التفاصيل البيبلوغرافية
| العنوان: |
High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm |
| المؤلفون: |
Supanat Chamchuen, Apirat Siritaratiwat, Pradit Fuangfoo, Puripong Suthisopapan, Pirat Khunkitti |
| المصدر: |
Energies, Vol 14, Iss 5, p 1238 (2021) |
| بيانات النشر: |
MDPI AG, 2021. |
| سنة النشر: |
2021 |
| المجموعة: |
LCC:Technology |
| مصطلحات موضوعية: |
power quality disturbance classification, optimal feature selection, probabilistic neural network, particle swarm optimization, artificial bee colony, Technology |
| الوصف: |
Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1996-1073 |
| Relation: |
https://www.mdpi.com/1996-1073/14/5/1238; https://doaj.org/toc/1996-1073 |
| DOI: |
10.3390/en14051238 |
| URL الوصول: |
https://doaj.org/article/2cc249fbeb5443368443f853df414cc5 |
| رقم الانضمام: |
edsdoj.2cc249fbeb5443368443f853df414cc5 |
| قاعدة البيانات: |
Directory of Open Access Journals |
Full Text Finder