Power System Transient Stability Research Articles

Evaluating the significance of samples in deep learning-based transient stability assessment

Deep learning-based transient stability assessment has achieved big success in power system analyses. However, it is still unclear how much of the data is superfluous and which samples are important for training. In this work, we introduce the latest technique from the artificial intelligence community to evaluate the significance of the samples used in deep learning model for the transient stability assessment. From empirical experiments, it is found that nearly 50% of the low-significance samples can be pruned without affecting the testing performance at the early training stages, thus saving much computational time and effort. We also observe that the samples with the fault-clearing time close to the critical clearing time often have higher significance indexes, indicating that the decision boundary learned by the deep network is highly related to the transient stability boundary. This is intuitive, but to the best of our knowledge, this work is the first to analyze the connection from sample significance aspects. In addition, we combine the stability scores with the significance index to provide an auxiliary criterion for the degree of stability, indicating the distance between a sample and the stability boundary. The ultimate goal of the study is to create a tool to generate and evaluate some benchmark datasets for the power system transient stability assessment analysis, so that various algorithms can be tested in a unified and standard platform like computer vision or natural language-processing fields.

Critical Clearing Time and Angle for Power Systems Postfault Stability Assessment

Transient stability analysis is a very important tool to deal with many behaviors of electrical power systems during and after being subjected to various disturbances. this paper propose a method for electrical power systems transient stability assessment using phase plane trajectories. A methodology for computing the critical stability conditions of generators is proposed. The critical conditions such as critical clearing time (CCT) and critical clearing angle (CCA) were obtained. The computation of CCA and CCT is curried out step by step using the characteristics of the faulted and postfault trajectories from given initial conditions until their intersection point. The angle and time values founded represent, by definition, the critical conditions of the system. The proposed algorithm can be used for complex models since it is based on solving systems of differential equations by iterative methods in the phase plane. The advantage provided by this method is it's accurate and small time consuming. To demonstrate the effectiveness of the proposed method, first, critical conditions calculation procedures are given, then the process used in judging power system stability is provided, finally, simulation results for various test cases of a single machine infinite bus (SMIB) system highlight the proposed methodology.

Transient stability assessment with time-adaptive method based on spatial distribution

For power system transient stability assessment (TSA), the differences of characteristics between critical situations are not obvious in the early post-fault stage, so high accuracy can’t be achieved. With the development of fault, the differences are more obvious, and the evaluation accuracy is improved. Towards the trade-off between accuracy and timeliness, a spatial distribution-based time-adaptive (SDTA) method is proposed. Firstly, several long short-term memory (LSTM) classifiers of different cycles construct a time-adaptive framework. Then, the overlapping region where misclassifications are likely to occur is selected according to the spatial distribution of training samples. The TSA results of samples in the non-overlapping region are directly output, and the reliability of samples in the overlapping region is evaluated. Finally, the distance ratio-based trust score (DRTS) is proposed as a reliability index. The reliability of overlapping samples is evaluated by employing the changing trend of DRTS. If the classifications at the current cycle are assessed as reliable, the TSA results are output. Otherwise, the system will wait for the features of the next cycle for prediction. The proposed method is tested in the IEEE-39 bus system and a realistic system. The case studies demonstrate that the scheme meets the requirements of TSA.

Composite Index for Comprehensive Assessment of Power System Transient Stability

This paper proposes a Principal Component Analysis (PCA)-based method to construct composite indices to rapidly assess power system transient stability. By assigning appropriate weights to a few selected sub-indicators, a composite index can be constructed to guarantee a better coverage of worst-case stability scenarios of the power system. This paper provides an automatic method to construct a composite index with four sub-indicators calculated by the integral of acceleration, speed deviation and rotor angle of generators over a certain integration period using PCA. The effectiveness of the sub-indicators is validated first, followed by a comprehensive example of process of composite index construction, and the assessment of the influence of the effect of integration period on the performance of the composite index. High accuracy of classification has been demonstrated using composite index with appropriate settings of the integration periods of the sub-indicators.

Survey transient stability of power system with penetration of wind power generation

Generation of electricity from wind power a renewable energy source, is continually attracting the attention of investors, researchers and electrical utilities. It has been predicted that the annual growth of wind power between 1998 and 2040 would be between 20% and 30%. This shows the increase in impact of the wind power generator to power system and the importance of understanding the behavior power wind generator following fault conditions that may develop at any point on the hosted network and consequently may affect the stability, the security as well as the quality of power system. Hence, the searching Stability of power system with connection of power wind generator is continually attracting the attention of researchers in the world. This paper displays the Surveying Stability of power system with connection of power wind generator when occurred the fault short circuit on the power system. The investigation is illustrated with network included 12 bus.

