광역정전 예방을 위한 분산형 부하 제어 방안에 대한 연구

Accurate real-time assessment of power systems voltage stability has been an active area of research in the past few decades. In the past decade, after the development of phasor measurement units (PMU), a lot of discussions has been going on phasor measurement techniques for real-time voltage stability. The fundamental idea behind these methods is to find the Thevenin equivalents of the system, and then determine the voltage stability margin based on the equivalent circuits. Some approaches also include the use of Artificial Neural Networks (ANN), for online monitoring of voltage stability margins. These methods are really fast as compared to the other methods. It has been shown that if we can obtain the phase angles and voltage magnitude in real-time from the phasor measurement units (PMU), then the voltage stability margins can be obtained in real-time and we can initiate voltage stability control methods. We are going to discuss Thevenin's equivalent methods and Artificial Intelligence methods in detail in this paper. We will also introduce the traditional methods which were earlier used for power systems stability assessment such as Time Domain methods, Static Methods, and Sensitivity methods. We are going to finally compare these methods and try to give general guidance on choosing a power stability method.

A reactive power planning method based on improved particle swarm optimization (IPSO) is presented. This method can improve the quality of initial particles and eliminate the boundary oscillation in traditional particle swarm optimization. The voltage stability margin index (VSI) is used to rank the load buses. Then reactive power compensators are installed on the buses with weak voltage stability. The maximization of voltage stability margin is included in the objective function of reactive power planning to improve system voltage stability. Its application in IEEE 30‐bus system illustrates that the real power loss is depressed with voltage stability margins increased simultaneously. Copyright © 2009 John Wiley & Sons, Ltd.

This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes. Due to the confidentiality of real PMU data and no public access to the real power systems infrastructure information, the lack of credible realistic data becomes a growing concern. Instead of constructing synthetic power grids and then producing synthetic PMU measurement data by time simulations, we propose a model-free approach to directly generate synthetic PMU data. we train the generative adversarial network (GAN) with real PMU data, which can be used to generate synthetic PMU data capturing the system dynamic behaviors. To validate the sequential generation by GAN to mimic PMU data, we theoretically analyze GAN's capacity of learning system dynamics. Further by evaluating the synthetic PMU data by a proposed quantitative method, we verify GAN's potential to synthesize realistic samples and meanwhile realize that GAN model in this paper still has room to improve. Moreover it is the first time that such generative model is applied to synthesize PMU data.

The development of Phasor Measurement Units (PMUs) in power system improves the possibilities of analyzing and monitoring the system dynamics. PMU data with its high sampling rate contains crucial information about the state of power system. Hence effective PMU data analysis can provide solution for most of the power system problems. This work proposes a data analysis method on PMU data. This combines the simplicity of passive islanding detection techniques and the robustness of artificial intelligence methods. PMU measurements are analyzed and classified to detect power system islanding. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) algorithms are used to classify islanding and non-islanding events. Both the models classified the events correctly with good accuracy. The test results also show a better performance for SVM classifier compared to ANN classifier.

In this paper, hybrid Artificial Neural Network and Genetic Algorithm (ANN-GA) approach for online monitoring of long-term voltage instability has been proposed. Thegenetic algorithm (GA) has been used to improve the accuracy of ANN by tuning its meta-parameters such as number of nodes in hidden layer, input and output activation function and learning rate. The proposed approach uses the voltage magnitude and phase angle obtained from phasor measurement units (PMUs) as the input vectors and the outputs is the Voltage Stability Margin Index (VSMI)vector. The effectiveness of the proposed approach is testedon New England 39-bus test system. The results of the proposed ANN-GA approach for voltage stability monitoring is compared with ANN model on same data set.

