Morphological Median Filter Research Articles

Adaptive Detection of Islanding and Power Quality Disturbances in a Grid-Integrated Photovoltaic System

Global photovoltaic (PV) generation is increasing steadily at about $$30\%$$ growth rate over the last decade. Depleting environment owing to extensive use of fossil fuels is expected to further continue with this growth rate. With such large PV penetration in the utility grid, perturbation-based active islanding detection methods are becoming detrimental, marred with issues like degradation of power quality and deteriorating system stability. This paper uses morphological filters combined with empirical mode decomposition (EMD) to implement an efficient adaptive signal processing-based detection for islanding as well as PQ disturbances. Two-stage morphological median filter (MMF-2) is used to overcome the noise vulnerability associated with EMD. Field programmable gate array implementation is developed for real-time detection of PQ events. Classification of power quality disturbances is obtained using a support vector machine classifier. The results demonstrate fast and accurate real-time detection under various noisy scenarios without applying any parameter perturbations.

Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line

The permanency of highly-reliable power supply is a core trait of an electric power transmission network. A transmission line is the main part of this network through which power is transmitted to the utility. These lines are often damaged by accidental breakdowns owing to different random origins. Hence, researchers are trying to detect and identify these failures at the earliest to avoid financial losses. This paper offers a new real-time fast mathematical morphology-based fault feature extraction scheme for detection and classification of transmission line faults. The morphological median filter is exploited to wrest unique fault features which are then fed as an input to a decision tree classifier to classify the fault type. The acquired graphical and numerical results of the extracted features affirm the potency of the offered scheme. The proposed scheme is verified for different fault cases simulated on high-voltage transmission line modelled using ATP/EMTP with varying system constraints. The performance of the stated technique is also validated for fault detection and classification on real-field transmission lines. The results state that the proposed method is capable of detecting and classifying the faults with adequate precision and reduced computational intricacy, in less than a quarter of a cycle.

Real‐time digital filtering algorithm for elimination of the decaying DC component using mathematical morphology

Fundamental phasor estimation is one of the most important jobs to be done by the digital protective relay (DPR). However, fault signals given to DPR consist of decaying DC (DDC) components, harmonics, and noise which can lead to inaccuracy in phasor estimation. Hence, for accurate results, digital filtering algorithms (DFAs) are mandatory to wipe out all these unwanted components. In modern DPR, discrete Fourier transform (DFT) is extensively used DFA for fundamental phasor estimation. However, the accuracy of DFT is affected by the presence of DDC. Hence, to tackle this problem, a new real-time fast DFA based on the mathematical morphology (MM) technique is put forward. First, the DDC components are filtered out using the morphological median filter (MMF) and are subtracted from the original fault signal. Afterwards, the fundamental phasor is extracted from the residual DDC-free signal using the DFT. Several computer-simulated, EMTP-generated, and real-field fault signals are used to examine the efficacy of the proposed DFA. In addition, the proposed DFA is compared with other existing techniques. The obtained results prove that the suggested DFA has vigorous performance in the presence of multiple DDC components, noise, and severe harmonic conditions with a low computational burden and high accuracy.

Compressive Sensing and Morphology Singular Entropy-Based Real-Time Secondary Voltage Control of Multiarea Power Systems

This paper presents an improved secondary voltage control (SVC) methodology incorporating compressive sensing (CS) for a multiarea power system. SVC minimizes the voltage deviation of the load buses while CS deals with the problem of the limited bandwidth capacity of the communication channel by reducing the size of massive data output from the phasor measurement unit (PMU) based monitoring system. The proposed strategy further incorporates the application of a morphological median filter (MMF) to reduce noise from the output of the PMUs. To keep the control area secure and protected locally, mathematical singular entropy (MSE) based fault identification approach is utilized for fast discovery of faults in the control area. Simulation results with 27-bus and 486-bus power systems show that CS can reduce the data size up to 1/10th while the MSE-based fault identification technique can accurately distinguish between fault and steady-state conditions.

Advanced signal processing techniques for multiclass disturbance detection and classification in microgrids

This study proposes the application of fuzzy assessment tree (FAT)-based short-time modified Hilbert transform (STMHT) as a new multiclass detection and classification technique, for a distributed generation (DG)-based microgrid. The time varying non-stationary power signal samples extracted near the target DG are initially de-noised by passing through the morphological median filter and then processed through the proposed STMHT technique for disturbance detection. Further based on the overlapping in the target attribute values, an FAT has been incorporated, which significantly classifies the different multiclass disturbances on a standard IEC microgrid model simulated in MATLAB/Simulink environment with highest precision in accuracy.

Strong Interference Separation Method based on Morphology-Median Filtering for Magnetotelluric Sounding Data in Ore Concentration Area

Strong Interference Separation Method based on Morphology-Median Filtering for Magnetotelluric Sounding Data in Ore Concentration Area