T. Y. Ji

Finite-Element Modeling for Analysis of Radial Deformations Within Transformer Windings

This paper develops computational models for undeformed and deformed transformers, using the finite-element method (FEM) to calculate frequency dependent parameters accounting for diamagnetic properties. In this manner, properly estimated inductances and capacitances can be derived and applied into a winding model for frequency response analysis (FRA). This research uses a hybrid winding model, so that frequency responses in the high frequency range ( >1 MHz) can be explored for the investigation of radial winding deformation. Meanwhile, computational models with respect to winding radial deformation are constructed, so that corresponding inductances and capacitances in specific radial deformed cases can be obtained by FEM. Therefore, the influence of the capacitances as well as the inductances can be taken into account for FRA of radial deformation in high frequencies. The frequency response in the undeformed case is compared with the experimental data to verify the accuracy of the frequency dependent parameters and mathematical winding models. The analyzed results in radial deformed cases are compared with the fault features derived from experimental studies reported in relevant literatures.

Identification of Power Disturbances Using Generalized Morphological Open-Closing and Close-Opening Undecimated Wavelet

This paper proposes a new technique, generalized morphological open-closing and close-opening undecimated wavelet (GMOCUW), based on which a power disturbance identification scheme is developed. In order to extract features of power disturbances, the proposed scheme employs undecimated wavelet transform for its advantage in retaining information and reducing waveform distortion, multiscale morphological analysis for its ability in frequency analysis, and generalized morphological open-closing (GMOC) and generalized morphological close-opening (GMCO) operations for their advantages in information preserving. Power system computer aided design/electro-magnetic transient in dc system (PSCAD/EMTDC) was employed to construct a test power system to simulate eight types of power disturbances. Additionally, a laboratory platform was established to generate power quality (PQ) signals under real operating conditions. The performance of GMOCUW has been compared with that of morphological gradient wavelet (MGW), new dual neural-network-based methodology (NDNM), S-transform (ST), and Daubechies 4 wavelet (DB4W). Comparison results have proved that, in power disturbance detection, GMOCUW is more accurate and faster than these methods.

Multistep Wind Power Forecast Using Mean Trend Detector and Mathematical Morphology-Based Local Predictor

This paper proposes a novel forecasting model based on a mean trend detector (MTD) and a mathematical morphologybased local predictor (MMLP) to undertake short-term forecast of wind power generation. In the proposed MTD/MMLP model, the nonstationary time series describing wind power generation is first decomposed by the MTD, which employs some new notions and conventional morphological operators. The decomposition yields two components-the mean trend, which reveals the tendency of the time series, and the stochastic component, which depicts the fluctuations caused by high frequency of the variability. Subsequently, the p-step forecast is conducted for these two components separately. The mean trend is forecasted on the basis of the least-square support vector machine (LS-SVM) model, while the p-step forecast for the stochastic component is carried out by the MMLP, which involves performing morphological operations employing a novel structuring element (SE) in the phase space. Finally, the forecast of wind power generation is achieved by combining the separate forecasts of two components. In order to evaluate the accuracy and stability of the MTD/MMLP model, simulation studies are carried out using the data obtained from three widely used databases sampled in different periods. The results demonstrate that the MTD/MMLP model provides a more accurate and stable forecast compared to the traditional methods.

Disturbance detection in the phase space through GK clustering

Power disturbances have always been an important issue in power system protection. This paper proposes an original approach to detect the disturbances in power system signals. The disturbance signal is firstly transformed to the phase space, where the disturbance-free signal and the disturbance form two irrelevant waveforms. GK clustering is used afterwards to distinguish the two clusters. Simulation studies are conducted on a batch of power system disturbances, which show that the proposed approach is capable of detecting and localizing the various disturbances of power systems.

Quasi-Monte Carlo Based Probabilistic Optimal Power Flow Considering the Correlation of Wind Speeds Using Copula Function

Wind farms commonly cluster in regions rich in wind resources. Thus, correlation of wind speeds from different wind farms should not be ignored when modeling a power system with large wind energy penetration. This paper proposes a probabilistic optimal power flow (POPF) technique based on the quasi-Monte Carlo simulation (QMCS) considering the correlation of wind speeds using copula functions. In this paper, a copula function is used to model the dependent structure of random wind speeds and their forecast errors. QMCS is employed in the sampling procedure to reduce computation burden. The proposed method is applied in probabilistic power flow (PPF). Furthermore, the PPF is used in the POPF problem that aims at minimizing the expectation and downside risk of fuel cost simultaneously. Simulation studies are conducted on a modified IEEE 118-bus power system with wind farms integrated in two areas, and the results show that the accuracy and efficiency are improved by the proposed method.

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.

Identification of current transformers saturation intervals using morphological gradient and morphological decomposition

This paper proposes a novel method to detect the saturation intervals of current transformer (CT) secondary current. The method features the waveform properties of the distorted secondary current, which has abrupt changes where saturation begins and ends. Two algorithms, the improved morphological gradient and the morphological decomposition, are designed to extract the saturation intervals, respectively, and the detection results are combined using the AND logic to give the final detection result. The proposed saturation detection scheme is implemented and tested on a sample power system model built in PSCAD/EMTDC. The simulation results show that the proposed method can accurately detect the onsets and ends of saturations under a variety of operation conditions.

Detection and classification of low-frequency power disturbances using a morphological max-lifting scheme

This paper presents a morphological max-lifting scheme for the detection and classification of low-frequency power disturbances. In order to extract waveform features of low-frequency disturbances, the proposed scheme employs mathematical morphology (MM) for its advantage in noise removing and max-lifting for its ability of information preserving. Afterwards, two aided variables are constructed to assist the classification of low-frequency disturbances. A variety of low-frequency power disturbances have been included in simulation studies and simulation results have demonstrated the effectiveness and feasibility of the proposed scheme.

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.