Nowadays, new trends in the industry of electricity generation are to enhance the power generation by employing distributed generation (DG) system which is mostly based on renewable generation sources such as wind, solar, etc. However, many power quality problems could arise on existing grid when DG is connected or the operation of distributed energy resources (DER) is not controlled properly. That's why, while integrating DG with the power grid, a seamless attention should be given to power generation and safe running of system. Several methods, having a diverse concept, have been divided into two main sets: linear and nonlinear controllers. The first group comes with PI controller and parameter feedback controller, and control by means of constant frequency with predictive techniques. The second group includes hysteresis current control and on-line optimization for predictive controllers. Additionally, new current control techniques with neural networks and fuzzy based controllers are also discussed. Selected methods associate the arrangement for the sake to demonstrate the described groups of the controller.
Reducing energy consumption is a critical issue in the design IntroductionReal-time systems are vital to industrialized infrastructure such as command and control, process control, flight control, space shuttle avionics, air traffic control systems and also mission critical computations [1]. In all cases, time has an essential role and having the right answer too late is as bad as not having it at all. In the literature, these systems have been defined as: "systems in which the correctness of the system depends not only on the logical results of computation, but also the time at which the results are produced" [1]. Such systems must react to the requests within a fixed amount of time which is called deadline. Scheduling algorithms of these systems may be considered one of the key components of a real-time system, which can either enable the system to thrive or bring it to its knees. Strict timing requirements must often be met within highly dynamic environments which do not lend themselves well to static scheduling algorithms. The level of uncertainty in dynamic, real-time environments is such as to require significant flexibility and adaptivity from real systems. Fuzzy logic contributions in this issue in the form of approximate reasoning, where it provides decisionsupport and expert systems with powerful reasoning capabilities bound by a minimum number of rules. Theoretically, fuzzy logic is a method for representing analog processes, or natural phenomena that are difficult to model mathematically on a digital computer. Therefore Fuzzy systems fit as scheduling algorithm building into the real-time system flexibility and adaptation to the uncertainty inherent in real-time environments and offer a means to improve several important characteristics of real-time systems.Since most of real time systems (devices) are battery powered. As the applications on these devices are being complicated, the energy consumption is also effectively increasing. So, minimizing energy consumption is a critical issue in the design of these systems, and techniques that reduce energy consumption have been studied at different levels in details [2].Dynamic voltage scaling (DVS) is a technique that varies the supply voltage and clock frequency (speed) based on the computation load to provide desired performance with the minimal amount of energy consumption in ubiquitous embedded systems.The power consumption has two essential components: dynamic and static power. The dynamic power consumption, which is the main component, has a quadratic dependency on supply voltage [3] and can be represented as:
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