Transient Stability Assessment Research Articles

GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-Temporal Disturbance Response Characteristics

GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-Temporal Disturbance Response Characteristics

Исследование влияния отклонений параметров моделей синхронных генераторов на результаты расчета динамической устойчивости электроэнергетической системы

One of the important tasks solved in planning the electric power system (EPS) operation is the assessment of its transient stability. Use of incorrect parameters for EPS element models, in particular synchronous generators (SG), contributes to the occurrence of errors when determining the operating conditions of power systems, and, therefore, can lead to a decrease in the reliability of EPS operation. This article is devoted to the study of the impact of errors in SG model parameters on the transient stability assessment results. The analysis presented in this article allows us to substantiate the relevance of the SG parameter estimation task using a “passive experiment”, such as synchronized phasor measurements. This research is based on the theory of electrical circuits, mathematical description of electrical machines and transients in EPS, as well as statistical analysis. Computational experiments have been carried out in the software package MATLAB using the simulation modeling environment “Simulink”. During the research, two different multi-machine EPS models have been used, including the well-known “IEEE 9-bus” system. To quantitatively assess the influence of SG parameter uncertainties on transient stability analysis, the values of the maximum permissible fault clearance time are determined, as well as the values of the maximum power output of generating equipment in prefault conditions. The authors have carried out a comprehensive analysis of the influence of deviations in SG model parameters on the maximum fault clearance time, as well as on the maximum power output of generating equipment in the prefault conditions for a given fault duration. During the study, two approaches have been considered; in each case, the EPS transient stability assessment has been performed almost automatically, and quantitative indicators have been computed for the influence of SG model parameter deviations on the outcome of EPS transient stability analysis. Analysis of the computational experiment results has demonstrated a significant influence of the SG model errors on the errors in calculating certain transient stability-related parameters. For the considered scenarios, the spread of errors in calculating the maximum fault clearance time reached 100 % relative to a base-case (errorless) result, and the error in calculating the maximum power output of generating equipment in prefault conditions for a given fault duration may reach 20 % relative to the errorless result.

An overview of machine learning applications in elastic power systems

Abstract. With the development of economy and the access of renewable energy, the power system is facing new challenges. Machine learning provides a new way for the intelligent management of power system through big data analysis, pattern recognition and predictive modeling. This paper reviews the application of machine learning technology in elastic power system, including load forecasting, transient stability assessment, intelligent scheduling and fault diagnosis. Machine learning will better improve the stability, reliability and economic benefit of the system. Future research will focus on algorithm optimization, model adaptability improvement and integration with traditional power system knowledge to promote the development of power system to a higher level of intelligence and automation.

Two-stage transient stability assessment using ensemble learning and cost sensitivity

Two-stage transient stability assessment using ensemble learning and cost sensitivity

Transient Stability Assessment in Power Systems: A Spatiotemporal Graph Convolutional Network Approach with Graph Simplification

Accurate and fast transient stability assessment (TSA) of power systems is crucial for safe operation. However, deep learning-based methods require long training and fail to simultaneously extract the spatiotemporal characteristics of the transient process in power systems, limiting their performance in prediction. This paper proposes a novel TSA method based on a spatiotemporal graph convolutional network with graph simplification. First, based on the topology and node information entropy of power grids, as well as the power flow of each node, the input characteristic matrix is compressed to accelerate evaluation. Then, a high-performance TSA model combining a graph convolutional network and a Gated Convolutional Network is constructed to extract the spatial features of the power grid and the temporal features of the transient process. This model establishes a mapping relationship between spatiotemporal features and their transient stability. Finally, the focal loss function has been improved to dynamically adjust the influence of samples with different levels of difficulty on model training, adaptively addressing the challenge of sample imbalance. This improvement reduces misclassification rates and enhances overall accuracy. Case studies on the IEEE 39-bus system demonstrate that the proposed method is rapid, reliable, and generalizable.

