Migratable Power System Transient Stability AssessmentMethod Based on Improved XGBoost

The data-driven transient stability assessment(TSA)of power systems can predict online real-time prediction by learning the temporal features before and after faults.However,the accuracy of the assessment is limited by the quality of the data and has weak transferability.Based on this,this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting(XGBoost)model.Firstly,the gradient detection method is employed to remove noise interference while maintaining the original time series trend.On this basis,a focal loss function is introduced to guide the training of theXGBoostmodel,enhancing the deep exploration of minority class samples to improve the accuracy of the model evaluation.Furthermore,to improve the generalization ability of the evaluation model,a transfer learning method based on model parameters and sample augmentation is proposed.The simulation analysis on the IEEE 39-bus system demonstrates that the proposed method,compared to the traditional machine learning-based transient stability assessment approach,achieves an average improvement of 2.16%in evaluation accuracy.Specifically,under scenarios involving changes in topology structure and operating conditions,the accuracy is enhanced by 3.65%and 3.11%,respectively.Moreover,the model updating efficiency is enhanced by 14–15 times,indicating the model’s transferable and adaptive capabilities across multiple scenarios.

Hybrid Analytical and Data-Driven Model Based Instance-Transfer Method for Power System Online Transient Stability Assessment

Data-driven methods are widely recognized and generate conducive results for online transient stability assessment.However,the tedious and time-consuming process of sample collection is often overlooked.The functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode,thereby highlighting the importance of collection efficiency.As a means to achieve high sample collection efficiency following the operation mode change,we propose a novel instance-transfer method based on compression and matching strategy,which facilitates the direct acquisition of useful previous samples,used for creating the new sample base.Additionally,we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection,where features of analytical modeling with special significance are introduced into data-driven methods.At the same time,a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models,enabling fast and accurate post-disturbance transient stability assessment.As a paradigm,we consider a scheme for online critical clearing time estimation,where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part,respectively.Derived results validate the credible efficacy of the proposed method.

Data-driven Transient Stability Assessment Using Sparse PMU Sampling and Online Self-check Function

Artificial intelligence technologies provide a newapproach for the real-time transient stability assessment (TSA)of large-scale power systems. In this paper, we propose a datadriven transient stability assessment model (DTSA) that combinesdifferent AI algorithms. A pre-AI based on the time-delay neuralnetwork is designed to locate the dominant buses for installingthe phase measurement units (PMUs) and reducing the datadimension. A post-AI is designed based on the bidirectionallong-short-term memory network to generate an accurate TSAwith sparse PUM sampling. An online self-check function of theonline TSA’s validity when the power system changes is furtheradded by comparing the results of the pre-AI and the post-AI.The IEEE 39-bus system and the 300-bus AC/DC hybrid systemestablished by referring to China’s existing power system areadopted to verify the proposed method. Results indicate that theproposed method can effectively reduce the computation costswith ensured TSA accuracy as well as provide feedback forits applicability. The DTSA provides new insights for properlyintegrating varied AI algorithms to solve practical problems inmodern power systems.

Transient Stability Analysis of Grid-connected Converters in Wind Turbine Systems Based on Linear Lyapunov Function and Reverse-time Trajectory

As the proportion of converter-interfaced renewable energy resources in the power system is increasing,the strength of the power grid at the connection point of wind turbine generators(WTGs)is gradually weakening.Existing research has shown that when connected with the weak grid,the stability of the traditional grid-following controlled converters will deteriorate,and they are prone to unstable phenomena such as oscillation.Due to the limitations of linear analysis that cannot sufficiently capture the stability phenomena,transient stability must be investigated.So far,standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability.However,the time-domain simulations have proven to be computationally too heavy,while analytical methods are difficult to formulate for larger systems,require many modelling assumptions,and are often conservative in estimating the stability boundary.This paper proposes and demonstrates an innovative approach to estimating the transient stability boundary via combining the linear Lyapunov function and the reverse-time trajectory technique.The proposed methodology eliminates the need of time-consuming simulations and the conservative nature of Lyapunov functions.This study brings out the clear distinction between the stability boundaries with different post-fault active current ramp rate controls.At the same time,it provides a new perspective on critical clearing time for wind turbine systems.The stability boundary is verified using time-domain simulation studies.

