Abstract: The invention relates to a method and device for diagnosing the operation of a photovoltaic module string. The method involves acquiring a series of current and voltage measurements forming a current versus voltage curve, known as the I(V) curve, and the steps of: transforming (46) into polar coordinates, each pair of current and voltage values of the I(V) curve, calculating (48-54) parameters of a first electrical model and second electrical model each approximating said I(V) curve, as well as a first cost representative of a deviation between said first model and said I(V) curve, and a second cost representative of a deviation between said second model and said I(V) curve, using a distance metric in the polar coordinate space, selecting (56) an electrical estimation model for estimating the I(V) curve and diagnosing (58) operation based on this selected electrical estimation model.

Abstract: A method determines a probe signal for acoustically characterising an electrochemical system, the probe signal being determined by an identification signal transmitted in a reference electrochemical system to induce a response from the reference electrochemical system.

Abstract: A method for determining an electrical model of a string of photovoltaic modules from a characteristic I(V) of the string includes detecting a first linear zone and a second linear zone of the characteristic I(V); initialising the parameters of a non-by-pass electrical model corresponding to a first operating condition, called a non-by-pass condition; optimising the parameters of the non-by-pass electrical model from a reference characteristic I(Vref) equal to I(V), determining the parameters of the electrical model corresponding to a second operating condition, called a by-pass condition, in order to obtain a by-pass electrical model from the characteristic determining, from the characteristic I(V) the best model among the non-by-pass model and the by-pass model.

Abstract: A method for determining the state of a system among a plurality of states, includes acquiring values of a reference physical quantity of the system corresponding to a plurality of points in an original space, each value being paired with one point of the plurality of points and with one state of the system; embedding a portion of the points in a representation space, the representation space being in bijection with a sub-variety of the original space, each point in the representation space being paired with one state; determining a pairing function that pairs any position of the original space with a position in the representation space; determining the position in the representation space of a point of the original space paired with an acquired value, and determining the state of the system from the position of the point paired with the acquired value in the representation space.

Abstract: A method for determining a corrected current-voltage curve of an electrical system, the method including the following steps: obtaining a first current-voltage characteristic curve of the electrical system, by varying the voltage across its terminals at a first measurement rate, obtaining a second current-voltage characteristic curve of the electrical system, by varying the voltage across its terminals at a second measurement rate, different from the first rate, using a single notional capacitance to model an intrinsic stray effect to be corrected between an input voltage without the stray effect and the output voltage, determining a correction value representative of the stray effect and a step of determining a corrected current value on the basis of the determined correction value.

Abstract: A method for determining a corrected current-voltage curve of an electrical system, the method including the following steps: obtaining a first current-voltage characteristic curve of the electrical system, by varying the voltage across its terminals at a first measurement rate, obtaining a second current-voltage characteristic curve of the electrical system, by varying the voltage across its terminals at a second measurement rate, different from the first rate, using a single notional capacitance to model an intrinsic stray effect to be corrected between an input voltage without the stray effect and the output voltage, determining a correction value representative of the stray effect and a step of determining a corrected current value on the basis of the determined correction value.

Abstract: A method for determining an electrical model of a string of photovoltaic modules from a characteristic I(V) of the string includes detecting a first linear zone and a second linear zone of the characteristic I(V); initialising the parameters of a non-by-pass electrical model corresponding to a first operating condition, called a non-by-pass condition; optimising the parameters of the non-by-pass electrical model from a reference characteristic I(Vref) equal to I(V), determining the parameters of the electrical model corresponding to a second operating condition, called a by-pass condition, in order to obtain a by-pass electrical model from the characteristic determining, from the characteristic I(V) the best model among the non-by-pass model and the by-pass model.

Abstract: The invention relates to a method for estimating drift in a solar radiation sensor (2) and for calibrating such a sensor, in which the radiation (GMES) measured by this sensor under its conditions of use and a radiation model (51) are taken into account.

Abstract: Method for detecting an electric arc in a photovoltaic installation, characterized in that it comprises the following steps: Measurement (E6) of voltage values at at least one site of the electrical circuit of the photovoltaic installation; Digitization (E8) of the measured voltage values so as to form a data sampling x; Computation (E10) of a first indicator of presence of an electric arc, on the basis of a statistical computation on the data sampling x, from among the variance, a value analogous to the variance, or the interquartile range of the data sampling; Computation (E10) of a second indicator of presence of an electric arc, on the basis of a statistical computation on the data sampling based on one or more estimations of the mean and/or of the median of the data sampling; Comparison (E12) of the first and second indicators of presence of an electric arc with respective thresholds so as to deduce therefrom the presence or absence of an electric arc within the photovoltaic installation.

Abstract: Method for detecting an electric arc in a photovoltaic installation, which includes measurement (E6) of voltage values at at least one site of the electrical circuit of the photovoltaic installation; digitization (E8) of the measured voltage values so as to form voltage data; formation (E11) of a window of n recent digitized voltage data; computation (E12) of a test value associated with the said window of the n voltage data; analysis (E13) of a test vector comprising m test values associated with m recent windows generating a quantity arising from this analysis representative of a risk of electric arc; comparison (E15) of this quantity arising with at least one threshold so as to deduce therefrom the presence or otherwise of an electric arc within the photovoltaic installation.

Abstract: A method and a system for evaluating the class of a test datum in a data space of dimension D where D?3, each datum belonging to at least one class grouping together several data, comprising: projecting a suite of reference data of the data space into a space of dimension Q where Q<D, the class of each reference datum being known, calculating a measurement of similarity of the test datum to each of the reference data, partitioning the projection space into a plurality of disjoint regions each containing the projection of one and only one reference datum, and finally evaluating the class of the test datum, this class being evaluated as being the same class as one of the reference data contained in one of the regions containing the reference data closest to the test datum in the sense of the similarity measurement.

Abstract: Method for detecting an electric arc in a photovoltaic installation, characterized in that it comprises the following steps: Measurement (E6) of voltage values at at least one site of the electrical circuit of the photovoltaic installation; Digitization (E8) of the measured voltage values so as to form a data sampling x; Computation (E10) of a first indicator of presence of an electric arc, on the basis of a statistical computation on the data sampling x, from among the variance, a value analogous to the variance, or the interquartile range of the data sampling; Computation (E10) of a second indicator of presence of an electric arc, on the basis of a statistical computation on the data sampling based on one or more estimations of the mean and/or of the median of the data sampling; Comparison (E12) of the first and second indicators of presence of an electric arc with respective thresholds so as to deduce therefrom the presence or absence of an electric arc within the photovoltaic installation.

Abstract: Method for detecting an electric arc in a photovoltaic installation, which includes measurement (E6) of voltage values at at least one site of the electrical circuit of the photovoltaic installation; digitization (E8) of the measured voltage values so as to form voltage data; formation (E11) of a window of n recent digitized voltage data; computation (E12) of a test value associated with the said window of the n voltage data; analysis (E13) of a test vector comprising m test values associated with m recent windows generating a quantity arising from this analysis representative of a risk of electric arc; comparison (E15) of this quantity arising with at least one threshold so as to deduce therefrom the presence or otherwise of an electric arc within the photovoltaic installation.

Abstract: The invention relates to a method for estimating drift in a solar radiation sensor (2) and for calibrating such a sensor, in which the radiation (GMES) measured by this sensor under its conditions of use and a radiation model (51) are taken into account.

Abstract: A method and a system for evaluating the class of a test datum in an original metric space, each datum belonging to at least one class grouping a plurality of data, includes a step of graphical representation of the spatial organization of a set of learning data of the original space in a representation metric space, a conjoint membership level indicating if any two data from the learning set belong to the same class. The method also includes a step of relating the test datum to the projections of the data from the learning set, the most probable class of the test datum being the class of the projections of the data from the learning set related to the test datum. Application: assistance with decision-making in discrimination, shape recognition.

Abstract: A method and a system for evaluating the class of a test datum in an original metric space, each datum belonging to at least one class grouping a plurality of data, includes a step of graphical representation of the spatial organization of a set of learning data of the original space in a representation metric space, a conjoint membership level indicating if any two data from the learning set belong to the same class. The method also includes a step of relating the test datum to the projections of the data from the learning set, the most probable class of the test datum being the class of the projections of the data from the learning set related to the test datum. Application: assistance with decision-making in discrimination, shape recognition.

Abstract: A method and a system for evaluating the class of a test datum in a data space of dimension D where D?3, each datum belonging to at least one class grouping together several data, comprising: projecting a suite of reference data of the data space into a space of dimension Q where Q<D, the class of each reference datum being known, calculating a measurement of similarity of the test datum to each of the reference data, partitioning the projection space into a plurality of disjoint regions each containing the projection of one and only one reference datum, and finally evaluating the class of the test datum, this class being evaluated as being the same class as one of the reference data contained in one of the regions containing the reference data closest to the test datum in the sense of the similarity measurement.