Anglia Ruskin Research Online (ARRO)
Browse
Qashou_2024.pdf (3.66 MB)

Investigation for estimating short-term temporal forecasting to produce patterns of failure in SmartGrids using GRU and LSTM algorithms

Download (3.66 MB)
thesis
posted on 2024-04-05, 13:30 authored by Akram Qashou

The generation of active power in renewable energy is dependent on several factors. These variables are related to the areas of weather, physical structure, control, and load behavior. Estimating the future value of the active power to be generated is difficult due to their unpredictable character. However, because of the higher precision required of the estimation, this problem becomes more complex if I examine a short-term temporal prediction. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms to perform the Short-term estimation. The environment, the operation, and the generated (normal or faulty) signal are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been realized to conduct an experiment. In addition, the LSTM and the GRU are compared to see how well they perform in this system. The proposed methods end findings outperform the current state-of-the-art as shown in the literature review.

History

Institution

Anglia Ruskin University

File version

  • Published version

Thesis name

  • PhD

Thesis type

  • Doctoral

Thesis submission date

2024-03-15

Legacy Faculty/School/Department

Faculty of Science and Engineering

Note

Accessibility note: If you require a more accessible version of this thesis, please contact us at arro@aru.ac.uk

Usage metrics

    ARU Theses

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC