Stability Assessment Of Power Systems Research Articles

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.

A Modular Multi-Technology Generator Model for EMT Stability Studies in Power Systems with Large Amounts of Converter-based Generation

This paper explores the integration of converter- based generation (CBG) into power systems, analyzing their impact on power system stability. For this purpose, a simulation tool consisting of comprehensive, aggregated models integrating several generation technologies into a power system for dynamic simulation has been developed to represent three prominent generation technologies: synchronous generation (SG), grid- following converters (GFL), and grid-forming converters (GFM). The proposed modular multi-technology generator model allows to set the penetration level of each technology at any bus of the system, thus facilitating stability assessment of power systems with large amounts of CBG. Electromagnetic-type (EMT) studies are carried out in a small test system to analyse the impact of the GFL/GFM generation ratio on power system stability. The results show the capability of the proposed tool for testing the impact of the generation mix in the stability of the system, highlighting the importance of GFM generation to operate in with low amounts or in absence of synchronous generators in the system.

Voltage stability assessment and contingency ranking in power systems based on modern stability assessment index

Electric loads steadily increase each year due to economic and population expansion, placing electric systems close to their stability limits. Also, the electrical network is a sophisticated system with various control, protection devices, and power electronics. Any malfunction in these components can disrupt system reliability. Therefore, ensuring safe and dependable power system operation requires continuous and precise voltage stability monitoring to prevent instability and cascading outages. Despite the existence of numerous stability indexes, their limitations often make them ineffective under varying operating conditions. In response to these challenges, this article proposes a modern stability assessment index (MSAI) designed specifically to assess voltage stability in different operating scenarios. The MSAI aims to accurately assess voltage stability and stress levels in power lines, enabling operators to proactively manage system reliability. One key advantage of the MSAI is its ability to determine the maximum allowable load and loading margin, which is crucial for maintaining stable operation under changing load conditions. In order to validate the effectiveness of the proposed index, extensive evaluations are conducted using IEEE 30, 57, and 118 bus test systems. The study meticulously compares the performance of the MSAI with other prominent stability indicators, highlighting its superiority in providing predictive insights into areas prone to stress and potential contingencies. By empowering operators with preemptive correction capabilities, the MSAI reduces the risk of successive outages and enhances overall system flexibility. The voltage stability assessment and contingency ranking were evaluated using the proposed MSAI via a smooth method in the MATLAB environment.

Intelligent Power System Stability Assessment and Dominant Instability Mode Identification Using Integrated Active Deep Learning.

Simulation analysis is critical for identifying possible hazards and ensuring secure operation of power systems. In practice, large-disturbance rotor angle stability and voltage stability are two frequently intertwined stability problems. Accurately identifying the dominant instability mode (DIM) between them is important for directing power system emergency control action formulation. However, DIM identification has always relied on human expertise. This article proposes an intelligent DIM identification framework that can discriminate among stable status, rotor angle instability, and voltage instability based on active deep learning (ADL). To reduce human expert efforts required to label the DIM dataset when building DL models, a two-stage batch-mode integrated ADL query strategy (preselection and clustering) is designed for the framework. It samples only the most helpful samples to label in each iteration and considers both information contents and diversity in them to improve query efficiency, significantly reducing the required number of labeled samples. Case studies conducted on a benchmark power system (China Electric Power Research Institute (CEPRI) 36-bus system) and a practical large-area power system (Northeast China Power System) reveal that the proposed approach outperforms conventional methods in terms of accuracy, label efficiency, scalability, and adaptability to operational variability.

L-Index-Based Technique for Voltage Collapse Prediction and Voltage Stability Enhancement in Electrical Power Systems

Recent years have witnessed a notable increase in the occurrence of blackouts, especially in developing nations, attributed to the continuously growing demand on modern power networks. Given that the demand shows no signs of abating and is projected to increase further in the coming years, additional research on power system stability is imperative. This study, therefore, investigates voltage stability assessment in power systems using the L-index methodology, focusing on the Nigerian 28-bus system and the IEEE system. The L-index offers a practical means of identifying weak buses and evaluating voltage stability margins. Calculating L-index values for load buses under diverse conditions identifies critical points, with higher values indicating vulnerability. The research investigates injecting reactive power at load buses to prevent collapse, comparing outcomes with and without compensation. Analyzing the L-index's performance across varied loading scenarios confirms its precision in predicting breakdown points and identifying critical buses. Load flow analysis of the Nigerian 28-Bus system reveals that only bus 16 exceeds voltage limits, while line analysis shows total power losses. Increasing loadability exposes bus 16 as the weakest, supported by its low voltage magnitude. The research confirms bus 16 as the system's weakest point, guiding corrective measures to enhance stability and prevent collapse. Utilizing Matlab for implementation, this study contributes valuable insights into system vulnerability and provides a framework for improving voltage stability in power systems.

