Modeling and Analysis of Voltage Sag Sources in Distribution Network

This paper analyses the different voltage sag phenomenon of the different voltage sag sources and summarizes the characteristics of the different voltage sag sources, including the amplitude of the voltage sag, the swell happening at the same time of the sag, the characteristics of the voltage changes during the sag and the three phases voltage balance or not. Most of the existing voltage sag source recognition methods and their disadvantages and application have been analyzed in this paper. Considering that in actual power quality monitoring network, many monitoring equipments only can provide parts of the voltage sag data, so a simple and practical method is proposed in this paper. Only the RMS voltages are calculated in this method. Based on the different voltage sag amplitudes, the voltage jump happening at the end of the sag(or the voltage changes in trends during the sag), the different deviations of the three-phase voltages and some other characters according to different sources, the characteristic value corresponding can be extracted, and then the classification of voltage sag caused by the short-faults, transformers energizing and large-capacity induction motor starting can be realized. The correctness of the method is proved by simulations.

The precise location of voltage sag sources plays an important role in formulating a voltage sag governance plan and clarifying the responsibility for the accident. The traditional location method of voltage sag sources is difficult to establish a precise mathematical model and locate the complex voltage sag sources accurately in the complex distribution network. In order to solve these problems, a location method for complex voltage sag sources based on random matrix theory is proposed in this paper. Firstly, the augmented matrix is constructed based on the influence factor data and the operation state data of the distribution network as the data source matrix, and the statistical characteristics of each data are analyzed by using the random matrix theory to determine the suspicious area of voltage sag source. Then, the disturbance signal of each node in the suspicious area of voltage sag source is analyzed by using the atomic algorithm and disturbance active power method, and it can determine the location of each disturbance source in the complex voltage sag event and the cause of voltage sag at each node. Compared with the existing model-based methods, the proposed method in this paper is a data-driven approach, which does not need the physical model and topology information. Furthermore, it can reduce the amount of data and improve the analysis efficiency on the basis of determining the suspicious area of the voltage sag source. Finally, the examples are given to show that the proposed method can accurately locate the disturbance sources in complex voltage sag events.

Voltage sag is one of the power quality disturbances most frequently by customers because it can cause economic loss for the customers especially industries and commercial customers. The main source of voltage sags are faults in transmission and distribution beside two others namely motor large starting and transformer energizing, both the voltage sags have less effect on the equipment. Voltage sags can cause degradation performance of the equipment, and it depends on the magnitude and the duration. The voltage sags that occur in the power system can be compensated with installed Static VAR compensator (SVC) and Static synchronous compensator (STATCOM) in the distribution system bus. The purpose of the study is to compare both types of the device used to mitigate voltage sags in simulation using Alternative transient program (ATP) software. The voltage sag results from the short circuit faults. The method proposes the installation of the two compensator devices alternatively at one of the IEEE thirteen busses systems. The result shows the STATCOM is better than the SVC to handle mitigation of voltage sags compared with the SVC.

Voltage dip has been identified as one of the common types of power quality disturbance in electrical power networks and drew a lot of attention in voltage quality research presently. It is regarded as the most costly power quality problem. It can be caused by starting electrical motor, switching of generators or bulk loads, transformer energizing and short circuits in the power networks. In recent time power utilities and customers have made an effort to improve the reliability of power network, but it has been so difficult to control the external factors that cause voltage dip. As a result voltage dip must be classified and diagnosed accurately so that proper mitigation measures can be implemented. Classification of voltage dips plays an important role in voltage dip mitigation, investigations, assessment and equipment immunity specifications. In this context, this paper develops a technique using artificial neural network (ANN) for voltage dip classification, which is based on the voltage dip windows defined by the South African utility ESKOM. The test is carried out on IEEE 9-bus system through simulation in DigSILENT Power Factory 14.0 software and the ANN model is trained, tested and validated in Matlab environment using neural network Toolbox.

Voltage sags are very frequent events associated with faults in the network, or with common operations like the energization of transformers or the starting of large size motors. Although their duration is very short, they can represent a critical problem for industrial plants, especially for those characterized by the presence of adjustable speed drives or, more generally, of sensitive loads, like power electronics devices. In this paper, a static series compensator (SSC) has been connected in a radial network feeding a thyristor bridge rectifier controlled load, for the compensation of voltage sags. The whole system has been simulated with the aid of the electromagnetic code PSCAD-EMTDC and the obtained results show that it is particularly suitable for those cases where the unsymmetrical faults are the responsible for the failure of the thyristor pulse logic, thus resulting in a substantial decrease of the DC voltage.

