Stability Assessment Of Power Systems 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 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.

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

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.

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.

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.

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.

Model of quasi-Z-source inverter-based PV power systems for stability studies of multi-terminal AC grid-connected PV power systems

Photovoltaic (PV) power systems are becoming one of the most popular green power systems. Power converters are used in these systems. Among them, quasi-Z-source inverters (qZSIs) are currently on the rise. However, instabilities can appear in multi-terminal AC grid-connected power electronics, such as PV power systems, due to the interaction between power electronic controls and the grid. Modeling of qZSI-based PV power systems is lacking in the literature and stability assessment of grid-connected qZSI-based PV power systems is not yet well understood. Accordingly, the present paper contributes with an s-domain dq-real admittance transfer matrix model of qZSI-based PV power systems derived from their small-signal state-space averaged model. The paper also describes a systematic procedure to study multi-terminal AC grid-connected qZSI-based PV power systems by the nodal admittance method, where the proposed qZSI-based PV power system model is incorporated into the nodal admittance matrix to study stability concerns. Thus, AC grid dynamics in the presence of qZSI-based PV power systems can be investigated using impedance-based stability criteria. The proposed model and the procedure are applied in a stability study of two applications where solutions for improving stability issues are presented. In these applications, the two main causes of instabilities in AC-grid connected PV-qZ systems are analyzed in detail. The study is validated by PSCAD/EMTDC simulation. .

Robust Power System Stability Assessment Against Adversarial Machine Learning-Based Cyberattacks via Online Purification

The increasing complexity associated with renewable generation brings more challenges to power system stability assessment (SA). Data-driven approaches based on machine learning (ML) techniques for stability assessment have received significant research interest and shown their promising performance. However, ML-based models are recognized to be vulnerable to adversarial disturbances, where a slight perturbation to power system measurements could lead to unacceptable errors. To address this issue, this paper develops a novel lightweight mitigation strategy, i.e., robust online stability assessment (ROSA), to enhance the ML-based assessment model against both white-box and the black-box adversarial disturbances (i.e., purification) in the online implementation. The ROSA involves a supervised learning-based module for the primary stability assessment and a self-supervised learning-based module. The two modules are trained jointly with different objective (loss) functions and implemented in sequence. A suitable purification objective and various time-series data augmentation methods are designed for SA applications to tackle adversarial disturbances adaptively. Case studies are performed, and the comparative results have clearly illustrated the competitive, robust accuracy against various adversarial scenarios and verified the effectiveness of the proposed online purification strategy.

Bidirectional Active Transfer Learning for Adaptive Power System Stability Assessment and Dominant Instability Mode Identification

Deep learning (DL) is a useful tool for power system stability assessment (PSSA) and dominant instability mode (DIM) identification. However, when faced with operational variability, the performance of DL models degrades. This paper proposes a bidirectional active transfer learning (Bi-ATL) framework for more adaptive PSSA and DIM identification, where the DL model is easier to adapt to unlearned operating conditions with fewer newly labeled instances. At the instance level, forward active learning and backward active learning are integrated to progressively build a mixed instance set by actively including the most label-worthy instances of new operating conditions and actively eliminating the most useless original operating condition instances. Then at the model parameter level, the mixed instance set is utilized to fine-tune the original DL model to new operating conditions. The Bi-ATL framework synthesizes three-way information of the instances and model of the original operating condition, and a few labeled instances of new operating conditions for more efficient adaptation. Intensive case studies conducted on a benchmark power system (CEPRI 36-bus system) and a real-world large-scale power system (Northeast China Power System-2131 bus) validate the efficacy and efficiency of the Bi-ATL framework as well as the role of the three-way information.

