Sag Events Research Articles

Voltage sag sensitive load type identification based on power quality monitoring data

This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

Analysis through mathematical model of power quality disturbances to improvement

Power quality studies have developed to analyses power quality data as the use of delicate electronic equipment has increased. The wavelet transformation method was developed to be more useful for analyzing different kinds of power quality events. In this research, we analyses the utilization of several wavelet types at various sizes and decomposition level of the system analyzing actual value of the power quality disturbances events in the transmission system. Signal created using MATLAB backdrop. Testing has been done on voltage sag, voltage swell, and transient events. This technique makes use of Wavelet transform and Artificial Neural Network (ANN) to identify and categories power quality disturbances in the power system. Comparing the suggested method to the standard approach, fewer characteristics are needed for identification. Artificial neural networks are taught to classify events using the feature that the wavelet extracts. The weight gained after neural network training is used to categories PQ concerns between the quantity demand and the price.

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

An innovative Dynamic Voltage Restorer (DVR) system based on Artificial Neural Network (ANN) technology, implemented in MATLAB Simulink, accurately detects, and dynamically restores voltage sags, significantly improving power quality and ensuring a reliable supply to critical loads, contributing to the advancement of power quality enhancement techniques. Voltage sags are a prevalent power quality concern that can have a significant impact on sensitive electrical equipment. An innovative approach to address voltage sags through the operation of a Dynamic Voltage Restorer (DVR) based on Artificial Neural Network (ANN) technology. The proposed system, developed using MATLAB Simulink, leverages the ANN's capabilities to accurately detect voltage sags and dynamically restore the voltage to the affected load. The ANN is trained using a comprehensive dataset comprising voltage sag events, enabling it to learn the intricate relationships between sag characteristics and optimal compensation techniques. By integrating the trained ANN into the DVR control scheme, real-time compensation for voltage sags is achieved. The effectiveness of the proposed system is rigorously evaluated through extensive simulations and performance analysis. The results demonstrate the superior performance of the ANN-based DVR in terms of voltage sag detection accuracy and restoration precision. Consequently, the proposed system presents an intelligent and adaptive solution for voltage sag compensation, ensuring a reliable and high-quality power supply to critical loads. This research contributes to the advancement of power quality enhancement techniques, facilitating the implementation of intelligent power system.

Voltage Sag Data Generation Based on the Probabilistic Diffusion Model

To effectively analyze and mitigate voltage sag phenomena using deep learning techniques, a diverse and representative dataset of voltage sag events is essential. However, the available voltage sag dataset obtained from monitoring systems is often limited in quantity and lacks comprehensive coverage of various sag types. In this paper, the achievement of generating high-quality voltage sag waveforms that closely resemble real-world scenarios is accomplished by leveraging the diffusion model’s ability to capture complex data distributions. Our study focuses on modifying the diffusion model to capture the specific characteristics of voltage sag waveforms. The results demonstrate the effectiveness of the proposed approach in generating realistic voltage sag datasets, highlighting its potential for various applications in power system analysis and planning.

A novel voltage sag detection method for analyzing charging quality of electric vehicle

Electric car charging quality is severely impacted by voltage sag in the distribution network. Voltage sag events frequently occur in conjunction with other power quality disturbances, which leads to signal waveform distortion, making it difficult to identify voltage sag characteristics in composite power quality disturbance signals. The voltage sag amplitude, phase angle jump, and duration are all accurately detected in the composite power quality disturbance signal thanks to the method's two-stage waveform decomposition model, which eliminates the interference of the transition segment by pinpointing the beginning and ending points of the voltage sag using DQ transform. Moreover, the designed simulation experiments further verify that our proposed method outperforms other mainstream approaches, and it can be well used to effectively detect voltage sags of different causes under various signals that disturb the composite power quality, with an average accuracy rate of over 98.86 %.

A study of the impact of voltage sag on sensitive loads at low-voltage distribution areas

Under the background of the dual carbon goal and the new power system, the power system becomes more complex and diverse, and voltage sag events caused by short-circuit faults, overloads, switching operations, large motor starting, and distributed generation access are more frequent. On the other hand, with the increasing sensitivity of low-voltage distribution network electrical equipment to power quality, especially for precision process control equipment using computers, power electronics, and automation technology, new energy generating units, and electric vehicle charging facilities, voltage sag accidents will cause difficult-to-estimate losses in equipment production and operation, and frequent voltage sag events have become one of the main power quality problems affecting reliable power supply and normal equipment operation. To address this problem, this paper analyzes the impact of voltage sag on low-voltage substation loads from a mechanism perspective, establishes a voltage sag tolerance curve for electronic appliances through experimental research, and simulates and verifies the effectiveness and correctness of the method in this paper using a 3 MW-rated induction generator connected to the distribution network as an example.

