5 research outputs found
Data analytics for performance evaluation under uncertainties applied to an industrial refrigeration plant
Get PDFArtificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems. In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system. The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows comparing the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.Peer ReviewedPostprint (published version
The Development of the Tool for the Power Quality Parameters Optimization in a Off-Grid System
Get PDFCílem této diplomové práce je navrhnout algoritmus pro optimalizaci kvality elektrické energie v ostrovním napájecím systému. Dodávka elektrické energie do této sítě pochází z obnovitelných zdrojů elektrické energie využívající sluneční záření. Off-Grid napájecí systém má také možnost akumulace přebytků elektrické energie pro následné pokrytí spotřeby v době, kdy nestačí aktuální dopad úhrnu slunečního svitu na fotovoltaické panely. Předmětem návrhu algoritmu je určit relevance spotřebičů, které ovlivní posouvání spotřeby v čase takovým způsobem, aby velikosti kvalitativních parametrů elektrické energie byly dodrženy dle stanovených limitů aktuální legislativy. Koncepce pro zajištění správné kvality elektrické energie neřeší důsledek negativních zpětných vlivů, ale poprvé jejich příčinu. Posouvání spotřeby je optimalizační prvek v algoritmu, který využívá prediktivních nástrojů, jež mohou do budoucna tvořit základy umělé inteligence nebo tzv. chytrých sítí. Vytvořený koncept algoritmu může sloužit jako podklad pro realizaci aktivního řízení energií 2.0, který momentálně řeší pouze energetickou soběstačnost. Implementace nové části algoritmu si klade za cíl řešit energetickou spolehlivost při řízení energií v testovací platformě Off-Gridu, jež je vybudován v areálu VŠB-TUO.The goal of this master thesis is a concept of algorithm for power quality optimization in an Off-Grid system. Researched network is fed from renewable source using solar energy with the ability to accumulate the excess of electric power to use it during the parts of the day, when the contribution of the total sunlight is not sufficient. The aim of this algorithm is how to determinate the relevancy of loads for proper operation of load shifting, which works for compliance standard’s limits of power quality. Load shifting concept for regular power quality is solving the consequences of the problems not the actual cause of them. It is an optimization element in algorithm, which is using predictive tools. These tools can become a basic ground for the creation of artificial intelligence or smart grids. The designed concept of algorithm can be used as the basis for the realization of active energy management 2.0. This management system currently deals only with energy self-sufficiency. The implementation of the new part in algorithm aims to solve the energy reliability management in the Off-Grid test platform, which has been built in campus of VŠB-TUO.410 - Katedra elektroenergetikyvýborn
The power quality forecasting model for off-grid system supported by multiobjective optimization
No full textMeasurement and control of electric power quality (PQ) parameters in off-grid systems has played an important role in recent years. The purpose is to detect or forecast the presence of PQ parameter disturbances to be able to suppress or to avoid their negative effects on the power grid and appliances. This paper focuses on several PQ parameters in off-grid systems and it defines three evaluation criteria that are supposed to estimate the performance of a new forecasting model combining all the involved PQ parameters. These criteria are based on common statistical evaluations of computational models from the machine learning field of study. The studied PQ parameters are voltage, power frequency, total harmonic distortion, and flicker severity. The approach presented in this paper also applies a machine learning based model of random decision forest for PQ forecasting. The database applied in this task contains real off-grid data from long-term one-minute measurements. The hyperparameters of the model are optimized by multiobjective optimization toward the defined evaluation criteria.Web of Science64129516950
