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.

Stability assessment of rock surrounding an I-beam supported retreating roadway

The installation of a back-wall guard-board is the key to successfully supporting underground retreating roadways in coal mines. Based on the coordinate support principle, and using an I-shaped steel support for the surrounding rock, a mechanical model was developed for the stability of the roadway support and surrounding rock. Analysis of the bearing capacity of the roof back-wall guard-board and modelling of the equations for the maximum deflection and the maximum compressive stress of the top and side beams of the I-shaped steel support were undertaken. Simultaneously, the model was used to calculate and analyse the stability of the top and side beams of the I-shaped steel support structure and analyse the criteria for their stability. The results provide a reliable theoretical basis for the judgment of the stability of the surrounding rock and support structure. The theoretical evaluation results are consistent with field data. Finally, the key support parameters of the top and side beams of the I-shaped steel support structure and the variation of the maximum deflection and the maximum compressive stress as affected by the influence of the guard-board length were investigated. It is concluded that, as the back-board length increases, the maximum compressive stress in the top beam of the I-shaped steel support increases while the compressive stress in the side beam decreases. The results show that the accuracy of judgment of the stability of a supported retreating roadway is improved, providing guidance for the design of such typical I-shaped steel support and back-board structures.

Formation Mechanism and Stability Assessment of the Colluvial Deposit Slope in Zuoyituo

The basic features of the colluvial deposit slope in Zuoyituo such as geological conditions, dimensions, slip surfaces and groundwater conditions are described concisely in this paper. The formation mechanism of the slope is discussed. It is considered that the formation of the colluvial deposit slope in Zuoyituo has undergone accumulation, slip, load, deformation and failure. The effects of rainfall on slope stability are categorized systematically based on existing methodology, and ways to determine the effects quantitatively are presented. The remained slip force method is improved by the addition of quantitative relations to the existing formulae and programs. The parameters of the colluvial deposit slope are determined through experimentation and the method of back-analysis. The safety factors of the slope are calculated with the improved remained slip force method and the Sarma method. The results show that rainfall and water level in the Yangtze River have a significant effect on the stability of the colluvial deposit slope in Zuoyituo. The hazards caused by the instability of the slope are assessed, and prevention methods are put forward.

Automatic identification of rock discontinuity and stability analysis of tunnel rock blocks using terrestrial laser scanning

Local geometric information and discontinuity features are key aspects of the analysis of the evolution and failure mechanisms of unstable rock blocks in rock tunnels.This study demonstrates the integration of terrestrial laser scanning(TLS)with distinct element method for rock mass characterization and stability analysis in tunnels.TLS records detailed geometric information of the surrounding rock mass by scanning and collecting the positions of millions of rock surface points without contact.By conducting a fuzzy K-means method,a discontinuity automatic identification algorithm was developed,and a method for obtaining the geometric parameters of discontinuities was proposed.This method permits the user to visually identify each discontinuity and acquire its spatial distribution features(e.g.occurrences,spac-ings,trace lengths)in great detail.Compared with hand mapping in conventional geotechnical surveys,the geometric information of discontinuities obtained by this approach is more accurate and the iden-tification is more efficient.Then,a discrete fracture network with the same statistical characteristics as the actual discontinuities was generated with the distinct element method,and a representative nu-merical model of the jointed surrounding rock mass was established.By means of numerical simulation,potential unstable rock blocks were assessed,and failure mechanisms were analyzed.This method was applied to detection and assessment of unstable rock blocks in the spillway and sand flushing tunnel of the Hongshiyan hydropower project after a collapse.The results show that the noncontact detection of blocks was more labor-saving with lower safety risks compared with manual surveys,and the stability assessment was more reliable since the numerical model built by this method was more consistent with the distribution characteristics of actual joints.This study can provide a reference for geological survey and unstable rock block hazard mitigation in tunnels subjected to complex geology and active rockfalls.

Preliminary establishment of a spinal stability scoring system for multiple myeloma

BACKGROUND Multiple myeloma is an incurable malignant plasma cell disorder that represents the most common primary malignant bone tumor.It commonly involves bone metastasis in multiple vertebral bodies,and the Spinal Instability Neoplastic Score scoring system may not be fully applicable to multiple myeloma(MM)patients.AIM To evaluate the spinal stability of patients with MM spinal involvement to guide their clinical treatment.METHODS By using the Delphi method,we collected and extracted information through a series of questionnaires and improved it via feedback.We also preliminarily established a spinal stability scoring system for multiple myeloma.RESULTS Fifteen clinicians completed a second round of questionnaires and compared their answers with those of the first round of questionnaires to identify significant comments or changes that required group discussions.As a result,no further feedback was used to improve the scoring system.After integrating the information from the expert consultation questionnaire,we established the initial scoring system for MM spine stability and used the scoring system to assess a series of representative clinical cases.The MM spinal stability scoring system was created by calculating the scores of the six separate components:location,pain,number of segments,physiological curvature,comorbidities,and neurological function.The minimum value was"0",and the maximum value was"24".A score of"0-10"indicated"spine stability",a score of"11-17"indicated"potential instability",and a score of"18-24"indicated"spine instability".Patients with a score of"11-24"need an intervention such as surgery.CONCLUSION The initial establishment of the MM spine stability scoring system provides a vital theoretical basis for the evaluation of spine stability in individuals with MM.

Stability Assessment of Some West African Okra （Abelmoschus caillel~ Genotypes in Nigerian Genebank

Stability among 50 accessions of West African okra （Abelmoschus caillei） was assessed under three diverse ecological environments at Abeokuta, Ibadan and Mokwa in Nigeria during 2005 and 2006 cropping season. The accessions were grown in a Randomized Complete Block Design with three replications; data were collected on 5-10 randomly selected plants from each plot. Only 20 accessions were subjected to stability analysis on the basis of yield across the three environments. The joint regression analysis, deviation means square were computed using Eberhart and Russell method and complemented with Francis and Kannenberg method. The regression coefficients of accessions mean yields on the environmental index resulted in regression coefficients ranging in values from 0.5549 to 1.6667. OAA/96/175-5328, NGAE-96-011 and NGAE-96-0060 were among the superior genotypes with high yield performance. The large variation in regression values indicated large differences in genotype response to different environments. It suggests that stability concept of Ebelhart and Russell could be modified to use any yield components that has strong correlation with yield for stability analysis. The two promising accessions ofA. caillei （NGAE-96-011 and NGAE-96-0060） needed to be further tested on farmers＇ field to obtain on-farm data, alter which it should be recommended for official registration and released by the National Committee on Naming, Registration and Release of Crop varieties in Nigeria.

Strategies for Advancing Road Construction Slope Stability: Unveiling Innovative Techniques for Managing Unstable Terrain

This comprehensive review paper explores various aspects of geotechnical engineering, with a focus on the management of unstable terrains, numerical methods for solving complex soil and consolidation problems, rheological analysis of suspensions and muddy soils, and stability analysis of slopes. It begins by examining the unique physicochemical properties of cohesive sediments, including cohesion and specific surface area. The temporal evolution of deposit concentration and average bed concentration in unstable terrains is discussed, along with settling behavior of isolated particles and hindered settling using empirical equations. Key sedimentation theories, such as Kynch’s theory, and geotechnical consolidation theories, including Terzaghi’s consolidation equation and Gibson’s theory, are presented. The investigation interrelates these theories and principles to offer a holistic view of managing unstable terrains. It also addresses the challenges associated with experimental determination of constitutive relationships and presents alternative simplification methods proposed by researchers. Additionally, it delves into numerical methods for solving nonlinear partial differential equations governing soil behavior, emphasizing the need for numerical frameworks and discussing various techniques and associated challenges. The rheological analysis section covers material flow behavior, rheological behavior models, and the rheological properties of water and cohesive sediment mixtures. Fundamental geotechnical calculations, constitutive laws, and failure criteria are explained, highlighting their relevance in geotechnical engineering applications. This paper provides a multidimensional perspective on geotechnical engineering, offering valuable insights into soil properties, consolidation processes, numerical methods, rheological analysis, and slope stability assessment for professionals in the field.

Three-dimensional pseudo-dynamic reliability analysis of seismic shield tunnel faces combined with sparse polynomial chaos expansion

To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.

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.

Physics-informed transient stability assessment of microgrids

With the integration of a voltage source converter(VSC),having variable internal voltages and source impedance,in a microgrid with high resistance to reactance ratio of short lines,angle-based transient stability techniques may find limitations.Under such a situation,the Lyapunov function can be a viable option for transient stability assessment(TSA)of such a VSC-interfaced microgrid.However,the determination of the Lyapunov function with the classical method is very challenging for a microgrid with converter controller dynamics.To overcome such challenges,this paper develops a physics-informed,Lyapunov function-based TSA framework for VSC-interfaced microgrids.The method uses the physics involved and the initial and boundary conditions of the system in learning the Lyapunov functions.This method is tested and validated under faults,droop-coefficient changes,generator outages,and load shedding on a small grid-connected microgrid and the CIGRE microgrid.

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.

Smart and fast reinforcement design for anti-dip bedding rock slopes

Rock slopes are usually reinforced by a number of rock bolts due to the high efficiency and low price.However,where should the rock bolts be installed is still a troublesome issue.For anti-dip bedding rock slopes(ABRSs),the installation position of rock bolts is a controlling factor that determines the reinforcement effect.In this work,a theoretical method is firstly proposed for assessing the stability of ABRSs reinforced by rock bolts using a limit equilibrium model.A comparison of theoretical calculations and numerical results was conducted to test the correctness of the theoretical method.Based on the stability assessment of ABRSs,we introduce adaptive moment estimation method(Adam)to optimize the installation location of rock bolts.Using Adam optimizer,the optimal layout of rock bolts with the maximum factor of safety can be determined,and the factor of safety of the slope increases by about 25%using the same amount of rock bolts but with different installation locations.The proposed method enables the fast stability analysis and supporting design for reinforced ABRSs,which paves the way to smart supporting design of slopes.

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.

A landscape persistence-based methodological framework for assessing ecological stability

Ecological stability is a critical factor in global sustainable development,yet its significance has been overlooked.Here we introduce a landscape-oriented framework to evaluate ecological stability in the Qingzang Plateau(QP).Our findings reveal a medium-high stability level in the QP,with minimal changes over recent years.The driving factors vary across landscape types,with climate and anthropogenic factors emerging as crucial determinants.While anthropogenic factors are strong but unstable due to policy changes and economic development,climatic factors exert a consistent influence.Based on our results,we propose site-specific ecological conservation and restoration measures.The ecological stability assessment framework provides a practical tool to understand the link between environmental conditions and ecosystems.

Reliability analysis of earth slopes using hybrid chaotic particle swarm optimization

A numerical procedure for reliability analysis of earth slope based on advanced first-order second-moment method is presented,while soil properties and pore water pressure may be considered as random variables.The factor of safety and performance function is formulated utilizing a new approach of the Morgenstern and Price method.To evaluate the minimum reliability index defined by Hasofer and Lind and corresponding critical probabilistic slip surface,a hybrid algorithm combining chaotic particle swarm optimization and harmony search algorithm called CPSOHS is presented.The comparison of the results of the presented method,standard particle swarm optimization,and selected other methods employed in previous studies demonstrates the superior successful functioning of the new method by evaluating lower values of reliability index and factor of safety.Moreover,the presented procedure is applied for sensitivity analysis and the obtained results show the influence of soil strength parameters and probability distribution types of random variables on the reliability index of slopes.

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.

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.