Research on Power Angle Characteristics of One-Machine Infinite-Bus Power Systems of Mixed Gauss and Poisson Stochastic Excitation

Background: A high percentage of renewable energy and a high percentage of power electronic devices are connected to the power system, which leads to the diversification and complexity of stochastic excitation, and the traditional single-excitation stochastic model is no longer applicable. Objective: The study aimed to solve the problem that the high proportion of renewable energy and the high proportion of power electronic equipment are connected to the power system, which leads to the diversification and complexity of stochastic excitation and makes the traditional stochastic model of single excitation no longer applicable. Methods: Firstly, stochastic differential equations for power systems have been modelled with mixed Gaussian white noise and Poisson white noise excitation. Secondly, the Milstein-Euler predictor-corrector method has been developed to solve the stochastic differential equation model of the power system. Finally, the influence of Gauss white noise and Poisson white noise on the power system stability under different excitation intensities has been analyzed. The rationality and correctness of the model have been verified by the simulation of a one-machine infinitebus (OMIB) system. Results: The stochastic differential equation model of a power system with Gauss white noise and Poisson white noise excitation has been established and its angle stability has been analyzed. Increasing the Gaussian white noise and Poisson white noise excitation intensity can lead to an increase in the fluctuation of the power angle curve, as well as an increase in the standard deviation and expected value of the power angle mean curve, which may decrease the stability of the power system. Conclusion: This study provides a reference for stochastic power systems modeling and efficient simulation, and has important application value for power system stability assessment and safety evaluation as well as related patent applications.

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.

Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions.

With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To address this problem, this work develops a robust representation learning approach to enhance DSA performance against multiple input data loss conditions in practice. Specifically, focusing on the short-term voltage stability (SVS) issue, an ensemble representation learning scheme (ERLS) is carefully designed to achieve data loss-tolerant online SVS assessment: 1) based on an efficient data masking technique, various missing data conditions are handled and augmented in a unified manner for lossy learning dataset preparation; 2) the emerging spatial-temporal graph convolutional network (STGCN) is leveraged to derive multiple diversified base learners with strong capability in SVS feature learning and representation; and 3) with massive SVS scenarios deeply grouped into a number of clusters, these STGCN-enabled base learners are distinctly assembled for each cluster via multilinear regression (MLR) to realize ensemble SVS assessment. Such a divide-and-conquer ensemble strategy results in highly robust SVS assessment performance when faced with various severe data loss conditions. Numerical tests on the benchmark Nordic test system illustrate the efficacy of the proposed approach.

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 Extended Continuation Power Flow Method for Static Voltage Stability Assessment of Renewable Power Generation-Penetrated Power Systems

The continuation power flow method (CPF) has been extensively used for obtaining the static voltage stability limit of power systems, which provides important guidance for the stable operation. However, the conventional CPF cannot account for the dynamic characteristics of renewable power generation, thus resulting in prominent errors for renewable power generation-penetrated power systems. In this paper, we investigate the static voltage stability assessment (SVSA) of power systems with high penetration of rgb0,0,0renewable power generation (RPG). An extended Jacobian matrix is firstly established by considering the dynamic characteristics of synchronous generators (SG) and renewable power generation in constant AC voltage control mode (CVRPG). Based on the extended Jacobian matrix, an extended CPF method is proposed for SVSA of the power system and a reactive power sensitivity index is proposed to indicate the voltage support capability of nodes. Simulation results show that the proposed extended CPF method has higher accuracy than conventional CPF in assessing the static voltage stability of power systems with high penetration of RPG. It is also revealed that that the voltage support capability of each node rises as the proportion coefficient is increases.

Stability assessment of forestry plant power system based on improved long short-term memory network

The stability assessment of forestry power system plays a vital role in many aspects. First of all, the stability of the power system is directly related to the production efficiency, employee safety and environmental sustainability of the factory. If the power system is unstable, it may cause power outage or power fluctuations, which will interrupt the production and turn the production line, which will lead to a reduction in production capacity and the increase in production costs. In addition, the stability of the power system is directly related to the security of employees. Sudden power failure or voltage fluctuations may cause improper equipment operation, and may even cause accidents and damage. Therefore, by evaluating the stability of the power system, potential security risks can be reduced. In addition, unstable power systems may cause waste and increase environmental burden. In order to improve the stability of the power system, this article proposes an improved LSTM (long and short memory) model of improved stability prediction. This model uses the power system to operate data, including current, voltage, frequency, and other parameters, and external factors related to weather conditions and load changes to predict the stability of the power system. Compared with traditional methods, this method performs well in predicting the failure and abnormalities of the power system, and has the ability to evaluate high accuracy and stability. Therefore, the improved LSTM model method can be regarded as a powerful tool for power system management and maintenance.

