Method For Transient Stability Assessment Research Articles

Assessment of Power System Stability During Transients Using Deep Residual Shrinkage Network and CBAM Integration

Aiming at the noise that may exist in the data collection and transmission of power system synchronous phasor measurement unit (PMU), as well as the imbalance between stable and unstable samples, making the model likely to tilt to most kinds of samples, thus affecting the evaluation results of power system transient stability model, a transient stability assessment method integrating Convolutional Block Attention Module (CBAM) and deep residual shrinkage network is proposed. First, the mapping relationship between the input values and the labels of the stability results is established, and the electrical quantities are transformed into the form of convolutional feature maps to be input into the model. Second, channel attention module and spatial attention module are introduced, and soft threshold function is used to automatically learn the noise threshold and reduce the interference of the noise. As the model tends to favor the majority of the samples during training, gradient harmonized mechanism (GHM) loss function is introduced to improve the attention of the minority of samples as well as the training effect and evaluation performance of the model. Finally, the model is tested by simulation on the IEEE39 node system.

An Improved Neural Lyapunov Method for Transient Stability Assessment of Networked Microgrids

An Improved Neural Lyapunov Method for Transient Stability Assessment of Networked Microgrids

A multi-task transient stability assessment method adapted to topology changes using multi-graph sample and aggregate-attention network

Transient stability assessment (TSA) plays a pivotal role in guiding power grid risk control strategies. However, it faces challenges when dealing with complex multi-graph inputs generated by pre-fault, fault occurrence, and post-fault states. Meanwhile, most previous research studies neglected the assessment of the transient stability level. To address this, we propose a multi-task transient stability assessment (MTTSA) approach. In MTTSA, we introduce a multi-graph sample and aggregate-attention network (GraphSAGE-A) designed to capture stability features even amidst topology changes. A multi-head attention mechanism and local normalization method are adopted for a better extraction of the global and contextual information. Additionally, we introduce a quantified transient stability risk index considering the transient stability boundary and incorporate a multi-task dense structure to enhance MTTSA’s performance. Empirical tests, under changing operating conditions, conducted on the IEEE 39-bus system showcase a significant performance improvement with the proposed MTTSA method.

Transient Stability Assessment of Power Systems Based on the Transformer and Neighborhood Rough Set

Modern power systems are large in size and complex in features; the data collected by Phasor Measurement Units (PMUs) are often noisy and contaminated; and the machine learning models that have been applied to the transient stability assessment (TSA) of power systems are not sufficiently capable of capturing long-distance dependencies. All these issues make it difficult for data mining-based power system TSA methods to have sufficient accuracy, timeliness, and robustness. To solve this problem, this paper proposes a power system TSA model based on the transformer and neighborhood rough set. The model first uses the neighborhood rough set to deal with the redundant features of the power system trend data and then uses the transformer model to train the TSA model, in which various normalization methods such as Batch Normalization and Layer Normalization are introduced in the process to obtain better evaluation performance and speed up the convergence rate of the model. Finally, the model is evaluated by two evaluation indicators, F1−measure and accuracy, with values of 99.61% for accuracy and 0.9972 for F1−measure, as soon as the tests on noise contamination and missing data test results on the IEEE39 system show that the NRS-Transformer model proposed in this paper is superior in terms of prediction accuracy, training speed, and robustness.

Transfer Learning for Transient Stability Predictions in Modern Power Systems Under Enduring Topological Changes

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the IEEE 39-bus test system, the IEEE 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art alternatives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distributed energy resources and tackle their intermittence, sometimes event-driven due to disruptive events (e.g., faults), and sometimes operator-triggered for maintenance activities and responsive control to return the system back to its normal operating condition. This article is motivated by the need for an efficient and computationally-attractive approach for online situational awareness and real-time transient stability monitoring and assessment of the power grid under an enduring topological change, where the main goal is to identify the power system operating states (SOSs) and critical generators (CGs) in case of instability. Instead of training a new model for each topological change, this paper proposes an adaptive power system transient stability assessment (TSA) method that uses transfer learning (TL) which in return reduces the training time yet with less data than a newly-trained model for each topology change scenario.

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Neural Networks Based Lyapunov Functions for Transient Stability Analysis and Assessment of Power Systems

This article presents the Lyapunov functions based on the artificial intelligence (AI) method for transient stability assessment and the determination of stability region (SR). First, Lyapunov stability theory and the definition of SR are introduced. Then, the characteristics of neural networks as a general function approximator are employed as the Lyapunov function learner, and the Lyapunov function is constructed combined with stochastic gradient descent (SGD). Then, the falsifier's task is to find the state vectors that violate Lyapunov stability conditions, and the counterexamples would be added to the training set for the function learner to accelerate convergence. After obtaining the Lyapunov function of power system, the estimation of SR boundary can be represented by the maximum level set of Lyapunov function. Finally, the IEEE 9-bus 3-machine system is used as test system to demonstrate the validity and effectiveness of the proposed construction method of Lyapunov functions for power system transient stability analysis.

A data-driven method for online transient stability monitoring with vision-transformer networks

Online transient stability assessment (TSA) plays an important role in power system planning and operation. The massive integration of renewable energy sources into the grids increases the risks of relay malfunction; however, the existing TSA approaches only apply to scenarios where the fault could be successfully cleared, and are not applicable under relay failure scenarios. Besides, the post-fault TSA methods normally rely on the accurate fault clearance information, which would be affected by the clock synchronization errors in relays. Thus, this paper proposes an online monitoring system that assesses transient stability based on the current operating condition, which is independent of the accurate moments of fault occurrence and clearance provided by fault indicators or relays signals; the transient stability prediction during the fault duration provides the system operators with an early warning of system instability in the event of relay failures. Moreover, an adaptively adjusted criterion for instability is proposed to balance the false alarm and misclassification rates. Furthermore, novel Vision-Transformer-based models for both TSA and unstable generators identification are built in this paper. Compared with other networks, the self-attention structure in the Transformer leads to a global receptive field and higher resolution for capturing time-series information in each sample. Our experiments on the IEEE-39 system show that the proposed method can assess transient stability at each moment in different operation states. It outperforms existing machine learning-based models in terms of accuracy and robustness in TSA and unstable generators identification tasks.

An online power system transient stability assessment method based on graph neural network and central moment discrepancy

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems. Conventional offline time-domain simulation-based stability assessment methods may no longer be able to face changing operating conditions. In this work, a graph neural network-based online transient stability assessment framework is proposed, which can interactively work with conventional methods to provide assessment results. The proposed framework consists of a feature preprocessing module, multiple physics-informed neural networks, and an online updating scheme with transfer learning and central moment discrepancy. The t-distributed stochastic neighbor embedding is used to virtualize the effectiveness of the proposed framework. The IEEE 16-machine 68-bus system is used for case studies. The results show that the proposed method can achieve accurate online transient stability assessment under changing operating conditions of power systems.

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of today’s interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. We devise a quantum TSA (QTSA) method to enable efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth quantum circuit (HELD) is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD training and 3) A systematical analysis on QTSA’s performance under various quantum factors is performed. QTSA underpins a foundation of quantum machine learning-enabled power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

Transient voltage stability assessment of renewable energy grid based on residual SDE-Net

For the purpose of assessing the transient voltage stability of renewable energy grid more effectively and to measure the epistemic uncertainty of the evaluation results, a power system transient voltage stability assessment method based on residual stochastic differential equation network (SDE-Net) is proposed in this paper. Considering the transient stability characteristics is chronological, the proposed network takes the time series of the basic physical quantity measurement data of the power system as the input. The internal features of time series data are extracted by residual convolution block based stochastic differential equation network in this paper. Meanwhile, the proposed method introduces the focal loss function to eliminate the imbalance of the samples and improve the accuracy of transient stability assessment. In particular, the drift network for learning prediction of stability and the diffusion network for learning uncertainty measurement in residual SDE-Net are trained respectively. As a result, case study on modified IEEE 30- bus system demonstrate the residual SDE-Net’s superior performances on both transient voltage stability assessment and stability evaluation result confidence estimation.

