This papers deal with a new Adaptive Direct Power Control for Doubly-Fed Induction Generator of 1.5 MW. The main feature of the proposed strategy is based on the replacement of the fixed switching table by an adaptive one. The online update of the adaptive switching table depends on the reactive power variation and past switching sequences. The proposed adaptive direct power control is compared with Vector Control and Classical Direct Power Control. The robustness of the proposed control scheme against parameter, load and wind speed variations have done with success. The main performance of the Adaptive Direct Power Control strategy is the reduction of powers ripples, thus reduce of torque ripple on the shaft of the turbine.

Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented.