Causes Of Voltage Sag Research Articles

Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing

In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for voltage sag disturbance in distribution network based on TCN-Attention neural network and Flink flow calculation, which has important practical significance for controlling voltage sag and reducing economic losses. This method uses Temporal Convolutional Network (TCN) to extract the cross time nonlinear characteristics of voltage sag time series data, which effectively solves the problems of long-term dependence on time series and low training output efficiency of existing time series models. In order to further improve the recognition accuracy of the model, Attention mechanism is introduced to mine the duration relationship in voltage sag data. At the same time, the method also constructs a parallel real-time monitoring platform based on Flink streaming computing framework, embeds the TCN-Attention voltage sag cause identification model generated by training, so as to realize real-time identification and monitoring analysis of voltage sag disturbances at each monitoring point of the distribution network. In this paper, various voltage sags are simulated on IEEE 14 bus system using PSCAD software, and the proposed method is verified and tested. The deep learning fusion model has high recognition accuracy for the cause of voltage sag, and the flow computing platform has excellent performance in time delay and throughput indicators, and can realize the parallel real-time monitoring and analysis of voltage sag causes in distribution network.

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.

Implementation of Voltage Sag Relative Location and Fault Type Identification Algorithm Using Real-Time Distribution System Data

One of the common power quality (PQ) problems in transmission and distribution systems is the voltage sag that affects the sensitive loads. Losses and problems caused by the voltage sag in the power system can be reduced by correctly determining the relative location of the voltage sag. This paper proposes a novel algorithm to classify voltage sag relative location and fault type, which is the main root cause of voltage sag, based on the actual voltage and current data before and during the voltage sag. The performance of the algorithm is investigated by performing a numerical simulation utilizing MATLAB/Simulink. Moreover, the proposed algorithm is integrated into the power quality monitoring system (PQMS) of the real distribution system and tested. The results show that the performance of the proposed method is satisfactory.

Power Quality Improvement by using Dynamic Voltage Restorer

The integration of renewable energy sources, smart grid systems and extensive usage of power electronic devices, micro-controlled based device, variable speed drives etc. causes a number of power quality issues including electrical harmonics, voltage sag, voltage swell and imbalance creates an impact on the efficiency of electrical equipment. the energy maintaining poor power quality will affect consumers and their utility equipment’s. Power quality means quality of the normal voltage supplied to your facility. Voltage provided should be as close as possible to nominal voltage. The waveform must be pure sine wave free from any harmonics and other disturbances. The growing use of microprocessors and electronic equipment has made us to focus on power quality. Equipment and machinery can be damaged or even fail when subjected to power anomalies. Some of the power quality issues are voltage sag, swell, harmonics, flicker, interruption etc. Among these issues voltage sag is an important issue. This paper investigates about the causes of voltage sag, consequences and its mitigation. Many mitigation devices are available and the most economic effective solution is Dynamic Voltage Restorer (DVR). Simulation has been carried out to evaluate performance. A DVR is simulated in MATLAB/SIMULINK using synchronous reference frame theory.

Research on a Location Method for Complex Voltage Sag Sources Based on Random Matrix Theory

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.

Analysis and Investigation on Causes of Voltage Sag Based on A Novel Apriori Algorithm

As more and more power users are increasingly demanding the quality of electricity, the losses caused by voltage sags are becoming more and more serious. Therefore, it is very important to analyze the cause of the voltage sag to prevent the voltage sag in time. This paper proposes a new algorithm that combines Apriori correlation analysis algorithm and cluster analysis algorithm to analyze the causes of voltage sags. Because some typical climatic conditions also have an important influence on the cause of voltage dips, The data is initially processed using climate factors as clustering indicators, and then the correlation analysis between typical electrical characteristics and voltage sags is performed, and strong association rules are finally obtained. According to the calculation and analysis of examples, some factors with high correlation with the causes of voltage sag are found, which will provide theoretical support for the prevention of voltage sag and provide ideas for further research on the causes of voltage sag.

D-STATCON model for voltage sag mitigation

<span>Development of a FACTS device model for mitigating voltage sag is presented. It deals with a static synchronous condenser (STATCON) to be used in the AC distribution networks. Simulation with the help of a commercial software Matlab/Simulink was explored to investigate its characteristics and performance during the voltage sag occurrence. The considered main cause of voltage sag was the starting of high capacity asynchronous motor. A voltage source inverter based on the phase-controlled thyristor components was implemented in this application to compensate the change in reactive power of the system. The concerns have been put on the influence of initial operation point and DC capacitance. The study results show the capability of D-STATCON model to reduce the consequence of voltage sag following the starting of high-power asynchronous motor in the distribution network. A fast response of the condenser work confirmed the desired performance.</span>

Fractionally delayed Legendre wavelet transform based detection and optimal features based classification of voltage sag causes

Detection of underlying causes of voltage sag is more crucial than detection of voltage sag from the mitigation point of view. Therefore, this paper proposes the detection and classification of the eight causes of voltage sag. This approach augments the conventional technique of wavelet transform by designing the fractionally delayed Legendre wavelet as the signal processing tool. The unique event characteristics are sought from approximation, and detailed signal coefficients which are obtained by using 4-level multiresolution analysis. To achieve a higher classification rate, the best optimal features are rummaged around the extracted features by employing the ant lion optimization algorithm. This algorithm retains only the suitable features as per the fitness criterion based on the classification accuracy and number of features. The representative feature set is classified using the classifier ensemble employing plural majority voting. The classifier ensemble has three members which are support vector machine, probabilistic neural network, and multilayer neural network. Better performance of the classifier ensemble is observed as compared to individual members in classification of voltage sag causes. The efficacy of the proposed methodology has been validated in the presence of noise also. This approach has successfully classified the causes of voltage sag using optimal features with good accuracy in the case of both noiseless signals and noisy signals.

A New Approach to Determine the Direction and Cause of Voltage Sag

Event source locating is very important to improving the power quality level. This paper presents a method to determine the relative location of the voltage sag source according to the cause. For this, the concept of the relative location of the source is defined first. Then, the main causes of voltage sag are classified and their characteristics are discussed. From these investigations, the rules to determine the relative location of event source are proposed for each type and the overall algorithm to identify the relative location and the kind of event source is presented. Finally, the proposed method is applied to the IEEE 13-bus test system and it is verified that the method can help to pinpoint the accurate location of the event source.

A METHOD TO DETERMINE THE RELATIVE LOCATION OF VOLTAGE SAG SOURCE FOR POWER QUALITY DIAGNOSIS

Event source locating is very important to improve the power quality level. And it is a helpful way to find the location of the power quality event source to determine the direction of event at each monitor. This paper presents a method to determine the relative location of the source of voltage sag. For this, the relative location of the source is defined first. Then, the main causes of voltage sag are classified and their characteristics are discussed, respectively. From these investigations, the rules to determine the relative location of event source for each cause are proposed and the overall algorithm to identify the relative location and the kind of event source is organized. Finally, the proposed method is applied to the IEEE 13bus test system and it is verified that the method can be helpful to pinpoint the accurate location of the event source.