Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Zhu, Juner; Luo, Tianyun; Chen, Jiajun; Xia, Yunrui; Wang, Chenxi; Liu, Mingbo

doi:10.1109/access.2019.2938099

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The objective function for parameter estimation using the least squares method is given as follows: F P,A (t) = P A,measured − P A,3alculated (39) f P,B (t) = P B,measured − P B,3alculated (40) f P,C (t) = P C,measured − P C,3alculated…”

Section: Composite Load Modelmentioning

confidence: 99%

Three-Phase-Measurement-Based Load Modeling Technique Using Unbalanced Fault Data

Han¹

2020

IEEE Access

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This study presents a load modeling technique using unsymmetrical fault data. Typically, three-phase balanced fault data are prerequisite for load modeling; however, the existing technique utilizes only positive sequence data. Obtaining symmetrical data from a real power system is very difficult as three-phase faults are very rare. In this study, I aimed to improve existing methods designed for estimating load parameters from three-phase symmetrical fault data via load modeling of unsymmetrical faults. The proposed method corrects the existing algorithm using positive sequence data along with three-phase data, namely phases-A, -B, and -C. The equation for each phase of the load model is obtained and subsequently used for load parameter estimation. The load model parameters are estimated using the data obtained from phases-A, -B, and -C, unlike in existing load models that use only positive sequence data. Unsymmetrical faults occur more frequently in a system than symmetrical faults, and numerous opportunities for load model parameter estimation are available. Applying this algorithm to the three-phase data collected using phasor measurement unit-like data acquisition devices is expected to considerably increase both the parameter estimation accuracy and frequency during load modeling parameter estimation in the future.INDEX TERMS Load modeling, measurement-based approach, parameter estimation, three-phase data, unbalanced disturbances, asymmetrical fault.

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Section: Composite Load Modelmentioning

confidence: 99%

Three-Phase-Measurement-Based Load Modeling Technique Using Unbalanced Fault Data

Han¹

2020

IEEE Access

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Improving the performance of deep learning-based classification when a sample has various appearances

Jin

Jiao

Wang

et al. 2022

Journal of Experimental & Theoretical Artificial Intelligen

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Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Cited by 3 publications

References 35 publications

Three-Phase-Measurement-Based Load Modeling Technique Using Unbalanced Fault Data

Three-Phase-Measurement-Based Load Modeling Technique Using Unbalanced Fault Data

Improving the performance of deep learning-based classification when a sample has various appearances

Modeling Energy Demand—A Systematic Literature Review

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