Power Swing Research Articles

Analyzing Impact of Different Mother Wavelets on Power Swing and Fault Detection

Wavelet Transform represents the improved form of Fourier Transform of the signal having wide range in signal processing and pattern re-cognition. This paper studies application of discrete wavelet transform detection of two commonly existing abnormalities in power system including different fault types and power swings. This paper proposes four mother wavelet types namely: Battle Lemarie (Piecewise Linear Spline), Beylkin, Daubechies and Symlet for differentiating faults from power swing by extracting voltage and current signals and evaluating signal wavelet energy. Comparison study is commented at the end for varied fault locations. The proposed study is implemented on a 400 kV, 50 Hz parallel transmission line network developed in MATLAB software

A fast, simple and local protection scheme for fault detection and classification during power swings based on differential current component

A fast, simple and local protection scheme for fault detection and classification during power swings based on differential current component

Simulation and validation of a Francis turbine load rejection procedure using OpenFOAM CFD code

Abstract The effect of off-design operation on the health of hydraulic turbines is known to be adverse, with substantial compromise in efficiency, due to evolvement of flow instability in the draft tube and consequent pressure fluctuation. The worst-case scenario may entail resonance between developed pressure fluctuation and system natural frequencies and therefore lead to power swings, possible mechanical vibrations and machine wear and tear. Present global energy ambition, however, necessitates frequent off-design operation and as such development of accurate numerical methods to determine the anomalous flow field in the turbomachine is mandated. To this end, a load rejecting transient operation of a model high head Francis turbine from design operating point to part load condition is simulated using a hybrid turbulence modelling approach. A detailed analysis of the draft tube velocity field is carried out after validation of the test case with experimental investigations that have discerned similar results. Spatio-temporal features, such as central flow stagnation, flow separation, central flow recirculation and development of a precessing vortex core, which are embodiments of the vortex breakdown phenomenon are thoroughly analysed and discussed. Finally, the draft tube flow is visualized using streamlines on the meridional plane and tracked as the turbine transitions from design to part load operating conditions.

Comparison of Pattern Recognition Techniques of Loss of Excitation and Power Swing Using Machine Learning

Comparison of Pattern Recognition Techniques of Loss of Excitation and Power Swing Using Machine Learning

Estimation of polarity of slope of instantaneous current increment for power swing immune protection of series compensated line

Estimation of polarity of slope of instantaneous current increment for power swing immune protection of series compensated line

A novel setting free approach to differentiate fault and power swing using support vector machine

A novel setting free approach to differentiate fault and power swing using support vector machine

Envelope based symmetrical fault detection during power swing for series compensated lines

Envelope based symmetrical fault detection during power swing for series compensated lines

Power Swing in Systems With Inverter-Based Resources—Part I: Dynamic Model Development

Power Swing in Systems With Inverter-Based Resources—Part I: Dynamic Model Development

Power Swing in Systems With Inverter-Based Resources—Part II: Impact on Protection Systems

Power Swing in Systems With Inverter-Based Resources—Part II: Impact on Protection Systems

Wavelet Synchro-Squeezing Transform and Dynamic Threshold Supported Symmetrical Power Swing Technique for Modern Transmission Network

Wavelet Synchro-Squeezing Transform and Dynamic Threshold Supported Symmetrical Power Swing Technique for Modern Transmission Network

Numerical investigation on vortex characteristics in a low-head Francis turbine operating of adjustable-speed at part load conditions

Numerical investigation on vortex characteristics in a low-head Francis turbine operating of adjustable-speed at part load conditions

Optimization design of an innovative francis draft tube: Insight into improving operational flexibility

Optimization design of an innovative francis draft tube: Insight into improving operational flexibility

Signal Envelope-Based Adaptive Power Swing Blocking

This research paper proposes detection of faults during power swings by using a peak envelope-based method to prevent incorrect distance relay operation. Distance relays should not operate during power swings except to remove a correctly detected fault event. In this study, distinction between faults and power swings is achieved by correctly observing fault occurrence based on dynamic thresholds generated by peak envelopes. The method utilizes a DC offset current in determining the fault inception and fifth harmonic voltage to assess the fault clearance. If the current surpasses the calculated threshold due to fault existence, the relay is allowed to operate. Re-blocking the relay after fault clearance is achieved in the case of a higher value of the voltage than the threshold value. Current and voltage data obtained from two different test systems simulated in PSCAD for various scenarios have been used in the proposed algorithm developed in MATLAB. In order to show the effectiveness of the proposed method, the results on the second test system have also been compared to the results of selected methods in the literature. It has been perceived that the developed method has a high accuracy and provides an appropriate time range for relay and breaker operation.

Comparative assessment of fault detection methods in transmission line during power swing: analyzing single and hybrid networks for the southern Kerala grid

The blocking function of power oscillation is a highly effective feature found in distance relays. Its purpose is to prevent unintentional tripping of transmission lines. It is crucial to accurately differentiate between faults and power oscillations to minimise the occurrence of widespread power outages and monetary losses. Therefore, the detection and classification of faults arising from power swings are critical challenges in ensuring the smooth operation and overall health of transmission lines, particularly in extra-high-voltage (EHV) and ultra-high-voltage (UHV) power systems. Modern methods rely on machine learning methods which are in their infancy. The paper proposes to identify various fault conditions in transmission lines during power swing by using a hybrid Neural Network architecture. In this research, different designs such as 2-D convolution neural network (2D-CNN), 1-D convolution neural network (1D-CNN), long short-term memory (LSTM), and CNN-LSTM hybrid networks are investigated to understand the effect of various deep learning approaches. Along with the southern Kerala grid, the standard 9-bus system is selected as a test case to examine various fault circumstances of the system during power swing. Multiple tests were conducted to determine the optimal deep learning architecture, including model parameters and configurations, for accurate fault detection. For the 9 bus system, the 1D CNN network performs better with an accuracy of 98.70%, and for the Kerala grid, both networks are competitive, but the CNN-LSTM Hybrid method slightly outperforms the 1D CNN with an accuracy of 92.40%.

