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Peak power tracking system Proteus model is proposed to design a maximum power point tracking techniques for photovoltaic systems. The simulator is good substitute to implement and verify the performance of MPPT algorithms in the laboratory. The proposed simulator has the same comportment as physical prototype. Simulated results of tracking voltage, tracking current and tracking power have been analyzed. The theoretical operation of the proposed model confirms the good performance even under solar irradiation variation.

In the paper, the main contribution is to understand the challenges related with operating silicon solar cells in Algerian Sahara, in order to improve the economical output of photovoltaic power systems. The evaluation consists the extraction of electrical parameters and visual observations. Tested PV modules have been subjected to extreme weather conditions for many years in field. Desert climate pose several challenges for the photovoltaic applications. The output power of solar cells drop up to 30 % compared to manufacturing status and physical material defects have mainly contribution in failure of polycrystalline solar cells in desert climate.

Desert climate affects the durability of photovoltaic panels that leading to a drop in their lifetime. the following work reviews the failure modes and performance degradation of standard panels in Sahara Desert climate. The study carried out on several silicon cells after years fielded in Algeria Sahara. Visual inspection shows the influence of high temperature combined by high ultra violet. The review on failure mode and performance degradation in the paper could be used to assessing the accelerated lifetime of silicon solar cells in desert climate, in order to improve the standard photovoltaic panels performance in desert climate.

Nowadays, dissolved gas analysis (DGA) technology of strategic electrical power transformers is universally practiced. The proposed approach is based on mapping the triangle of Duval into Bayesian network (BN). This approach eliminates the uncertainty that exists in the decision making regarding the nature of the faults due to gases that are trapped in the transformer, and faults that results in more gas percentages simultaneously. The model accurately provides transformer fault diagnosis and prediction abilities by calculating the probabilities of released gases and further predicting the failures based on their relationships in the Bayesian network. A case study is presented at the end of the paper to show the feasibility of the method on a 15.5 / 6.6 kV 20 MVA step-down transformer.