High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods
Power quality disturbances (PQDs) occur as the use of non-linear load and renewable-based micro-grids increases. This paper presents a new algorithm that consists of the discrete orthogonal S-transform (DOST) in the feature extraction stage, compressive sensing (CS) in the feature reduction stage, a...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10214567/ |
| _version_ | 1828717461198864384 |
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| author | Muhammad Abubakar Arfan Ali Nagra Muhammad Faheem Muhammad Mudassar Muhammad Sohail |
| author_facet | Muhammad Abubakar Arfan Ali Nagra Muhammad Faheem Muhammad Mudassar Muhammad Sohail |
| author_sort | Muhammad Abubakar |
| collection | DOAJ |
| description | Power quality disturbances (PQDs) occur as the use of non-linear load and renewable-based micro-grids increases. This paper presents a new algorithm that consists of the discrete orthogonal S-transform (DOST) in the feature extraction stage, compressive sensing (CS) in the feature reduction stage, and a deep stacking network (DSN) for the automatic classification of single and multiple PQDs. It compresses the extracted feature matrix (orthogonal S-matrix coefficients) to minimize the computational process and provide more diversified features. Firstly, PQDs data is generated from a modified IEEE 13 bus system with wind grid integration, both synthetically and in real time. Moreover, compressive measurements of 24 types of multiple PQDs events and nine types of single PQDs events of synthetic and real data, and 12 type of three-phase single and multiple PQDs from the modified IEEE wind grid integration are fed to a proposed DSN classifier for PQD recognition. The DOST-based CS feature extraction technique achieves good robustness and time-frequency localization while retaining useful information. The DSN classifier method utilizes a Batch-mode gradient as a fine-tune, which has less noise gradient and improved efficiency of PQD classification. A noise level of 20 dB to 50 dB is considered. Other models, such as k-Nearest Neighbor (KNN), Multiclass Support Vector Machine (MSVM), and ensemble learner methods, are also developed to compare the efficiency. The high classification results demonstrate that the DOST-CS feature extraction and the DSN classifier have high precision in identifying multiple power quality events, even in noisy conditions. |
| first_indexed | 2024-03-12T14:28:43Z |
| format | Article |
| id | doaj.art-c87b8076ca3d4e82b4a578da002091ca |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:28:43Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c87b8076ca3d4e82b4a578da002091ca2023-08-17T23:00:31ZengIEEEIEEE Access2169-35362023-01-0111855718558810.1109/ACCESS.2023.330437510214567High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network MethodsMuhammad Abubakar0https://orcid.org/0000-0001-6902-6549Arfan Ali Nagra1https://orcid.org/0000-0002-2149-8165Muhammad Faheem2https://orcid.org/0000-0003-4628-4486Muhammad Mudassar3https://orcid.org/0000-0002-9310-6343Muhammad Sohail4Department of Computer Science, Lahore Garrison University, Lahore, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore, PakistanSchool of Technology and Innovations, University of Vaasa, Vaasa, FinlandDepartment of Technology, The University of Lahore, Lahore, PakistanDepartment of Computer Software Engineering, MCS, National University of Sciences and Technology, Islamabad, PakistanPower quality disturbances (PQDs) occur as the use of non-linear load and renewable-based micro-grids increases. This paper presents a new algorithm that consists of the discrete orthogonal S-transform (DOST) in the feature extraction stage, compressive sensing (CS) in the feature reduction stage, and a deep stacking network (DSN) for the automatic classification of single and multiple PQDs. It compresses the extracted feature matrix (orthogonal S-matrix coefficients) to minimize the computational process and provide more diversified features. Firstly, PQDs data is generated from a modified IEEE 13 bus system with wind grid integration, both synthetically and in real time. Moreover, compressive measurements of 24 types of multiple PQDs events and nine types of single PQDs events of synthetic and real data, and 12 type of three-phase single and multiple PQDs from the modified IEEE wind grid integration are fed to a proposed DSN classifier for PQD recognition. The DOST-based CS feature extraction technique achieves good robustness and time-frequency localization while retaining useful information. The DSN classifier method utilizes a Batch-mode gradient as a fine-tune, which has less noise gradient and improved efficiency of PQD classification. A noise level of 20 dB to 50 dB is considered. Other models, such as k-Nearest Neighbor (KNN), Multiclass Support Vector Machine (MSVM), and ensemble learner methods, are also developed to compare the efficiency. The high classification results demonstrate that the DOST-CS feature extraction and the DSN classifier have high precision in identifying multiple power quality events, even in noisy conditions.https://ieeexplore.ieee.org/document/10214567/Multiple power quality disturbances identificationcompressed sensingdiscrete orthogonal S-transformdeep neural networkwind-grid distribution |
| spellingShingle | Muhammad Abubakar Arfan Ali Nagra Muhammad Faheem Muhammad Mudassar Muhammad Sohail High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods IEEE Access Multiple power quality disturbances identification compressed sensing discrete orthogonal S-transform deep neural network wind-grid distribution |
| title | High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods |
| title_full | High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods |
| title_fullStr | High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods |
| title_full_unstemmed | High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods |
| title_short | High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods |
| title_sort | high precision identification of power quality disturbances based on discrete orthogonal s transforms and compressed neural network methods |
| topic | Multiple power quality disturbances identification compressed sensing discrete orthogonal S-transform deep neural network wind-grid distribution |
| url | https://ieeexplore.ieee.org/document/10214567/ |
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