Sag Events Research Articles

Assessment of Wavelet Types in DWT-based Power Quality Disturbance Detection Method

Power quality parameters are the limit values that allow an electrical device to function as intended without significant loss of performance and life expectancy. It is important to detect power quality disturbances quickly and reliably to ensure reliability and continuity of the power system. Today, unlike conventional methods, signal processing-based disturbance and fault detection methods have gained great importance. Wavelet transform (WT)-based disturbance detection methods are the most prominent among these methods. In this study, the performance of the discrete wavelet transforms (DWT)-based disturbance detection method used in the detection of voltage sag and voltage swell events were evaluated for different wavelet types. Process calculation load, response to event signal and performance values were compared for different wavelet types. The results will be an important source for the selection of appropriate wavelets in the methods using WTs.

Bayesian Network-Based Process Downtime Cost Determination of an Industrial Plant

Industrial plants utilize sensitive equipment to produce their products and meet their financial targets. Equipment downtime caused by power quality issues such as voltage sag affects production and entails cost hence poses a threat to their ability to deliver their financial objectives. This research aims to determine the response of industrial equipment to sag events and quantify the downtime cost caused by interruption in the production process. The study used the voltage tolerance curve to determine the individual equipment response to sag events and the Bayesian Network to establish the network structure of the production process. The probability of process interruption and the associated downtime losses was computed using a mathematical software. The research shows a strong relationship between the equipment’s response to voltage sag events and the production downtime cost and highlights the importance of the immunity of equipment to voltage sags.

Voltage Sag Data Sampling Method for Papermaking Process Based on Compressed Sensing Technology

Important production equipment and sensitive load in the papermaking process are susceptible to the voltage sag event, and production may be interrupted in severe cases. Sampling and recording this event are conducive to judge and analyze the cause of production interruption. To effectively reduce the number of samples and storage costs of the voltage sag data, this paper studies and discusses a data sampling method based on compressed sensing technology, designs an experimental device for verifying the method, proposes relevant indicators and test steps for measuring the performance of the method. The results show that the voltage sag data per unit sampling time is much lower than the minimum value specified by the traditional data sampling method and the original signal can be accurately recovered. Moreover, combined with the actual situation of papermaking, the method is featured by good anti-noise robustness and strong scalability.

Effects of voltage sag on the performance of induction motor based on a new transient sequence component method

Voltage sag is one of the most common power quality disturbances in industry, which causes huge inrush currents in stator windings of induction motors, and adversely impacts the motor secure operation. This paper firstly introduces a 2D Time-Stepping multi-slice finite element method (2D T-S multi-slice FEM) which is used for calculating the magnetic field distribution in induction motors under different sag events. Then the paper deduces the transient analytical expression of stator inrush current based on the classical theory of AC motors and presents a separation method for the positive, negative and zero sequence values based on instantaneous currents. With this method, the paper studies the influences of voltage sag amplitude, phase-angle jump and initial phase angle on the stator positive- and negative-sequence peak currents of 5.5 kW and 55 kW induction motors. This paper further proposes a motor protection method under voltage sag condition with the stator negative-sequence peak currents as the protection threshold, so that the protection false trip can be avoided effectively. Finally, the calculation and analysis results are validated by the comparison of calculated and measured stator peak value of the 5.5 kW induction motor.

Impact of SMES Unit on DC-Link Voltage of DFIG during Various Types and Level of Faults

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Accurate Identification of Point-on-Wave Inception and Recovery Instants of Voltage Sags and Swells

Voltage sags and swells are power quality events commonly observed in power systems; however, none of the existing methods allows determining their point-on-wave inception and recovery instants (and consequently, their duration) accurately in all cases. The primary goal of this paper is to determine these instants with little or no delay, by calculating the absolute rms voltage difference between two adjacent sliding windows. The proposed method is based on the assumption that this difference is maximum when the sample under analysis corresponds to either the inception or the recovery instant. This method is valid for both sag and swell events, with or without transients. Evaluation of the proposed method performance for different types of events shows that it is robust and highly accurate in determining the inception and recovery instants. The estimation error for the majority of the events analyzed is either zero or one sample (each sample corresponds to a phase angle difference of 2.81 $^\circ$ ), while the worst performance is three samples.