Transient Stability Assessment Based on Gated Graph Neural Network With Imbalanced Data in Internet of Energy

Transient stability assessment (TSA) plays an important role to ensure the safe operation of the power system in Internet of Energy (IoE). Many time-domain simulation (TDS)-based and transient energy function (TEF)-based methods have been proposed to assess the transient stability of the power system. With the wide area measurement system (WAMS) and the phasor measure units (PMUs) applied to observe the real-time data, methods of TSA based on the machine learning and data-driven are continuously studied. These kinds of methods can only assess the transient stability of the power system when subjected to large disturbances. However, these kinds of methods cannot infer the type of event which leads to the collapse of the power system. In this article, the gated graph neural network (GGNN) is applied to assess the power system transient stability and infer the type of event leading the instability of the power system. First, conditional generative adversarial network (CGAN) is applied to generate unstable samples making the training data more balanced. With the balanced data graph-structured and used to train the GGNN-based TSA model, the GGNN-based TSA model achieves better performances. Finally, the real-time data is input into the trained TSA model and the transient stability of the power system is assessed. When the power system is considered unstable, the proposed TSA model can also infer the type of event leading the instability of the power system, classifying the unstable state to the corresponding event. Simulations performed on the New England 39-bus system verify the effectiveness of the proposed method.

Transient Stability of Hybrid Power Systems Dominated by Different Types of Grid-Forming Devices

This paper investigates the transient stability of power systems co-dominated by different types of grid-forming (GFM) devices. Synchronous generators (SGs and VSGs) and droop-controlled inverters are typical GFM devices in modern power systems. SGs/VSGs are able to provide inertia while droop-controlled inverters are generally inertialess. The transient stability of power systems dominated by homogeneous GFM devices has been extensively studied. Regarding the hybrid system jointly dominated by heterogeneous GFM devices, the transient stability is rarely reported. This paper aims to fill this gap. It is found that the synchronization behavior of the hybrid system can be described by a second-order motion equation, resembling the swing equation of SGs. Moreover, two significant differences from conventional power systems are discovered. The first is that the droop control dramatically enhances the damping effect, greatly affecting the transient stability region. The second is that the frequency state variable exhibits a jump at the moment of fault disturbances, thus impacting the post-fault initial-state location and stability assessment. The underlying mechanism behind the two new characteristics is clarified and the impact on the transient stability performance is analyzed and verified. The findings provide new insights into transient stability of power systems hosting heterogeneous devices.

A new method for operating point free power system transient stability situational awareness based on single machine equivalent approach

Transient stability assessment is an essential element for situational awareness and decision making process of a power system operator in maintaining the secure operation. Continuous efforts have been made for devising methods to assess the transient stability. However, the existing methods have dependency on pre-fault operating condition. This article aims to report a new method, towards deriving contingency wise operating point free transient stability assessment. The method is based on single machine equivalent (SIME) approach and has two phases (a) off line tuning (b) online application. In offline tuning phase each plausible contingency is characterized by an operating point free parameter called mean critical return angle over the specified operating region. In online application phase, transient stability for a given plausible contingency is predicted using SIME approach. Two new severity indices namely stable severity index and unstable severity index are proposed and evaluated for assessing the degree of stability and instability. Further, these indices are used in contingency screening. The proposed method is tested on 3-machine 9-bus system and, 10-machine 39-bus system. Results shows that the proposed method is simple and can aid the existing methods in providing reliable transient stability situational awareness to the system operator.

Semi-Supervised Ensemble Learning Framework for Accelerating Power System Transient Stability Knowledge Base Generation

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration.

A Bibliometric Analysis of Power System Planning Research During 1971–2020

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research.

A decentralized emergency control scheme for power System transient stability enhancement based on model predictive control

This paper proposes an emergency control scheme based on decentralized model predictive control (MPC) to prevent transient instabilities occurring in power systems. After an onset of transient instability due to a contingency, the control scheme acts on the intercept valves of the steam turbines and the excitation systems of the generators that are available for stabilizing the system. Using the proposed method, the system’s operational constraints as well as the turbine actuator dynamics are properly taken into account, and an objective function towards stabilization is minimized at every sampling time to generate an optimal control law. The method encompasses a decentralized control strategy allowing a straightforward implementation that does not require a communication system, nor does it suffer from its inherent delays, where each participating generator unit is equipped with an MPC controller that relies on only local measurements. Following a critical contingency, the controllers force the generators to remain in synchronism and bring the system back to a stable operating condition. The performance and efficacy of the proposed method have been evaluated and demonstrated by simulations performed on two test systems, the WSCC 9-bus and the IEEE 68-bus systems. It has been shown that the controllers, which are designed independently from the contingencies and operating conditions, are effective in restoring the system’s stability and are robust even when the system is subjected to structural changes due to integration of power electronics interfaced non-synchronous generator units.

Stability analysis and Simulation of power system based on Matlab

Abstract Abstact: The stability analysis of power system is an important process to measure the security and stability level of the power system and the construction and development plan. The stability of the power system is divided into static stability, transient stability and dynamic stability. The stability of the system is judged by different analysis methods for different forms and sizes of interference. The construction of large power grid is developing rapidly, and the powerful computing function of Matlab can adapt to the more and more huge power system stability calculation tasks. As a Matlab sub module, Simulink can properly assist the stability calculation of power system with its powerful and stable computing function and simple visual modular graphics. In this paper, the concept of power system stability is first proposed, the static stability and transient stability of power system are briefly introduced. Secondly, the static / transient stability of power system is simulated with Simulink, and the measures to improve the static / transient stability of power system are proposed.

