Detection and Classification of Power Quality Disturbances Using LSTM †
2. Long Short-Term Memory (LSTM)
2.1. Recurrent Neural Networks (RNN)
2.2. RNN Architecture
2.3. Long Short-Term Memory (LSTM)
2.4. LSTM Network Architecture
3. Results and Discussions
3.1. Description of the Simulation Environment
3.2. Proposed Methodology
3.3. Model Implementation and Training
- The first output of the LSTM function is for the waveform class, which is defined by six sets. These sets are the interruption, sag, normal, flicker, swell, and surge. Any output value, which does not belong to these sets, represents the distortion. The first output of the LSTM network system can assume values between 0 and 3, as shown in Table 1.
- The second output of the LSTM function is for harmonics indication, which is partitioned into two function sets. The labels of these sets are Pure and Harmonics, as shown in Table 1.
3.4. Results and Discussions
- The LSTM has performance values ranging from 96.3674% to 99.7458%.
- A value of 40 dB SNR and the harmonics do not have much of an effect on the detection results, which shows the effectiveness of the LSTM model.
- An SNR of 30 dB is considered a high noise ratio even if the final accuracy was not deduced higher than 3%.
- Merging the normal signal with PQ disturbances at an SNR of 30 dB and harmonics gives an accuracy of around 96%.
- The worst-case PQ disturbances with harmonics and noises were clearly and successfully detected and classified, which proves the LSTM’s robustness.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|PQ Problem||LSTM Output|
|First LSTM output|
|Normal sine wave||1|
|Second LSTM output|
|Events||Pure Signal||With SNR 40||With SNR 30 dB||With Harmonics||With Harmonics and SNR 30 dB|
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Dekhandji, F.Z.; Recioui, A.; Ladada, A.; Moulay Brahim, T.S. Detection and Classification of Power Quality Disturbances Using LSTM. Eng. Proc. 2023, 29, 2. https://doi.org/10.3390/engproc2023029002
Dekhandji FZ, Recioui A, Ladada A, Moulay Brahim TS. Detection and Classification of Power Quality Disturbances Using LSTM. Engineering Proceedings. 2023; 29(1):2. https://doi.org/10.3390/engproc2023029002Chicago/Turabian Style
Dekhandji, Fatma Zohra, Abdelmadjid Recioui, Athmane Ladada, and Taha Slimane Moulay Brahim. 2023. "Detection and Classification of Power Quality Disturbances Using LSTM" Engineering Proceedings 29, no. 1: 2. https://doi.org/10.3390/engproc2023029002