Synchronized phasor measurement unit (PMU) data contain rich information about power systems, hence the quality of estimates is of utmost concern. The existing methodologies for estimation rely on certain assumptions regarding the error in the measurement data. This paper revisits a key assumption specifically the Gaussian character of the error. A quantification of the PMU error yields its nature and statistical properties including its dependence on various sections of the PMU instrumentation channel (supposedly the major source of error in the PMU data). The non Gaussian nature of the error is asserted using various null hypotheses tests and a novel Gaussian mixture model based clustering technique is proposed to characterize and relate the errors present in PMU measurement data to the saturation in current transformer, cable length and the PMU burden. The proposed approach is tested using both real and synthetic PMU datasets. The ultimate goal of the paper is towards creating a PMU error emulator for testing and research of data analytic algorithms focused on crucial WAMS based applications.

Power system blackouts have become a serious problem for electric utilities especially in recent years. Different forms of system instability have emerged in recent blackouts, such as voltage instability and frequency instability. To counteract each form of system instability, special algorithms have been designed in the protection system, e.g. Under Frequency Load Shedding (UFLS) and Under Voltage Load Shedding (UVLS) schemes. One of the major weaknesses of these algorithms is that combination of different forms of instability is not considered in their design, while any one form of instability may not occur in its pure form. This is particularly true in highly stressed systems and for cascading events. This paper presents an adaptive algorithm to combine UVLS and UFLS schemes. The purpose of this method is to enhance the flexibility of under frequency relays and increase the security of power system during large disturbances by improving system voltage stability margins. In the proposed algorithm, loads with greater voltage decay are shed sooner. In this way, locations of load shedding become dependent to the location of disturbance and voltage stability margins of the system are enhanced. Indeed, load shedding is performed faster for severe events accompanying large voltage or frequency declines. Using this load shedding method, faster reactions could be obtained for major system failures. This way, system blackouts could be better controlled. Performance of the proposed method is compared with the conventional UFLS method. In this paper, dynamic model of the Khorasan HV network of Iran national grid is used for simulation. This network includes 75 high voltage 400 kV and 132 kV buses with about 2700 MW of generation. Performance of the schemes has been evaluated using dynamic simulations as well as the P-V curves, and reactive power margins for a number of events. Simulation results show that conventional UFLS algorithm will not necessarily result in acceptable voltage stability margins for the system following a disturbance. Meanwhile, the proposed algorithm improves voltage stability margins. The proposed algorithm is suitable for saving system stability after occurrence of major system disturbances.

This study proposes a deep learning-based fault detection method for intelligent distribution networks using Phasor Measurement Unit (PMU) data. With the increasing development of intelligent distribution systems, the need for fast and accurate fault detection systems is crucial to improve the reliability and resilience of the power grid. Utilizing PMU data, which provides real-time information on voltage, current, and frequency, enables more precise and rapid fault detection. In this study, we developed a deep learning model that uses Long Short-Term Memory (LSTM) to sequentially process PMU data and detect faults such as short circuits, phase faults, and line outages. The model was trained on a PMU dataset covering a wide range of normal and fault conditions in the distribution network. Evaluation results show that the proposed model is capable of detecting faults with >98% accuracy and has a faster detection time compared to traditional detection methods. This approach also demonstrates the ability to identify fault types with a high degree of reliability and reduces the risk of system failure due to detection delays. By using deep learning methods, this study contributes to improving the reliability of intelligent distribution systems and provides a basis for the application of PMU technology in more efficient and automated distribution network monitoring and maintenance.

Introduction of Synchronized phasor measurement unit (PMU) in power system is a solution for monitoring system wide disturbances. PMUs provide precise information about a system’s phasors such as magnitude and phase angle of sine waves of the system. PMU data is a valuable source when determining the post-mortem of a fault, or a system disturbance. It can be unveiled what time the system began to falter and exactly what instabilities the system experienced. Knowing the cause of such instabilities will allow enhancement of the current system protection system. This will help in minimizing risk, disruptions or total system collapse. PMUs are synchronized via global positioning system (GPS). For wide area monitoring (WAM), synchronization of the PMUs allows for interconnected systems to be monitored simultaneously, giving real time records. This paper explores the impact of PMU in modern power system, deployment strategies of PMU network around Texas Tech University, importance of PMU data collected from the network, correlation of events by data analysis and focus on how the time stamped information is valuable for grid stability. The ongoing smart architecture of grid will increase dependency and importance on PMU based network in future.

