Fuzzy Expert System for Determining State of Solar Photo Voltaic Power Plant Based on Real-Time Data

The basis of reliability engineering is reliability theory. Reliability analysis is critical for developing different alternatives for system improvement during the design, configuration, and tuning stages, which are essential to the efficient operation of a complicated system. This paper deals with the estimation of the reliability of a degradable system with imperfect coverage and uncertain information about system components. The Weibull intuitionistic fuzzy set (WIFS) concept was used to deal with the uncertainties in the data. The knowledge of a trapezoidal intuitionistic fuzzy number (TrIFN) is presented, as well as its arithmetic operations. Trapezoidal intuitionistic fuzzy numbers (TrIFN) are applied to signify the failure rate of system.

Section: Introductionmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

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Reliability Estimation of a Degradable System using Intuitionistic Fuzzy Weibull Lifetime Distribution

Patrai,

Gupta

2023

“…In an attempt to maintain the energy supply, the development of alternative energy sources concerning renewable energy is now growing increasingly. The employment of renewable energy sources such as solar 1,2) , wave 3,4) , geothermal 5,6) , tidal 7,8) , and wind 9,10) have been widely investigated to evaluate their potency.…”

Section: Introductionmentioning

confidence: 99%

Numerical study on the Effect of Rectangular and Triangular Counter-Rotating Vortex Generators on the H-Rotor Wind Turbine Performance

Yaningsih¹,

Tjahjana²,

Budiana³

et al. 2023

Vortex generators (VGs) in the form of small fins are attached to the blade of the Hrotor wind turbine to improve the performance. Many studies provide the utilization VGs both experimentally and numerically. However, the investigation of the full scale of counter-rotating VGs in rotor blades is still rarely found. In the current study, two shapes of VGs were investigated to evaluate wind turbine performance. VGs in rectangular and triangular shapes were attached under the identical blade geometry of 375 mm of chord length and the blade attachment of 30% from the chord length. VGs have a similar height and length of 6.5 mm and 13 mm, respectively. The turbine was constructed with three blades that rotate under 1,650 mm of the rotor diameter and 1,000 mm of rotor height. The chordwise position (x/c) of the VGs was at 10%, 15%, 20%, and 25%. The computational fluid dynamics with the unsteady Reynolds Average Navier Stokes (URANS) model were employed. We found that VGs behave in different performances. The rectangular VGs had the maximum power coefficient (Cp) value at the x/c of 20%, while the triangular VGs had the maximum Cp value at the x/c of 25%. However, both VGs show the maximum Cp in the value of 0.467 with a 1.5 Tip Speed Ratio. To evaluate the performance improvement, the blade with VGs was compared with the blade without VGs. It was found that using the VGs improved the performance of the wind turbine by around 45.68% and 47.24%, respectively, for triangular and rectangular shapes. The flow field characteristics in terms of turbulent kinetic energy and flow velocity were also presented in this study to gain a better understanding of how the VGs work. The result revealed that the presence of the VGs significantly improves the performance of the turbine.

“…One of the challenging problems is controlling bipedal walking robots. The bipedal walking robot is an advanced anthropomorphic robot that can mimic the human ability to walk 7) . It is designed and modeled in human like and walking by the knowledge of the human gait.…”

Section: Introductionmentioning

confidence: 99%

A Trajectory Control for Bipedal Walking Robot Using Stochastic-Based Continuous Deep Reinforcement Learning

Surriani,

Wahyunggoro,

Adha Imam Cahyadi

2023

The bipedal walking robot is an advanced anthropomorphic robot that can mimic the human ability to walk. Controlling the bipedal walking robot is difficult due to its nonlinearity and complexity. To solve this problem, recent studies have applied various machine learning algorithms based on reinforcement learning approaches, however most of them rely on deterministic-policybased strategy. This research proposes Soft Actor Critic (SAC), which has stochastic policy strategy for controlling the bipedal walking robot. The option thought deterministic and stochastic policy affects the exploration of DRL algorithm. The SAC is a Deep Reinforcement Learning (DRL) based algorithm whose improvement obtained through the augmented entropy-based expected return allows the SAC algorithm to learn faster, gain exploration ability, and still ensure convergence. The SAC algorithm's performance is validated with a bipedal robot to walk towards the straight-line trajectory. The number of the reward and the cumulative reward during the training is used as the algorithm's performance evaluation. The SAC algorithm controls the bipedal walking robot well with a total reward of 384,752.8.