Power System Transient Stability Research Articles

Transient Energy Dissipation Control of Energy Storage Devices in Wind Power Generation Systems

The transient stability of power systems with high penetration of wind power generation can be significantly improved through the utilization of variable virtual inertia if the energy source of rotational inertia can be expanded by energy storage devices (ESDs). This paper first establishes the relationship of energy conversion between ESD and mechanical kinetic energy inside a synchronous generator (SG). Then, the impact of the controllable inertia on system frequency characteristics and power angle oscillation is analyzed using the small-signal model corresponding to this system. Based on the analysis of the change of transient energy at different stages in the transient process, the control of transient energy dissipation is proposed employing ESDs for reliable inertia support. Finally, a typical simulation system with high penetration of wind power generation is carried out to demonstrate the viability of the proposed control scheme. The test results show that the fluctuation of SG’s power angle and system frequency can be reduced by regulating the variable inertia in transient events, significantly improving the reliability of the inertia support provided by the ESDs.

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

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

Transient Stability Improvement on Power Network using Static Synchronous Compensator: A Case of The Nigerian 330 KV Electricity Grid

This study deals with power system transient stability, enhanced via a static synchronous compensator (STATCOM). The system’s steady state and transient responses were modelled with Newton-Raphson-based power flow and modified Euler-based swing equation, respectively. Simulations were performed with and without STATCOM, considering the Nigerian 34-bus power grid. A balanced three-phase fault was used for the transient stability analysis. The voltage magnitudes of Katampe (0.9373), Kaduna (0.9209), Kano (0.9381), Jos (0.8295), and Gombe (0.7795) in the grid violated the statutory voltage limit of 0.95 to 1.05 p.u. before compensation and were improved to 0.9650, 0.9550, 0.9789, 0.9630, and 0.9550 p.u., respectively after STATCOM was applied. The system’s total real line losses before and after compensation were 57.887 and 55.623 MW, respectively. Fault application on bus 20 (Akangba T.S) caused loss of synchronism on generator 14 when the critical fault clearing time (CFCT) exceeded 0.019 s while generators 2 to 13 experienced marginal stability. The STATCOM application restored stability beyond the CFCT of 0.019 s due to compensation provided by the device. Fault introduction on bus 22 drove Egbin, the largest system’s generator corresponding to generator 7 on the swing curve into marginal stability along with generators 3 to 6 and 8 to 14 while generator 2 lost synchronism beyond a fault CFCT of 0.0130 s. However, the STATCOM application restored the system’s stability. STATCOM aided the transient stability improvement of the Nigerian 34-bus power network for effective operation.

Application of Static Synchronous Compensator for Improvement of Power System Transient Stability

Transient stability is the power system’s ability to maintain synchronism when exposed to severe disturbances such as loss of generation, rapid load changes, transmission lines tripping, switching operations and faults. Transient instability causes cascading outages, system collapse and economical losses. This study employed static synchronous compensator (STATCOM) for power system transient stability improvement. The system’s steady state and transient responses were respectively modelled with Newton-Raphson power flow and modified-Euler linearized swing equations. A balanced three-phased short circuit fault was introduced on selected buses for transient stability analysis. The steady state and transient responses of the system were simulated with and without STATCOM. The critical fault clearing time (CFCT) was obtained from the generators’ swing curves. The IEEE 9-bus and 14-bus power networks were used as test cases. The voltage magnitudes of all buses in both networks fell within the specified 0.95 to 1.1 p.u. voltage limit with and without compensation. The total active line loss on IEEE 9-bus and 14-bus networks which were 4.641 and 13.514 MW without compensation reduced to 4.600 and 13.014 MW, respectively with STATCOM’s application. The total reactive line losses of both networks which were 92.160 and 31.393 MVAr without compensation decreased to 90.160 and 30.693 MVAr, respectively with STATCOM’ utilization. The CFCT for fault on bus 8 of the IEEE 9-bus system without compensation were 0.2100 and 0.2700 s for generators 2 and 3, respectively. The system’s stability was, however, extended beyond 0.2700 s due to compensation from STATCOM. In the case of IEEE 14-bus system, the CFCT for fault on bus 6 was 0.36364 s for generators 2, 3, 4 and 5, respectively without compensation. The STATCOM inclusion, however, enhanced the system’s stability beyond 0.36366 s where all the four had lost synchronism. This work established that the use of STATCOM on the considered networks appropriately extended the CFCT such that the stability of the systems was not lost in the event of fault application via robust compensation of reactive power.