Transient voltage stability assessment and margin calculation based on disturbance signal energy feature learning

With the increasing variation of the network topology and the high complexity of the processing measurement data, the transient voltage stability assessment of the new power system is facing significant challenges in low accuracy and high time costs. To address the shortcomings of the existing method and apply it to online assessment, this paper proposes an assessment method based on feature learning for disturbance signal energy (DSE) from bus voltages. Firstly, the relationship between DSE and system transient voltage stability is established, and the calculation of DSE from bus voltage time series is detailed. Subsequently, a transient voltage stability assessment method based on the ID3 Decision Tree algorithm and DSE is proposed. Finally, by employing the Support Vector Machine (SVM) to construct the optimal boundary in the feature space formed by the key buses, the transient voltage stability margin (TVSM) for specific scenarios is proposed. Simulation results on the IEEE 39-bus system demonstrate that the proposed method can rapidly and accurately assess the transient voltage stability of the system and calculate the stability margin, providing intuitive and interpretable results with high engineering application value.

Research on Graph Multi-Attention Neural Network for Power System Transient Stability Assessment

Abstract Diagnosing the stability of power grids based on artificial intelligence technology is a research trend. The existing artificial intelligence stability analysis methods rely on a large number of fault case data, while the graph neural network method ignores the correlation characteristics of nodes themselves and the correlation of long-distance nodes. In order to solve the problems, the graph multi-attention neural network (GMANN) was proposed. The self-attention, which characterizes the correlation between different state quantities of a single node, is proposed, firstly. Then, long-distance association attention, which represents the correlation of distant nodes in the power grid, was proposed. The long-distance correlation attention and the adjacent correlation attention in graph attention network are fused to form a global attention that reflects the global importance of the power grid. The channel attention that characterizes the importance of different pooling methods of graph convolutional network is extracted and used to obtain the importance of different convolution operations. Finally, based on the multi-source attention fusion strategy, global attention and channel attention are embedded in the graph attention neural network to form a multi-level evaluation model to achieve power grid stability evaluation efficiently guided by multiple attentions. The proposed GMANN is verified based on simulated fault cases obtained from the 10-machine 39-node system in New England. The results show that the accuracy of the GMANN can reach 97.34%, which is better than other methods. And the missed judgment rate of important indicators is significantly better than other methods.

Design-Oriented Transient Stability Assessment for Droop-Controlled Converter Considering Grid Voltage Phase-Angle Jump

Design-Oriented Transient Stability Assessment for Droop-Controlled Converter Considering Grid Voltage Phase-Angle Jump

Contrastive-Active Transfer Learning-Based Real-Time Adaptive Assessment Method for Power System Transient Stability.

The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model's adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method.

Physics-informed neural networks for phase locked loop transient stability assessment

A significant increase in renewable energy production is necessary to achieve the UN’s net-zero emission targets for 2050. Using power-electronic controllers, such as Phase Locked Loops (PLLs), to keep grid-tied renewable resources in synchronism with the grid can cause fast transient behavior during grid faults leading to instability. However, assessing all the probable scenarios is impractical, so determining the stability boundary or region of attraction (ROA) is necessary. However, using EMT simulations or Reduced-order models (ROMs) to accurately determine the ROA is computationally expensive. Alternatively, Machine Learning (ML) models have been proposed as an efficient method to predict stability. However, traditional ML algorithms require large amounts of labeled data for training, which is computationally expensive. This paper proposes a Physics-Informed Neural Network (PINN) architecture that accurately predicts the nonlinear transient dynamics of a inverter with a PLL-based controller under fault with less labeled training data. The proposed PINN algorithm can be incorporated into conventional simulations, accelerating EMT simulations or ROMs by over 100 times. The PINN algorithm’s performance is compared against a ROM and an EMT simulation in PSCAD for the CIGRE benchmark model C4.49, demonstrating its ability to accurately approximate trajectories and ROAs of a inverter with a PLL-based controller under varying grid impedance.

A new transient phenomenon caused by active current dynamics of grid-following converters during severe grid faults

Existing transient stability analysis of grid-following (GFL) converters mainly focuses on the dynamics of the phase-locked loop (PLL), while current loop dynamics are usually neglected due to their faster response than PLL. However, this article reveals that active current may not be able to track its reference quickly during severe grid faults even with high current loop bandwidth, which leads to a non-negligible impact on the transient stability of GFL converters. Furthermore, this article discusses the intrinsic mechanism of why active current cannot track its reference quickly during severe grid faults and establishes a refined third-order transient synchronization model that offers a more accurate assessment of transient stability during severe grid faults than the conventional second-order model.