Voltage sag source location based on multi-layer perceptron and transfer learning

The existing voltage sag source localization only utilizes one simulation type or measurement data based on sampled waveform data. Moreover, it requires more storage space and transmission channels and cannot combine the advantages of simulated and measurement data, resulting in poor applicability of the model. Hence, this paper proposes a voltage sag source locating method based on transfer learning from the sag information in the sag event list combined with the grid structure data. Firstly, the location features of the sag source are extracted from the degree of sag impact, network structure, and sag-type information based on the characteristics of simulated and measurement data that characterize the position of the sag source, and they are collectively used as inputs to the model. Then, the simulated data is used to build a multi-classification model based on the multi-layer perceptron with the line number as the classification number, and the measurement data is employed to fine-tune the model parameters to achieve transfer learning. Finally, voltage sag source localization is achieved based on the trained multi-classification model. The correctness of the proposed method in this paper is verified through simulation and actual measurement in a specific area of East China.

Exponential Smoothing of Quadrature Amplitude Modulation for Power Quality Disturbance Detecting and Classification

AbstractIn this study, exponential smoothing (ES)‐based quadrature amplitude modulation (QAM) is applied to detect and classify the power quality (PQ) events with high accuracy and low complexity. Considering the IEEE‐1159 standards, in the simulation environment, 14 different power quality distortion signals are produced. Each of these signals is modulated at the fundamental frequency by QAM. The high‐frequency constituents in the in‐phase and quadratic components resulting from the QAM are filtered by the ES. PQ events are detected and classified by extracting features from the filtered in phase and quadrature component. The performance of the proposed method is examined by taking randomly selected data samples for each PQ event, and its performance is and , respectively, at noiseless and dB SNR environments. The obtained results are compared with the others in the literature for the detection and classification of PQ events, and it is shown that the proposed method has better performance in noisy and noiseless conditions. In addition, to verify that the proposed method can be applied to real systems, oscillating transitions produced by capacitor banks in Simulink environment and voltage sag and swell events are generated in experimental environment. The proposed method successfully detected and classified PQ events. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A Power-Angle-Based Adaptive Overcurrent Protection Scheme for Grid-Forming Inverter Under Large Grid Disturbances

As the capacity of renewable energy generation increases, grid-forming (GFM) inverters are deemed as promising solutions for low inertia power grids. However, power-electronic-based inverters have limited overcurrent capability, so additional overcurrent protection schemes are necessary. More importantly, the stability should not be jeopardized by using additional overcurrent protection schemes. However, GFM inverters with the conventional current reference limiting method tend to be unstable under large grid disturbances such as grid frequency drop. To address this problem, this paper proposes a virtual power angle limiting method. The main idea of this method is to limit the output power by limiting the power angle instead of directly limiting the current reference. Thus, the power synchronization control law is still satisfied when the overcurrent protection is triggered, so that the stability can be maintained. Besides, after redesigning the power angle limiting value, the proposed method is also suitable for grid voltage sag cases. A significant advantage of the proposed method is that the output current can be limited automatically during grid voltage sag or frequency drop events without the need of fault detection or tuning control structures and parameters. Simulation and experimental results have verified the effectiveness of the proposed method.

Voltage sag source location based on sag event amplitude information

Voltage sag source location provides a solution to improve the operation and maintenance efficiency and resolve disputes between power supply and consumption. The existing sag source locating methods are based on the fault recording system’s sampling value waveform data, which requires more storage space and transmission channels. Based on sag event list with easy access and high real-time performance and takes the line as the location result, this paper proposes a voltage sag source locating method based on sag event amplitude information. Firstly, Based on the voltage amplitude of the monitoring nodes recorded in the sag event list, a matrix of voltage amplitude of disturbed monitoring nodes is formed. Secondly, the voltage amplitude and fault type of the disturbed nodes are used as inputs, and the line number is used as a classification number. The multi-classifier model based on multi-layer perceptron is trained to locate the voltage sag source. Finally, the method in this paper is verified by IEEE39 node simulation model and the actual monitoring data of sag events in an area in East China. The results show that the method is effective and feasible. Compared with the existing commonly used multi classification algorithms, it is found that the accuracy of sag source location in this method is 10% ∼ 20% higher than other multi classification algorithms.

