Scientists in Algeria have developed a cheap solution to optimize the cleaning activities for all PV systems. The proposed approach works “effectively” without heavy data requirements, according to its makers.
Researchers at the Université De Ghardaia van Algeria have developed a new method to improve PV cleaning schedules that guarantee independence of extensive data sets.
“Our method functions effectively without heavy data requirements”, the main author of the research, Charaf Abdelkarim Mosbahtold PV -Magazine. “It combines maximum power point tracking (MPPT) techniques, metahuristic optimization and an intelligent cleaning score mechanism (ICSM).”
In the study “Smart and cost -effective optimization of photovoltaic cleaning schedules“Published in Energy and buildingsThe research team explained that the Method ensures “optimum” decision -making in both real -time and offline settings, which reduces it unnecessarily cleaning Operations and improving the overall energy yield. “What strikes our approach is the seamless integration with existing MPPT systems, which means that immediate deployment is possible without further investments in hardware or data infrastructure,” said Mosbah.
The scientists explained that cleaning activities require two fundamental steps – assessing cleanliness and making a decision – and noted that cleaning only becomes “essential” when the dust accumulates and remains on the module surfaces, whereby the analysis focuses on errors with constant or gradual characteristics.
The proposed method distinguishes itself between two different MPPT operations modes: the exploration phase, when the algorithm actively searches for the MPP; And the operating phase, when the MPP is reached. It also makes a distinction between partial shadow and uniform radiation modes and defines the search room of each decision-be-variable independent, the scientists said that it is not the case with many other algorithms.
The new methodology uses ICSM to simplify the detection of dust accumulation. It initially evaluates whether the PV system works normally or under partial shade, whereby the data is analyzed in real time in combination with MPPT or later evaluated offline. “With this function, the method can be applied during downtime of the system, so that maintenance tasks, including planning of cleaning planning, can be used efficiently without disturbing energy production,” the research group emphasized.
The new approach also uses an intelligent counter to collect the results of the data analysis. “Our experimental results showed an accuracy of 98.4% when predicting optimal cleaning Schedules, achieving superior performance compared to three other benchmarkalgorithms, “said Mosbah.
The research team claims that their new solution can be applied to all PV systems, including large-scale sun factories, without extra costs.
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