Recently, solar power generation is significantly contributed to growing renewable sources of electricity all over the world. The reliability and availability improvement of solar photovoltaic (PV) systems has become a critical area of interest for researchers. Reliability, availability, and maintainability (RAM) is an engineering tool used to address operational and safety issues of systems. It aims to identify the weakest areas of a system which will improve the overall system reliability. In this paper, RAM analysis of grid-connected solar-PV system is presented. Elaborate RAM analysis of these systems is presented starting from the sub-assembly level to the subsystem level, then the overall system. Further, an improved Reliability Block Diagram is presented to estimate the RAM performance of seven practical grid-connected solar-PV systems. The required input data are obtained from worldwide databases of failures, and repair of various subassemblies comprising various meteorological conditions. A novel approach is also presented in order to estimate the best probability density function for each sub-assembly. The monitoring of the critical subassemblies of a PV system will increase the possibility not only for improving the availability of the system, but also to optimize the maintenance costs. Additionally, it will inform the operators about the status of the various subsystems of the system.
In the two-coupled distillation column process, keeping the tray temperatures within a specified range around their steady state values assures the specifications for top and bottom product purity. The two-coupled distillation column is a 4 Input/4 Output process. Normally, control engineers decouple the process into four independent loops. They assign a PID controller to control each loop. Tuning of conventional PID controllers is very difficult when the process is subject to external unknown factors. The paper proposes a Brain Emotional Learning Based Intelligent Controller (BELBIC) to replace conventional PID controllers. Moreover, the values of BELBIC and PID gains are optimized using a particle swarm optimization (PSO) technique with minimization of Integral Square Error (ISE) for all loops. The paper compares the performance of the proposed PSO-BELBICs with that of conventional PSO-PID controllers. PSO-BELBICs prove their usefulness in improving time domain behavior with keeping robustness for all loops.
Multiple-input multiple-output (MIMO) with N Input/ N Output processes are characterized by significant interactions between their inputs and outputs. The control of MIMO processes is usually implemented using sets of singleinput single-output (SISO) loop controllers, which requires proper input-output pairing and development of decoupling compensator unit. In this paper, a generalized decoupling technique is proposed. The proposed technique uses relative gain array (RGA) to select proper pairing and particle swarm optimization (PSO) technique to estimate the optimal elements' values of steady state decoupling compensation matrix constituting the decoupling compensator unit. The proposed technique is applied on 4 Input/ 4 Output two coupled distillation columns process, it proves remarkable success in minimizing the interaction between every input and all outputs except that output has been proper paired with.
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