In this paper a new method is presented for voltage sag source location based on directional overcurrent (DOC) relay information. In this method the current (during sag and presag) is the only variable which is measured by relay/power quality (PQ) monitor. Then relative location of voltage sag source is determined using the variation of the magnitude of measured current positive-sequence component and the sign of its phase-angle jump. The performance of the proposed method is compared with distance relay (DR) and phase change in sequence current (PCSC) methods by using PSCSD/EMTDC and MATLAB on a large scale actual regional network including transmission and sub-transmission levels. The results show the good performance of the new method and its unique applicability in cases where only currents are recorded.

Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform

This study presents a new approach for voltage sag assessment based on the concept of ‘area of severity’ (AOS). The network regions, where the fault occurrences will simultaneously lead to voltage sags at different sensitive load points, can be determined by performing an AOS analysis. The impact rankings of the network lines and buses are also addressed. The impact rankings are determined according to the contribution degree of each line and bus faults to the total expected number of voltage sags in the power system. The concepts of AOS and the impact ranking are useful in establishing efficient planning for the mitigation of voltage sags, and for evaluating the relationship between sensitive load points and system voltage sag performance.

Voltage support in industrial distribution systems in presence of induction generator-based wind turbines and large motors

This paper presents an inclusive set of EMTP models used to simulate the cause of voltage sags such as short circuits, transformer energizing, induction motor starting. Voltage sag is usually described as characteristics of both magnitude and duration, but it is also necessary to detect phase angle jump in order to identify sags phenomena and finding the solutions, especially for sags due to short circuits. In case of the simulation of voltage sags due to short circuit, their effect on the magnitude, duration and phase-jump are studied.

Voltage sag is considered to be the most serious problem of power quality. It is caused by faults in the power system or by the starting of large induction motors. Voltage sag causes about 80% of the power quality problems in power systems. One of the main reasons for voltage sag is short circuit fault, which can be compensated for by a distribution static compensator (DSTATCOM) as an efficient and economical flexible AC transmission system (FACTS) device. In this paper, compensation of this voltage sag using DSTATCOM is reviewed, in which a sliding mode control (SMC) technique is employed. The results of this control system are compared with a P+Resonant control system. It will be shown that this control system is able to compensate the voltage sag over a broader range compared to other common control systems. Simulation results are obtained using PSCAD/EMTDC software and compared to that of a similar method.

Voltage sag is a serious problem in power systems. To reduce voltage sag, it is necessary to identify and classify voltage sag sources. However, due to insufficient voltage sag data, our paper designs a CM-eCNN model through few-shot learning. Firstly, this paper introduces the CM-eCNN model and studies how to improve the extraction efficiency of the constellation model to feature map through the attention mechanism. Then, it introduces the training method of few-shot learning. Finally, our model training with few-shot learning has better performance than other models, which proves effectiveness of the model.

This paper presents an equivalent self-consistent electrothermal circuit model for power integrity analysis of large on-chip power distribution networks. Two coupled circuits are used to co-simulate the electrical and thermal behavior of the power grid. After a steady-state analysis, the order of the circuit is strongly reduced by means of a node clustering technique. The obtained low-order circuit allows a cost-effective complete power integrity analysis, including dynamic analysis and evaluation of time-domain features like voltage droop. As a case-study, a 45-nm chip power grid is analyzed: the full circuit for the electrothermal model with 4 million nodes is reduced by a factor of about 3500×, with a relative error on the solution below few percent.

Voltage sags is a problem affecting the electrical qualities. In this paper, effects of large induction motors on voltage sag characteristic are discussed. Moreover, the influences of different mechanical loads of the motors on voltage recovery are investigated. The study results show that reactive power contribution owing to the motor's trapped flux linkage prevents fast collapse of the voltage, resulting in non-rectangular shape of sag. The motor's negative torque induced during fault interval causes speed loss. The constant torque of mechanical load gives rise to the most speed loss, leading to a longer reacceleration and a great delay in voltage recovery.

This paper presents a study developed on a three-phase 11-MW synchronous motor with a solid salient-pole rotor of a blower drive system of a blast furnace at the Companhia Siderurgica Belgo-Mineira, Joao Monlevade, Brazil. A mechanical model including motor, gears, couplings, and blower is used in a simulation study to evaluate the torsional dynamics and stresses during motor startup and voltage sag conditions. In addition, the operation of the synchronous motor under voltage sag is analyzed with a comparative study between complete and approximated models of the motor. The utilization of a dynamic voltage restorer to protect the drive system under this abnormal situation is also evaluated.