Generalized Short-Circuit Ratio Based Distributed Real-Time Stability Assessment of Renewable Power Systems

Generalized Short-Circuit Ratio Based Distributed Real-Time Stability Assessment of Renewable Power Systems

Basin delimitation and stability assessment for power systems in the projective space

This paper presents a method for determining the global basin boundary of the power systems. To show the general outline of the basin, the power system is shrink to the projective space. First, a new vector field is induced according to Poincare’s method. After the transformation, the structural stability of the system remains the same. The singularities at infinity are obtained by the proposed shrinking-projection method. The boundary of the basin which is extended to the infinity are complemented by the singularities at infinity in the projective space. The stability of the power system is then evaluated based on the distribution of the singularities at infinity. Finally, the proposed method is applied to the power system with perturbations to study the characteristic of the stochastic basin boundary. The validity of the proposed method is demonstrated on a 9-bus, 11-bus and 68-bus test system.

Cybersecurity Analysis of Data-Driven Power System Stability Assessment

Machine learning-based intelligent systems enhanced with internet-of-things (IoT) technologies have been widely developed and exploited to enable the real-time stability assessment of a large-scale electricity grid. However, it has been extensively recognized that the IoT-enabled communication network of power systems is vulnerable to cyberattacks. In particular, system operating states, critical attributes that act as input to the data-driven stability assessment, can be manipulated by malicious actors to mislead the system operator into making disastrous decisions and thus cause major blackouts and cascading events. In this paper, we explore the vulnerability of the data-driven power system stability assessment, with a special emphasis on decision tree-based stability assessment (DTSA) approaches, and investigate the feasibility of constructing a physics-constrained adversarial attack (PCAA) to undermine the DTSA. The PCAA is formulated as a nonlinear programming problem considering the misclassification constraint, power limits, and bad data detection, computing potential adversarial perturbations that reverse the “stable/unstable” prediction of the real-time input while remaining invisible/stealthy. Extensive experiments based on the IEEE 68-bus system are conducted to evaluate the impact of PCAAs on predictions of DTSA and their transferability.

Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data

AbstractThe social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system.

A comparative study on frequency scanning techniques for stability assessment in power systems incorporating wind parks

A comparative study on frequency scanning techniques for stability assessment in power systems incorporating wind parks

Review of Grid Stability Assessment Based on AI and a New Concept of Converter-Dominated Power System State of Stability Assessment

Artificial intelligence (AI) has been increasingly used for power system stability assessment due to its fast evaluation speed compared to conventional time-domain methods. This paper reviews four types of classic grid stability assessment (GSA) methods based on AI in the recent literature firstly where different AI algorithms from the literature are summarized and compared. Moreover, as the number of power converters using grid forming control intensively increases in the modern system, the influence of the converter parameters on grid stability needs to be investigated. In this context, the concept of the converter-dominated power system state of stability (CDPS-SOS) assessment based on AI is qualitatively discussed. The CDPS-SOS assessment can reveal the system stability margin by considering the converter control parameters and grid bus voltages. Overall, this paper aims to give an overview of AI-based stability assessment for the traditional grid and to provide a new stability assessment concept and inspiration for solving the challenges of future converter-dominated grids.

Multi-task transient stability assessment of power system based on graph neural network with interpretable attribution analysis

Transient power angle stability analysis and voltage stability analysis are the key basis of power system security and stability control. The diversified operation modes of power system put forward higher requirements for real-time and topology generalization of transient stability assessment. Based on Convolution Neural Network (CNN) and Graph Attention Networks (GAT), this paper uses the wide-area transient response features to evaluate the transient angle stability and voltage stability of power system. Firstly, CNN is used to encode the temporal variation characteristics of transient features of electrical components in hidden layer space. Secondly, the encoded hidden layer features are mapped into graph data samples according to the spatial topological correlation of electrical components, so as to train and build a multi-task transient stability assessment model based on GAT. The model can adaptively capture the interaction relationship between encoded hidden layer features of topologically correlated components, and realizes real-time evaluation of transient angle stability and transient voltage stability by perceiving the temporal–spatial correlation of transient features. Finally, Shapley additive explanation (SHAP) method is used to explain the decision-making mechanism of the model and extract the key features that affect the transient stability, so as to enhance the ability of power grid operators to perceive the transient stability situation. The results of IEEE-39 system show that the transient stability assessment model has good accuracy and topology generalization, and the results of interpretable analysis are in line with the conclusions of traditional mechanism cognition, which helps to improve the credibility of the model results.