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.

Integrated algorithm evaluation of a scalable digital power management innovation model

Maintaining contemporary society’s steady progress depends on the reliable functioning of the electrical grid. The complexity and nonlinearity of today’s power networks make it difficult to manage them using conventional mathematical modeling approaches. To get around these problems, researchers have started using machine learning methods, such transient stability evaluation that is based on machine learning. This research suggests a way to evaluate the transient stability of power systems by making use of the complex feature extraction capabilities of multi-layer perceptron (MLP) networks. Another approach is to suggest an ensemble learning model that uses the easy-ensemble undersampling technique to deal with power system transient stability data that is unbalanced. This method preserves unstable power system samples, performs multiple undersampling on stable samples and combines unstable and undersampling samples into multiple new balanced training sets for MLP model training. The final ensemble model is obtained through voting integration strategy. Through conducting a comparative evaluation of individual models and ensemble models, this paper discovers that the ensemble learning models exhibit superior sensitivity and accuracy in effectively addressing system instability.

Deep Learning-Based Equivalent Modelling of Hybrid RES Plant for Efficient, Repetitive Power System Transient Stability Studies

The paper presents the methodology for dynamic equivalent modelling of hybrid renewable energy source (HRES) plant with the aim of obtaining highly accurate time domain HRES plant power responses at any time during the year. The focus is on equivalent modelling for transient stability studies. Historical HRES plant production dataset and transmission network short-circuit fault statistical data, along with unsupervised data mining and deep learning techniques, represent a basis of the modelling procedure. Dynamic equivalent model (DEM) is developed in the form of deep Long Short Term Memory network. Real and imaginary parts of voltage at the point of common coupling (PCC) are model inputs, while real and reactive power at the PCC are model outputs. The paper also proposes a practical approach for selecting the adequate DEM at any time in a year based on the forecasted HRES plant operating scenario only. Model performance is evaluated on a large number of case studies using the HRES plant consisting of three renewable energy sources. The results have shown that only a few DEMs are required for representing the annual HRES plant dynamic performance and obtaining reliable transient system stability results.

Effect of photovoltaic penetration level and location on power system transient stability during symmetrical and unsymmetrical faults

Due to the constant growth in the power demand and the raised risks from unsustainable energy resources, it is essential to continue investigating the operations of renewable energy sources (RESs), such as photovoltaic (PV) systems. Integrating conventional power system with PV plants creates challenges that affect the overall stability of the power system. In this work, the effects of PV penetration levels, PV location, type of faults, and inertia reduction of the conventional synchronous generators on the transient stability of the power system are examined. This is done on 9-bus system based on the critical clearing time values required for the operation of protective devices. It is found that higher PV penetration levels increase the critical clearing times resulting in an improved stability response. Further, the stability of the system under distributed- or concentrated-PV generation depend highly on the location of the PV. Moreover, extensive analysis on the impact of a variety of faults on systems stability are provided (symmetrical vs. unsymmetrical, permanent vs. temporary). The findings of this study indicate that systems experiencing symmetrical faults had lower critical clearing times and tend to be less stable than systems under unsymmetrical faults. Also, the system stability is notably more impacted by permanent faults as opposed to temporary ones with a case of an increase of 80 ms (29.62%). Finally, the negative effect of conventional system inertia reduction on the systems stability is demonstrated with differences reaching 250 ms (28% reduction).

A Comprehensive Analysis of PINNs for Power System Transient Stability

The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face limitations due to their time-consuming and computationally demanding nature. This paper introduces physics-informed Neural Networks (PINNs) as a promising solution for these challenges, especially in scenarios with limited data availability and the need for high computational speed. PINNs offer a novel approach for complex power systems by incorporating additional equations and adapting to various system scales, from a single bus to multi-bus networks. Our study presents the first comprehensive evaluation of physics-informed Neural Networks (PINNs) in the context of power system transient stability, addressing various grid complexities. Additionally, we introduce a novel approach for adjusting loss weights to improve the adaptability of PINNs to diverse systems. Our experimental findings reveal that PINNs can be efficiently scaled while maintaining high accuracy. Furthermore, these results suggest that PINNs significantly outperform the traditional ode45 method in terms of efficiency, especially as the system size increases, showcasing a progressive speed advantage over ode45.

