Transient Stability Assessment Research Articles

Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset

Transient stability preventive control (TSPC) ensures that power systems have a sufficient stability margin by adjusting power flow before faults occur. The generation of TSPC measures requires accuracy and efficiency. In this paper, a novel model interpretation-based multi-fault coordinated data-driven preventive control optimization strategy is proposed. First, an augmented dataset covering the fault information is constructed, enabling the transient stability assessment (TSA) model to discriminate the system stability under different fault scenarios. Then, the adaptive synthetic sampling (ADASYN) method is implemented to deal with the imbalanced instances of power systems. Next, an instance-based machine model interpretation tool, Shapley additive explanations (SHAP), is embedded to explain the TSA model’s predictions and to find out the most effective control objects, thus narrowing the number of control objects. Finally, differential evolution is deployed to optimize the generation of TSPC measures, taking into account the security and economy of TSPC. The proposed method’s efficiency and robustness are verified on the New England 39-bus system and the IEEE 54-machine 118-bus system.

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.

Transient Voltage Stability Assessment Method based on gcForest

Aiming at the fact that the traditional method cannot quickly and accurately judge the power system transient voltage stability, and the deep neural network method needs many sample data sets and has a large number of hyperparameters, a power system transient voltage stability assessment method based on deep forest (gcForest) is proposed. gcForest effectively extracts features through ensemble learning, multi-layer feature transformation, and dynamically adjusting the number of model layers. Finally, this method is applied to transient voltage stability assessment of an electrolytic aluminium load-intensive area in Guangxi Power Grid. The results show that the method has high accuracy, the feasibility and effectiveness of this method are verified. This method can assist dispatchers to judge the transient voltage stability of the power grid.

An optimized algorithm for optimal power flow based on deep learning

With the increasing requirements for power system transient stability assessment, the research on power system transient stability assessment theory and methods requires not only qualitative conclusions about system transient stability but also quantitative analysis of stability and even development trends. Judging from the research and development process of this direction at home and abroad in recent years, it is mainly based on the construction of quantitative index models to evaluate its transient stability and development trend. Regarding the construction theories and methods of quantitative index models, a lot of results have been achieved so far. The research ideas mainly focus on two categories: uncertainty analysis methods and deterministic analysis methods. Transient stability analysis is one of the important factors that need to be considered. Therefore, this paper proposed an optimized algorithm based on deep learning for preventive control of the transient stability in power systems. The proposed algorithm accurately fits the generator’s power and transient stability index through a deep belief network (DBN) by unsupervised pre-training and fine-tuning. The non-linear differential–algebraic equation and complex transient stability are determined using the deep neural network. The proposed algorithm minimizes the control cost under the constraints of the contingency by realizing the data-driven acquisition of the optimal preventive control. It also provides an efficient solution to stability and reliability rules with similar safety into the corresponding control model. Simulation results show that the proposed algorithm effectively improved the accuracy and reduces the complexity as compared with existing algorithms.

An ensemble model for wide-area measurement-based transient stability assessment in power systems

Transient stability assessment involves the determination of whether all generators in the power system would maintain synchronous operation when it is subjected to a large disturbance. Assessing the transient stability status of a power system in an accurate and timely manner is of high importance in order to plan and initiate relevant corrective control actions where required. The wide-area monitoring systems enable to acquire synchronized measurements of important system variables from geographically dispersed locations via the phasor measurement units (PMUs), which can be used for measurement-based transient stability assessment. In this work, a stacked ensemble model that uses post-contingency wide-area synchrophasor measurements of bus voltage magnitude, voltage angle, frequency and rate of change of frequency is proposed to classify the post-contingency transient stability status of a power system to be unstable or stable. The model includes four individual long short-term memory network classifiers as base-learners and a LightGBM classifier as a meta-learner. An important differentiation of this work is that the proposed method does not assume that each bus is equipped with a PMU. Furthermore, robustness of the proposed model is analyzed in the presence of measurement noise and against the scenario of missing measurements. The performance of the proposed ensemble model is investigated in two generic test systems, the Nordic and the 127-bus WSCC test systems, as well as the practical Turkish power system.

