scholarly journals A Knowledge-Informed Simplex Search Method Based on Historical Quasi-Gradient Estimations and Its Application on Quality Control of Medium Voltage Insulators

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 770
Author(s):  
Xiangsong Kong ◽  
Dongbin Zheng
Keyword(s):  
Quality Control ◽  
Weight Control ◽  
Control Method ◽  
Search Method ◽  
Experimental Simulation ◽  
Process Conditions ◽  
Medium Voltage ◽  
Simplex Search ◽  
Simplex Search Method ◽  
Gradient Based

Quality control is of great significance for the economical manufacturing and reliable application of medium voltage insulators. With the increasingly stringent quality control requirement, traditional quality control methods in this field face a growing challenge on their efficiency. Therefore, this study aims to achieve quality specifications by optimizing process conditions with the least costs. Thus, a knowledge-informed simplex search method was proposed based on an idea of knowledge-informed optimization to enhance the optimization efficiency. Firstly, a new mathematical quantity, quasi-gradient estimation, was generated following a reconstruction of the simplex search from the essence and the development history of the method. Based on this quantity, the gradient-free method possessed the same gradient property and unified form as the gradient-based methods. Secondly, an implementation of the knowledge-informed simplex search method based on historical quasi-gradient estimations (short for GK-SS) was constructed. The GK-SS-based quality control method utilized the historical quasi-gradient estimations for each simplex generated during the optimization process to improve the method’s search directions’ accuracy in a statistical sense. Finally, this method was applied to the weight control of a kind of post insulator. The experimental simulation results showed that the method is effective and efficient in the quality control of medium voltage insulators.

scholarly journals Quality Control for Medium Voltage Insulator via a Knowledge-Informed SPSA Based on Historical Gradient Approximations

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 146 ◽  
Author(s):  
Xiangsong Kong ◽  
Jiaming Guo ◽  
Dongbin Zheng ◽  
Ji Zhang ◽  
Wei Fu
Keyword(s):  
Quality Control ◽  
Process Parameters ◽  
Weight Control ◽  
Control Method ◽  
Parameters Optimization ◽  
Experimental Simulation ◽  
Iteration Step ◽  
Step Size ◽  
Medium Voltage ◽  
Voltage Insulator

Medium voltage insulators are essential and versatile components in electrical engineering. Quality control of the manufacturing process for the insulators has a significant role in their economic production and reliable operation. As the quality of medium voltage insulator is mainly affected by the process parameters of the automatic pressure gelation process (APG), the optimal process settings are required to achieve a satisfactory quality target. However, traditional process parameters’ optimization methods are often cumbersome and cost-consuming. Moreover, the operational cost of APG for insulator production is relatively high. Therefore, the determination of the optimal settings becomes a significant challenge for the quality control of insulators. To address the above issues, an idea of knowledge-informed optimization was proposed in this study. Based on the above idea, a knowledge-informed simultaneous perturbation stochastic approximation (SPSA) methodology was formulated to reduce the optimization costs, and thus improve the efficiency of quality control. Considering the characteristics of SPSA, the historical gradient approximations generated during the optimization process were utilized to improve the accuracy of gradient estimations and to tune the iteration step size adaptively. Therefore, an implementation of a quality control strategy of knowledge-informed SPSA based on historical gradient approximations (GK-SPSA) was thus constructed. In this paper, the GK-SPSA-based quality control method was applied to the weight control of a kind of post insulators. The experimental simulation results showed that the revised knowledge-informed SPSA was effective and efficient on quality control of medium voltage insulators.

An Efficient Hybrid Evolution Strategy Algorithm with Direct Search Method for Global Optimization

2019 ◽  
Vol 9 (3) ◽  
pp. 63-78
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Global Optimization ◽  
Adaptive Evolution ◽  
Optimization Problems ◽  
Search Method ◽  
Local Solution ◽  
Evolution Strategy ◽  
Direct Search Method ◽  
Simplex Search ◽  
Simplex Search Method ◽  
Restart Strategy

The main purpose of this article is to demonstrate how evolution strategy optimizers can be improved by incorporating an efficient hybridization scheme with restart strategy in order to jump out of local solution regions. The authors propose a hybrid (μ, λ)ES-NM algorithm based on the Nelder-Mead (NM) simplex search method and evolution strategy algorithm (ES) for unconstrained optimization. At first, a modified NM, called Adaptive Nelder-Mead (ANM) is used that exhibits better properties than standard NM and self-adaptive evolution strategy algorithm is applied for better performance, in addition to a new contraction criterion is proposed in this work. (μ, λ)ES-NM is balancing between the global exploration of the evolution strategy algorithm and the deep exploitation of the Nelder-Mead method. The experiment results show the efficiency of the new algorithm and its ability to solve optimization problems in the performance of accuracy, robustness, and adaptability.

Inverse Heat Transfer Analysis of Porous Extended Surface Using Simplex Search Method

2013 ◽  
Author(s):  
Rohit Kumar Singla ◽  
Ranjan Das ◽  
Arka Bhowmik ◽  
Ramjee Repaka
Keyword(s):  
Heat Transfer ◽  
Temperature Field ◽  
Temperature Measurement ◽  
Thermal Conditions ◽  
Search Method ◽  
Heat Transfer Analysis ◽  
Critical Parameters ◽  
Simplex Search ◽  
Simplex Search Method ◽  
Extended Surface

This work deals with the application of the Nelder-Mead simplex search method (SSM) to study a porous extended surface. At first, analytical expression for calculating the local temperature field has been derived using an implicit Runge-Kutta method. The heat transfer phenomenon is assumed to be governed by conductive, naturally convective and radiative heat transfer, whereas the diffusion of mass through the porous media is also taken into account. Then, using the SSM, critical parameters such as porosity, permeability, and thermal conductivities of the extended surface have been predicted for satisfying a prescribed temperature field. It is found that many alternative solutions can meet a given thermal requirement, which is proposed to offer the flexibility in selecting the material and regulating the thermal conditions. It is observed that the allowable error in the temperature measurement should be limited within 5%. It is also found that even with few temperature measurement points, very good reconstruction of the thermal field is possible using the SSM.

A fin design employing an inverse approach using simplex search method

2013 ◽  
Vol 49 (7) ◽  
pp. 1029-1038 ◽  
Author(s):  
Ranjan Das ◽  
Ashis Mallick ◽  
K. T. Ooi
Keyword(s):  
Search Method ◽  
Inverse Approach ◽  
Simplex Search ◽  
Simplex Search Method ◽  
Fin Design
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