embodiment;

FIG. 3 is a sectional view showing a model of a part of a solar panel according to an exemplary embodiment of the present description;

FIG. 4 is a flowchart showing steps in a method for monitoring and controlling one or more solar panels according to an exemplary embodiment of the present description;

FIG. 5 is a flowchart showing several steps of the method of FIG. 4 in more detail according to an exemplary embodiment of the present description;

FIG. 6 is a flowchart showing steps for initializing and reading the method of FIG. 5 in more detail according to an exemplary embodiment;

FIG. 7 is a flowchart showing a processing step of FIG. 5 in more detail according to an exemplary embodiment;

FIG. 8 is a flowchart showing a first part of an enrichment step of the method of FIG. 5 in more detail;

FIG. 9 is a flowchart showing a second part of the enrichment step of the method of FIG. 5 in more detail;

FIG. 10 is a flowchart showing a third part of the enrichment step of the method of FIG. 5 in more detail;

FIG. 11 is a flowchart showing a fourth part of the enrichment step of the method of FIG. 5 in more detail; FIG. 12 is a flowchart showing a first part of an analysis step of the method of FIG. 5 in more detail;

FIG. 13 is a flowchart showing a second part of the analysis step of the method of FIG. 5 in more detail;

FIG. 14 is a flowchart showing a first part of a step of interpreting the method of FIG. 5 in more detail; and

FIG. 15 is a flowchart showing a second part of the step of interpreting the method of FIG. 5 in more detail.

Description of the embodiments

The same elements have been designated by the same references in the various figures. In particular, the structural and / or functional elements common to the different embodiments may have the same references and may have identical structural, dimensional and material properties.

Unless otherwise specified, when referring to two elements connected together, this means directly connected without intermediate elements other than conductors, and when referring to two elements coupled together, it means that these two elements can be connected or be linked through one or more other elements.

In the following description, when reference is made to absolute position qualifiers, such as the terms "front", "rear", "top", "bottom", "left", "right", etc., or relative, such as the terms "above", "below", "upper", "lower", etc., or to orientation qualifiers, such as the terms "horizontal", "vertical", etc., reference is made unless otherwise specified in the orientation of the figures or to a solar installation in a normal position of use.

Unless otherwise specified, the expressions "approximately", "approximately", "substantially" and "of the order of" mean within 10%, preferably within 5%.

Figure 1 schematically shows a solar energy system 100 comprising, in the example of Figure 1, two solar energy installations each comprising one or more solar panels (SOLAR PANEL (S)) 102, 104 or a group of solar panels coupled in series, in parallel, or in a network, each solar panel corresponding for example to a solar panel of photovoltaic equipment integrated in a building (BIPV) or another type of solar panel comprising a matrix of solar cells photovoltaic. The solar panels or the groups of solar panels 102, 104 are for example different solar panels of the same building, or could be located at a distance from each other, for example on different buildings.

The solar energy system 100 further comprises a monitoring and diagnostic system (MONITORING AND DIAGNOSTICS) 106, which is for example in communication with each of the solar panels or groups of solar panels 102, 104. The monitoring system monitoring and diagnostics 106, for example, receives performance data from each of the solar panels or groups of solar panels 102, 104, and in some embodiments transmits control signals to the solar panels or groups of solar panels 102, 104 For example, each solar panel or group of solar panels 102, 104 includes a control module (CTRL MOD) 108 to manage communications with the monitoring and diagnostic system 106 and to control the operation of the panel. solar panel or group of solar panels, comprising for example deactivation of the solar panel or group of solar panels in the event of faulty operation.

[0046] For example, performance data, such as the amount of electrical energy produced during several sample energy recovery periods, is collected by each solar panel or group of solar panels 102, 104 and is transmitted to the system 106. The System 106, for example, is capable of accurately identifying whether performance is degraded and capable of taking appropriate action to ensure near-optimal operating efficiency for each solar panel or group of solar panels, even when the panels are solar panels or groups of solar panels 102, 204 are in urban environments. For example, the appropriate action might be to send an electronic communication, e.g. email, text message, calendar entry, etc., to one or more members of a maintenance team to notify them of the need. on-site repairs. This has the advantage of allowing some or all of the on-site inspections to be avoided, thereby reducing the cost.

Although Figure 1 illustrates a case where there are two solar panels or groups of solar panels monitored by the monitoring and diagnostic system 106, in alternative embodiments there could be any number of solar panels or groups of solar panels which are monitored by a single central monitoring and diagnostic system 106.

FIG. 2 schematically represents the monitoring and diagnostic system 106 of the solar energy system 100 of FIG. 1 in more detail according to an exemplary embodiment. The system 106 comprises for example a processing device (PROCESSING DEVICE) 202 comprising one or more processors under the control of instructions stored in an instruction memory (INSTRUCTION MEMORY) 204. Another memory (MEMORY) 206, which is for example part of the same memory device or of a device different from the memory 204, stores for example performance data (PERFORMANCE DATA) 208, thermal response models (THERMAL RESPONSE MODELS) 210 for solar panels or given solar panel groups, and an INSTRUMENTS TABLE 212, described in more detail below.

The system 106 also comprises for example an operator display (OPERATOR DISPLAY) 214 coupled to the processing device 202, and a communication interface (COMMS INTERFACE) 216 also coupled to the processing device 202, and supporting communications with the solar panels or groups of solar panels (SOLAR PANELS) to be monitored. For example, the communication link between system 106 and each of the solar panels or groups of solar panels may include wired and / or wireless connections, including one or more wired or wireless networks.

We will now describe in more detail an example of thermal response model 210 of a solar panel, with reference to Figure 3.

FIG. 3 is a sectional view showing a nodal physical model of a part of a solar energy panel according to an exemplary embodiment of the present description. In particular, FIG. 3 represents an example of a panel 302 of a BIPV matrix having an air gap in front of a roofing membrane, and comprising opaque modules. In the example of Figure 3, the panel 302 comprises a layer forming a roof (ROOF), a layer forming an air gap (AIR GAP), comprising a chamber 304 and a frame 306, a sheet of back (BACK) forming a solar panel support formed adjacent to the air gap, a photovoltaic (PV) layer formed adjacent to the backsheet, and a glass cover layer (GLASS) that covers the photovoltaic PV layer .

The nodes in the center of each layer represent the temperature of the layer. Figure 3 also defines the following temperatures:

Ti: temperature of the indoor ambient air;

Tf: air temperature in the air gap;

Ta: temperature of the outside ambient air; and

Ts: effective temperature of the sky.

