Power System Transient Stability Assessment Research Articles

Opportunities and Challenges in AI Based Modern Power System

Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Integrated Data-Driven Power System Transient Stability Monitoring and Enhancement

While many promising data-driven power system transient stability assessment (TSA) studies have been recently reported, very few of them further propose efficient data-driven solutions for follow-up control actions, e.g., generator tripping, against potential instability. To address this inadequacy, this work develops an integrated data-driven transient stability monitoring and enhancement (TSMAE) approach that can reliably and efficiently handle various emergency situations in real time. First, by introducing the emerging spatial-temporal synchronous graph convolutional network (STSGCN), wide-area spatial-temporal features w.r.t. system stability are sufficiently learned to reliably implement online TSA. Then, to handle impending instability in a tractable manner, remedial actions are quickly taken based on intelligent critical generator identification (CGI). Specifically, with the help of the STSGCN again, the potential effects of tripping individual generators on system stabilization are efficiently predicted from the spatial-temporal perspective. Based upon that, the most critical generators for tripping are adaptively selected to enhance system stability. Numerical test results on a realistic provincial power grid of China illustrate the efficacy of the proposed TSMAE approach.

A Novel Data-Driven LSTM-SAF Model for Power Systems Transient Stability Assessment

A Novel Data-Driven LSTM-SAF Model for Power Systems Transient Stability Assessment

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.

Approaching the Transient Stability Boundary of a Power System: Theory and Applications

Estimating the stability boundary is a fundamental and challenging problem in transient stability studies. It is known that a proper level set of a Lyapunov function or an energy function can provide an inner approximation of the stability boundary, and the estimation can be expanded by trajectory reversing methods. In this paper, we streamline the theoretical foundation of the expansion methodology, and generalize it by relaxing the request that the initial guess should be a subset of the stability region. We investigate topological characteristics of the expanded boundary, showing how an initial guess can approach the exact stability boundary locally or globally. We apply the theory to power system transient stability assessment, and propose expansion algorithms to improve the well-known Potential Energy Boundary Surface (PEBS) and Boundary of stability region based Controlling Unstable equilibrium point (BCU) methods. Case studies on the IEEE 39-bus system well verify our results and demonstrate that estimations of the stability boundary and the critical clearing time can be significantly improved with modest computational cost. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of determining whether a power system can retain transient stability subject to large disturbances such as short-circuit fault and generator tripping. The most well-known approaches to such a problem are the direct methods that can avoid time-costly simulations and “directly” determine the transient stability by estimating the stability boundary. Nevertheless, such estimations are generally too conservative. This paper proposes a new expansion method to improve the direct methods, which allows the initial estimation to lie partially outside the real boundary and hence enables applications to both global and local direct methods. Our method can expand the initial estimation towards the real stability boundary, thus reducing the conservativeness and providing a more accurate stability assessment such as the critical clearing time (CCT) of a fault. In this paper, we first mathematically establish the expansion theory and then propose numerical algorithms for power system applications. Simulations on the IEEE 39-bus benchmark verify our approach and show that the conservativeness can be significantly reduced with modest computational cost. Our approach can be further extended to scenarios where a valid Lyapunov (energy) function is unavailable.

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.

An Automated and Interpretable Machine Learning Scheme for Power System Transient Stability Assessment

Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are often not explainable. This paper handles both the automation and interpretability issues of ML-based TSA. An automated machine learning (AutoML) scheme is proposed which consists of auto-feature selection, CatBoost, Bayesian optimization, and performance evaluation. CatBoost, as a new ensemble ML method, is implemented to achieve fast, scalable, and high performance for online TSA. To enable faster deployment and reduce the heavy dependence on human expertise, auto-feature selection and Bayesian optimization, respectively, are introduced to automatically determine the best input features and optimal hyperparameters. Furthermore, to help operators understand the prediction of stable/unstable TSA, an interpretability analysis based on the Shapley additive explanation (SHAP), is embedded into both offline and online phases of the AutoML framework. Test results on IEEE 39-bus system, IEEE 118-bus system, and a practical large-scale power system, demonstrate that the proposed approach achieves more accurate and certain appropriate trust solutions while saving a substantial amount of time in comparison to other methods.

Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology

This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic stability assessment is achieved. The selected research topic relates to the issue of changing the structure and parameters of modern power systems. The key features of modern power systems include the following: decreased total inertia caused by integration of renewable sources energy, stricter requirements for emergency control accuracy, highly digitized operation and control of power systems, and high volumes of data that describe power system operation. Arranging emergency control in these new conditions is one of the prominent problems in modern power systems. In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. Transient stability of a power system was analyzed as the base function. Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. In the case of the test sample, accuracy of instability classification for XGBoost was 91.5%, while that for Random Forest was 81.6%. The accuracy of algorithms increased by 10.9% and 1.5%, respectively, when the topology of the power system was taken into account.

Power System Transient Stability Assessment Model Construction Using Improved SVM

Power System Transient Stability Assessment Model Construction Using Improved SVM

Combination with Continual Learning Update Scheme for Power System Transient Stability Assessment.

In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cramér Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving.

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.

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.

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.

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.

Networked Time Series Shapelet Learning for Power System Transient Stability Assessment

While many machine learning approaches have been widely applied to power system online dynamic stability assessment, how to sufficiently learn spatial-temporal correlations from system transients without losing the interpretability is still a challenging issue. In this paper, a novel networked time series shapelet learning approach is proposed to learn spatial-temporal correlations for transient stability assessment (TSA) in an interpretable manner. Specifically, a network impedance-based adjacency matrix is first introduced to characterize spatial networked correlations. Based on graph structural regularization, this matrix is effectively incorporated into the subsequent learning procedure as spatial constraints. Taking time series trajectories acquired from multiple buses as the inputs, networked shapelet learning is heuristically performed to learn critical sequential features, i.e., networked shapelets, for TSA model derivation. With the learning procedure strategically guided by inherent spatial-temporal correlations of the system, the obtained data-driven TSA model is able to perform highly reliable and interpretable online TSA. Numerical test results on the IEEE 39-bus test system and the realistic GD Power Grid in China verify the superior performances of the proposed approach.