Power system transient stability assessment based on the multiple paralleled convolutional neural network and gated recurrent unit

In order to accurately evaluate power system stability in a timely manner after faults, and further improve the feature extraction ability of the model, this paper presents an improved transient stability assessment (TSA) method of CNN + GRU. This comprises a convolutional neural network (CNN) and gated recurrent unit (GRU). CNN has the feature extraction capability for a micro short-term time sequence, while GRU can extract characteristics contained in a macro long-term time sequence. The two are integrated to comprehensively extract the high-order features that are contained in a transient process. To overcome the difficulty of sample misclassification, a multiple parallel (MP) CNN + GRU, with multiple CNN + GRU connected in parallel, is created. Additionally, an improved focal loss (FL) function which can implement self-adaptive adjustment according to the neural network training is introduced to guide model training. Finally, the proposed methods are verified on the IEEE 39 and 145-bus systems. The simulation results indicate that the proposed methods have better TSA performance than other existing methods.

An Interpretable Deep Learning Method for Power System Transient Stability Assessment via Tree Regularization

Deep learning (DL) techniques have shown promising performance for designing data-driven power system transient stability assessment (TSA) models. However, due to the deep structure of the DL, the resulting model is always a black-box and hard to explain, which hinders its practical adoption by the industry. This paper proposes an interpretable DL-based TSA model to balance the TSA accuracy and transparency. The proposed method combines the strong nonlinear modelling capability of a deep neural network and the interpretability of a decision tree (DT). Through regularizing DL-based model with the average DT path length in the training process, the proposed interpretable DL-based TSA method can visually explain the TSA decision-making process. Simulation results have shown that the proposed method can deliver highly accurate TSA results and interpretable TSA decision-making rules, which can be used for designing preventive control actions.

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.

Synchrophasor-Based Online Transient Stability Assessment Using Regression Models

An online post-fault transient stability assessment method is proposed in this study using synchrophasor or PMU measurements. Initially, a post-fault multimachine system is converted into a suitable one machine infinite bus (OMIB) system using the single machine equivalent (SIME) method. Thus, the - trajectory obtained through the OMIB system enabled a normalized transient stability index to be calculated offline. By using synchrophasor measurements before and during the fault as inputs, the regression model can be trained offline to predict the normalized stability margins. Following a fault, the synchrophasor measurements are used as input to this trained model for online stability margin prediction. If the predicted margin is negative, then the post-fault power system is indicated to be unstable. Alternatively, positive values for the predicted margin identify the system as stable. The proposed assessment method is verified using the New England (NE) 39 bus test system. The results obtained are then compared with offline simulations.

Improved Multi-point estimation method based probabilistic transient stability assessment for power system with wind power

With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels.

Time-Adaptive Transient Stability Assessment Based on the Gating Spatiotemporal Graph Neural Network and Gated Recurrent Unit

With the continuous expansion of the UHV AC/DC interconnection scale, online, high-precision, and fast transient stability assessment (TSA) is very important for the safe operation of power grids. In this study, a transient stability assessment method based on the gating spatiotemporal graph neural network (GSTGNN) is proposed. A time-adaptive method is used to improve the accuracy and speed of transient stability assessment. First, in order to reduce the impact of dynamic topology on TSA after fault removal, GSTGNN is used to extract and fuse the key features of topology and attribute information of adjacent nodes to learn the spatial data correlation and improve the evaluation accuracy. Then, the extracted features are input into the gated recurrent unit (GRU) to learn the correlation of data at each time. Fast and accurate evaluation results are output from the stability threshold. At the same time, in order to avoid the influence of the quality of training samples, an improved weighted cross entropy loss function with the K-nearest neighbor (KNN) idea is used to deal with the unbalanced training samples. Through the analysis of an example, it is proved from the data visualization that the TSA method can effectively improve the assessment accuracy and shorten the assessment time.

