Dust Accumulation Effects on the Performance of Photovoltaic Panels: An Experimental Study in the Algerian Region of El-Oued

This paper examines the dust accumulation impact on the performance of photovoltaic panels in the Algerian region of El-Oued, where two similar photovoltaic panels were analyzed: a clean reference photovoltaic panel (PVr) and a dirty targeted photovoltaic panel (PVt) with 14.5 g/m² of dust. The data was collected on May 4th and 13th, 2022, through experimental works and numerical validation. The results show that dust significantly reduces the PV performance. On May 4th, 2022, the PVr produced 330.89 Wh, compared to 216.72 Wh for the PVt, with a difference of 34.65%. On May 13th, 2022, PVr generated 414.01 Wh, while PVt produced 271.16 Wh, with a difference of 34.67%. In terms of PV power generation, PVr reached maximum values of 52.82 W and 66.28 W on May 4th and 13th, respectively, compared to 34.5 W and 43.29 W for PVt. The PVr performance varied between 5.85% and 6.56%, while that of PVt was limited to 3.82% and 4.29%. These results highlight the importance of keeping photovoltaic panels clean to ensure optimal energy production, especially in desert environments like El-Oued,. Moreover, the study confirms that regular panel maintenance is essential to minimize power reduction due to dust and guarantee maximum panel efficiency

Open-circuit fault diagnosis in three-phase induction motor using model-based technique

The presence of an open-circuit fault subjects a three-phase induction motor to severely unbalanced voltages that may damage the stator windings consecutively causing total shutdown of systems. Unplanned downtime is very costly. Therefore, fault diagnosis is essential for making a predictive plan for maintenance and saving the required time and cost. This paper presents a model-based diagnosis technique for diagnosing an open-circuit fault in any phase of a three-phase induction motor. The proposed strategy requires only current signals from the faulty machine to compare them with the healthy currents from an induction motor model. Then the errors of comparison are used as an objective function for a genetic algorithm that estimates the parameters of a healthy model, which they employed to identify and localize the fault. The simulation results illustrate the behaviours of basic parameters (stator and rotor resistances, self-inductances, and mutual inductance) and the number of stator winding turn parameters with respect to the location of an open-circuit fault. The results confirm that the number of stator winding turns are the useful parameters and can be utilized as an identifier for an open-circuit fault. The originality of this work is in extracting fault diagnosis features from the variations of the number of stator winding turns

Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

The widespread use of three-phase cage induction motors in so many critical industrial, commercial and domestic applications means that there is a real need to develop online diagnostic systems to monitor the state of the machine during operation. This paper presents a hybrid diagnostic system that combines a model-based strategy with a fuzzy logic classifier to identify abnormal motor states due to single-phasing or inter-turn stator winding faults. Only voltage and current measurements are required to extract the fault symptoms, which are represented as model parameters variations in an equivalent virtual healthy motor, negating the need to use complex models of faulty machines. A trust-region method is used to estimate the machine model parameters, with the final decision on the type, location and extent of the fault being made by the fuzzy logic classifier. The proposed diagnostic system was experimentally verified using a 1.0 hp three-phase test induction motor. Results show that the proposal method can efficiently diagnose single phasing and inter-turn stator winding faults even when operating with unbalanced supply voltages and in the presence of significant levels of measurement noise