Dynamic performance comparison of DFIG and FCWECS during grid faults

Among several types, variable speed-based wind turbine generator (WTG) is the most popular type installed worldwide. This type of WTG is able to extract 5% more energy from wind speed compared to the fixed speed WTG. There are two kinds of variable speed based WTG; Doubly Fed Induction Generator (DFIG) and Full Converter Wind Energy Conversion System (FCWECS). DFIG and FCWECS are placed at the first and second topWTG installation worldwide since 2004. However, both of them are very sensitive to the grid dip fault and may violate the allowable margins identified by various international Fault Ride Through (FRT) codes. This paper aims to investigate the responses of DFIG and FCWECS during certain level of grid dips and compare their performanceunder such event. Results show some differences of the performance of DFIG and FCWECS during voltage sag event, however the voltage profile at the point of common coupling is much better in case of DFIG. Results also recommend that DFIG can be effective when connected to weak grids whilst FCWECS is preferably to be connected to strong grids.

Sag and Flicker Reduction Using Hysteresis-Fuzzy Control-Based SMES Unit

The issue of power quality is one of the prominent factors in determining the reliability of power systems. It is inevitable to consider power quality issues and include appropriate mitigation techniques in the design of any future power system project construction. Voltage sag and flicker are the two most common short disturbance types of power quality issues that frequently occur in power systems. A widely accepted mitigation strategy to enhance the power quality of disturbed networks is by connecting a flexible ac transmission system. In this paper, a superconducting magnetic energy storage (SMES) unit along with hysteresis-fuzzy controller to suppress the severe impacts of voltage sag and flicker events on the power quality of a power system is proposed, implemented, and tested. The proposed SMES configuration with hysteresis current control and fuzzy logic controller is effective, simple, and easy to implement. Simulation results show the ability of the proposed SMES controller in compensating sag and/or flicker distortions.

Voltage Sag Source Identification Based on Few-Shot Learning

Efficient identification of the voltage sag sources is significant in the power quality studies. This paper presents a novel method for voltage sag source identification which performs automatic feature extraction and shows a superior performance regardless of the insufficient amount of training samples. In the proposed strategy, the input data are preprocessed and fetched into the feature extractor, which is designed based on the convolutional neural network. Then the weighted k-nearest neighbor classifier generates the identification results. In the training period, the few-shot learning technique is harnessed, and the siamese network is constructed such that the proposed model learns efficiently even with a small number of samples. The proposed scheme is implemented in Python and PyTorch framework. Case studies and comparisons with other methods are carried out on 700 samples of voltage sag events in Jiangsu Province, China. Experimental results show the superiority of the proposed method over other identification methods in the tested cases.

Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

An Adaptive Approach for Voltage Sag Automatic Segmentation

Voltage sag characterization is essential for extracting information about a sag event’s origin and how sag events impact sensitive equipment. In response to such needs, more characteristics are required, such as the phase-angle jump, point-on-wave, unbalance, and sag type. However, the absence of an effective automatic segmentation method is a barrier to obtaining these characteristics. In this paper, an automatic segmentation method is proposed to improve this situation. Firstly, an extended voltage sag characterization method is described, in which segmentation plays an important role. Then, a multi-resolution singular value decomposition method is introduced to detect the boundaries of each segment. Further, the unsolved problem of how to set a threshold adaptively for different waveforms is addressed, in which the sag depth, the mean square error, and the entropy of the sag waveform are considered. Simulation data and field measurements are utilized to validate the effectiveness and reliability of the proposed method. The results show that the accuracies of both boundary detection and segmentation obtained using the proposed method are higher than those obtained using existing methods. In general, the proposed method can be implemented into a power quality monitoring system as a preprocess to support related research activities.

