L. L. Zhang

Morphology Singular Entropy-Based Phase Selector Using Short Data Window for Transmission Lines

This paper presents a new technique called morphology singular entropy (MSE), based on which a phase selector for transmission lines is developed. MSE combines mathematical morphology, singular value decomposition (SVD), and entropy theory, making it insensitive to noise and easy to extract the features of the fault-induced transients. Voltage signals are used as inputs of the proposed MSE-based phase selector. Each signal is processed by a multiscale morphological filter first, and a matrix consisting of the outputs of the filter is formed. By decomposing the matrix using SVD, the singular values are obtained, and then the entropy in association with this signal can be calculated. Afterwards, in order to improve the sensitivity and reliability of the phase selection, four classification indices derived from the entropies are defined. The phase selection is performed by comparing these four indices to a preset threshold. Simulation data generated using PSCAD/EMTDC and real-life data have been employed to verify the performance of the proposed method.

Inrush identification by applying improved Morphological Gradient Algorithm

Inrush current identification is one of the most important aspects in transformer differential protection. However, the traditional methods of inrush identification are not reliable in some particular cases. This paper presents a novel scheme for three phase transformer protection, which effectively identifies inrush currents. In the proposed method, the improved Morphological Gradient Algorithm is employed to extract the features of inrush. Simulation studies have been performed to demonstrate the accuracy and efficiency of the new approach using PSCAD/EMTDC and MATLAB. The proposed method is able to distinguish between an internal fault and inrush, even under CT saturation condition, and its required calculation is simple.

An Identification Method Based on Mathematical Morphology for Sympathetic Inrush

Sympathetic interaction between transformers is a quite normal phenomenon in power systems. For the purpose of preventing transformer differential protection relays from malfunction, this paper proposes a morphological method for the identification of sympathetic inrush, which is the first time when mathematical morphology is applied in this field. Since the waveform of differential current is symmetrical in an internal fault case while asymmetrical in a sympathetic inrush case, the proposed method uses a morphological operator to extract the peaks and valleys of the differential current to distinguish sympathetic inrush. Considering the possible current-transformer (CT) saturation conditions, this paper combines a morphological gradient with a weighted mathematical morphological operator to improve the effectiveness of the proposed method. The proposed method is evaluated on data collected from simulation cases established in PSCAD/EMTDC and from laboratory experiments, respectively. Identification results have verified that by comparing with the traditional second harmonic restrain method, the proposed method can distinguish sympathetic inrush from an internal fault current more accurately and more effectively, even when the CT is fully saturated.

Identification of Dominant Low Frequency Oscillation Modes Based on Blind Source Separation

On-line monitoring and analysis of low frequency oscillation (LFO) are important for stability and security of a power system. This paper proposes a blind source separation (BSS) based method for LFO modal analysis of one-channel measured signal, which consists of a second-order blind identification (SOBI) algorithm and the Hilbert transform (HT) technique. This is the first time when a BSS technique is applied as a modal decomposition tool in this field. The proposed SOBI-HT based dominant mode identification method combines an iteration procedure with an identification procedure to determine the model order, and then identify dominant modes. The performance of the proposed method is evaluated on numerical simulation signals and a record of real data, verifying its effectiveness and accuracy.

Current Transformer Saturation Detection Using Morphological Gradient and Morphological Decomposition and Its Hardware Implementation

In this paper, the improved morphological gradient algorithm and the morphological decomposition algorithm are used to detect the saturated sections of a secondary current, and the detection results are combined by the AND logic so as to provide a more accurate and reliable detection result. The proposed method is implemented in a system on chip, whose core is a field-programmable gate array. To evaluate the performance of the proposed method as well as its hardware implementation, a simple double-side power supply system model is built in RSCAD, and a test platform involving a real-time digital simulator is set up accordingly. The simulation results have shown that the proposed method can correctly identify the saturated sections under various conditions, and its hardware implementation has illustrated that the proposed method can be used in real time in practice.

Paired bacteria optimizer for power system fault signal identification

The accurate detection and location of a fault is crucial for power systems applications such as protective relaying and phasor measurement. As fault signals have unique mathematical expressions, this paper proposes a computational intelligence based method to estimate the parameters of the fault signal towards the minimum difference between the input signal and the reconstructed one. The optimization algorithm employed is paired bacteria optimizer (PBO), which is fast, accurate and stable. Hence, it is most applicable to solving such problems, as fault detection/location and fault signal identification require fast and accurate estimation of the signals parameters. The performance of PBO has been demonstrated in this paper, and simulation studies have shown the superiority of the proposed method over traditional algorithms.