Safety assessment of waste rock dump built on existing tailings ponds

The construction of waste rock dumps on existing tailing ponds has been put into practice in China to save precious land resources. This work focuses on the safety assessment of the Daheishan molybdenum mine waste rock dump under construction on two adjoining tailings ponds. The consolidation of the tailings foundation and the filling quality of the waste rock are investigated by the transient electromagnetic method through detecting water-rich areas and loose packing areas, from which, the depth of phreatic line is also estimated. With such information and the material parameters, the numerical method based on shear strength reduction is applied to analyzing the overall stability of the waste rock dump and the tailings ponds over a number of typical cross sections under both current and designed conditions, where the complex geological profiles exposed by site investigation are considered. Through numerical experiments, the influence of soft lenses in the tailings and possible loose packing areas in the waste rock is examined. Although large displacements may develop due to the soft tailings foundation, the results show that the waste rock dump satisfies the safety requirements under both present and designed conditions.

Static information, K-neighbor, and self-attention aggregated scheme: a transient stability prediction model with enhanced interpretability

Data-driven preventive scanning for transient stability assessment(DTSA)is a faster and more efficient solution than time-domain simulation(TDS).However,most current methods cannot balance generalization to different topolo-gies and interpretability,with simple output.A model that conforms to the physical mechanism and richer label for transient stability can increase confidence in DTSA.Thus a static-information,k-neighbor,and self-attention aggre-gated schema(SKETCH)is proposed in this paper.Taking only static measurements as input,SKETCH gives several explanations that are consistent with the physical mechanisms of TSA and provides results for all generator stability while predicting system stability.A module based on the self-attention mechanism is designed to solve the locality problem of a graph neural network(GNN),achieving subgraph equivalence outside the k-order neighborhood.Test re sults on the IEEE 39-bus system and IEEE 300-bus system indicate the superiority of SKETCH and also demonstrate the rich sample interpretation results.

Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Transient stability assessment(TSA)is of great importance in power system operation and control.One of the usual tasks in TSA is to estimate the critical clearing time(CCT)of a given fault under the given network topology and pre-fault power flow.Data-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of samples.However,the influence of network topology on CCT is hard to be analyzed and is often ignored,which makes the models inaccurate and unpractical.In this paper,a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the problem.The model is a weighted sum of several sub-models.Each sub-model only uses the data of one topology to construct a kernel regressor.The weights are determined by both the topological similarity and numerical similarity between the samples.The similarities are decided by the parameters in Mahalanobis distance,and the parameters are to be trained.To reduce the model complexity,sub-models within the same topology category share the same parameters.When estimating CCT,the model uses not only the sub-model which the sample topology belongs to,but also other sub-models.Thus,it avoids the problem that there may be too few data under some topologies.It also efficiently utilizes information of data under all the topologies.Moreover,its decision-making process is clear and understandable,and an effective training algorithm is also designed.Test results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.

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 prefault 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.

Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

The recent development of phasor measurement technique opens the way for real-time post-disturbance transient stability assessment(TSA).Following a disturbance,since the transient instability can occur very fast,there is an urgent need for fast TSA with sufficient accuracy.This paper first identifies the tradeoff relationship between the accuracy and speed in post-disturbance TSA,and then proposes an optimal self-adaptive TSA method to optimally balance such tradeoff.It uses ensemble learning and credible decision-making rule to progressively predict the post-disturbance transient stability status,and models a multi-objective optimization problem to search for the optimal balance between TSA accuracy and speed.With such optimally balanced TSA performance,the TSA decision can be made as fast as possible while maintaining an acceptable level of accuracy.The proposed method is tested on New England 10-machine 39-bus system,and the simulation results verify its high efficacy.

Power system transient stability assessment based on the multiple paralleled convolutional neural network and gated recurrent unit

In order to accurately evaluate power system stability in a timely manner after faults,and further improve the feature extraction ability of the model,this paper presents an improved transient stability assessment(TSA)method of CNN+GRU.This comprises a convolutional neural network(CNN)and gated recurrent unit(GRU).CNN has the feature extraction capability for a micro short-term time sequence,while GRU can extract characteristics contained in a macro long-term time sequence.The two are integrated to comprehensively extract the high-order features that are contained in a transient process.To overcome the difficulty of sample misclassification,a multiple parallel(MP)CNN+GRU,with multiple CNN+GRU connected in parallel,is created.Additionally,an improved focal loss(FL)func-tion which can implement self-adaptive adjustment according to the neural network training is introduced to guide model training.Finally,the proposed methods are verified on the IEEE 39 and 145-bus systems.The simulation results indicate that the proposed methods have better TSA performance than other existing methods.

Information Entropy Based Prioritization Strategy for Data-driven Transient Stability Batch Assessment

Transient stability batch assessment(TSBA)is es-sential 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"contin-gencies)to be assessed at the same time.The purpose of this work is to construct a data-driven method to early terminate time-domain 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 cas-caded 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.