Analysis of Wavelet Transform applied to the segmentation of disturbance signals with different sampling rates

Andrade, Luciano Carli Moreira de; Oleskovicz, Mário; Fernandes, Ricardo Augusto Souza

doi:10.1109/pesgm.2014.6939189

Cited by 8 publications

(3 citation statements)

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“…To develop detection and segmentation algorithms suitable for low-cost hardware of the LDN, the typical methodologies proposed in the literature cannot be used. The solutions based on wavelet transform [ 48 , 49 ], STFT, and the auto-regressive model [ 50 ] require high computational power, and, as a consequence, expensive hardware. In the design of the detection method algorithm, the PQ events taken into consideration are the most typical ones defined by international standards and in the literature [ 14 , 22 , 23 , 24 , 25 , 26 , 34 , 35 , 36 , 37 , 38 , 39 , 51 ].…”

Section: Locally Distributed Node Algorithmmentioning

confidence: 99%

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Carní,

Lamonaca

2024

Sensors

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The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally Distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification; otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods.

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Section: Locally Distributed Node Algorithmmentioning

confidence: 99%

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Carní,

Lamonaca

2024

Sensors

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“…The application of DWT in power system analysis becomes a popular tool nowadays due to its capability to analyse non-stationary power spectrum [5,[11][12][13][14][15][16]. WT developed by Morlet has been widely used in numerous studies such as in medical, electrical and geophysics.…”

Section: Discrete Wavelet Transform Theorymentioning

confidence: 99%

Determination of Different Fault Features in Power Distribution System Based on Wavelet Transform

Asman*,

Aziz,

Kadir

et al. 2019

IJRTE

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Nowadays, there are various signal processing methods that have been studied by many researchers in order to detect faults in power lines. From previous literature, signal processing that works based on time frequency analysis has been proven to accurately detect faults at high speed. In this study, wavelet transform is adopted to analyse fault occurrences on power line of distribution network. Three types of faults due to lightning, switching and short circuit fault were analysed based on their voltage waveform profiles. ‘Daubechies’ 4 (db4) mother wavelet and four levels decomposition were implemented to extract the features. Approximation at level 4 (A4) and detail coefficient at level 1 to 4 (D1-D4) were extracted to evaluate the energy, skewness, and kurtosis. Based on the results, lightning showed the highest energy, skewness and kurtosis compared to the short circuit and switching voltage waveform. Therefore, these features can be utilized as the new parameters for fault detection in a power system network

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“…Smart grid data has been subjected to run-length encoding for compression and encrypted using lightweight methods [12]. Wavelet transform has been used to divide voltage and current disturbance signals at various sampling rates [13]. Resumable load data compression has been employed to reduce complexity in the smart grid [14].…”

Section: Introductionmentioning

confidence: 99%

Smart Grid Data Denoising and Compression Using Wavelet Packet Transform

Jadhav,

Mahajan

2023

MMEP

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The efficient operation of the Smart Grid is contingent on the accurate analysis of power signals, which are often compromised by disturbances. These power signals, captured by quality monitors, generate substantial volumes of data, thereby necessitating effective compression strategies to facilitate manageable data transfer and collection. Besides mitigating the costs associated with data storage, transmission, and encryption, these compression techniques must ensure minimal reconstruction error to avoid distortion in the original signal. Moreover, it becomes imperative to eliminate noise for the attainment of high-quality signals, critical for disturbance detection. In this paper, a novel method has been developed employing lower-order wavelets (Db3, Db2, Db2, Db2, and Db1). This method decomposes the signal from the first to fifth level utilizing wavelet Packet Transform, testing the efficacy on Phasor Measurement Unit data. Simulation results demonstrate enhanced data compression and noise reduction compared to previous designs, with the signal being approximately reconstructed. This innovative approach offers a facile, efficient, economical, and time-saving solution for smart grid data management, marking a significant advancement in this field.

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Analysis of Wavelet Transform applied to the segmentation of disturbance signals with different sampling rates

Cited by 8 publications

References 8 publications

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Determination of Different Fault Features in Power Distribution System Based on Wavelet Transform

Smart Grid Data Denoising and Compression Using Wavelet Packet Transform

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