System Transient Stability Research Articles

Dynamic control of grid-following inverters using DC bus controller and power-sharing participating factors for improved stability

Integrating Grid-Following Inverters (GFLs) into power systems presents significant stability challenges, particularly in systems with reduced strength and high renewable energy penetration. This study delves into the dynamic interactions between parallel synchronous generators (SGs) and GFLs, highlighting the impact of reduced system inertia and transient stability margins. A novel DC bus controller is proposed to enhance the inertia and stability of GFLs during grid disturbances by dynamically adjusting power references based on load demand. Through mathematical modelling, small signal analysis, and MATLAB simulations, the study evaluates the effects of inverter-based resources (IBRs) on power system stability. The proposed GFL configuration demonstrates improved stability control, achieving an 80 % increase in stability under weak grid conditions. This paper provides insights into optimized control strategies, emphasizing the importance of power-sharing participation factors (PSPF) for effective GFL integration in modern power systems with high renewable energy penetration.

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.

Optimal Control Strategy to Analyse the Networked Control System Stability

It is crucial to preserve the networked control systems (NCS) transient performance and stability because adding uncertain parameters degrades system performance and introduces instability. Thus, the analysis of NCS system performance under uncertain conditions, such as disturbance, is the focus of this paper. The performance of NCS is demonstrated using control action and some appropriate stability conditions. The simulation diagram in result section displays effectiveness of proposed methodology and shows a comparative analysis of the response signal with and without control action along with disturbance in NCS. Experiments conducted in the MATLAB Simulink environment demonstrate the efficacy of the suggested methodology.

Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning

Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.

Mobileception-ResNet for transient stability prediction of novel power systems

Power system transient stability prediction (TSP) is particularly important as power systems change and evolve, including the rapid growth of renewable energy, the proliferation of electric vehicles, and the construction of smart grids. Traditional time-domain simulation methods are time-consuming and cannot achieve online prediction. Direct methods are poorly adapted and cannot be applied to complex power systems. Existing machine learning algorithms only classify the transient stability without providing the degree of transient stability of the system. Therefore, a fast and accurate power system TSP method is needed to assist operators in implementing timely measures to improve the stability of the power system running. This study proposes a Mobileception-ResNet network, Mobileception-ResNet is formed by Inception-ResNet-v2, MobileNet-v2, and a fully connected layer. In this study, Mobileception-ResNet and nine comparison models are experimented on two node systems, i.e., the IEEE 10–39 and 69–300 systems. In the IEEE 10–39 system, the root mean square error, mean absolute error, and mean absolute percentage error of Mobileception-ResNet are 44.13 %, 36.74 %, and 39.96 % lower, and the coefficient of determination is 0.04 % higher, respectively, when compared to the comparative model with the best evaluation indicator; in the IEEE 69–300 system, the corresponding values are 2.6 %, 12.83 %, 12.55 %, and 0.01 %, respectively.

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.

Impact of inductive and resistive fault current limiters for transient stability Improvement based on difference between accelerating and decelerating areas

The utilization of fault current limiters (FCLs) provides an effective approach to alleviate the negative consequences associated with short circuit currents. This equipment can affect other system specifications. In this paper, the effects of FCL on power system transient stability are investigated by a theoretical-comparative study. In this study, different FCL impedances (inductive and resistive) are analyzed and compared considering two locations for FCL installation and different locations of fault occurrence in a single-machine infinite bus system. To evaluate the power system's transient stability, the authors adopt an approach based on the equal area criterion, which calculates the difference between accelerating and decelerating areas. Another issue addressed in this article is the fault-clearing time effect on the power system's transient stability as FCL is installed in the power grid. The results have been confirmed through the use of MATLAB software.

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

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.

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.

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.

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.

Simulation of transient response of PID controller in an automated electro-pneumatic system using a single-acting cylinder in a clinical ventilator

Patients with COVID-19 are not eligible for any therapy. Patients who have had respiratory failure and are unable to provide oxygen via noninvasive means obtain supportive care in ICUs. Since the onset of the outbreak, every sick COVID-19 patient has received oxygen via a mechanical ventilator. This study describes and simulates the transient stability of systems in an automated pressure regulator utilizing a single-acting cylinder in a clinical ventilator. These components include horizontal controllers, control devices, connecting tubes, and PID for electro-pneumatic control. Increased system stability and nonlinearity in electro-pneumatic actuator systems are accomplished by the implementation of PID. The redesigned PID control architecture was enhanced with alternative acceleration feedback through the close loop with an integral control method to get the system stable. This introduces the standard value N from the outside vicious circle and applies a form control law to integrate all reference control supply through into the gadget. Even as proportional gain (Kp) gets increased, the controller output would increase proportionately while maintaining the exact degree of accuracy. A derivative term boosts the ability of the Kd regulator to "detect" malfunctions. The integral term of the Ki controller minimizes its set point distortion. The system was updated to make it feasible for transferrable knowledge and competencies by incorporating real industrial components. The completed fluid control system was simulated through FluidSIM, which is frequently helpful for educational purposes.

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.

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.

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).

Impact of High PV Penetration on Transient Stability — a Case Study on the U.S. ERCOT System

This study was performed to assess the impact of high photovoltaic (PV) penetration on transient stability using a series of hypothetical models of the ERCOT system. This was done by calculating the critical clearing time (CCT) when faulting various busses. Varying levels of PV penetration were used to study the system transient stability. For each case 15% wind penetration was also included. It was found that under the PV volt/Var control with plant-level GE SolarControl settings, PV penetration at lower level can slightly improve the transient stability in the ERCOT system model. However, at approximately 54% penetration of renewables (39% PV and 15% wind), transient stability starts to show declining trend. This value has slight variance depending on PV distribution.

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.

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost