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

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 mechanism of voltage sags due to three main voltage sag sources, such as line faults, startup of large induction motor and transformer energization, was analyzed theoretically. Based on the typical structure of the distribution network of a large industrial enterprise, the paper presented a simplified analytical model for voltage sag sources and conducts a test with the above three kinds of voltage sag sources in Matlab/Simulink. The results show the proposed model is correct and suitable for the analysis of distribution network.

Voltage sag according to the IEEE standard is a momentary decrease (10% - 90%) in the root mean square (rms) voltage magnitude where the duration is longer than half a cycle and less that one minute. The common causes of voltage sag are faults or short circuit in the system, starting of large loads and faulty wiring. Dynamic voltage restorer (DVR) is a custom power device that is used to compensate voltage sag. The DVR generally consists of voltage source converter (VSC), injection transformers, filters and energy storage (battery). The control strategy and the implementation of the control strategy using DSP board TMS320F2812 will be briefly discussed in the paper. Simulation results of the control strategy for voltage sag and swell will be presented. This paper will focus on the design and the commissioning of a 3 kVa DVR lab prototype. Experimental results of a working DVR lab prototype will be presented.

In the current energy landscape, power quality (PQ) emerges as a critical concern. Even when there is no fault on a line, PQ issues are common in all power networks since 90% of power systems’ loads are variable or inductive in nature. Variable loads cannot be avoided; hence, PQ concerns such as voltage swelling and sag will always arise. Voltage sag is one of the main issues within a distribution network, resulting in financial losses for the utility company and the customer. The Dynamic Voltage Restorer (DVR) effectively addresses voltage sags and minimizes total harmonic distortion (THD) in the distribution network. This paper proposed a novel control strategy to increase the PQ in a system. A Frilled Lizard Optimization-optimized fuzzy PI controller is proposed in this work to control the inverter. This proposed method improves the DVR’s ability to correct voltage sag and reduce total harmonic distortion as soon as possible. The PI control scheme is utilized initially to reduce the oscillations and remove the steady-state error. To increase the tendency rate of the error to zero, the PI method is applied to a fuzzy logic-based compensatory stage. The proposed approach is validated using pro-type models, as well as mathematical and Simulink modelling. In the Results Section, the performance of the proposed controllers with the DVR is tabulated and compared with other DVR controller schemes described in other research papers.

Underground cables are being increasingly constructed in modern power distribution networks, however overhead lines are still present, making the system more exposed to faults. Faults from medium voltage networks propagate throughout the network and then experienced by low-voltage customers as voltage sags. Voltage sags have become increasingly important when considering the various power quality issues that cause inconvenience to customers. For some customers voltage sags cause, especially high costs. In this paper, different fault types at the medium voltage level and the sag propagated throughout the network are analyzed. Asymmetrical faults can cause problems for protection devices since they propagate differently throughout the distribution network, and distort the phase to ground voltages on the different voltage levels. Experimental results obtained from the Bosnia and Herzegovina distribution system show the significance of appropriate voltage sag propagation understanding. The power distribution companies should understand the voltage sags experienced in their networks and develop strategies for decreasing the influence of voltage sags. Thus, voltage sags analysis should be an important part of power distribution planning and an important element in a comprehensive power system analysis.

The voltage sag disturbance stands out as the most evident waveform change that is detected in electric networks, since the presence of these events in the network causes damages to the consumers. The first step in diagnosing the problem is to identify the location in the distribution system that is connected to the source causing the sinking disorder. This work presents a methodology based on clustering algorithm combined with decision rule to point out the region (cluster) that aggregates the place of origin. Clustering algorithm is responsible for analyzing the voltage signal data from different measurement nodes and separating these data into clusters. Then the Partial Decision Trees (PART) algorithm is responsible for defining the decision rule set that will confront the characteristics of each cluster and define which group aggregates the disturbance source location. For the clustering task, the k-means and DBSCAN clustering algorithms are evaluated and compared. The methodology was evaluated using the IEEE 34-bus test feeder system and the results show a hit rate higher than 98%.

Voltage sag is one of the major power quality (PQ) problems, and has been the focus of PQ studies due to the impact on sensitive industrial loads and costs led by the damages and maintenance. Voltage sag source location is significant for the customers and suppliers to solve the issue between them, as well as for possible mitigation. Five main methods (the disturbance power and energy method, the slope of system trajectory method, the real current component method, the resistance sign–based method, and the distance relay method) are reviewed first. However, these methods used single criteria, and their effect is limited as the literature shows. This paper presents a pattern recognition way to locate the source of voltage sag. In the proposed method, features for pattern recognition are extracted first, based on these five methods. Then, the thought of source location by pattern classification is discussed with three steps in detail, and support vector machine (SVM) is applied in the case. The nonl...

Magnitude and duration are two main features used to characterize voltage sag. Many studies have been done in analyzing the factors that only impact the magnitude of the sag. As another feature of voltage sag, the duration is determined by the coordination of protection system. The fault caused voltage sag is usually assumed to be cleared by the primary protection system simply, the impact of protection coordination on sag duration is not considered by existing studies. In order to estimate voltage sag duration more precisely, this paper firstly uses the concept of Markov state space to establish the probabilistic modeling of protection coordination according to protection configuration, and then estimates sag frequency by utilizing the protection coordination probability and setting value of protection system. In addition, the proposed method was applied to the IEEE 57-bus test system, the simulation results show that the proposed method is simple and can give more accurate answers on voltage sag frequency corresponding to different duration.