A New 2-D Multi-Slice Time-Stepping Finite Element Method and Its Application in Analyzing the Transient Characteristics of Induction Motors Under Symmetrical Sag Conditions

Voltage sag is one of the most common power quality disturbances in industry, which causes huge inrush current in the windings of induction motors, and adversely impacts the motor’s secure operation. For better understanding of the operation and protection of induction motors, there is a need for a detailed research of the transient characteristics of induction motors during sag events. In this paper, we first proposed a novel 2-D multi-slice field-circuit-motion coupling time-stepping finite element method (2-D multi-slice FCM coupling T-S FEM) for calculating the transient performance of the induction motor with skewed rotor bars. In the proposed method, the equations of field, circuit, and motion are expressed in a nodal matrix and coupled together. Based on this method, we have analyzed the electromagnetic properties of a 5.5-kW induction motor with skewed rotor bars under symmetrical voltage sag conditions. Then, the influences of voltage sag’s sag magnitude, phase-angle jump, and initial phase angle on the stator inrush peak currents of the 5.5 kW and a 55-kW induction motors is studied. Finally, the proposed method is compared with 3-D FEM, the traditional 2-D multi-slice FEM and the measured data. The results show that the proposed method can reduce the computation time significantly with high precision.

Single-Phase DVR with Semi-Z-Source Inverter for Power Distribution Network

A novel topology of single-phase dynamic voltage restorer (DVR) is proposed to compensate the load voltage during voltage sag and voltage swell events. The semi-Z-source converter is utilised in its inverter operating mode for this proposed topology. The reduction in the number of active switches in the inverter and also in the current conduction path without compromising the output voltage range is the merit of the semi-Z-source inverter-based DVR against the commonly used full-bridge inverter-based DVR. The load voltage contains harmonics within the permissible limits when the compensation is performed by the semi-Z-source inverter-based DVR. The need for the filter circuit is eliminated; hence, the magnitude difference and the phase shift in the inverter output voltage due to the filter circuit are not present in the semi-Z-source inverter-based DVR. The proposed topology is cheap compared to the full-bridge inverter-based DVR topology. The simulations performed in MATLAB/Simulink environment are presented to validate the performance of the proposed topology. A comparison between the semi-Z-source inverter-based DVR and full-bridge inverter-based DVR is presented in this paper. The novel topology of DVR using semi-Z-source inverter is proved to be a better solution to the full-bridge inverter-based DVR.

VOLTAGE SAG SENSITIVITY OF INDUSTRIAL VECTOR CONTROLLED INDUCTION MOTOR DRIVES – A COMPARATIVE STUDY

Sensitivity of adjustable speed drives (ASDs) on voltage sag events represents one of the most challenging problems in modern industrial facilities. In this paper, a comprehensive experimental verification of vector-controlled ASDs is conducted under the most-frequent sag types. The obtained results are faced with static and dynamic requirements in speed and torque controlled applications. Besides influence of DC-link parameters, selection of the applied control method and the controller settings can have crucial impact on performance deterioration. Examined industrial ASDs exhibited voltage sag susceptibility with fault error codes under the deeper voltage sags, while under the voltage sags with residual voltage above under-voltage limit they showed speed degradation.

Research on classification of voltage sag sources based on recorded events

In recent years, with the wide application of power electronic devices and stringent requirement of industrial process, voltage sag has caused much attention in many countries. To design effective and efficient mitigation and management strategies, it is essential to have accurate classification of voltage sag sources. In this study, based on the analysis of massive voltage sag events in the grid recorded by online power quality monitoring systems in several different regions in China, a more extensive and practical classification of voltage sag sources is given. The voltage sags are divided into eight categories due to short-circuit faults with symmetric and asymmetric faults included, transformer energising, induction motor starting, lightning faults, self-extinguishing faults, the combined action of short-circuit faults and heavy load starting, upgrade faults and multi-stage voltage sags. For each category of the voltage sag sources, both the instantaneous value and the root mean square value of the waveform of the typical recorded events are given. In addition, the reasons are analysed and the waveform characteristics are summarised for each category of the voltage sag sources. Finally, the frequencies of eight categories under different voltage levels and the statistical result analysis are given.