Design and Implementation of Probabilistic Transient Stability Approach to Assess the High Penetration of Renewable Energy in Korea

Recently, because of the many environmental problems worldwide, Korea is moving to increase its renewable energy output due to the Renewable 3020 Policy. Renewable energy output can change depending on environmental factors. It is for this reason that institutions should consider the instability of renewables when linked to the electric system. This paper describes the methodology of renewable energy capacity calculation based on probabilistic transient stability assessment. Probabilistic transient stability assessment consists of four algorithms: first, to create probabilistic scenarios based on the effective capacity history of renewable energy; second, to evaluate probabilistic transient stability based on transient stability index, interpolation-based transient stability index estimation, reduction-based transient stability index calculation, etc.; third, to implement multiple scenarios to calculate renewable energy capacity using probabilistic evaluation index; and finally, to create a probabilistic transient stability assessment simulator based on Python. This paper calculated renewable energy capacity based on large-scale power system to validate consistency of the proposed paper.

Analysis of power system transient stability characteristics with the application of massive transient stability simulation data

A large number of simulation results will be generated during the calculation of the power system digital simulation. Manual analysis of massive numerical results is inefficient and error-prone. Besides, some imperceptible operating rules of power system operating mode may be ignored. In order to solve these problems, a transient stability assessment method based on deep learning is proposed in this paper. By analyzing simulation data, the relationship between power grid stability characteristics and the set operation mode is constructed. According to these research results, the calculation and analysis of power system transient stability will be effectively supported.

A topology adaptive high-speed transient stability assessment scheme based on multi-graph attention network with residual structure

Reliable and fast transient stability assessment (TSA) is significantly required for the power system emergency control. We propose a topology adaptive high-speed transient stability assessment (HSTSA) scheme, where the inputs of the model adopt only the pre-fault state and the dynamics at the fault occurrence snapshot. A novel multi-graph attention network with residual structure (ResGAT) is designed to capture the stability characteristics. ResGAT applies improved graph attention mechanism to enhance its adaptability to the power system topology changes and the residual structure helps to avoid network degeneration. Meanwhile, a new piece-wise transient stability index (PSI) is proposed for the stability level prediction. Integration of both the stability category and the stability level results increase the precision of the HSTSA scheme. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models and its robustness under various scenarios. .

Transient stability assessment for power system with wind farm considering the stochasticity

Transient stability assessment for power system with wind farm considering the stochasticity

Transient stability assessment model with parallel structure and data augmentation

Critical situations are difficult to predict reliably by the machine learning-based transient stability assessment (TSA) methods. Therefore, the practicality of the data-driven TSA is limited. A parallel TSA framework constructed by two basic predictors and a comprehensive decider (CD) is proposed to achieve fast and reliable real-time transient stability assessment (RTSA). A cost-sensitive method is utilized for stacked sparse auto-encoders to establish two basic predictors with opposite evaluation biases. Then, the outputs of the two basic predictors are sent to the CD. Finally, the stability of the non-critical cases can be judged directly, and the critical cases are suggested to be analyzed by other methods. Besides, in order to enhance the reliability of the parallel predictor, a simple data augmentation approach with Gaussian white noise is employed to expand the classification boundaries. A fault severity factor is introduced to filter basic critical samples for data augmentation to improve the performance of the proposed framework. The effect of the proposed strategy is verified on the IEEE-39 bus system and a realistic regional system.

Versatile and Robust Transient Stability Assessment via Instance Transfer Learning

To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics. The domain knowledge on how the disturbance effect will propagate from the fault location to the rest of the network is leveraged to recognise the dominant conditions that determine the stability of a system. Accordingly, we introduce a new concept called Fault-Affected Area, which provides crucial information regarding the unstable region of operation. This information is embedded in an augmented dataset to train an ensemble model using an instance transfer learning framework. The test results on the IEEE 39-bus system verify that this model can accurately predict the stability of previously unseen operational scenarios while reducing the risk of false prediction of unstable instances compared to standard approaches.

Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning

Transient stability assessment (TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time (CCT), which is a function of the pre-fault power flow. TSA can be regarded as the fitting of this function with the pre-fault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression, which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples. Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be regarded as a fundamental issue in the transient learning strategy. In fact, MDTFs extraction is raised to make a trade-off between paradoxically intertwined indices, namely the accuracy and processing time of TSP. To this end, we offer a bi-mode hybrid feature selection scheme called BMHFSS for extracting MDTFs in high dimensional transient multivariate time series (TMTS). First, we used the TMTS, which are effective features on TSA. Next, the trajectory-based filter-wrapper mode (TFWM) is applied on TMTS to surmount the curse of dimensionality in two phases. In the filter phase, statistical and intrinsic characteristics of the TMTS in the form of agglomerative hierarchical clustering (AHC) are measured, and relevant TMTS (RTMTS) is selected according to obtained weight. In the wrapper phase, the RTMTS is entered into the trihedral kernel-based approach, including both fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) to find the intersected most RTMTS (IMRTMTS). As a complementary step, the filter-wrapper scenario in point-based mode (PFWM) is conducted for selecting MDTFs per time series in IMRTMTS. Finally, the aggregated MDTFs (AMDTFs) are tested to verify their efficacy for TSP based on cross-validation. The results show that the proposed framework has prediction accuracy greater than 98 % and a processing time of 52.94 milliseconds for TSA.

A Preventive Transient Stability Control Strategy Through Individual Machine Equal Area Criterion Framework

Power system transient stability assessment (TSA) and control is a complex problem. TSA from the perspective of individual machine Kimbark curves forms a distinctive approach. On the other hand, transient stability constrained optimal power flow (TSC-OPF) has been adopted widely for determining preventive control actions against transient instabilities under a given contingency. This paper presents a strategy for transient stability control, which mainly integrates the concepts of individual machine methods based TSA and global TSC-OPF. The proposed workflow is mainly based on the most severely disturbed machines (MDM) and their critical trajectories. These are utilized in deciding the number of TSC-OPF transient stability constraints, the minimum upper time limit for TSC-OPF solution interval, and security-based transient stability constraint of individual machines. Further, the application of the proposed method is explained with respect to stabilization of three different transient instability scenarios viz., transient instability originates from first swing instability, transient instability originates from multi swing instability but leads to first swing instability as fault duration increases, and transient instability originates from multi swing instability, but never leads to first swing instability as fault duration increases. IEEE 39 bus and 118 bus test system are considered for testing the proposed control strategy. The proposed approach, which expresses the transient stability constraints in global TSC-OPF in terms of some selective machine (i.e MDMs) critical trajectories not only forms a distinctive approach but also provide a smooth reconciliation between economy of operation and security level while ensuring transient stability under a given contingency.

Renewable energy power system transient stability analysis and assessment

In this paper, in-depth analysis, and evaluation of the transient stability of a renewable energy power system are carried out, and a transient stability evaluation method based on stacked autoencoders is proposed. The method breaks through the traditional two-stage evaluation mode. The method proposes a deep network model with a multi-branch structure for different physical natures of different measured data. With the help of different sparse denoising coding networks (branches), features are extracted from each measurement separately and fused on top of the model to finally complete the transient stability assessment. The simulation results show that the method can effectively handle the simultaneous input of multiple measurements, and has the advantages of high accuracy and robustness in noisy environments. To effectively determine the model hyperparameters, the orthogonal test is used for model selection, which can significantly reduce the hyperparameter seeking space and save computational resources. The simulation results show that the method can effectively use multiple measurements to improve the accuracy of wind power prediction. Also, the input variable selection index based on mutual information can provide a good guide for further improvement of model performance.

Hybrid analytical and data-driven modeling based instance-transfer method for power system online transient stability assessment

Data-driven methods are widely concerned and show good effects in online transient stability assessment. However, tedious and time-consuming sample collection process is always neglected. In power systems, continuous changes in operation mode require repetitive sample collection process, underlining importance of collection efficiency. To achieve high sample collection efficiency after operation mode change, a new instance-transfer method based on compressing and matching strategy is proposed, by which useful previous samples are inherited directly for new sample base construction. Further, a hybrid model is put forward to guarantee rationality in sample similarity comparison and selection, where high-importance features of analytical models are introduced to data-driven methods. Meanwhile, data-driven method is also utilized to realize fast error correction of analytical models in the hybrid model, helping achieve fast and accurate post-disturbance transient stability assessment. As a paradigm, scheme for online critical clearing time (CCT) estimation is concerned, where integrated extended equal area criterion (IEEAC) and extreme learning machine (ELM) are used as analytical model and data-driven error correction model part respectively. Results validate effectiveness of the proposed method.

