Abstract:Stability metrics are used to quantify a system’s ability to maintain equilibrium under disturbances. We did not identify the proposition of a stability metric using sensitivity analysis within the literature. This work proposes a system stability metric and its application to an electrical repowering system. The methodology for applying the proposed metric comprises: (i) system parameters sensitivity analysis and spider diagram construction, (ii) determining the array containing the line segments inclination … Show more
“…The calculated sensitivity indices presented values between and , that is, the least sensitive parameter is relevant to the system as well as the most sensitive parameter. In other systems, it is common to verify sensitivity indices of certain parameters close to zero and others, above 48 , 51 , 54 , 55 .…”
Section: Discussionmentioning
confidence: 99%
“…Sensitivity analysis allows measuring the impact generated on system outputs due to variations in input variables 45 – 47 . Figure 1 , adapted from Paiva 48 , illustrates the sensitivity analysis process for a system with n input parameters given by . Figure 1 Sensitivity analysis cycles using the one-at-a-time method.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…variations in input variables [45][46][47] . Figure 1, adapted from Paiva 48 , illustrates the sensitivity analysis process for a system with n input parameters given by x…”
This work proposes a system complexity metric and its application to Intensive Care Unit (ICU) system. The methodology for applying said complexity metric comprises: (i) parameters sensitivity indices calculation, (ii) mapping connections dynamics between system components, and (iii) system’s complexity calculation. After simulating the ICU computer model and using the proposed methodology, we obtained results regarding: number of admissions, number of patients in the queue, length of stay, beds in use, ICU performance, and system complexity values (in regular or overloaded operation). As the number of patients in the queue increased, the ICU system complexity also increased, indicating a need for policies to promote system robustness.
“…The calculated sensitivity indices presented values between and , that is, the least sensitive parameter is relevant to the system as well as the most sensitive parameter. In other systems, it is common to verify sensitivity indices of certain parameters close to zero and others, above 48 , 51 , 54 , 55 .…”
Section: Discussionmentioning
confidence: 99%
“…Sensitivity analysis allows measuring the impact generated on system outputs due to variations in input variables 45 – 47 . Figure 1 , adapted from Paiva 48 , illustrates the sensitivity analysis process for a system with n input parameters given by . Figure 1 Sensitivity analysis cycles using the one-at-a-time method.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…variations in input variables [45][46][47] . Figure 1, adapted from Paiva 48 , illustrates the sensitivity analysis process for a system with n input parameters given by x…”
This work proposes a system complexity metric and its application to Intensive Care Unit (ICU) system. The methodology for applying said complexity metric comprises: (i) parameters sensitivity indices calculation, (ii) mapping connections dynamics between system components, and (iii) system’s complexity calculation. After simulating the ICU computer model and using the proposed methodology, we obtained results regarding: number of admissions, number of patients in the queue, length of stay, beds in use, ICU performance, and system complexity values (in regular or overloaded operation). As the number of patients in the queue increased, the ICU system complexity also increased, indicating a need for policies to promote system robustness.
This paper presents a tracking method to improve the performance of switched reluctance generators in areas with low wind speeds. The main contribution of this work is the proposal of an advanced control system that employs dynamic tracking techniques to optimize the energy efficiency and response of the switched reluctance generators under various operating conditions. The methodology encompasses the construction of the generator, the development of a wind turbine model, and the coupling of the turbine with the switched reluctance generators. Simulations are conducted using real wind speed data to evaluate the use of switched reluctance generators in wind energy generation. The performance of the switched reluctance generators is improved by applying a tracking technique that operates on the converter’s turn‐off angle, which controls the generator’s switching. The tracking and optimization techniques implemented in this work maximize the performance of switched reluctance generators in wind energy generation, both in standalone and self‐excited operations. The results demonstrate that satisfactory wind energy generation can be achieved using the SRG, with wind speeds of 5.40 m/s producing approximately 1.5 kW without optimization and around 1.65 kW with optimization, reaching about 70% efficiency with a custom‐built generator. This makes the technology viable for regions characterized by low wind speeds.
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