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Reprint of the 1863 ed. published by C. F. Winter, Leipzig. ; Mode of access: Internet. ; 2

Soiling losses are a major concern for remote power systems that rely on photovoltaic energy. Power loss analysis is efficient for the monitoring of large power plants and for developing an optimal cleaning schedule, but it is not adapted for remote monitoring of standalone photovoltaic systems that are used in rural and poor regions. Indeed, this technique relies on a costly and dirt sensitive irradiance sensor. This paper investigates the possibility of a low-cost monitoring of cleaning interventions on photovoltaic modules during daytime. We believe that it can be helpful to know whether the soiling is regularly removed or not, and to decide if it is necessary to carry out additional cleaning operations. The problem is formulated as a classification task to automatically identify the occurrence of a cleaning intervention using a time window of temperature, voltage and current measurements of a photovoltaic array. We investigate machine learning tools based on Logistic Regression, Support Vector Machines, Artificial Neural Networks and Random Forest to achieve such classification task. In addition, we study the influence of the temporal resolution of the signals and the feature extraction on the classification performance. The experiments are conducted on a real dataset and show promising results with classification accuracy of up to 95%. Based on the results, three implementation strategies addressing different practical needs are proposed. The results may be particularly useful for non-governmental organizations, governments and energy service companies to improve the maintenance level of their photovoltaic facilities.

Soiling losses are a major concern for remote power systems that rely on photovoltaic energy. Power loss analysis is efficient for the monitoring of large power plants and for developing an optimal cleaning schedule, but it is not adapted for remote monitoring of standalone photovoltaic systems that are used in rural and poor regions. Indeed, this technique relies on a costly and dirt sensitive irradiance sensor. This paper investigates the possibility of a low-cost monitoring of cleaning interventions on photovoltaic modules during daytime. We believe that it can be helpful to know whether the soiling is regularly removed or not, and to decide if it is necessary to carry out additional cleaning operations. The problem is formulated as a classification task to automatically identify the occurrence of a cleaning intervention using a time window of temperature, voltage and current measurements of a photovoltaic array. We investigate machine learning tools based on Logistic Regression, Support Vector Machines, Artificial Neural Networks and Random Forest to achieve such classification task. In addition, we study the influence of the temporal resolution of the signals and the feature extraction on the classification performance. The experiments are conducted on a real dataset and show promising results with classification accuracy of up to 95%. Based on the results, three implementation strategies addressing different practical needs are proposed. The results may be particularly useful for non-governmental organizations, governments and energy service companies to improve the maintenance level of their photovoltaic facilities.

Soiling losses are a major concern for remote power systems that rely on photovoltaic energy. Power loss analysis is efficient for the monitoring of large power plants and for developing an optimal cleaning schedule, but it is not adapted for remote monitoring of standalone photovoltaic systems that are used in rural and poor regions. Indeed, this technique relies on a costly and dirt sensitive irradiance sensor. This paper investigates the possibility of a low-cost monitoring of cleaning interventions on photovoltaic modules during daytime. We believe that it can be helpful to know whether the soiling is regularly removed or not, and to decide if it is necessary to carry out additional cleaning operations. The problem is formulated as a classification task to automatically identify the occurrence of a cleaning intervention using a time window of temperature, voltage and current measurements of a photovoltaic array. We investigate machine learning tools based on Logistic Regression, Support Vector Machines, Artificial Neural Networks and Random Forest to achieve such classification task. In addition, we study the influence of the temporal resolution of the signals and the feature extraction on the classification performance. The experiments are conducted on a real dataset and show promising results with classification accuracy of up to 95%. Based on the results, three implementation strategies addressing different practical needs are proposed. The results may be particularly useful for non-governmental organizations, governments and energy service companies to improve the maintenance level of their photovoltaic facilities.

Soiling losses are a major concern for remote power systems that rely on photovoltaic energy. Power loss analysis is efficient for the monitoring of large power plants and for developing an optimal cleaning schedule, but it is not adapted for remote monitoring of standalone photovoltaic systems that are used in rural and poor regions. Indeed, this technique relies on a costly and dirt sensitive irradiance sensor. This paper investigates the possibility of a low-cost monitoring of cleaning interventions on photovoltaic modules during daytime. We believe that it can be helpful to know whether the soiling is regularly removed or not, and to decide if it is necessary to carry out additional cleaning operations. The problem is formulated as a classification task to automatically identify the occurrence of a cleaning intervention using a time window of temperature, voltage and current measurements of a photovoltaic array. We investigate machine learning tools based on Logistic Regression, Support Vector Machines, Artificial Neural Networks and Random Forest to achieve such classification task. In addition, we study the influence of the temporal resolution of the signals and the feature extraction on the classification performance. The experiments are conducted on a real dataset and show promising results with classification accuracy of up to 95%. Based on the results, three implementation strategies addressing different practical needs are proposed. The results may be particularly useful for non-governmental organizations, governments and energy service companies to improve the maintenance level of their photovoltaic facilities.

We theoretically introduce a type of topological dipole soliton propagating in a Floquet topological insulator based on a kagome array of helical waveguides. Such solitons bifurcate from two edge states belonging to different topological gaps and have bright envelopes of different symmetries: fundamental for one component, and dipole for the other. The formation of dipole solitons is enabled by unique spectral features of the kagome array which allow the simultaneous coexistence of two topological edge states from different gaps at the same boundary. Notably, these states have equal and nearly vanishing group velocities as well as the same sign of the effective dispersion coefficients. We derive envelope equations describing the components of dipole solitons and demonstrate in full continuous simulations that such states indeed can survive over hundreds of helix periods without any noticeable radiation into the bulk. ; Y.V.K. and S.K.I. acknowledge funding of this study by RFBR and DFG according to Research Project No. 18- 502-12080. A.S. acknowledges funding from the Deutsche Forschungsgemeinschaft (Grants No. BL 574/13-1, No. SZ 276/19-1, and No. SZ 276/20-1). Y.V.K. and L.T. acknowledge support from the Government of Spain (Severo Ochoa CEX2019-000910-S), Fundació Cellex, Fundació Mir-Puig, Generalitat de Catalunya (CERCA). V.V.K. acknowledges financial support from the Portuguese Foundation for Science and Technology (FCT) under Contract No. UIDB/00618/2020. ; Peer Reviewed ; Postprint (author's final draft)