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State assessment of 110-220 kV intelligent substation based on multisensor fusion algorithm control and image vision

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FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Zhao, Weiyan Cui, Ao Fang, Ming Yang, Liang Chen, Kang Gong, Xuqiang Yunnan Power Grid Co Ltd Transmiss Branch Kunming Peoples R China

With the development of modern information technology, intelligent substation technology has been widely used, which greatly promotes the development of power grid. The information integration platform of intelligent substations realizes panoramic data collection and data sharing of substations. With the continuous development of the economy and society, it is crucial to improve the power regulation ability of intelligent substations, make scientific use of intelligent technology, and optimize the patrol level of intelligent substation patrol robots to better meet the operation needs of substations. In the actual substation construction, the flexibility of equipment utilization in intelligent substations should be fully considered. In addition, the number of substations should be controlled to improve the utilization efficiency of patrol robots. This paper mainly summarizes the monitoring technology of intelligent substations, the positioning technology of inspection robots, and the multi-sensor control technology to lay a foundation for the follow-up inspection work of intelligent substations.

关键词： state assessment intelligent substation multisensor fusion algorithm control image vision

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Gamification From Below as by Form of Resistance: algorithm control, Precarity, and Resistance Dynamic of Indonesian Gig Workers

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NEW TECHNOLOGY WORK AND EMPLOYMENT 2024年第0期

作者： Novianto, Arif Univ Gadjah Mada Fac Social & Polit Sci Dept Publ Policy & Management Yogyakarta Indonesia

In the digital gig economy, algorithms serve as primary tools for controlling workers within the labor process. One form of this control is through gamification, which aims to align labor supply with consumer demand, encourage quality service, and enforce worker discipline. This study surveyed of 662 couriers or gig workers in Gojek's Gosend Sameday service. Findings of this study show that Gosend Sameday drivers receive low pay and lack social protection. In light of these precarious conditions, Gojek builds the gamification scheme to incentivize hard work. In responding to the gamification created unilaterally by Gojek (gamification from above), many drivers rejected it. Resistance manifests in two forms: infrapolitics and collective resistance. Infrapolitics is part of the weapon of the weak, which is carried out by rejecting unsatisfactory orders, voicing complaints on social media, and exploiting loopholes in the Gojek driver application. Conversely, collective resistance was carried out with mass actions, strikes, picketing, organizing, and forming unions to build gamification from below. This study contributes to the understanding of how infrapolitics can inadvertently consent to platform capitalism hegemonic regime, suggesting that while drivers may it rejects gamification, the platform continues to influence labor dynamics. Collective resistance, on the other hand, challenges gamification from above and attempts to build gamification from below to create a fairer labor environment for platform drivers.

关键词： algorithm control gamification from below gig worker precarity labor process platform driver resistance

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Applying machine learning to low-knowledge control of optimization algorithms

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COMPUTATIONAL INTELLIGENCE 2005年第4期21卷 372-387页

作者： Carchrae, T Beck, JC Natl Univ Ireland Univ Coll Cork Dept Comp Sci Cork Constraint Computat Ctr Cork Ireland Univ Toronto Dept Mech & Ind Engn Toronto Intelligent Decis Engn Lab Toronto ON Canada

This paper addresses the question of allocating computational resources among a set of algorithms to achieve the best performance on scheduling problems. Our primary motivation in addressing this problem is to reduce the expertise needed to apply optimization technology. Therefore, we investigate algorithm control techniques that make decisions based only on observations of the improvement in solution quality achieved by each algorithm. We call our approach "low knowledge" since it does not rely on complex prediction models, either of the problem domain or of algorithm behavior. We show that a low-knowledge approach results in a system that achieves significantly better performance than all of the pure algorithms without requiring additional human expertise. Furthermore the low-knowledge approach achieves performance equivalent to a perfect high-knowledge classification approach.

关键词： algorithm selection algorithm control machine learning classifiers scheduling

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On sample size control in sample average approximations for solving smooth stochastic programs

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2013年第2期55卷 265-309页

作者： Royset, Johannes O. USN Postgrad Sch Dept Operat Res Monterey CA USA

We consider smooth stochastic programs and develop a discrete-time optimal-control problem for adaptively selecting sample sizes in a class of algorithms based on variable sample average approximations (VSAA). The control problem aims to minimize the expected computational cost to obtain a near-optimal solution of a stochastic program and is solved approximately using dynamic programming. The optimal-control problem depends on unknown parameters such as rate of convergence, computational cost per iteration, and sampling error. Hence, we implement the approach within a receding-horizon framework where parameters are estimated and the optimal-control problem is solved repeatedly during the calculations of a VSAA algorithm. The resulting sample-size selection policy consistently produces near-optimal solutions in short computing times as compared to other plausible policies in several numerical examples.

关键词： Stochastic programming Sample average approximations Sample size selection algorithm control

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REMOTE control OF NONLINEAR MOTION FOR MECHATRONIC MACHINE BY MEANS OF CoDeSys COMPATIBLE INDUSTRIAL controlLER

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第6期24卷 1661-1667页

作者： Korobiichuk, Igor Dobrzhansky, Oleksandr Kachniarz, Maciej Ind Res Inst Automat & Measurements PIAP Jerozolimskie 202 PL-02486 Warsaw Poland Zhytomyr State Technol Univ Chernyakhovskogo Str 103 UA-10005 Zhtomr Ukraine

The given article describes the hardware-software complex aimed to teach students how to control the mechatronic mechanisms remotely by means of standard industrial automation. The purpose of this paper is to consider the following problems: the curved trajectory movement algorithm control, the corresponding software development for the industrial controllers using CoDeSys environment, CoDeSys software package usage for the development of interactive HTML-applications with WEB-interface.

