Multi Scale Recurrence Quantification Analysis for Clustering Harmonics on Microgrid Systems (original) (raw)
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International Journal of Electrical and Computer Engineering (IJECE), 2024
Power quality disturbances (PQD) threaten electrical power systems, especially in distributed generation with renewable energy sources and in smart grids where PQD takes a complex form. Providing accurate information on the status and characteristics of the electrical signal facilitates the identification of practical solutions to this threat. In this paper, a variational mode decomposition (VMD) signal processing tool is proposed to analyze complex PQD. In VMD, the input signal is decomposed into different band-limited intrinsic mode functions (IMF) or non-recursively reconstructed modes. The input signal analysis by VMD, which considers the frequency values and spectral decomposition for each mode, describes the changes in the input waveform, and the IMFs help extract the behavioral patterns of these disturbances. A new hybrid particle swarm optimizationtechnique for order of preference by similarity to ideal solution (PSOTOPSIS) algorithm is also proposed to classify the disturbances based on the features extracted from the signals decomposed using VMD. The performance of this method is then extensively validated by using different PQDs (including complex, stationary, and non-stationary (PQDs) and through a comparison with deep learning methods, such as convolutional and recurrent neural networks. Results show that VMD has several advantages over Fourier, wavelet, and Stockwell transforms, such as its lack of any modal aliasing effect, its capability to diagnose disturbances across four noise levels, and its ability to separate harmonics from other events. The proposed VMD in combination with PSO-TOPSIS performs more accurately than the other methods across all noise levels.
Exponential Method for Determining Optimum Number of Clusters in Harmonic Monitoring Data
International Journal of Computer and Electrical Engineering, 2012
Clustering is an important process for finding and describing a variety of patterns and anomalies in multivariate data through various machine learning techniques and statistical methods. Determination of the optimum number of clusters in data is the main difficulty when applying clustering algorithms. In this paper, an exponential method has been proposed to determine the optimum number of clusters in power quality monitoring data using an algorithm based on the Minimum Message Length (MML) technique. The optimum number of clusters has been verified by the formation of super-groups using Multidimensional Scaling (MDS) and link analysis with power quality data from an actual harmonic monitoring system in a distribution system in Australia. The results of the obtained super-group abstractions confirm the effectiveness of the proposed method in finding the optimum number of clusters in harmonic monitoring data.
Metrological Aspects of Inter -Harmonic Identification and Grouping in Electrical Power Systems
The main purpose of this paper is to present some metrological aspects of identification and grouping of harmonics and interharmonics and calculation of total harmonic. The Virtual Instrument for identification of distorted signal spectrum is described and the demonstration may take place at the presentation of the paper at the oral or poster session. We define the total harmonic and interharmonic distortion coefficient and analyse its sensitivity to variation in fundamental and interharmonic frequency and sensitivity to incompleteness of samples for five different cases: rectangular window, triangular, Hanning, Hamming and Blackman windows. The virtual test signal was selected arbitrarily. This signal was processed first by the virtual harmonic analyser, in such a way that all components were identified without any errors, and then the signal was analysed according to the requirements specified in IEC 61000-30-4 draft standard, in which also Harmonic Groups (HG) and Interharmonic G...
Faster Islanding Detection of Microgrid Based on Multiscale Mathematical Morphology
2020
Faster and reliable islanding detection of microgrid is necessary to protect the equipment and maintenance personnel. Voltage and frequency are two important parameters needs to be controlled in microgrid; Voltage and frequency stability of microgrid depends merely on main grid during grid connected mode and depends on individual controllers during islanding mode. This research proposes a time domain technique based on multi-scale mathematical morphology (MMM) for islanding detection. The proposed technique uses multiscale dilation-erosion difference filter (MDEDF) with peak value of the signal. The performance of the proposed technique is validated in IEEE-13bus system with different cases such as mismatch in real power, reactive power, load switching, motor switching; L-G fault .The results validate the accuracy and efficacy of the proposed technique and also compared with recently published work.
Energies, 2021
In the field of microgrids (MGs), steady-state power imbalances and frequency/voltage fluctuations in the transient state have been gaining prominence owing to the advancing distributed energy resources (DERs) connected to MGs via grid-connected inverters. Because a stable, safe power supply and demand must be maintained, accurate analyses of power system dynamics are crucial. However, the natural frequency components present in the dynamics make analyses complex. The nonlinearity and confidentiality of grid-connected inverters also hinder controllability. The MG considered in this study consisted of a synchronous generator (the main power source) and multiple grid-connected inverters with storage batteries and virtual synchronous generator (VSG) control. Although smart inverter controls such as VSG contribute to system stabilization, they induce system nonlinearity. Therefore, Koopman mode decomposition (KMD) was utilized in this study for consideration as a future method of data-d...
The broad diffusion of renewable energy-based technologies has introduced several open issues in the design and operation of smart grids (SGs) when distributed generators (DGs) inject a large amount of power into the grid. In this paper, a theoretical investigation on active and reactive power data is performed for one active line characterized by several photovoltaic (PV) plants with a great amount of injectable power and two passive lines, one of them having a small peak power PV plant and the other one having no PV power. The frequencies calculated via the empirical mode decomposition (EMD) method based on the Hilbert-Huang transform (HHT) are compared to the ones obtained via the fast Fourier transform (FFT) and the wavelet transform (WT), showing a wider spectrum of significant modes mainly due to the non-periodical behavior of the power signals. The results obtained according to the HHT-EMD analysis are corroborated by the calculation of three new indices that are computed starting from the electrical signal itself and not from the Hilbert spectrum. These indices give the quantitative deviation from the periodicity and the coherence degree of the power signals, which typically deviate from the stationary regime and have a nonlinear behavior in terms of amplitude and phase. This information allows to extract intrinsic features of power lines belonging to SGs and this is useful for their optimal operation and planning.
A novel scheme for island detection in microgrids based on fuzzy c-means clustering technique
2021
Microgrids (MGs) are capable to work at different operation modes, namely grid-connected or islanded, which make a significant change in the network fault current level. These changes may lead to problems and should be detected fast to do the proper protection actions accordingly and prevent blackouts. Moreover, some island detection methods suffer from the drawbacks of high computation burden and time-consuming procedure of training data to detect the islanded mode. For this purpose, in this paper, a faster and less computation burden island detection scheme without the need for training data is proposed which detects the islanded mode by analyzing the fault current data obtained from a continuous sampling using the phasor measurement unit (PMU). The sampled data are utilized in the fuzzy c-means (FCM) clustering to determine the network operation mode. The proposed scheme works in two phases. In the offline phase, the root mean square (RMS) of the current amplitude for islanded mo...