Employing instantaneous positive sequence symmetrical components for voltage sag source relative location

Modern industrial facilities are vulnerable to voltage sags and short interruptions as they were in the past to sustainable interruptions. Therefore, modern reliability assessment should include voltage sags and short interruptions. Hence, in order to improve power system reliability it is necessary to mitigate these voltage variations. One of the first steps towards this objective is to locate the source of these disturbances. This paper investigates a number of methods for sag source relative location. Said methods are based on different electrical parameters. Using instantaneous positive sequence components, five novel extended methods for voltage sag source location are proposed for accommodating the transient nature of voltage sag events and for improving unsuitable operation of methods on asymmetrical sags and earth faults. Due to the fact that short circuits are the main reason resulting in voltage sags, this paper focuses on the location of voltage sag sources due to line faults. A comparative analysis between the existing methods and the five novel extended methods is performed by applying extensive numerical simulations in a Brazilian regional transmission and sub-transmission utility. Finally, the effectiveness of all methods is obtained. The results show that the novel extended methods are more effective and among them, the extended methods based on reactive power can locate sag sources with an accuracy of 88%. Also, implementation of the novel extended methods on signal processors is easier with respect to the already implemented methods.

Power Quality Improvement of Grid Connected Wind Farms through Voltage Restoration Using Dynamic Voltage Restorer

Increasing sensitivity of the loads with respect to power quality has gained the interest of power system analysis and power quality improvement techniques. The voltage sags or swells which are characterized by rms voltage variations outside the normal operating range of voltages due to faults may lead to improper disconnection of wind turbines. This paper deals with the effective voltage sag/swell mitigation using Dynamic Voltage Restorer (DVR), to regulate the terminal voltage of the wind farm. The DVR utilizes a feed forward vector control based algorithm to generate the PWM based firing signals for injecting appropriate compensation voltages. The actual wind farm field data of the voltage sag and swell events during fault conditions are re- created using MATLAB/Simulink and restored by employing DVR. The simulation results are shown to verify the operation of DVR during balanced voltage sag and swell conditions.

A Sparse Sampling Approach to Dynamic Sub-Cycle Decomposition of Apparent Power in General Polyphase Networks

We present a dynamic decomposition of apparent power in a polyphase network, based on measurements taken within a portion of a single cycle of the ac fundamental. We show that two time samples suffice for a definition that provides an attractive trade-off between computational cost and dynamic performance. The sub-cycle (or near-instantaneous) metrics that we introduce connect the time- and frequency-domain approaches in a natural way, and address the key weaknesses of the existing instantaneous decompositions. Furthermore, our power metrics are amenable to refinements via symmetrical components, thus connecting with concepts commonly used in system protection. We illustrate the sub-cycle decomposition via two polyphase examples: one of conceptual interest, the other an actual industrial voltage sag event.

Instantaneous positive‐sequence current applied for detecting voltage sag sources

This study proposes a method for detecting voltage-sag sources, using only the measurements of line currents. Instantaneous symmetrical components are introduced for accommodating the transient nature of voltage sag events, where a magnitude and phase change in the instantaneous positive-sequence current is used as a criterion. The proposed method is compared with conventional phasor-based methods known from the literature. All the discussed methods for voltage sag source detection, both the already known and the proposed ones, were evaluated by applying extensive simulations, laboratory tests and field testing. When compared with the phasor-based methods, the proposed method showed faster response, a higher degree of confidence and superior effectiveness, especially for transient voltage sag events in networks with distributed generation and active loads.

Economic analysis of losses due to voltage sag on industries using sensitivity method and comparison with cost of mitigation devices

Voltage sag indices are used to quantify the performance of the power supply during individual events for individual sites and the whole system. In this paper, a questionnaire is designed for survey and data is collected from the different industries. For calculating financial losses due to sag events, a probabilistic method is used. The aim of this survey is to find out the answer of some questions like an effect of voltage sag on Indian industries i.e., which type of industries suffers most. In this paper also losses due to voltage sag are compared with the cost of different mitigation devices.