Analysis of interlocking faults and reliability evaluation methods for communication networks of power information physical systems

The information physics system realizes the integration and interaction between them by the 3C technique. The application of scheduling automation, Substation Automation and Online Fault Recovery Technology Improves Emergency Response Capability, and Information Security issues are also embedded in the energy system. Power grid operation is facing more threats, and state space and failure scenarios are also more abundant, which poses new research needs and challenges for power system stability assessment. First, the physical interaction of information is analyzed. From the perspective of function, the CPPS hierarchy corresponds to the physical Electric power system, the information sub-station system and the main station system respectively. Then, taking the information node as the fault detection sample point, the chain fault model of the power information physical system is studied, and the abnormal response of the power information system is classified and identified, Finally, a failure response model and a failure recovery model are built. Finally, a simulation example is given for verification.

ANN based smart sensing of short-term voltage stability assessment of modern power system

This study addresses the critical challenge of Short-term Voltage Stability (STVS) in the contemporary electric grid system, characterized by diverse static loads, induction motors, electronic loads, electric vehicle charging stations, and renewable energy sources. High-power sensing elements are identified for power system stability and transient measurements, and the existing system is explored for fault measurement and stability maintenance. The analysis employs an IEEE 14-bus dynamic model with two distinct load configurations. In the first configuration, the static load at bus 14 is replaced with a composite load, encompassing static loads, induction motors, electronic loads, and EV charging stations. For the second novel configuration at bus 14, the static load is substituted with a composite load and a solar photovoltaic (PV) generator operating in reactive power compensation mode. This addition of the PV generator compensates for elevated reactive power demand in the 14-bus system. The study conducts voltage stability analysis in two phases, evaluating the voltage profile and transmission line load ability in both scenarios during the first phase. In the second phase, STVS is simulated by briefly opening the circuit breaker in bus 2. The results reveal notable improvements in the voltage profile of all buses, a 6 % enhancement in power factor at bus 14 in the dynamic model with the novel configuration, and a 3 % increase in the loading factor of the IEEE Bus 14 system. The dynamic model incorporating a PV generator and composite load in bus 14 demonstrates superior STVS performance compared to the setup with only a composite load. The abstract concludes by highlighting the development of real-time voltage profile assessment using Artificial Neural Network (ANN) topology, leveraging results from the Continuous Power Flow (CPF) analysis of the novel model with a PV generator and composite load in the far end bus. This restructuring ensures a logical and clear organization, encompassing an introduction to the problem, a description of methods employed, presentation of results, and a succinct conclusion summarizing the key findings.

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.

Influence of the Load Models in the Dynamic Voltage Stability of an Electric Power System

Voltage stability plays a very important role during the planning and design stages of an electric power network as well as during the system operation. In the last years in various countries worldwide, several power network collapses (blackouts) caused by voltage problems have been reported. This can be produced by a lack of sufficient reactive power reserve during heavy load or by the occurrence of severe contingencies. In this paper it is studied and analysed the influence of the load models in the dynamic voltage stability assessment of an electric power system. It was used the BPA test power network. A severe contingency situation was simulated to perform the study. The automatic voltage regulators of the generating units and the turbine speed governors were modelled as well as the transformer taps. The simulation results were obtained using the commercial transient software package EUROSTAG. Finally some conclusions that provide a better understanding of the voltage collapse phenomena are pointed out.

Investigating the Performance of MLE and CNN for Transient Stability Assessment in Power Systems

Investigating the Performance of MLE and CNN for Transient Stability Assessment in Power Systems

Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks

Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks

Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment

Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few events from where power system dynamics can be learnt. To mitigate this imbalance, we propose a novel data augmentation strategy preserving the dynamic characteristics to be learnt. The augmentation is performed using Variational Mode Decomposition. The detrended and the augmented data are tested for distributions similarity using Kernel Maximum Mean Discrepancy test. In addition, the effectiveness of the augmentation methodology is validated via training an Encoder DNN utilizing original data, testing using the augmented data, and evaluating the Encoder’s performance employing several metrics.

Simultaneous Assessment of Multiple Aspects of Stability of Power Systems With Renewable Generation

Modern power systems are operating much closer to stability limits due to the integration of uncertain Renewable Energy Sources (RES)-based generations and the decommissioning of traditional synchronous generations. Hence, different aspects of power system stability assessment have come into research focus again. Conventional system stability assessment typically uses dedicated stability indices describing a single aspect of system stability (e.g., voltage, frequency, angularity) or composite indices developed for one aspect of system stability. Such assessments can be ambiguous as the impacts of new devices and technologies on different aspects of system stability can be both, positive and negative to different extents. This paper introduces a probabilistic framework and two composite stability indices for the simultaneous assessment of multiple aspects of power system stability. The application of the indices is demonstrated on a range of case studies with modified IEEE 9-bus and modified IEEE 68-bus test systems without/with RES in a mixed Matlab and DigSilent PowerFactory simulation environment.