Real-time power system generator tripping control based on deep reinforcement learning

In case of faults or severe disturbances, the power system will enter an emergency operation state. After the system instability is detected, oscillation and blackout will occur in the system if effective control measures are not taken in time. Generator tripping control (GTC) is the most effective emergency control measure. In view of the mismatch between the traditional GTC algorithm and the transient stability assessment method based on machine learning, a new real-time GTC method is needed. In this paper, a three-part control framework is designed for the GTC problem. The control agent is endowed with decision-making ability by interacting with the simulation environment in the offline pre-learning part. Then the trained agent is transplanted to the online application which can help system operators make decisions. Meanwhile, the agent is updated with real data to be better adapted to the actual system in the online learning part. A deep reinforcement learning algorithm, deep deterministic policy gradient (DDPG) is employed to train the control agent in this framework. A modified DDPG algorithm and the corresponding reward function are designed for the GTC problem. Convolution neural network (CNN) is added to the DDPG network, by which the training time of the agent is shortened and the generalization ability of the algorithm is improved. Trained with simulation data and real system experience, the control agent can determine control strategies timely according to the system operating conditions. Simulation results on the IEEE-39 bus system and the realistic regional power system of Eastern China show the effectiveness, generalizability, and timeliness of the decision algorithm.

Deep learning-based transient stability assessment framework for large-scale modern power system

When severe disturbance occurs in power system, lack of efficacious information about transient stability state is a key challenge for power network operator. Especially for the system operating in boundary of security constraints due to economic reasons, it becomes more prominent. With the extensive deployment of phasor measurement units (PMUs), abundant historical data of power system have been collected and the data driven method based on machine learning model has become important tool to solve transient stability assessment (TSA) problem. However, the implementation of data-driven TSA method is difficult due to the characteristics of huge feature number and variable network topology of large-scale modern power system. In order to address this issue, a novel deep learning-based online TSA framework is proposed in this article. Firstly, parallel convolution algorithms are employed to address redundant input features. secondly, a novel machine learning model, deep forest is employed to train a TSA model. Some improvements are implemented for better assessment performance: 1) The internal feature transmission of deep forest is adjusted for higher assessment accuracy. 2) A fast update scheme is proposed based on active learning technique and graded strategy. 3) The cost-based method is employed to address class-imbalance training data. The test results on three power systems show that the proposed TSA framework has advantages in prediction accuracy, training speed, and update time. It is suitable for application of large-scale modern power system.

A hierarchical power system transient stability assessment method considering sample imbalance

In order to make full use of the dynamic information contained in the electrical quantity response trajectories, improve the reliability of critical sample prediction results, and correct the bias problem caused by sample imbalance to model prediction, a transient stability assessment (TSA) method based on bidirectional long short-term memory (BiLSTM) network is proposed. The method takes the dynamic trajectories of the underlying measurement data as the inputs, abstracts the features step by step from the multivariate time series through the multilayered neural network, and then establishes the nonlinear mapping relationship between the input feature and the stability category. In this paper, BiLSTM is improved by introducing truncation threshold and penalty coefficient into the loss function to give higher weights to hard samples and unstable samples, thus optimizing the gradient descent direction. Furthermore, this method enables sustainable hierarchical prediction and effectively reduces uncertain samples. The experimental results on the New England 39-bus system integrated with wind farm show that the proposed method significantly reduces the missing alarm rate of unstable samples and the false alarm rate of stable samples, and improves the credibility of the prediction results of critical samples.