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The fault location problem is one of the most important issues in power system operation and control. To obtain an experimental system for teaching and training electrical engineering students, this paper performed a study to design and implement a real-time fault location laboratory-scale model from practical hardware components. The impedance-based fault location method is embedded in the system to determine the distance to fault in the transmission line. Furthermore, the monitoring and controlling program is designed by the Matlab App Designer – A new professional app to create the graphical user interface and use the integrated editor quickly. Several fault types including three-phase fault, phase-to-phase fault, phase-to-ground fault are created to evaluate the performance of the fault location experimental system. The real-time measurement results which are acquired and observed on the user guide interface of the program confirm the effectiveness of the experimental system; therefore, the system can be considered a powerful tool for electrical engineering students.

This paper presents an architecture of the Doubly-Fed Induction Generator based on Wind Energy Conversion System (DFIG-WECS) to study with the Low Voltage Ride Through (LVRT) capability. The proposed architecture consists of three main parts namely the wind turbine, electric generator, control systems. The wind turbine and gearbox are modeled by a servo motor driven by using an inverter system. The electric generator uses the Wound Rotor Induction Generator (WRIG). The control system composes of the rotor-side converter (RSC) and the grid-side converter (GSC). The integration of the digital signal processor (DSP) with the Simulink/Matlab software of the vector control scheme is implemented to control both RSC and GSC. Besides, to protect the overcurrent of the generator’s rotor and overvoltage of the DC-link, the crowbar and chopper circuits and the logical scheme between the crowbar and the RSC are also proposed. To approve the control algorithms implemented in DSP as hardware in the loops, the proposed 0.8kW DFIG-WESC system has been performed under the experimental environment considering LVRT requirement in German grid code.

The detection, mitigation, and classification of power quality (PQ) disturbances have been issues of interest in the power system field. This paper proposes an approach to detect and classify various types of PQ disturbances based on the Stockwell transform (ST) and decision tree (DT) methods. At first, the ST is developed based on the moving, localizing, and scalable Gaussian window to detect five statistical features of PQ disturbances such as the high frequency of oscillatory transient, distinction between stationary and non-stationary, the voltage amplitude oscillation around an average value, the existence of harmonics in a disturbance signal, and the root mean square voltage at the internal period of sag, swell or interruption. Then, these features are classified into nine types, such as normal, sag, swell, interruption, harmonic, flicker, oscillatory transient, harmonic voltage sag, and harmonic voltage swell by using the DT algorithm that is based on a set of rules with the structure &ldquo, if&hellip, then&rsquo, &rsquo, This proposed study is simulated using MATLAB simulation. The IEEE 13-bus system, the recorded real data based on PQube, and the experiment based on the laboratory environment are applied to verify the effectiveness.

The flexible alternating current transmission systems (FACTS) have been widely used in modern power systems. Because of the presence of the FACTS devices, distance relays in transmission lines may inaccurately locate fault locations. Therefore, it is significant to find a mechanism for locating fault in transmission lines connected to FACTS in which a static var compensator (SVC) is investigated in this work. Based on the development of a phasor measurement unit (PMU) with global positioning system (GPS), this paper proposes a new method for calculating apparent impedance seen by the distance relay location while a short-circuit fault occurs in a transmission line connected the SVC to the midpoint of the line. According to the method, sampled voltage and current measurement at the relay and SVC locations are synchronized using PMUs and the synchronized measurements are then used to calculate a new apparent impedance and to locate the fault location in the line. The method in this paper has the capability for fast calculation and it also has the robustness for identifying different fault types in power systems. Matlab/Simulink software is applied to simulate the study results and to evaluate the correctness of the modeling and effectiveness of the proposed method for locating fault in this paper.

This paper focuses on analyzing and evaluating impact of a Static Var Compensator (SVC) on the measured impedance at distance protection relay location on power transmission lines. The measured impedance at the relay location when a fault occurs on the line is determined by using voltage and current signals from voltage and current transformers at the relay and the type of fault occurred on the line. The MHO characteristic is applied to analyze impact of SVC on the distance protection relay. Based on the theory, the authors in this paper develop a simulation program on Matlab/Simulink software to analyze impact of SVC on the distance protection relay. In the power system model, it is supposed that the SVC is located at mid-point of the transmission line to study impact of SVC on the distance relay. The simulation results show that SVC will impact on the measured impedance at the relay when the fault occurs after the location of the SVC on the power transmission line.

This paper proposes a method to monitor, detect and classify voltage disturbances including voltage sag, voltage swell, voltage interruption, and long duration voltage variations. The detail coefficient in discrete wavelet transform is used to extract the start and end times of the voltage disturbance. The decomposed level of multi-resolution analysis in discrete wavelet transform is calculated according to the sampling frequency of the voltage signal for determining the approximation coefficient that only contains the fundamental frequency band. Then an adaptive linear neural network is used to estimate the voltage magnitude from the approximation coefficient. Finally, the disturbance will be classified into groups of voltage disturbances based on the characteristics according to the rules in IEEE Std. 1159-2009. The voltage waveforms based on theoretical signal models and actual voltage waveforms which recorded by power quality monitoring equipment are applied to evaluate the proposed method. The simulation results show that the proposed method can detect and classify voltage disturbances better than traditional methods based on root mean square and conventional adaptive linear neural network techniques.