Entropy based prioritization strategy for data-driven transient stability batch assessment

Transient stability batch assessment (TSBA) is essential for dynamic security check in both power system planning and day-ahead dispatch. It is also a necessary technique to generate sufficient training data for data-driven online transient stability assessment (TSA). However, most existing work suffers from various problems including high computational burden, low model adaptability, and low performance robustness. Therefore, it is still a significant challenge in modern power systems, with numerous scenarios (e.g., operating conditions and N-k contingencies) to be assessed at the same time. The purpose of this work is to construct a data-driven method to early terminate timedomain simulation (TDS) and dynamically schedule TSBA task queue a prior, in order to reduce computational burden without compromising accuracy. To achieve this goal, a time-adaptive cascaded convolutional neural networks (CNNs) model is developed to predict stability and early terminate TDS. Additionally, an information entropy based prioritization strategy is designed to distinguish informative samples, dynamically schedule TSBA task queue and timely update model, thus further reducing simulation time. Case study in IEEE 39-bus system validates the effectiveness of the proposed method.

Transient Stability Assessment of IEEE 9-Bus System Integrated Wind Farm

The use of wind energy is increased due to the high demand for sustainable energy. The penetration of wind energy in electrical networks might have several effects on load flow and power system stability. In this research, the transient stability of the IEEE 9-Bus system integrated with Doubly Fed Induction Generator (DFIG) is analyzed. Additionally, different penetration levels of a wind farm are considered. With a 5% penetration of wind energy, the maximum power angle of the synchronous generator is around 129 deg, which is quite similar to the existing system. In contrast, the power angle increases to 140 deg after adding more wind turbines with 15% wind farm penetration. Then, the system loses stability with a 25% penetration of wind energy. The results indicate that the high penetration of wind energy has a destabilizing impact on the studied network. Moreover, the location of the wind farm affects transient stability. This research intends to contribute towards assessing the stability of the power system integrated DFIG. Hence, this study will support the increase of using wind energy in power systems rather than conventional power plants and evaluate the stability to enable the reliability of alternative energy sources in the grid.

A Direct Bounded Control Method for Transient Stability Assessment

Abstract The paper proposes a direct method for transient stability assessment, which is more efficient than traditional time-domain simulation methods. To achieve this objective, a new bounded control law for the model of a power system is designed. This yields superior closed-loop transient performances when compared to those achievable with traditional automatic voltage regulators and power system stabilizers. The designed control law allows defining an energy-based Lyapunov function which is instrumental in assessing transient stability properties of the post-fault system. A case study on a single machine infinite bus power system model is presented to illustrate the merits of the proposed method.

Single Value Decomposition to Estimate Critical Clearing Time of a Power System Using Measurements

The transient stability analysis of large-interconnected power systems using time-domain simulations (TDS) is a significant challenge since it represents a huge computational cost. Besides, for dynamic security assessment is required have a quick response. Consequently, recent approaches are relying on using the wide-area measurement system combined with other techniques to perform transient stability assessment and counteract the drawbacks of the TDS method. However, these approaches still requiring to perform TDS to set initial parameters. This paper proposes a new algorithm to estimate the critical clearing time (CCT) based on the eigenvalue calculation and the singular value decomposition using data from wide-area measurement systems. The proposed algorithm uses the phase angles of the voltage phasors measurements at the generation buses to represent the dynamics of the internal angles of the generators. First, from a set of signals, a measurement matrix is formed using a sliding window. Then, a threshold based on the maximum singular value and the dominant eigenvalue of the measurement matrix are computed. Finally, the CCT is estimated using the dominant eigenvalue (the most energetic eigenvalue) and the threshold. The proposed algorithm is evaluated using the Kundur four-machine system and New England 39-Bus system. Its performance contrasts to the CCT calculated using the classical TDS. The simulation results demonstrated acceptable precision of the CCT against TDS. Also, it presents robustness against the effect of the noise in the measurements. Therefore, it is suitable for online applications.