关键词： algorithm control industrial controller interactive interface nonlinear motion software

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Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES Variants

Integrated vs. Sequential Approaches for Selecting and Tunin...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick Wang, Hao Doerr, Carola Back, Thomas Leiden Inst Adv Comp Sci Leiden Netherlands Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781450371285

When faced with a specific optimization problem, deciding which algorithm to apply is always a difficult task. Not only is there a vast variety of algorithms to select from, but these algorithms are often controlled by many hyperparameters, which need to be suitably tuned in order to achieve peak performance. Usually, the problem of selecting and configuring the optimization algorithm is addressed sequentially, by first selecting a suitable algorithm and then tuning it for the application at hand. Integrated approaches, commonly known as Combined algorithm Selection and Hyperparameter (CASH) solvers, have shown promise in several applications. In this work we compare sequential and integrated approaches for selecting and tuning the best out of the 4,608 variants of the modular Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We show that the ranking of these variants depends to a large extent on the quality of the hyperparameters. Sequential approaches are therefore likely to recommend sub-optimal choices. Integrated approaches, in contrast, manage to provide competitive results at much smaller computational cost. We also highlight important differences in the search behavior of two CASH approaches, which build on racing (irace) and on model-based optimization (MIP-EGO), respectively.

关键词： algorithm Selection algorithm control Iterative Optimization Heuristics Black-Box Optimization

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A Meta-heuristic for Improving the Performance of an Evolutionary Optimization algorithm Applied to the Dynamic System Identification Problem 8

A Meta-heuristic for Improving the Performance of an Evoluti...

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8th International Joint Conference on Computational Intelligence

作者： Ryzhikov, Ivan Semenkin, Eugene Sopov, Evgenii Siberian State Aerosp Univ Dept Syst Anal & Operat Res Krasnoyarskij Rabochij Ave 31 Krasnoyarsk 660014 Russia

ISBN: (纸本)9789897582011

In this paper a meta-heuristic for improving the performance of an evolutionary optimization algorithm is proposed. An evolutionary optimization algorithm is applied to the process of solving an inverse mathematical modelling problem for dynamical systems. The considered problem is related to the complex extremum seeking problem. The objective function and a method of determining a solution perform a class of optimization problems that require specific improvements of optimization algorithms. An investigation of algorithm efficiency revealed the importance of designing and implementing an operator that prevents population stagnation. The proposed meta-heuristic estimates the risk of the algorithm being stacked in a local optimum neighbourhood and it estimates whether the algorithm is close to stagnation areas. The meta-heuristic controls the algorithm and restarts the search if necessary. The current study focuses on increasing the algorithm efficiency by tuning the meta-heuristic settings. The examination shows that implementing the proposed operator sufficiently improves the algorithm performance.

关键词： Restart Operator Meta-heuristic algorithm control Dynamical System Linear Differential Equation Evolutionary Strategies System Identification

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RETRACTED: A simple algorithm for reducing the operation frequency of residential water pumps during peak hours of power consumption (Retracted Article)

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ENERGY SCIENCE & ENGINEERING 2018年第4期6卷 253-271页

作者： Ahmad, Hussein Atikol, Ugur Eastern Mediterranean Univ Dept Mech Engn Via Mersin 10 Gazimagusa Turkey

In developing countries where water supply pressure is low, or frequent water outages and electricity shortages happen, domestic end-users are forced to install water pump-storage systems, consisting of water pumps and ground-level and rooftop tanks to satisfy their daily water demands. This contributes to increasing electrical energy consumption, particularly during peak electricity demand hours. This study presents a simple, practical, computational, and cost-effective shifting water level control algorithm to manage water-energy nexus, by reducing pump-storage system electric energy consumption during peak hours. The proposed algorithm requires a simple modification to the existing water level control scheme, by installing an additional float switch in the rooftop water tank below the currently available float switch that is usually adjusted to trigger the pump when the level in the tank drops by 5-10% from the maximum level. Based on the simulation results, the algorithm preserves the domestic end-users' comfortable daily water demand and reduces water pump energy consumption during peak hours by 90%. During off-peak hours, the control algorithm triggers the pump to refill the rooftop tank based on the upper float switch when water level drops by 5%, while during peak hours, the pump is triggered only when the water drops by 30%. The performance of the algorithm is found to be comparable to the performance of the model predictive control (MPC) approach developed for the same purpose, but MPC needs a high computation capacity and a complex analog feedback level sensor. The algorithm succeeds in reducing and shifting pump energy consumption under various possible operation scenarios and water demand disturbances. A mathematical model is developed for the domestic water pump-storage system using Matlab/Simscape to cope with the complexity of solving nonlinear fluid flow equations and measure the data required to develop the control algorithm. The performance of the

关键词： algorithm control load shifting mathematical model water-energy nexus

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