Bonds between the nodes in Figure 3 represent a thermal coupling between the nodes. In particular, towards / from the glass layer there is a coupling by convection with the outside ambient air at the temperature Ta, a long-wave coupling with the sky at the temperature Ts and a conductive coupling with the PV of the PV layer. To / from the PV layer there is a conductive coupling with the glass layer and with the backsheet. To / from the back layer there is a conductive coupling with the PV cells of the PV layer and with the frame 306 around the air gap, natural convection with the air gap and a long wave coupling with the roof. The long wave radiant heat flux between the backsheet and the roof is shown as qr in Fig. 3. To / from frame 306 there is a conductive coupling with the backsheet and with the roof. Towards / from the air gap there is natural convection towards the back sheet and towards the roof, and advection from the rising air: air is replaced according to an air flow, for example from the outside ambient air. If the temperatures of the air node and the rising air differ, the air flow is equivalent to heat transfer. To / from the roof there is a conductive coupling with the frame 306, natural convection to the air gap and to the indoor ambient air at temperature Ti, and a long wave coupling to the foil. back.

FIG. 3 also shows radiation from the sun (SUN), the glass layer receiving unreflected incident solar radiation and also radiation reflected from the PV cells of the PV layer, and the PV layer receiving radiation transmitted through the glass layer.

Of course, Figure 3 is only an example of a thermal model of a solar panel, and this model could be adapted to other structures.

A generalized solar collector (GSC) model approximates a solar installation in the form of a set of nodes, corresponding for example to those of FIG. 3, each being defined by an energy equilibrium equation, properties internal and terms to describe the thermal couplings between the nodes and with boundary conditions. All nodes can contain photovoltaic elements on the upper side and the lower side, which convert some of the incident shortwave radiation into electricity. In the context of a thermal model, this photovoltaic response represents a reduction in the heat dissipated in the node. The system 106 is for example arranged to take a GSC model associated with a given solar panel or group of solar panels, the model being defined by a set of parameters, and to automatically generate an associated system of equilibrium equations. energy. Indeed, the goal is to determine the temperature and intensity of solar radiation at the level of the photovoltaic layer for a given set of environmental conditions.

For example, the mathematical problem to be solved is defined by the following equation:

[0060] [Math 1] where A 'represents a conductance matrix, B' represents couplings at boundary conditions, C represents thermal capacities of the system, U is a vector listing boundaries and sources, T represents the temperature vector to be solved, and represents the first derivatives of the temperatures of the vector T.

The solution in the stable state is obtained by inverting the conductance matrix as follows:

[Math 2]

By adopting A = C ^_1 A ', B = C ^_1 B', where A ^-1 is the inverse of the matrix A, and C ^-1 is the inverse of the matrix C, and by rearranging for , the solution in the unstable state of the Math 1 equation at time t + δt is found by integration:

[Math 3]

Assuming a linear variation at the element level of the inputs:

[Math 4] the solution in the unstable state of the GSC model becomes: [Math 5] or :

[Math 6] and :

[Math 7]

Taking an example of a photovoltaic matrix integrated in a building with an air gap before the roof membrane and comprising opaque modules, like the example of FIG. 3, the vectors / matrices T, U, A ', B' and C are for example the following:

[Math 8]

[Math 9]

[Math 10] or :

[Math 11]

[Math 12]

[Math 13]

[Math 14]

[Math 15]

[Math 16]

[Math 17] and

[Math 18] where R _ij gives the thermal resistance between nodes i and j, h _c and h _r represent convection and radiation heat transfer coefficients respectively, Gi gives the net shortwave radiation absorbed minus any photo-conversion, and h _Tfin represents the contribution of advection. FIG. 4 is a flowchart illustrating steps in a method for monitoring and diagnosing one or more solar panels or a group of solar panels according to an exemplary embodiment of the present description. The method of FIG. 4 is for example carried out by the monitoring and diagnostic system 106 of FIGS. 1 and 2. This method is for example carried out, in series or in parallel, for each solar panel or group of solar panels to be monitored. In some embodiments, the method is performed at regular intervals for each solar panel or group of solar panels, such as once a week or once every two weeks, to ensure relatively rapid detection of a new one. degradation of an installation.

In a step 401, performance data is received from the solar panel or from the group of solar panels, such as the solar panels or the groups of solar panels 102 or 104 of FIG. 1. This data subset comprises by example data indicating an amount of energy collected over a plurality of energy recovery periods covering for example a duration of at least three days, and for example a duration of two weeks or more. For example, the data indicates an average current and / or average voltage generated by the solar cells of the solar panel or group of solar panels during each energy recovery period. Each energy recovery period lasts, for example, one hour or less. The subset of data can be downloaded to system 106 from the solar panel or the group of solar panels at one time. Alternatively, the data subset can be downloaded progressively as it is captured. In a step 402, the data subset is classified, on the basis for example of at least one threshold, into at least two classes, one of which is a nominal class corresponding to recovery conditions d stable energy, and the other of which is an abnormal class corresponding to unstable energy recovery conditions. For example, the data value corresponding to each energy recovery period is classified as corresponding to either a nominal period or an abnormal energy recovery period, in which there can be any number of values per class, and all data values could be classified as nominal.

The classification is for example based on one or more indicators, and for example based on one or more of three indicators CI, PR and DS, where:

- CI is a clarity index indicating an intensity and a stability of solar radiation, the stability indicating the extent of the variation of the intensity over time;

- PR is a performance ratio indicating a ratio between electrical energy and incident radiation, normalized by the expected value of the ratio under reference conditions; and

- DS is a data simulation report showing a difference between a measured power output of the solar panel or solar panel array and a predicted nominal thermal and electrical response of the solar panel or solar panel array.

In a step 403, the expected performances of the solar panel or of the group of solar panels are characterized on the basis of the data values classified as nominal, that is to say the values captured during the nominal periods. For example, this involves determining an expected amount of energy production for a given amount of available solar power and given environmental conditions, such as ambient temperature, wind speed, etc.

In a step 404, the characterization of the solar panel or of the group of solar panels in the step 403 is used to detect degradation of the installation. This implies for example a deduction of the losses in the energy recovered during the abnormal periods, and for example a comparison of the losses deduced with a threshold to determine whether the performance of the installation has become notably degraded. For example, a degradation is identified when the losses reach a threshold TH, the threshold being for example set to lose between 5 and 20%, and for example around 10%.

In a step 405, a reaction is for example carried out in the case where a degradation of the installation is detected. For example, in some cases the reaction might be to turn off all or part of the degraded solar panel or group of solar panels in order to prevent further degradation until repairs can be made at the site. . In addition or instead, the reaction may involve generating an alarm on operator display 214, or sending an electronic message to one or more members of a maintenance crew, to indicate that the solar panel or group of solar panels has been found to have degraded performance.

FIG. 5 is a flowchart illustrating an example of the implementation of steps 401 to 404 of the method of FIG. 4 according to an exemplary embodiment of the present description.

Step 401 of FIG. 4 involves for example an initialization step (INITIALIZATION) 501 and a read step (READ) 502, step 402 for example involves a step processing (PROCESS) 503 and an enrichment step (ENRICH) 504, and steps 403 and 404 involve for example an analysis step (ANALYZE) 505 and an interpretation step (INTERPRET) 506.

FIGS. 6 to 15 are flowcharts representing the steps 501 to 506 of FIG. 5 in more detail according to an exemplary embodiment.

FIG. 6 represents the initialization and reading steps 501, 502 of the method of FIG. 5 in more detail according to an exemplary embodiment.

The initialization step 501 is for example based on configuration data 602 stored for example in the memory 204 or 206 of the monitoring and diagnostic system 106. This step prepares for example the set of parameters and the parameters. options to be used for analyzing the solar panel or group of solar panels to be monitored. In general, unique confirmation data 602 is prepared for each installation, since it includes parameters for access to specific data, and characteristics of the installation for simulations, as well as analysis options, a list. filters and derived variable definitions.

During the initialization step, the instrument table 212 is for example defined for the solar panel or the group of solar panels to be monitored, and is stored in the memory 206. The table links the names of variables in data sources with the names of internal variables used by the algorithm. The instrument table 212 lists, for example, the instruments that provide input data to the system, such as sensors at each solar panel or group of solar panels, groups of sensors, sensors at weather stations, etc. . For example with regard to groups, there may be have a single set of data values for all PV cells of each solar panel or group of solar panels, or a plurality of sets of data values, each set corresponding to a different sub-array of the solar panel or group of solar panels solar panels.

After step 501, a subroutine 603 is executed to process input data, this subroutine involving sub-steps 604 to 613. During subroutine 603, primary and secondary data is imported from available sources, as defined by the instrument table. The primary data includes, for example, performance data received from the solar panel or from the group of solar panels to be monitored. The secondary data is for example data which comes from outside the system 100 of FIG. 1, and can for example include weather data corresponding to the energy recovery periods with which the performance data is associated.

Step 604 involves, for example, the reading of primary data from the primary source, represented by monitoring data 605 in FIG. 6. For example, the primary data comprises samples indicating the electrical power generated during each period. energy recovery, each sample corresponding for example to a voltage and current reading or to an electric power reading. Additionally, the primary data may include readings from other sensors at the solar panel or group of solar panels, such as readings from one or more temperature sensors, readings from one or more temperature sensors solar radiation intensity, readings from one or more wind speed sensors, etc. Step 604 can include the storage of data read in a standard data structure, generally different from the source format. The conversion is for example managed by specifying the source format in the configuration data 602. For example, the source data format is the SQL (Structured Query Language) format, the XLS (Microsoft Excel) format, or the CSV (Character Separated Values) format (the names "Microsoft" and "Excel" may correspond to trademarks).

In a step 606, one enters a loop concerning the obtaining and the processing of the secondary data, which can comprise one or more sets of data. The loop involves steps 607 through 613.

In step 607, external data 608 corresponding to a first set of secondary data of the secondary data is for example received from one or more secondary sources.

In a step 609, the sampling frequency of the set of secondary data is for example adapted to that of the set of primary data, if this is appropriate. For example, this may involve decimating the samples of the secondary data set and / or interpolating the secondary data samples to coincide with the sampling times of the primary data.

In a step 610, time offsets are applied to the secondary data if this is appropriate. For example, the secondary dataset may be relevant for a different geographic location than the solar panel or group of solar panels, and so some adjustments may be applied to make the data more accurate for the actual location of the solar panel. or group of solar panels. For exemple, the secondary data can indicate times of sunrise or sunset for a given location, and time offsets can be applied to adjust these times to the location of the solar panel or group of solar panels that is being monitored.

In a step 611, the set of secondary data is for example concatenated with the set of primary data.

In a step 612, the concatenated data set, which corresponds to raw data, is for example pre-filtered to remove data which is inconsistent, such as altered or non-digital data values or altered dates and times. . Any missing row is for example inserted to impose a constant sampling rate.

In step 613, it is determined whether there are additional secondary data sets to be processed, and if so, the method returns to step 606 at the start of the loop. Otherwise, the next step is a step 614.

Step 614 involves checking the raw data set to verify whether the remaining data set is empty after the concatenation and pre-filtering steps. If the raw data cannot be trusted, the method eg exits step 615, and an error message is eg displayed on operator display 214 to inform the operator. If the data is ok, this data forms RAW DATA 616 to be processed in the following steps.

FIG. 7 is a flowchart showing the processing step 503 of FIG. 5 in more detail according to an exemplary embodiment. The processing step 503 involves the processing of the raw data 616.

In a step 701, if this is appropriate, a spatial aggregation of groups of instruments is carried out. For example, the instrument table indicates cases where two or more instruments or sensors form a group. As an example, there may be multiple temperature sensors at the solar panel or group of solar panels, and an aggregate value is for example generated by calculating the average of the temperature readings, or a weighted average of the readings of temperature. More generally, spatial aggregation can involve the aggregation of readings based on a calculation of standard deviation (std), mean, normalized value (znorm), mean difference (diff), a maximum difference (dmax) or the identity of an outlier (dmxi).

In some embodiments, in a step

702, it is determined whether the raw data set includes valid data readings representing the electrical power (P) produced by the solar panel or array of solar panels, ambient temperature (T) at the solar panel or array. of solar panels, and the wind speed (W) at the solar panel or group of solar panels. If any one of these three data readings is not available, the method for example stops in a step 703 and an error message is for example generated to alert the operator. If these three data reads are available, the method proceeds to step 704.

In step 704, it is determined whether there are data readings available indicating the amount of solar radiation Gi at the solar panel or group of solar panels. For example, each solar panel or group of solar panels may include one or more sensors to sense the intensity of ambient light. Alternatively, readings may be available from one or more external sites close to the facility. solar energy, and for example within a radius of 5 km around the solar energy installation, or for example be deduced from satellite data or weather forecast calculations.

If no such reading is available, in a step 705 it is determined for example whether the solar radiation Gi for the solar panel or the group of solar panels can be estimated on the basis of a measurement of the direct current. (DC) produced by the solar panel or group of solar panels. If not, the method for example stops in a step 706 and an error message is for example generated to alert the operator. If DC current readings are available, the solar radiation Gi in the plane of the solar panel or group of solar panels is for example estimated in an operation 707 based on the DC current readings.

After step 707, or after step 704 if Gi solar radiation readings are found present, the next step is step 708.

In step 708, the raw data is for example filtered on the basis of the ranges of values authorized for the variables. A series of filters are applied to the raw data in combination, each including a test on the instantaneous values of a single variable, and the failed rows are discarded from the data set. For example, a filter can test whether solar irradiance readings are in the range (0; 1500), or for a temperature sensor that has been configured to read -999 in the event of a sensor fault, a filter can be set to exclude such erroneous values.

In a step 709, derived variables are for example generated on the basis of the raw data set filtered, according to definitions provided in configuration 602. A derived variable is defined as being a variable calculated from the instantaneous variables of one or more variables. Derived variables share the same time base as raw data variables. For example, this involves the generation of the instantaneous performance ratio PR, which corresponds to the ratio between the output power and the incident radiation, normalized by an expected solar panel efficiency determined under standard test conditions, and for example defined in a specification sheet of solar panel or group of solar panels.

Additionally, one, some or all of the following derived variables are for example also generated in step 709:

- I * V. : electrical power P calculated by the product of a current reading I and a voltage reading V; pvpr: theoretical power assuming a constant performance ratio, for example 90% or 75%; pvpow: simple linear model of photovoltaic power, defined by Math 21 below;

- NOCT: nominal operating cell temperature of each solar panel or group of solar panels, defined by Math 22 below;

- Pdc: Gi: instantaneous performance ratio, using DC power;

- P: Pnoct: ratio between the power generated by the solar panel or the group of solar panels and the power

NOCT.

[Math 19] where η _ref is the conversion efficiency at the reference panel temperature T _ref and for the incident radiation G _ref . The coefficients β and γ describe the dependence as a function of the temperature (T _pv) and of the radiation (I _rr) of the efficiency of the panel, and A is the collection surface.

[Math 19] where the symbols containing the NOCT index refer to values under nominal cell operating temperature conditions, Gi is the radiation in the incident plane as mentioned previously, η _ref is the photovoltaic conversion efficiency under the conditions of reference, τα is the product of the optical transmittance and the absorbance, V _v is the wind speed, and the coefficients cV _1, cV ₂ describe the functional dependence of the wind speed in the temperature of the solar panels.

In a step 710, the corrected data set comprising the derived variables is for example pre-filtered to generate a corrected data set (CORRECTED DATA) 711. The same pre-filtering step 612 as that of FIG. 6 is eg applied, to eliminate non-numeric and altered derived variables. This data can also be exported in a step 712 to a persistent file in CSV format, to allow subsequent analysis to be undertaken using corrected data without the need to apply previous steps, if desired. FIG. 8 is a flowchart representing a first part of the enrichment step 504 of the method of FIG. 5 in more detail according to an exemplary embodiment. The enrichment step is for example performed on the corrected data set 711.

[0100] The method of FIG. 8 involves for example a subroutine 801 for enriching, on the basis of configuration data (config), the information concerning the local environment of the solar panel or of the group of solar panels. The aim of this subroutine is to produce an indicator with which one can distinguish periods of environmental conditions which are favorable for a nominal power production by the solar panel or the group of solar panels. The method involves, for example, the determination of an increased clarity index CI. The local lightness index indicates the degree of cloudiness, shading and variability in solar radiation at the solar installation. FIG. 8 includes a method comprising steps 802 to 807 based on a solar model of a clear sky, which is for example a default method. Figure 8 also indicates that, in addition or as an alternative, another environmental enrichment process 808 could be used.

[0101] In certain embodiments, in step 802, local radiation information is obtained, for example from a weather station, indicating for example the extent of the cloudiness.

In step 803, a clear sky calculation is for example carried out to predict the quantity of solar radiation received by the solar panel or the group of solar panels when the sky is clear. The incident flux in the plane of the solar panel or group of solar panels is calculated from the position of the sun, the solar flux extraterrestrial, and an estimate of the attenuation of solar radiation by the atmosphere as a function of optical depth. In step 803, the clear sky calculation is used to obtain an initial estimate of sunny conditions, via a ratio between simulated beam radiation and total radiation measured at noon.

[0103] In step 804, the sunniest days are identified by classifying the days by daily integrated solar radiation, and the daily accumulated lightness indicator obtained in step 803. If the sunniest day does not include cloudy periods, it is adopted for the total radiation profile under clear sky conditions, including diffuse radiation. Otherwise, a composite clear day is constructed from sunny periods in the selection of the sunniest days.

[0104] In step 805, the horizon and shading effects of the solar panel or group of solar panels are inferred, for example on the basis of the daily local radiation profile for light conditions, estimated in the step 804. The local diffuse profile is copied to all the days in the sample. Corrections are then applied for periods with zero beam radiation, setting the diffuse flux equal to the total measured radiation. For periods of absence of data, the diffuse flow is for example set to zero. The result is an estimate of local diffuse radiation taking into account contributions from the immediate environment and the horizon.

In step 806, the total clear sky radiation is calculated by adding the simulated beam radiation, obtained from step 803, and the local diffuse flux, obtained from step 805. The CI clarity index is defined as the ratio of total radiation to total clear sky radiation.

In step 807, the value of CI is for example modified to read specific values during periods without data, during the night (zero clear sky radiation), and during dawn and dusk (low component. simulated beam). The moving average and fluctuating components of the total radiation are calculated, for example for a period of 1 hour, which values are then used to mark unstable intervals in the IC.

[0107] After the increased CI clarity index has been deduced using steps 802 to 807, and / or using alternative method 808, the next step is step 809.

[0108] In step 809, it is determined whether in-plane solar radiation has been estimated in step 707 of FIG. 7 using DC current measurements. If so, in a step 810, a more precise estimate of in-plane solar radiation can be generated based on the mean horizontal total solar radiation Gh if available in the data, using Perez's model. This provides an absolute peak radiation measurement independent of the electrical response of the array. For example, Perez's model is described in more detail in the following publications: publication by Perez, R., Seals, R., Ineichen, P., Stewart, R., Menicucci, D., entitled "A new simplified version of the Perez diffuse irradiance model for tilted surfaces ", 1987, Solar Energy 39 (3), 221-232; the publication by Perez, R., Ineichen, P., Seals, R., Michalsky, J., Stewart, R., entitled "Modeling daylight availability and irradiance components from direct and global irradiance", 1990, Solar Energy 44 (5 ), 271-289; and the publication of

Perez, R. and. al. titled "The Development and Verification of the Perez Diffuse Radiation Model ", 1988, SAND88-7030, the contents of these three publications being incorporated herein by reference to the extent permitted by law.

After step 810, or after step 809 in the case where the solar radiation in the plane has not been estimated using DC current measurements, a step 811 is performed. Step 811 involves determining whether an interior building temperature Tin is available. If not, in a step 812, the values of the outside temperature will be used in the next step in the data enrichment process.

[0110] FIG. 9 is a flowchart showing the following steps 901 to 906 of the enrichment step 504 of the method of FIG. 5.

In step 901, a subroutine is implemented to enhance the performance of the system, this subroutine involving steps 902 to 906. This subroutine aims for example to deduce another indicator to be used for classifying the data of step 402, this indicator being a ratio between data and simulated electrical power (DS) assuming a nominal system response to uniform environmental conditions. In particular, the simulations provide for example, on the basis of the generalized solar collector model, a predicted matrix temperature and a predicted electrical output of the matrix as a function of the measured environmental conditions. A range of configurations can be simulated in this step in order to obtain information with which we can test hypotheses concerning the state of the PV array, and estimate for example the loss of performance due to integration into the building.

In step 902, for example, a simulation is carried out on the basis of a reference PV system, and in particular of a PV system of the same type as that of the panel. solar panel or group of solar panels, but without taking into account the integration of the solar panel or group of solar panels in a building. The simulation provides for example an estimate of the average matrix temperature of the solar panel or the group of solar panels and the DC power produced by the solar panel or the group of solar panels. For example, this simulation is based on the generalized solar collector model, for example as defined by the preceding Math 7 through Math 20 equations, and uses the measured environmental conditions as inputs.

In step 903, the BIPV system is for example simulated in a manner similar to the simulation carried out in step 902, but taking into account the integration of the solar panel or of the group of solar panels of the installation. as solar panels integrated into a building. For example, this involves a GSC simulation arranged to describe a thermal coupling to a roof or a facade, with an air gap, as shown in figure 3. Multiple simulations can be performed, by adapting input variables. and parameters to estimate uncertainties and consider different assumptions, eg degradation induced by aging, reduced air flow rates, high ambient temperatures, etc. This will now be described in more detail with reference to a step 904, which is shown in more detail in FIG. 10.

[0114] FIG. 10 is a flowchart representing examples of simulations carried out in step 904 of FIG. 9. These examples represent optional variants to complete the information generated by steps 902 and 903. For example, the simulation of the integrated PV system is improved by a step 1001, in which a series of simulation variants is carried out to estimate systematic errors in the simulation. Here, systematic errors include those occurring from uncertainties in GSC inputs. A default sequence of calculations is for example defined in step 1002:

- the solar panel or group of solar panels assuming a lower limit, an upper limit and a nominal mean value of the predicted air flow passing through the air gap of the panel;

the solar panel or group of solar panels assuming a relatively high ambient temperature in the vicinity of the panel;

- the solar panel or group of solar panels assuming a relatively high local wind speed; and

- the solar panel or group of solar panels assuming a constant interior temperature of the building.

[0116] Instead of estimating systematic errors as in step 1001, variant simulation configurations can be considered to estimate the impact of variations in the GSC parameters (PV characteristics) and are for example carried out in a step 1003 A sequence of default simulation variants is defined in a step 1004: the non-integrated solar panel or group of solar panels and the integrated solar panel or group of solar panels assuming relatively low performance, defined by poor optical transmittance and reflectance. and reduced electrical efficiency;

- the solar panel or group of solar panels integrated without aging;

- the solar panel or group of solar panels assuming a lower limit, an upper limit and a nominal average value of the predicted air flow passing through the air gap of the panel (s). This set of variants is identical to the first set in step 1003 except that in this instance the goal is to estimate the effect of the restricted air gap.

In addition to the simulations carried out in steps 1001 to 1004, one or more simulation variants can be carried out in a step 1005.

Referring again to FIG. 9, in a step 905, the ratio DS between data and model is for example determined for the electric power generated by the solar panel or group of solar panels, for the calculated predicted nominal power. in steps 902 and 903. The result of any further simulation is used to quantify the possible deviations.

In step 906, the power cuts are marked in the indicators Pdc: Gi and DS. The ratios are set to a constant value for any interval in which the measured power is zero.

[0120] After step 906, the process continues in FIG. 11.

[0121] FIG. 11 is a flowchart representing the following steps 1101 to 1110 of the enrichment step 504 of the method of FIG. 5.

[0122] In step 1101, it is determined whether there is a Tpv array temperature reading available for the solar panel or the group of solar panels. If so, in a step 1102, temperature prediction errors and heating correlations are for example calculated. A heating correlation is defined as the difference between PV temperature and ambient temperature, normalized by incident solar radiation. Then, in a step 1103, any temperature divergence is for example marked in the indicator Pdc: Pbipv. The report is set at different constant values depending on whether a significant overestimation or underestimation is observed, or a temperature divergence combined with a bad DS value is observed.

[0123] After step 1103, or after step 1101 in the case where no Tpv array temperature reading was available, step 1104 is performed, involving determining whether sub-array data is available. , allowing to perform a sub-matrix analysis. If so, in a step 1105, the presence of any heterogeneity between the sub-matrices of the solar panel or of the group of solar panels is for example marked. For example, heterogeneity is detected based on a calculation of the variance between measurements, such as power measurements generated by strings of PV cells or modules in the array.

In step 1106, it is determined whether both a current reading ItoG and an external reading Gext are available as measurements Gi of solar radiation. If so, in a step 1107, the measurement of Gi based on the current reading ItoG is replaced by the measurement Gext of external solar radiation for the following steps of the analysis.

After step 1107, or after step 1106 in the case where only one ItoG current reading is available, the enriched data (ENRICHED DATA) 1108 are ready. In some embodiments, this enriched data is exported to data storage 1109 and / or is represented in one or more tables or graphs on operator display 214 as represented by an operation 1110.

[0126] FIG. 12 is a flowchart showing a first part of the analysis step 505 of the method of FIG. 5 in more detail. [0127] In a step 1201, a daily weather profile is for example obtained for the location located at or near the solar panel or the group of solar panels for each of the energy recovery periods. These data are for example based on data coming from one or more weather stations in the vicinity of the solar panel or the group of solar panels, and / or based on other sources such as images from satellites, etc.

In a step 1202, a nominal BIPV simulation is carried out on the basis of the meteorological profile for each energy recovery period.

In a step 1203, a classification subroutine involving steps 1204 to 1210 is for example carried out on the enriched performance data. This subroutine implements step 402 of FIG. 4. Each of the energy recovery periods is for example classified into one of at least two categories, corresponding to nominal and abnormal periods. The classification is based, for example, on a comparison of one, some or all of the indicators PR, CI and DS, determined during previous steps as has been described previously, with one or more thresholds.

[0130] The classification algorithm can be based on a supervised approach and / or an unsupervised approach.

An unsupervised approach 1204 involves for example a grouping / decision tree algorithm 1205 to automatically generate subsets, and a step 1206 which assigns the subsets to the different classes.

A supervised approach 1208 involves for example an Mfilter filtering matrix comprising preset thresholds, while a supervised approach 1209 involves for example an Mfilter filtering matrix comprising thresholds defined from a file stored for example by the memory 206 of the system 106 of FIG. 2.

In one embodiment, the Mfilter filter matrix is of the form:

[0134] [Math 20] where C ₀ to C _n are classes, X _c , _min and X _c , _max are minimum and maximum thresholds for class c of an indicator V _x , and Z _{c, min} and Z _{c, max} are minimum thresholds and maximum for class c of an indicator V _z .

In one embodiment, there are 11 classes defined as follows:

-1: default class if no criteria from another class are met;

0: night;

1: zero power of daybreak or nightfall;

2: zero radiation, corresponding to a deactivated state of the solar panel or of the group of solar panels;

3: no data;

4: zero / low power (failure);

5: unstable cloudy conditions;

6: poor performance (questionable);

7: nominal sunny;

8: nominal cloudy;

9: sub-matrix flag (heterogeneity).

[0136] Of course, the above list is only an example of the classes available. Classes 7 and 8 correspond to nominal periods, and the other classes correspond, for example, to abnormal periods. In some modes of realization, the energy recovery periods classified as nominal represent between 10 and 20 percent of the total number of energy recovery periods.

[0137] By way of example, the following classification rules could be applied based on the augmented clarity index CI, the performance report PR, and the data simulation report DS.

[0138] Classes 0-4 are for example assigned to an energy recovery period when the radiation conditions are low / zero, indicating either a night period or a period of very low radiation, or a failure of the system.

[0139] Class 5 is for example assigned to an energy recovery period when the CI data indicates cloudy conditions, the DS data indicates an underestimation or an overestimation of the power produced, and the performance ratio is low. , indicating poor performance under unstable conditions.

[0140] An underestimation of the power generated implies that the actual or current data indicates a power significantly greater than that estimated / predicted, for example the data being greater than the estimate by 5 percent or more.

[0141] An overestimation of the power generated implies that the actual data indicates a power significantly lower than that estimated / predicted, for example the data being less than the estimate by 5 percent or more.

[0142] Class 6 is for example assigned to an energy recovery period when the CI data indicates a sunny period, the DS data indicates an underestimation or an overestimation of the power generated, and the performance ratio is low, indicating poor performance in sunny conditions.

[0143] Class 7 is for example assigned to an energy recovery period when the CI data indicates a sunny period, the DS data indicates an agreement or practically an agreement between the estimated power and the effective power, and the ratio of performance is medium to high, indicating that the system is working well in sunny conditions.

[0144] An agreement or nearly an agreement between the estimated power and the effective power corresponds, for example, to data lying within a limit of 5% with respect to the estimate.

[0145] Class 8 is for example assigned to an energy recovery period when the CI data indicates a cloudy period, the DS data indicates an agreement or almost an agreement between the estimated power and the effective power, and the ratio of performance is medium to high, indicating that the system is functioning well in cloudy conditions.

[0146] Class 9 is for example affected if a heterogeneity in the matrix has been noted.

[0147] After the classification subroutine, the result of the classification can be represented in a step 1210, for example in one or more tables or graphs, and displayed on the operator display 214. The method then continues with example in figure 13.

[0148] FIG. 13 is a flowchart showing the following operations 1301 to 1318 of the analysis step 505 of the method of FIG. 5 in more detail.

In step 1301, a performance characterization subroutine is implemented involving steps 1302 to 1317. The subroutine aims to characterize the nominal behavior of the system using regression models and then extrapolate the result to the full data set in order to characterize the full performance of the solar panel or group of solar panels.

[0150] In step 1302, the performance data is filtered to include only the subsets falling in the nominal class.

In a step 1303, it is determined whether current Idc and voltage Udc measurements are available for the solar panel or the group of solar panels. If so, a loop involving steps 1304 through 1310 is performed to fit the Photovoltaic Array Performance Model (PAPM) to the nominal data subsets. The PAPM model describes the electrical response of a solar panel or group of solar panels using a system of equations. The PAPM model and the corresponding equations are for example described in the publication by King, D., Boyson, W., & Kratochvill, J. entitled "Sandia Report: Photovoltaic Array Performance Model", 2004, Albuquerque, New Mexico and Livermore, California: Sandia National Laboratories, contents of which are incorporated herein by reference to the extent permitted by law. In particular, the relevant equations correspond for example to equations (1) to (8) on page 9 of this publication, where lmp and Vmp correspond to Idc and Udc in the present description, and Te and To represent Tpv and the ambient outside temperature respectively.

[0151] The loop begins at step 1304 and is repeated for each sub-array of each solar panel.

In step 1305, it is determined whether a temperature measurement Tpv of the current sub-matrix of the solar panel or of the group of solar panels is available. If it is the case, in a step 1306, the PAPM performance model is fitted to the data set based on the current measurement Idc and the temperature reading Tpv, and / or the PAPM is fitted to the data set based on Udc voltage measurement and Tpv temperature reading. The step calculates the adjustment values, the adjustment residuals (res), the adjusted parameters (par), and the regression estimation errors associated with each parameter (err).

Then, in a step 1307, the product of the adjusted results Idc * Udc is calculated to obtain a power estimate from the PAPM.

[0154] After step 1307, or after step 1305 if no matrix temperature Tpv is available for the current submatrix, a step 1308 is for example carried out. In step 1308, the PAPM performance model is fitted to the data set based on the current Idc and based on the generalized solar collector model (GSC) and / or the PAPM performance model is fitted to the 'dataset based on voltage Udc and based on temperature of the Generalized Solar Collector Model (GSC).

Then, in a step 1309, the power PAPM estimate is for example calculated from the product Idc * Udc, obtained by using the temperature of the generalized solar collector model (GSC).

After step 1309, it is determined for example in a step 1310 whether all the sub-matrices have been processed, and if not, the loop is repeated for a following sub-matrix.

[0157] Once all the sub-matrices have been processed, the result at the output 1311 of the loop is a PAPM characterization for some or all of the sub-matrices of the matrix on the basis of the power Pmp in using the GSC model and in some cases taking into account the temperature Tpv.

[0158] In addition to or as an alternative to fitting the nominal data sets to the PAPM model, a similar technique can be applied based on the PVUSA model (Photovoltaics for Utility Scale Applications - photovoltaic devices for commercial applications). ) in a step 1312. The PVUSA model is for example described in more detail in the publication by Dows, RN, & Gough, EJ entitled "PVUSA procurement, acceptance, and rating practices for photovoltaic power plants (No. DOE / AL / 82993 - 21) ", 1995, Pacific Gas and Electric Co., San Ramon, CA

(United States). Dept. of Research and Development, Bechtel Corp., San Francisco, CA (United States), contents of which are incorporated herein by reference to the extent permitted by law.

[0159] Further, also in addition to or as an alternative to fitting nominal data sets to the PAPM model, a similar technique can be applied based on the PVPOW model, defined by Math 22, involving steps 1313 through 1316.

[0160] In step 1313, the nominal data subsets are filtered to include only those covering the midday period.

In step 1314, it is determined whether a temperature measurement Tpv of the solar panel or of the group of solar panels is available. If so, in step 1315 the PVPOW performance model is fitted to the data set based on the Tpv temperature reading. On the other hand, if no Tpv temperature measurement is available, in a step 1316, the PVPOW performance model is fitted to the data set based on the GSC model. [0162] After steps 1311, 1312, 1315, 1316, in a step 1317, the characterization results for the nominal energy recovery periods are for example extrapolated to cover the entire data sample, in other words all periods of energy recovery, thus characterizing the solar panel or group of solar panels as if it had been operating at its nominal level at all times. Extrapolation involves the use of regression parameters, obtained by fitting models to the nominal subset, to predict the expected nominal response for environmental conditions encountered in other subsets.

[0163] The extrapolation of step 1317 results in ANALYZED DATA 1318.

[0164] FIG. 14 is a flowchart showing a first part of the interpretation step 506 of the method of FIG. 5 in more detail. The steps in Figure 14 are based on the analyzed data 1318.

[0165] A subroutine 1401 comprising steps 1402 to

1416 is used to define a set of integrated measures or more generally quantities that are inferred from data spanning multiple intervals, referred to herein as a set of measures. The predefined set of metrics is then applied to different temporal aggregations in subsequent steps in Figure 15.

[0166] Step 1402 involves, for example, the specification of a set of measures container for the quantities to be defined during steps 1403 to 1409.

[0167] In step 1403, measurements of data characteristics are defined. These include, for example, the number of data values, the duration of the interval, the number of usable data, etc. [0168] In step 1404, instant indicators are defined. These include for example the number of data points per indicator category PR, CI and DS, for example the number of data points that correspond to sunny conditions, and / or the number of data points that correspond to good performance, etc.

[0169] In step 1405, accumulated energy measurements are determined. These include for example the total incident solar radiation, the total solar radiation in open conditions, the measured total energy generation of the solar panel or group of solar panels, the predicted total energy generation of the solar panel or group of solar panels according to GSC simulation, etc. These also include the accumulated values of adjustment results from steps 1302 through 1317 of Fig. 13, the total energy and the sum of adjustment residuals.

[0170] In step 1406, ratings are defined using measured performance. For example, a power rating calculation includes a regression fit to linear models of power as a function of environmental conditions, the results of which are used to estimate the power of the solar panel or group of solar panels extrapolated to conditions. reference. The linear models discussed in step 1406 include, for example, the PVUSA evaluation under reference conditions of incident radiation of 1000 W / m, an ambient temperature of 20 ° C, and a wind speed of 1 m / s. of accumulated performance is also for example specified in a step 1406, defined as being the ratio between the total energy generated and the total incident radiation. In step 1407, additional power evaluations are for example defined using a combination of measured and simulated data obtained from the GSC simulation. Assessments include, for example:

- a PVGSC assessment, based on the pvpow model, with measured power and simulated solar panel temperature GSC, and extrapolated for incident radiation reference conditions of 1000 W / m and solar panel temperature of 25 ° C. The evaluated power, the regression estimator error, and the sum of residuals are included in the definition. Variants include replacing the returned value with electrical efficiency, or the apparent temperature coefficient

- a PVGSC adjustment evaluation, identical to the PVGSC adjustment except that the adjusted predicted power obtained from step 1316 of FIG. 3 is used

- a PVUSA evaluation using the predicted GSC model.

[0172] In step 1408, it is determined whether Tpv array temperature data is available. If so, in step 1409 the set of performance metrics is extended to include amounts inferred using that data. For example, the PVPOW ratings described above are determined using the measured matrix temperature. A Ross coefficient is also specified, for example, defined as being the gradient of the correlation between the incident radiation and the difference between the temperature of the solar panel and the outside ambient temperature.

[0173] In step 1410, it is determined whether data for matrix current and voltage, Idc and Udc is available. If so, in step 1411 the set of performance metrics is extended to include power ratings based on the PAPM. In a step 1411, we enter a loop involving steps 1412 to 1415 applied to each sub-matrix of the solar panel or of the group of solar panels. Here, sub-matrix refers to each part of the solar panel or group of solar panels for which separate current and voltage measurements are available. The PAPM evaluations are for example calculated at the sub-matrix level. In step 1412, it is determined whether a temperature measurement Tpv of the solar panel or of the group of solar panels is available. If no Tpv temperature measurement is available, in a step 1413, the PAPM evaluations are calculated using temperatures predicted by the GSC model. If temperature readings are available, in step 1414, PAPM ratings are calculated using temperature readings. [0176] In steps 1413 and 1414, the PAPM evaluations are for example specified for a current and a voltage extrapolated for reference conditions of incident radiation of 1000 W / m ² , and a solar panel temperature of 25 ° C. PAPM rated power is defined as the product of the current and voltage ratings. [0177] In step 1415, if there are other sub-matrices, the loop 1411 is continued for the next sub-matrix. If all the sub-matrices have been processed, the algorithm goes to step 1416. [0178] In step 1416, a combined set of measurements is generated and the process then continues in FIG. 15. [0179] FIG. 15 is a flowchart showing subsequent steps of the interpretation step of the method of FIG. 5 in more detail. In a step 1501, the classified and characterized subsets of data are for example aggregated over time in order to deduce the integrated performance measures specified in the set of measures defined in steps 1402 to 1416. The integration is by example performed over the full duration of the analysis, although it may also be performed over shorter periods in some embodiments.

In a step 1502, we enter a loop involving steps 1503 to 1515 applied to each class.

In step 1503, it is determined whether an additional subset filter should be applied to a class, and if so, the filter is applied in step 1504. A filter in step 1504 comprises for example a filter intended to select a particular time of the day.

[0183] After step 1504, or after step 1503 if no subset filter is to be applied, steps 1505, 1506, 1507 and 1508 are performed to respectively determine whether the time base for aggregation temporal must be in hours, (hh), in days of the year (doys), in weeks of the year (woys) and / or over a period defined by the operator. The time bases are for example defined by the user and one, some or all of the time bases can be applied.

Step 1509 then involves, for example, entering a sub-loop applied to each of the time bases involving steps 1510, 1511 and 1512.

[0185] In step 1510, mean deviation and standard deviation calculations are performed on each variable found in the data set for the time base, and in step 1511 the quantities defined in the set of measures are calculated. In step 1512, it is determined whether there are other time bases to be taken into account, and if so, the method returns to step 1509. When there is no longer any other time bases to be taken into account, the next step is step 1513.

[0187] Step 1513 involves, for example, determining whether current Idc and voltage Udc measurements are available for the solar panel or the group of solar panels. If so, in a step 1514 another aggregation is carried out to obtain a combined PAPM evaluation for all the sub-matrices.

After step 1514, or after step 1513 if there are no measurements of current Idc and voltage Udc, in step 1515 it is determined whether there are other classes to be to treat. If so, the process returns to the start of the loop 1502. Once all the classes have been processed, the next step is a step 1516.

[0189] In step 1516, a photovoltaic yield analysis is for example carried out on the basis of the data subsets aggregated over time, thus generating a set of measurements (MEASURES) 1517. In certain embodiments, this includes the variation of yield over given periods, such as the daily variation.

In step 1518, the measurements 1517 are for example stored in memory, and in step 1519, the measurements 1517 are for example represented in one or more graphs or tables, which are for example displayed for an operator on operator display 214 of Figure 2.

[0191] In certain embodiments, in a step

1520, a current state of the solar panel or group of solar panels is determined based on some or all of the measurements. For example, a flag of yields 1521, an evaluation flag 1522 and / or a fault flag 1523 are generated if one or more of the measurements exceed a given threshold or a given allowable range. Indeed, these flags provide for example a binary judgment of the state of the system, and indicate to an operator the moment when an action must be taken. In some embodiments, activating a flag can also trigger an automatic reaction.

The yield flag is for example activated as a function of a comparison between the yield extrapolated for measured values and simulated values. For example, extrapolated efficiency is the product of the integrated performance ratio during sunny periods, corresponding to the ratio of generated energy to incident energy, and incident energy over the full analysis period. A discrepancy between the extrapolated and simulated returns indicates that the nominal behavior differs from the model's expectations. In certain embodiments, the test is conditioned on the fact that there is sufficient nominal data, for example in the form of a percentage of the total data, the nominal data representing for example at least 10 percent of the total data.

[0193] For example, the yield flag is activated when:

[Math 21] and

[Math 22] where t _name is the duration of nominal periods, t _tot is the total duration of the analysis period, Ydat is the extrapolated yield data and Y _sim is the extrapolated simulation data.

[0194] In some embodiments, a yield warning flag is also set when:

[Math 23]

The evaluation flag is for example activated as a function of a comparison of the nominal PVGSC evaluations adjusted to the measured power and to the simulated power. The PVGSC assessment is for example based on the simple PV power model combined with simulated GSC matrix temperatures. In some embodiments, the adjustment is limited to nominal data over the midday period, since this corresponds, for example, to the period of greatest accuracy of the model. In some embodiments, the test is conditioned by a relatively good fit between the model and the data.

[0196] For example, the evaluation flag is activated when:

[Math 24] and

[Math 25] where P _dat is the power data, δ _pdat is the standard deviation of the power data, P _sim is the simulated power, and δ _Psim is the standard deviation of the simulated power. [0197] In some embodiments, a yield warning flag is also set when:

[Math 26]

The fault flag is for example activated as a function of a comparison between the efficiency during subassemblies classified as faulty and the expected nominal efficiency. The expected nominal efficiency is for example deduced from the simple power model with simulated GSC temperatures. In some embodiments, the model is fitted to nominal data during the midday period, then is extrapolated to other periods and integrated. As with the yield flag, in some embodiments the test is conditioned on whether there is sufficient nominal data, for example as a percentage of the total data, the nominal data representing for example at least 10% of total data.

[0199] For example, the yield flag is activated when:

[Math 27] and

[Math 28] where t _nom is the duration of the nominal periods, ttot is the total duration of the analysis period, Ydat is the extrapolated yield data and Y _sim is the extrapolated simulation data.

[0200] In some embodiments, a fault warning flag is also activated when: [Math 29] and / or when:

[Math 30] where tfauit is the duration of the defective periods.

[0201] An advantage of the embodiments described here is that one or more solar panels can be monitored remotely, making it possible to reduce the number of costly visual inspections. The embodiments described here also allow the rapid detection of any degradation in the efficiency of a solar energy installation, including for solar installations in urban environments, where there are many factors that can impact the efficiency. . In addition, by filtering data to identify nominal periods to use as a reference, it is possible to accurately identify when the performance of a solar panel or group of solar panels becomes significantly degraded and when an intervention. must be programmed to avoid a significant loss of the potential energy that can be recovered.

Various embodiments and variants have been described. Those skilled in the art will understand that certain features of these various embodiments and variations could be combined, and other variations will be apparent to those skilled in the art.

[0203] For example, although a method based on the three key indicators CI, PR and DS has been described, it will be clear to the person skilled in the art that variants of process could be based on just one or two of these indicators.

Finally, the practical implementation of the embodiments and variants described is within the abilities of those skilled in the art based on the functional indications given above.

Claims

A system for monitoring the operation of one or more solar panels (102, 104), the system comprising: a communication interface (216) arranged to receive performance data indicating the power generated by said one or more solar panels ( 102, 104) during a plurality of energy recovery periods; and

- a processing device (202) arranged to filter the performance data to classify at least one energy recovery period as a nominal period during which the solar radiation was stable, and to process the performance data to detect degraded operation of said one or more solar panels (102, 104) based on performance data during said at least one nominal period.

2. System according to claim 1, in which the processing device (202) is arranged to carry out the classification on the basis of a comparison of at least one parameter (CI, PR, DS) with at least one threshold.

3. System according to claim 2, wherein the processing device (202) is arranged to select said at least one threshold such that at least 10%, and for example between 10% and 20%, of the recovery periods. of energy are classified as nominal.

4. System according to claim 2 or 3, wherein said at least one parameter comprises at least one element of the group consisting of:

- a clarity index (CI) indicating an intensity and / or a stability of solar radiation during each energy recovery period;

- a performance report (PR) indicating a performance report electrical performance of said one or more solar panels (102, 104) relative to a simulated behavior of said one or more solar panels (102, 104) during each energy recovery period; and - a data simulation report (DS) indicating a difference between a measured output power of said one or more solar panels (102, 104) and a predicted nominal thermal and electrical response of said one or more solar panels (102, 104) during each energy recovery period.

The system according to claim 4, wherein the processing device (202) is arranged to perform the classification on the basis of the clarity index (CI), the performance report (PR) and the data simulation report. (DS).

System according to any one of claims 1 to 5, wherein the processing device (202) is arranged to use a nominal sub-assembly, corresponding to the performance data during said at least one nominal period, to characterize the performances. solar panels, and, by extrapolation and comparison with performance observed during one or more periods of questionable behavior, to assess losses during these periods.

The system according to any one of claims 1 to 6, wherein the processing device (202) is further arranged to generate and transmit to said one or more solar panels (102, 104) a control signal based on the. degraded operation detected.

The system of claim 7, wherein the control signal is a deactivation signal for deactivating said one or more solar panels (102, 104).

9.A method of monitoring the operation of one or more solar panels (102, 104), the method comprising:

- receiving, through a communication interface (216), performance data indicating the power generated by said one or more solar panels (102, 104) during a plurality of energy recovery periods; filtering, by a processor (202), the performance data to classify at least one energy recovery period as a nominal period during which the solar radiation was stable; and

- processing the performance data by the processing device (202) to detect degraded operation of said one or more solar panels (102, 104) based on the performance data during said at least one nominal period.

10. The method of claim 9, wherein the classification is based on a comparison of at least one parameter (CI, PR, DS) with at least one threshold.

11. The method of claim 10, wherein at least 10%, and for example between 10% and 20%, of the energy recovery periods are classified as nominal.

12. The method of claim 10 or 11, wherein said at least one parameter comprises at least one member of the group consisting of: a clarity index (CI) indicating an amount of solar radiation;

- a performance report (PR) indicating an electrical performance ratio of said one or more solar panels (102, 104) compared to a simulated behavior of said one or more solar panels (102, 104); and - a data simulation report (DS) indicating a difference between a measured output power of said one or more solar panels (102, 104) and a predicted nominal thermal and electrical response of said one or more solar panels (102, 104).

13. The method of claim 12, wherein the classification is based on the Clarity Index (CI), Performance Report (PR) and Data Simulation Report (DS).

14. A method according to any one of claims 9 to

13, in which a nominal subset, corresponding to the performance data during said at least one nominal period, is used to characterize the performance of the solar panels, and by extrapolation and comparison to the performance observed during one or more periods of questionable behavior, to assess losses during these periods.

15. A method according to any one of claims 9 to

14, further comprising generating and transmitting to said one or more solar panels (102, 104) a control signal based on the degraded operation detected.

16. The method of claim 15, wherein the control signal is a deactivation signal for deactivating said one or more solar panels (102, 104).

17. Non-transient storage medium storing computer instructions which, when executed by a processing device, cause the implementation of the method of any one of claims 9 to 16.