Highly efficient relay triggering circuit for fault detection during Power swings

This paper introduces a Discrete Wavelet Transform based very simple and fast acting algorithm with multi-resolution analysis to sense all types of faults in presence of power swings using current signal analyzation. The algorithm confirms very quick and efficient detection of various fault types in the first signal decomposition level of signal. The novelty of proposed algorithm lies in use of special type of Battle Lemarie mother wavelet having an advantage of perfect symmetry ensuring decomposition into B-Spline or same order polynomials capturing excellent speed and time-frequency localization of signal. The algorithm is tested for different fault parameters such as fault resistance, fault distance and time of initiation of faults considering EHV double circuit transmission line network and IEEE 9 Bus system developed in MATLAB environment. The proposed algorithm is capable of detecting all types of faults consistently within minimum time of 0.001 sec.

Parameter Estimation of Power System Oscillation Signals under Power Swing Based on Clarke–Discrete Fourier Transform

Accurate knowledge of oscillation parameters (i.e., frequency, amplitude, phase, and damping factor) is crucial for control strategies of power systems under power swing. This paper presents a method for the parameter estimation of power system oscillation signals under power swing based on Clarke–DFT. The proposed method provides accurate parameter estimation of damped sinusoidal signals for both balanced and unbalanced systems, which performs well even in the presence of harmonics. In the meantime, the negative frequency components in the spectra of the damped sinusoidal signals, which are caused by system imbalance, are calculated accurately using complex-valued interpolated DFT. To verify the performance of the proposed method, simulations are performed under balanced and unbalanced conditions. The results of the simulations confirm the effectiveness of the proposed method either in unbalanced or harmonic conditions.

MFCDFT and impedance characteristic-based adaptive technique for fault and power swing discrimination

ABSTRACT In today’s fast-changing power systems, it is vital to have a protection system that can distinguish between power swing and fault conditions. With advanced protective devices (Digital Relays), an appropriate protection strategy can be established to identify power swing and fault conditions individually. The proposed algorithm uses the sliding window concept, which requires very little computational data unless any abnormal conditions exist in the power system. Modified Full Cycle Discrete Fourier Transform (MFCDFT) is more effective in the extraction of the fundamental phasors from the original complex signal by effectively removing the harmonics and DC components. An adaptive impedance characteristic-based MFCDFT is utilized for the discrimination of power swing and fault in this work. Several fault scenarios and power swings are generated on the test system concurrently and independently from the relay station at varied distances. PSCAD/EMTDC is utilized on a single-machine infinite bus system to analyze various test conditions. The simulation is utilized in this case to collect data from various test circumstances. After obtaining the data, the MFCDFT algorithm along with adaptive impedance estimation is performed using MATLAB code. After examining various test results, it is discovered that the suggested technique correctly recognizes power swings and various fault scenarios. It can also identify the difference between stable and unstable power swings. According to the results, the proposed technique is proficient at spotting fault and power swing conditions. In addition, the algorithm ensures dependable operation with quick detection and avoids maloperation under a typical swing condition.

A Curve Fitting–Based Directional Protection of Transmission Lines Using Genetic Programing

In modern power systems, there is a growing need for protection techniques that are fast, precise, and stable to enhance the reliability and stability of power networks. This paper introduces an advanced technique for fault direction estimation that addresses key limitations in existing methods. Our proposed algorithm eliminates the dead‐zone issue, effectively handling both forward and reverse faults regardless of their location, including faults near relay regions. Using genetic programing (GP), we derive a closed‐form equation to drive the fault direction estimation algorithm, ensuring accurate and reliable results. The algorithm demonstrates exceptional speed and reliability in fault detection and direction estimation, even under challenging conditions such as current transformer (CT) saturation, power swing, and source strength variations. It is also insensitive to fault resistance and fault inception angle, increasing its robustness. A notable advantage of this algorithm is its efficiency at low sampling frequencies, making it highly adaptable for industrial applications and compatible with modern software and hardware requirements. We validated the algorithm through simulations on the electromagnetic transients program (EMTP‐RV) platform and with field data from the Manesht 230 kV substation in Ilam province, Iran. The results confirm the algorithm’s impressive speed and high reliability.

Comparison of Power Swing Characteristics and Efficacy Analysis of Impedance-based Detections in Synchronous Generators and Grid-following Systems

Comparison of Power Swing Characteristics and Efficacy Analysis of Impedance-based Detections in Synchronous Generators and Grid-following Systems

Out-of-Step Detection based on Phasor Measurement Unit

The electrical power systems operate as a huge, interconnected network that extends across a large area. In the power system, there is an equilibrium between generated power and a load. Any disturbance in the system, such as a fault or a change in load, will lead to imbalance and electromechanical oscillations. As a result, the power flow between two areas varies. This is known as a power swing." Large system disturbances could lead to large rotor angle deviations between groups of generators, resulting in a loss of synchronism between generators or between interconnected systems. This is known as an out-of-step condition. To avoid equipment damage and power outages, the interconnected area must be isolated as soon as possible before the electrical system loses synchronization. In this paper, PMU data is used to measure the current, voltage, and phase angle of the three phases at both ends of two interconnected area power systems. The measured data is then used to distinguish between a power swing or fault condition and predict the future phase angle difference during the disturbances to evaluate the system stability condition. If a swing is detected, then it will be ascertained whether the swing is stable or not. The performance of the proposed method has been tested on a simulated system using MATLAB / Simulink software.