Dynamic assessment of the capabilities of SIPC and study its effect on the transient stability of power system

Dynamic assessment of the capabilities of SIPC and study its effect on the transient stability of power system

Intelligent analysis method for transient stability of power distribution system

According to the principle of transient stability of power distribution system, this paper establishes the evaluation model of system transient stability, and studies a new intelligent analysis method of transient stability of power distribution system. Improve the process of power system transient stability analysis through wavelet transform. The experimental results show that the intelligent analysis method of transient stability of power distribution system based on wavelet transform can effectively shorten the analysis time and improve the accuracy.

Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach

Power system control and transient stability analysis play essential roles in secure system operation. Control of power systems typically involves highly nonlinear and complex dynamics. Most of the existing works address such problems with additional assumptions in system dynamics, leading to a requirement for a complete and general solution. This paper, therefore, proposes a novel control framework for various power system control and stability problems leveraging a learning-based approach. The proposed framework includes a two-module structure that iteratively and jointly learns the candidate Lyapunov function and control law via deep neural networks in a learning module. Meanwhile, it guides the learning procedure towards valid results satisfying Lyapunov conditions in a falsification module. The introduced termination criteria ensure provable system stability. This control framework is verified through several studies handling different types of power system control problems. The results show that the proposed framework is generalizable and can simplify the control design for complex power systems with the stability guarantee and enlarged region of attraction.

A coordinated and steadily fault ride through strategy under short-circuit fault of the wind power grid connected system based on the grid-forming control

With the continuous improvement of the penetration level of wind power in the power system, the grid-forming mode of wind turbine will gradually become one of the main modes of system synchronization. The power electronic equipment based on grid-forming control has the ability of frequency dynamic regulation, reactive power voltage regulation, weak current network operation stability and island operation similar to the conventional synchronous generator, however, the problem of strong electromagnetic stress faced by power electronic equipment under grid-forming control will be the key factor restricting the wide application and promotion of grid-forming technology. In particular, the fault ride through problem of the wind turbine under the grid-forming control will face more severe challenges. This paper is based on a switched fault ride through strategy, that is, in case of power grid fault, it is quickly converted from grid-forming control under normal working conditions to the conventional current vector control, so as to facilitate fault current limiting and realize fast dynamic reactive power support. In this paper, a fault ride through optimization control strategy of grid-forming wind turbine is proposed. During the fault, the threshold setting of entering/ exiting the fault ride through control is optimized to form hysteresis control, so as to improve the switching misoperation of control strategy caused by the large fluctuation of grid voltage in the process of fault. Finally, simulation results show that the earlier switching back to the grid-forming control is not the more conducive to the stability of the system, and there is an optimal switching time. The results verify the proposed strategy can effectively improve the transient stability of power system with grid-forming mode of the wind turbine.

Analysis of the Effect of Changes in RLC Load on Transient Stability of Electric Power Systems

Today, in an effort to fulfill human electricity needs, a power system is needed to transmit electrical energy from generators to consumers. The occurrence of disturbances in the network can result in changes in system response for a certain time. Therefore, transient stability analysis is needed to determine the response that occurs in the network. Transient Stability is the ability of a power system to maintain synchronization after experiencing a sudden major disturbance. In this study, the researcher wanted to analyze the effect of the type of RLC load on the transient stability of a power system. Power flow and transient response of the grid network is expressed using ETAP software. The result, at the beginning generator is operated at stable condition, when the new load is connected to power system, generator output is changed depend the load types and there is oscillation in the generator output.

Artificial Intelligence Techniques for Power System Transient Stability Assessment

The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.

A comparative analysis of artificial neural network and support vector machine for online transient stability prediction considering uncertainties

ABSTRACT Power system transient stability is an integral part of power system planning and operation. Conventional approaches to assess transient stability are time consuming and hence, are not suitable for online application. Thus, this paper presents a comparative analysis of two different machine learning (ML) algorithms, i.e. artificial neural network (ANN) and support vector machine (SVM), for online transient stability prediction, considering various uncertainties (load, network topology, fault type, fault location, and fault clearing time). Time-domain simulations were conducted using DIgSILENT PowerFactory software for obtaining the training data for ML algorithms. MATLAB was used to apply the ML algorithms (ANN and SVM), and to draw a comparison between them. The classification accuracy of ANN and SVM was found to be 98.4% and 94.4% (fixed topology), respectively. The classification accuracy of ANN and SVM was found to be 97.7% and 93.2% (topology change), respectively. The training time of ANN was less than that of SVM (for both topology cases). These results for the IEEE 14-bus system demonstrated that both ANN and SVM can rapidly estimate the transient stability, considering uncertainties, with a reasonable accuracy; however, ANN outperformed SVM as its classification performance and computational performance was determined to be superior.

Tripping Time Delays Detection in Ethernet-Based Digital Relays and Their Influence on Transient Stability of Power Systems

Tripping Time Delays Detection in Ethernet-Based Digital Relays and Their Influence on Transient Stability of Power Systems