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

Maximum Lyapunov Exponent Based Nearest Neighbor Algorithm For Real-Time Transient Stability Assessment

In power systems, ensuring transient stability is paramount to prevent unforeseen blackouts and power failures. Transient stability assessment is crucial for the early detection and mitigation of instabilities, providing a rapid response to severe fault situations. The concept of the maximum Lyapunov exponent facilitates fast predictions for transient stability assessment after severe disturbances. This paper introduces an efficient maximum Lyapunov exponent algorithm for online transient stability assessment, representing the primary contribution of this work. This approach uses the time series data from the rotor angles of generators or the phase angles of generator terminal buses. Case studies are conducted on the Nordic Power System, with simulations performed in DigSilent PowerFactory. This study contributes by offering insights into the performance and adaptability of the proposed algorithm.

Transfer learning-based updating method of transient stability assessment model

An updating method for data-driven based transient stability assessment model of power systems is proposed in this paper. To cope with potential faults in the real-time operation of power systems, the parameters of models can be modified online through the proposed method. Firstly, according to the long-short term memory (LSTM) network, transient stability assessment models are initially trained offline by expected faults. Then, the feature distribution differences between expected faults and potential faults are evaluated by the time-sequence maximum mean discrepancy (TMMD). Compared with the traditional maximum mean discrepancy (MMD), the proposed TMMD approach takes the temporal properties of transient stability into account, which can reflect the relationship between fault samples and time series better. Finally, the parameters of models are adjusted by transfer learning to reduce the feature distribution differences, by which the updated models can be more suitable for the different but related potential faults. Therefore, through the proposed model updating method, the extensibility of evaluation models is greatly enhanced, which is more in line with the practical conditions of power systems. In this paper, the effectiveness of the proposed method has been demonstrated by the evaluation results in the IEEE 39-bus system and a realistic power system.

Transient Stability Assessment of Power Systems Based on CLV-GAN and I-ECOC

In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper. To address the issue of the small number of unstable samples in power systems, a sample generation model is constructed by combining a dual-encoder VAE with a GAN network. The model generates effective artificial samples to balance the sample ratio between categories by learning the latent distribution of aperiodic and oscillatory unstable samples from the distribution. The decomposition method based on an improved error-correcting output coding algorithm is applied to convert the multi-class problem into a decision fusion issue for binary models. This method improves the overall performance of the multi-class model, particularly significantly increasing the recognition accuracy of discrimination against oscillatory unstable samples and reducing the safety hazards in the operation of power systems. The simulation validation was conducted on the IEEE 39-bus and IEEE 140-bus systems to confirm the effectiveness of the proposed model.

Power System Transient Stability Preventive Control via Aptenodytes Forsteri Optimization with an Improved Transient Stability Assessment Model

Transient stability preventive control (TSPC), a method to efficiently withstand the severe contingencies in a power system, is mathematically a transient stability constrained optimal power flow (TSC-OPF) issue, attempting to maintain the economical and secure dispatch of a power system via generation rescheduling. The traditional TSC-OPF issue incorporated with differential-algebraic equations (DAE) is time consumption and difficult to solve. Therefore, this paper proposes a new TSPC method driven by a naturally inspired optimization algorithm integrated with transient stability assessment. To avoid solving complex DAE, the stacking ensemble multilayer perceptron (SEMLP) is used in this research as a transient stability assessment (TSA) model and integrated into the optimization algorithm to replace transient stability constraints. Therefore, less time is spent on challenging calculations. Simultaneously, sensitivity analysis (SA) based on this TSA model determines the adjustment direction of the controllable generators set. The results of this SA can be utilized as prior knowledge for subsequent optimization algorithms, thus further reducing the time consumption process. In addition, a naturally inspired algorithm, Aptenodytes Forsteri Optimization (AFO), is introduced to find the best operating point with a near-optimal operational cost while ensuring power system stability. The accuracy and effectiveness of the method are verified on the IEEE 39-bus system and the IEEE 300-bus system. After the implementation of the proposed TSPC method, both systems can ensure transient stability under a given contingency. The test experiment using AFO driven by SEMLP and SA on the IEEE 39-bus system is completed in about 35 s, which is one-tenth of the time required by the time domain simulation method.

Discriminative Signal Recognition for Transient Stability Assessment via Discrete Mutual Information Approximation and Eigen Decomposition of Laplacian Matrix

Discriminative Signal Recognition for Transient Stability Assessment via Discrete Mutual Information Approximation and Eigen Decomposition of Laplacian Matrix

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.

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.

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.