With the continued deployment of synchronized phasor measurement units (PMUs), high sample rate data are rapidly increasing the real time observability of power systems. Prior research has shown that the statistics of these data can provide useful information regarding network stability, but it is not yet known how this statistical information can be actionably used to improve power system stability. To address this issue, this paper presents a method that gauges and improves the voltage stability of a system using the statistics present in PMU data streams. Leveraging an analytical solver to determine a range of “critical” bus voltage variances, the presented methods monitor raw statistical data in an observable load pocket to determine when control actions are needed to mitigate the risk of voltage collapse. A simple reactive power controller is then implemented, which acts dynamically to maintain an acceptable voltage stability margin within the system. Time domain simulations on 3-bus and 39-bus test cases demonstrate that the resulting statistical controller can outperform more conventional feedback control systems by maintaining voltage stability margins while loads simultaneously increase and fluctuate.

Continuous assessment of voltage stability (VS) is an important aspect to safeguard electrical power system (EPS) operation. The conservative methods for online assessment in terms of stability check for voltage are extremely time consuming and also absurd for supervising any application online. In agreement with this, a model which is an amalgamation of the Salp swarm algorithm (SSA) and support vector machine (SVM) is aimed at supervising the VS in the paper. The method anticipated for the aforesaid problem utilises the magnitude of voltage and its corresponding phase angle which are attained from the PMU as the inputs to the ML model and the respective output are considered to be the voltage stability margin index (VSMI). The proficiency of the anticipated model (SSA – SVM) is verified by means of various test cases under the real-time scenario and compared with the same data set to attest its pre-eminence.

The traditional load shedding schemes––––under frequency load shedding (UFLS) and under voltage load shedding (UVLS), are designed independently and they constitute the last line of defense against frequency and voltage instability. However, the traditional load shedding schemes are not capable of dealing with combined instabilities. Moreover, a new Adaptive load shedding under load shedding method was proposed based on wide area measurement information just because the traditional load shedding schemes was not optimization algorithm. The main purpose of the method goal is to set up an emergency load shedding strategy, considering the recovery system of frequency stability at the same time, improved the voltage stability. This process has two steps, first of all, when considering the system disturbance amplitude and classifying the degree of interference according to system disturbance, Furthermore, the voltage sensitivity analysis was considered to determine the shedding location and corresponding shedding value. Finally, this algorithm was applied in IEEE 10-generator 39-bus test system. The implementation emergency of traditional and new shedding load two methods Under the system by different levels of power disturbances, comparing and analysand the feasibility and effectiveness of the new project. The results showed that the new method avoiding shedding over, more shedding part of the reactive power load, and then improving the system voltage, also avoiding damage to the voltage stability in the process of the system restore.

In this paper, a new disturbance detection method is proposed based on principal component analysis (PCA). The PCA-based method exploits the low-rank characteristics of synchrophasor data reported by multiple phasor measurement units (PMUs). The principal component (PC) scores of the low-rank data sets are used to propose a disturbance indicator. Moreover, an analytical disturbance detection threshold is designed that helps classify different events based on their severity. Finally, a PCA-based data compression technique is developed to reduce the archival requirements while preserving critical PMU data. Numerical simulations confirm that the proposed disturbance detection method efficiently detects a wide range of disturbances and is robust against limited low-quality and false PMU data.

A PMU data recovering method based on preferred selection strategy

This paper studies the fundamental dimensionality of synchrophasor data, and proposes an online application for early event detection using the reduced dimensionality. First, the dimensionality of the phasor measurement unit (PMU) data under both normal and abnormal conditions is analyzed. This suggests an extremely low underlying dimensionality despite the large number of the raw measurements. An early event detection algorithm based on the change of core subspaces of the PMU data at the occurrence of an event is proposed. Theoretical justification for the algorithm is provided using linear dynamical system theory. Numerical simulations using both synthetic and realistic PMU data are conducted to validate the proposed algorithm.