Control of grid-following converter-interfaced resources for improved transient stability of power systems

Power-electronic converters are increasingly utilized to integrate renewable and distributed energy resources (DERs) with conventional power grids. The increasing penetration of converter-interfaced resources (CIRs) could, in turn, influence the transient stability of traditional synchronous generators (SGs) in power systems due to their fast response and low inertia nature. This paper evaluates the transient stability of power systems containing SGs and grid-following CIRs under various control schemes and penetration of CIRs. The critical clearing time (CCT) of the SGs is used as the criterion for assessing transient stability following a fault. It is demonstrated that stability margins can be improved by actively controlling the grid-following CIRs during fault and post-fault periods. The proposed method is shown to increase the CCT compared to the conventional/alternative approaches, particularly the common practice that requires CIRs to inject only reactive power to support/regulate voltage during the fault period. It is also shown that with increased penetration of CIRs, if controlled appropriately, the transient stability of SGs can be improved, which is not commonly expected in low-inertia systems.

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control

Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control

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

Analytic Deep Learning and Stepwise Integrated Gradients-Based Power System Transient Stability Preventive Control

Trajectory sensitivity (TS) is a powerful alternative to linearize strong-nonlinear transient stability patterns. However, due to its incomplete view of an intact power system operational space, it can be readily to drive preventive control to stuck in local stationary solution. To mitigate this issue, an integrated gradient (IG)-based stepwise control method is proposed. The key idea is to develop IG to enable a control sensitivity taking both merits of local and global sensitivity, further help preventive control escape from local stationary points. Particularly, deep belief network (DBN) is firstly utilized to parameterize pattern between transient stability index (TSI) and steady-state dispatches. It is then leveraged to infer IG to linearize transient stability constraints in real-time. Interior point method is employed to solve the linearized model. A stepwise control strategy is finally proposed to adaptively tune the integrated interval of the applied IG, such that the preventive control path can be ensured to follow the trend of improving transient stability. Case studies on two benchmarks show that the proposed method outperforms traditional TS method in terms of efficiency and economy, and provides a promising way to enhance engineering sensitivity methods.

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.

Stability assessment of a transmission system with converted-interfaced generation by means of a transient stability-constrained optimal power flow

This paper proposes a transient-stability constrained optimal power flow (TSCOPF) algorithm that includes, together with synchronous generators, renewable power parks with nonsynchronous converter-interfaced generation (CIG) applying grid-forming and grid-following controls. The solution of the TSCOPF algorithm allows the comparison of control alternatives for CIG in terms of generation cost and contribution to power system transient stability. The algorithm is applied to a model of the Iberian Peninsula power system on which a 2021 incident consisting in the disconnection from the rest of continental Europe is analyzed. The effect of each control on the cost of maintaining the stability of the system is discussed.

Transient Stability Impact of Short Circuit Fault System Based on SVC And PSS Applications

Researchers are currently focusing on two solutions, namely Static VAR compensator (SVC) and power system stabilizer (PSS), to address different power system stability issues in large area interconnected power grids. To enhance transient stability, researchers have suggested a combined approach involving SVC and PSS. In this method, SVC and PSS coordinate and cooperate with each other to provide better stability assurance. To verify the feasibility of this method, simulation experiments were conducted using MATLAB. Simulated the situation of single-phase ground fault, two-phase ground fault, and three-phase ground fault in the power system, and tested the effectiveness of SVC and PSS respectively. The simulation results show that the combined use of SVC and PSS can significantly improve the transient stability of power systems and effectively reduce the impact of faults on the system. In summary, the combined use of SVC and PSS can effectively solve various power system stability issues that may arise in large-scale interconnected power grids. By conducting simulation experiments in MATLAB, researchers have verified the feasibility of this method under different fault conditions, providing strong support for practical applications.

An improved constraint-inference approach for causality exploration of power system transient stability

Transient stability is the key aspect of power system dynamic security assessment, and data-driven methods are becoming alternative measures of assessment. The current data-driven methods only construct correlations between variables while neglecting causal relationships. Therefore, they face problems such as poor robustness, which restrict their practical application. This paper introduces an improved constraint-inference approach for causality exploration of power system transient stability. Firstly, a causal structure discovery method of power system transient stability is proposed based on a PC-IGCI algorithm, which addresses the shortage caused by Markov equivalence and massive variables. Then, a relative average causal effect index is proposed to reveal the relationship between relative intervention strength and causal effects. The results of a case study verify that the proposed method can identify the causal structure between the transient stability variables entirely based on data. In addition, the causal effect sorting between “cause” and “outcome” of transient stability variables is revealed. This paper provides a new approach for data mining to uncover the causal mechanisms between variables in power systems and expand the capabilities of data-driven methods in power system application.