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.

For the voltage sag caused by short-circuit fault, transformer switching and induction motor starting in distribution network, an optimized support vector machine method based on Gray Wolf algorithm is proposed for voltage sag identification. The empirical mode decomposition method is used to analyze the voltage sag signal, obtain an inherent mode function set (IMFs), and calculate the energy entropy of each order IMF as the eigenvector. In order to solve the problem that the traditional support vector machine is easy to fall into local optimization in the process of optimization, a method of optimizing the penalty factor and kernel function parameters of support vector machine (SVM) through gray wolf algorithm (GWO) is proposed, a GWO-SVM classifier is constructed, and then the extracted feature vector is input into the GWO-SVM classifier to train and recognize the samples, So as to realize the automatic classification and identification of different types of voltage sag sources. The simulation results show the effectiveness of the extracted feature vector and GWO-SVM classifier. Compared with other five traditional methods, it is verified that it has fast speed and high precision.

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.

This paper presents a methodology for risk analysis applied to voltage sags in distribution networks. The basis of the methodology is presented, as well as the description of its implementation. Two evaluations are performed for a given distribution network: frequency of occurrence of voltage sags and the impact of the voltage sags on customer loads in statistical basis. Fault events are simulated using the Monte Carlo method, in which variables related to faults are determined according to their probability distributions. The magnitude and the duration of voltage sags and occurrence of interruptions are determined in the network points by considering the fault conditions and the line overcurrent protection devices characteristics. The results presented are indices of sag events and equipment breakdown for any desired voltage tolerance curves, which allows mapping the network in respect to risk, associated to sag events. The emphasis of the studies lies upon the practical questions that appear when industries already installed or planning installation are faced with decisions which are influenced by the rate of occurrence of voltage sags.

The advent of distributed generation (DG) units has posed many problems to the planning and operation of distribution networks (DS). The selection of the “best” site for the DG installation is a major problem in this context. Different strategies and methods have been developed in the recent years which deal with this problem. However, Complexity of solutions and inadequate manipulation have hindered their utilization by DS planners. This paper presents a robust and practical tool which helps the distribution network planner in getting the best possible location of DG unit taking into account both reliability of supply and voltage sag problems. The method suggested uses knowledge-base expert system (ES) to help in selecting the appropriate locations. The method suggested is based on economic and technical considerations. It investigates with economic loss due to both voltage sag and forced outage of DS components. It concerns with the effect of location of the added DG on the overall network performance during both steady-state when a forced outage occurring on one or more component of the DS and transients after sudden short-circuit. Using ES avoids mathematical complications of solving complex analytical model. By judicious rule-base ES, selection of optimum locations becomes clear and simple. The suggested method is explained and implemented through practical distribution network and its effectiveness is tested and discussed.

Voltage sag in a power system is an unavoidable power quality issue, and it is also an urgent concern of sensitive industrial users. To ensure the power quality demand and economical operation of the power system, voltage sag management has always drawn great attention from researchers around the world. The latest research that realizes the power quality conditioning has used dynamic voltage restorers (DVRs), static VAR compensator (SVCs), adaptive neuro-fuzzy inference systems (ANFISs), and fuzzy logic controllers based on DVR to mitigate voltage sag. These devices, methods, and control strategies that have been recently used for voltage sag mitigation have some limitations, including high cost, increased complexity, and lower performance. This article proposes a novel, efficient, reliable, and cost-effective voltage sag mitigation scheme based on a modular multilevel converter (MMC) that ensures effective power delivery at nominal power under transient voltage conditions. The proposed method, the MMC, compensates for the energy loss caused by voltage sags using its internal energy storage of the submodules, and ensures reliable power delivery to the load distribution system. Furthermore, control strategies are developed for the MMC to control DC voltage, AC voltage, active power, and circulating current. Detailed system mathematical models of controllers are developed in the dual synchronous reference frame (DSRF). Validation of the results of back-to-back MMC for dynamic load distribution system is analyzed which proves the effectiveness of the proposed scheme for voltage sag mitigation.

This paper proposes a fault location estimation method for distribution networks considering errors from measurement devices. The method determines the fault location based on the voltage sag and current magnitudes. The fault location is based on faulted section and fault distance measured from one end of the bus. Faulted section is determines by matching the measured voltage and current with a pre-developed data. For multiple faulted sections, ranking analysis is employed to rank the most likely faulted section. Fault distance is calculated from one end of the identified faulted section. By determining the faulted sections, a fault in homogenous and non-homogenous networks can be located. The method has been tested using an actual Malaysian 11 kV distribution network. Different values of measurement errors have been applied, which includes positive and negative values. The test results showed the effectiveness of the method under various uncertainty of error from measurement devices. The proposed method can serve as an alternative technique for estimating fault location in distribution networks.