Fast Support Vector Machine for Power Quality Disturbance Classification

The power quality disturbance (PQD) problem involves problems of voltage swell, voltage sag, power interruption, harmonics and complex events involving multiple PQD problems. The PQD problem attracted considerable attention from utilities, especially when renewable energy is getting a higher penetration. The PQD problem could downgrade the service quality, causing problems of malfunctions and instabilities. This paper proposed a simplified SVM technique to identify the PQD problem including the multiple PQD classification. With the simple structure proposed, the methodology could reduce a great deal of training data; requires much less memory space and saves computing time. An IEEE 14-bus power system was used to show the performance. Many tests were conducted, and the method was compared with an artificial neural network (ANN). Simulation results showed the shortened processing time and the effectiveness of the proposed approach.

A Data-Driven Self-Learning Evaluation Method of Voltage Sag Severity

Lightning is one of the main causes of voltage sags, and it is of great significance for subsequent analysis and governance to accurately evaluate the severity of the sag events caused by lightning. There are many uncertain factors between lightning fault events and voltage sag events. To evaluate the severity of voltage sag events caused by lightning, a data-driven self-learning evaluation method for voltage sag severity is proposed. According to a large number of online monitoring data of lightning positioning system and power quality monitoring system, the association rule mining algorithm based on incremental learning is used. And the rules are kept updating through the accumulation of historical data, which may give it the abilities of self-learning. The empirical analysis is carried out based on the monitoring data of a regional power grid. The results show that the method in this paper can accurately mine more valuable rules in reality and solve the problem of low efficiency of mining algorithm when the database changes dynamically.

Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid

Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively.

Homology detection method for voltage sags based on space vector and maximum mean discrepancy

A single voltage sag source may trigger multiple power quality monitors to record waveforms resulting in the redundancy of voltage sag monitoring data. To address this problem, a homology detection method for voltage sags based on a space vector and maximum mean discrepancy was proposed, which is conducive for locating accurate voltage sag sources and inverting sag event propagation paths. First, a waveform transformation method based on a space vector was proposed to eliminate the influence of the transformer on voltage sag propagation. Second, a two-sample test method for voltage sags based on the maximum mean discrepancy was proposed to detect whether any two voltage sag waveforms are caused by the same voltage sag source. The connected graph method was used to identify the number of sag sources; this avoids the difficulty in accurately selecting the number of clusters. The measured and simulation data of the IEEE 30-bus system were used for verification, and the results verified the correctness and effectiveness of the proposed method. The proposed method can be implemented in a power quality monitoring system as a pre-processing step to support related research activities.

Clustering Analysis of Voltage Sag Events Based on Waveform Matching

Voltage sags are a serious problem within power supplies, which pose threats to both residential electricity and industrial manufacturing. Since any one sag may be recorded by multiple monitoring devices from different substations, the issue of redundant information in data arises. In this regard, a novel method for voltage sag events based on projection technology, shape dynamic time warping (shapeDTW), and spectral clustering is proposed. The main contributions of this paper may be summarized as follows: (1) We present a new method for extracting the voltage anomaly waveform, which is a fast projection segmentation algorithm (FPSA). The voltage sag waveform is only a part of the voltage anomaly waveform, so the voltage anomaly waveform contains more information. (2) ShapeDTW and spectral clustering are used to match and cluster voltage anomaly waveforms, so as to achieve the normalization of voltage sag events. (3) In practical engineering, the proposed method in the paper can be used to obtain the impact of voltage sags, reduce computational complexity, and ease the workload of the operation and maintenance engineers. Experiments were conducted using voltage sag data from voltage sag events recorded by the 10 kV monitoring points in Beijing, China. The results showed the effectiveness and reliability of our proposed methods.

Dynamic Model and Converter-Based Emulator of a Data Center Power Distribution System

Data centers have become a widespread power electronics (PE) load, which has significant impact on the power grid. In order to investigate the data center load characteristics, this article proposes a complete dynamic model for a typical data center ac power distribution system. A generalized model with mode transition is proposed to coordinate different power stages in the data center power system. Meanwhile, to help evaluate the grid dynamic performance and transient stability, an all-in-one load data center power emulator is developed on a reconfigurable PE converter-based hardware testbed (HTB). The dynamic power model is digitized and simplified to be implemented in two local voltage source inverters on the HTB. This proposed data center power emulator has been verified experimentally in a regional network. Dynamic performances during voltage sag events and server load variations are emulated and discussed. The article details the design, development, and verification of the data center model and power emulator. The proposed model and emulator provide an effective, easy-to-use tool to better design data centers and study the interaction with the power system.

Online Parameter Identification of a Simplified Composite Load Model by Voltage Sag Events

Online Parameter Identification of a Simplified Composite Load Model by Voltage Sag Events

A novel voltage sag state estimation method based on complex network analysis

Voltage sag state estimation focuses on estimating the number of voltage sags at unmonitored sites. Existing methods require power quality monitors having to be allocated optimally, which are generally unpractical. To this end, an alternative estimation method is proposed for the scenario where the monitoring scheme is not optimal. Since there is a general rule in a rather long span that some network sites always record more sags than others even when short-circuit fault distribution varies, how power network structure impacts voltage sag is discussed firstly. The strength of interaction among sites, proved to be related to the number of sags, is quantified based on complex network theory analysis. The stronger the interaction between any two sites, the more possible sag events propagate from one site to another. Hence, according to the interaction field between monitored and unmonitored sites, the estimation model is built as the under-determined system of equations. Proposed method has been validated by different IEEE test systems, where the influences of fault distribution and monitoring scheme on estimation results are discussed. The results show that proposed method has better performance especially when the observability of monitoring scheme cannot be ensured. Moreover, the drawback of computation for traditional methods is overcome also, since there is no need to build estimation equations repeatedly.

Modified methods for voltage-sag source detection using transient periods

Real-time detection of the voltage sag sources' relative location requires fast and accurate methods. Therefore, in this paper, the transient period of voltage sags is used with useful detection information, which is not considered in the literature. In this context, this work firstly analyses the main positive-sequence phasor-based (PB) and instantaneous-based (IB) methods within both transient and steady-state periods of voltage sags caused by network faults and transformer energizing. Secondly, new methods are proposed using five different modifiers, applied in the transient period of voltage sags, i.e., half and one cycle time windows, to achieve a faster and more accurate response. These modifiers use the PB/IB criteria obtained from the existing methods, such as power, impedance, and current, and are applied as: The mean of the criterion changes, the first largest peak (FLP) on the criterion changes, the mean of the zero-mean criteria during a sag, the FLP of the zero-mean criteria during a sag, and the Trend (slope) of criteria's trajectories versus time. Voltage sag source detection methods are evaluated by applying 1992 simulated voltage sag events in a Brazilian regional power network. The results reveal that the proposed modifiers, used in the new methods, improve the ineffective existing methods by taking half/one cycle within a transient period of voltage sags. The modifiers also show an accuracy equal to other existing enhanced methods due to employing them within the transient period, thus evidencing their appropriateness. Correspondingly, a selection is made amongst the new modified methods in order to choose the most accurate time window (half or one cycle) for the methods. The selected modified methods are also tested by applying field measurements in a Slovenian power network to confirm their effectiveness in the transient short periods. According to a recommendation of the fastest and most accurate new methods in this study, an important application can be using the recommended methods as the directional function in the relays, along with an accurate voltage sag/fault inception time detection algorithm in real-time.

Analysis of the sensitivity of electronic active energy meters to voltage sags✰

This article presents a study on the sensitivity of electronic active energy meters when dealing with voltage sags, through the consideration of three distinct approaches. Initially, the analysis is performed for the two main power supply topologies found on the various commercially available energy meters. Following this, laboratory tests are performed that aim at obtaining the sensitivity curves, as well as the respective reset times after the occurrence of voltage sag, for six different energy meters. Finally, the sensitivity curves obtained for each of the meters under consideration are compared with real recordings of voltage sag events, acquired from a particular low voltage distribution system. The results found show that the sensitivity of electronic active energy meters can be very significant in some cases, where in practical situations this results in almost 1 h/week of unmeasured active energy consumed by consumers.