Classification of smart grid stability prediction using cascade machine learning methods and the internet of things in smart grid
| dc.authorid | 0000-0001-8577-3659 | en_US |
| dc.authorid | 0000-0001-7341-1714 | en_US |
| dc.authorid | 0000-0003-1840-9958 | en_US |
| dc.contributor.author | Önder, Mithat | |
| dc.contributor.author | Doğan, Muhsin Uğur | |
| dc.contributor.author | Polat, Kemal | |
| dc.date.accessioned | 2023-07-24T10:09:28Z | |
| dc.date.available | 2023-07-24T10:09:28Z | |
| dc.date.issued | 2023 | en_US |
| dc.department | BAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü | en_US |
| dc.description.abstract | In a smart grid, the main goals are to provide grid stability, improve power system performance and security, and reduce operations, system maintenance, and planning costs. The prediction stability of smart grid (SG) systems is essential in terms of power loss minimization and the importance of adequate energy policies. SG systems must accurately predict the energy demand and ensure the right amount of energy is available at the right time. If the prediction is inaccurate, it can lead to costly energy production or usage errors and create considerable inefficiencies in the power grid. Due to this, this manuscript offers five different cascade methods to detect the stability of SG systems. Detecting the stability of SG systems enables the grid to respond quickly to changes in demand and supply, improves system reliability, reduces power outages, and increases the overall efficiency of the grid. The present work proposed five different cascade methods with pre-processing, training and testing division, and the classification stages of the classification procedure for estimating SG stability. In the first pre-processing stage, the SG dataset is pre-proceeded with the feature selection (Relief, Fast Correlation-Based Filter (FCBF), and supervised attribute filter). The resampling (the bootstrapping), the Fuzzy C-Means Clustering-Based Feature Weighting (FCMFW), the resampling then feature selection (supervised attribute filter), and the feature selection (supervised attribute filter), then FCMFW. In the second stage, the training and testing division stage, the SG dataset was separated into three test and training data methods before the classification algorithm: The 5 Fold Cross Validation (FVC), 10 FVC, and hold-out (50-50%). In the third stage, the classification stage, five different classification algorithms, including Naive Kernel Bayes, Linear Support Vector Machine (SVM), Weighted K-Nearest Neighbors, Begged Trees, and Narrow Neural Network classifying algorithms, are used to classify the SG dataset. The simulation results of this study demonstrated that the suggested cascade ML system had achieved significant accuracy in predicting SG stability. The best cascade method is the feature selection (supervised attribute filter) + FCMFW + 10 FCV and then performing the bagged trees algorithm; thus, the new approach affords an accuracy of 99.9%. Furthermore, due to the rapid growth of ML techniques, sensors, and smart meters technologies, with Machine to Machine communication via the internet of things (IoT), the real-time identification process is made practical with higher accuracy. For this reason, our future research will focus on an IoT-based SG system, an E-stability determination system. Thanks to the proposed cascade method, the SG dataset can be classified easily, quickly, and reliably. E-stability determination systems can help to fast detect, predict, and respond, which is an important application of IoT on the grid systems. | en_US |
| dc.identifier.citation | Önder, M., Dogan, M. U., & Polat, K. (2023). Classification of smart grid stability prediction using cascade machine learning methods and the internet of things in smart grid. Neural Computing and Applications, 1-19. | en_US |
| dc.identifier.doi | 10.1007/s00521-023-08605-x | |
| dc.identifier.endpage | 19 | en_US |
| dc.identifier.issn | 0941-0643 | |
| dc.identifier.issn | 1433-3058 | |
| dc.identifier.scopus | 2-s2.0-85160333789 | en_US |
| dc.identifier.scopusquality | Q1 | en_US |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1007/s00521-023-08605-x | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12491/11334 | |
| dc.identifier.wos | WOS:000995245800001 | en_US |
| dc.identifier.wosquality | Q2 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.institutionauthor | Önder, Mithat | |
| dc.institutionauthor | Doğan, Muhsin Uğur | |
| dc.institutionauthor | Polat, Kemal | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer London Ltd. | en_US |
| dc.relation.ispartof | Neural Computing & Applications | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Smart Grid | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Support Vector Machine | en_US |
| dc.subject | Transient Stability | en_US |
| dc.subject | Feature-Selection | en_US |
| dc.title | Classification of smart grid stability prediction using cascade machine learning methods and the internet of things in smart grid | en_US |
| dc.type | Article | en_US |
