Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for eac...Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches,namely accuracy,training time,and model management complexity,a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First,the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently,K-means clustering was conducted on each line based on the similarity of their electrical features,employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally,a model effectiveness evaluation system consisting of five metrics was established,and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover,this approach allows for more flexible adjustments to line topology changes.展开更多
The traditional transient stability assessment(TSA)model for power systems has three disadvantages:capturing critical information during faults is difficult,aperiodic and oscillatory unstable conditions are not distin...The traditional transient stability assessment(TSA)model for power systems has three disadvantages:capturing critical information during faults is difficult,aperiodic and oscillatory unstable conditions are not distinguished,and poor generalizability is exhibited by systems with high renewable energy penetration.To address these issues,a novel ResGRU architecture for TSA is proposed in this study.First,a residual neural network(ResNet)is used for deep feature extraction of transient information.Second,a bidirectional gated recurrent unit combined with a multi-attention mechanism(BiGRU-Attention)is used to establish temporal feature dependencies.Their combination constitutes a TSA framework based on the ResGRU architecture.This method predicts three transient conditions:oscillatory instability,aperiodic instability,and stability.The model was trained offline using stochastic gradient descent with a thermal restart(SGDR)optimization algorithm in the offline training phase.This significantly improves the generalizability of the model.Finally,simulation tests on IEEE 145-bus and 39-bus systems confirmed that the proposed method has higher adaptability,accuracy,scalability,and rapidity than the conventional TSA approach.The proposed model also has superior robustness for PMU incomplete configurations,PMU noisy data,and packet loss.展开更多
With the continuous expansion and increasing complexity of power system scales,the binary classifica-tion for transient stability assessment in power systems can no longer meet the safety requirements of power system ...With the continuous expansion and increasing complexity of power system scales,the binary classifica-tion for transient stability assessment in power systems can no longer meet the safety requirements of power system control and regulation.Therefore,this paper proposes a multi-class transient stability assessment model based on an improved Transformer.The model is designed with a dual-tower encoder structure:one encoder focuses on the time dependency of data,while the other focuses on the dynamic correlations between variables.Feature extraction is conducted from both time and variable perspectives to ensure the completeness of the feature extraction process,thereby enhancing the accuracy of multi-class evaluation in power systems.Additionally,this paper introduces a hybrid sampling strategy based on sample boundaries,which addresses the issue of sample imbalance by increasing the number of boundary samples in the minority class and reducing the number of non-boundary samples in the majority class.Considering the frequent changes in power grid topology or operation modes,this paper proposes a two-stage updating scheme based on self-supervised learning:In the first stage,self-supervised learning is employed to mine the structural information from unlabeled data in the target domain,enhancing the model’s generalization capability in new scenarios.In the second stage,a sample screening mechanism is used to select key samples,which are labeled through long-term simulation techniques for fine-tuning the model parameters.This allows for rapid model updates without relying on many labeled samples.This paper’s proposed model and update scheme have been simulated and verified on two node systems,the IEEE New England 10-machine 39-bus system and the IEEE 47-machine 140-bus system,demonstrating their effectiveness and reliability.展开更多
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 s...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.展开更多
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 s...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.展开更多
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 tech...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.展开更多
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 b...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.展开更多
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 ac...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.展开更多
Deep learning technology is identified as a valid tool for transient stability assessment(TSA).Moreover,the superior performance of the TSA model depends on generously labeled samples.However,the power grid is dynamic...Deep learning technology is identified as a valid tool for transient stability assessment(TSA).Moreover,the superior performance of the TSA model depends on generously labeled samples.However,the power grid is dynamic,and some topologies or operation conditions change substantially.The traditional method generates a significant quantity of samples for each specific topology.Nonetheless,generating these labeled samples and establishing TSA models is very time-consuming.This paper proposes a high-quality sample generation framework based on data-driven methods to build a high-quality offline samples database for TSA model training and updating.Firstly,the representative topologies provided by the system operator are clustered into four different categories by density-based spatial clustering of applications with noise(DBSCAN).Thus the corresponding samples are collected.Then,when a new topology is encountered in the online application,scenario matching is used to match the most similar topology category.After that,instance-based transfer learning is implemented from a database of the best-matched topology category.Finally,a deep convolutional generative adversarial network(DCGAN)is constructed to mitigate the class imbalance problem.That is,unstable scenarios occur far more rarely than stable scenarios.Consequently,a high-quality and balanced TSA model training and updating database is constructed.The comprehensive test results on the Central China Power Grid illustrate that the proposed framework can generate high-quality and balanced TSA samples.Furthermore,the sample generation time is dramatically shortened.In addition,the metrics of accuracy,reliability and adaptability of the TSA model are significantly enhanced.展开更多
Weighted least-square support vector machine(WLS-SVM)is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units(PMUs).T...Weighted least-square support vector machine(WLS-SVM)is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units(PMUs).This method considers the directional overcurrent relays(DOCRs)for the transmission system,whereas in previous studies,the effect of protective mechanisms on the transient stability was largely ignored.When protective relays are activated in power system,the configuration of the power system is altered to mitigate the risk of the power system becoming unstable.The present study considers the operation of DOCRs in transmission lines for the transient stability so that the proposed method can respond to changes in the configuration of the case study system.In addition,WLS-SVM is employed for an online assessment of the transient stability.WLS-SVM not only is effective in response due to its faster speed,but also is resistant to noise and has excellent performance against the measurement errors of PMUs.To extract the characteristics of the vectors that are fed into the WLS-SVM algorithm,principal component analysis is used.The findings of the suggested technique reveal that it has higher accuracy and optimum performance,as compared to the extreme learning machine method,the adaptive neuro-fuzzy inference system method,and the back-propagation neural network method.The proposed technique is validated in the New England 39-bus system and the IEEE 118-bus system.展开更多
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 topo...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.展开更多
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 ...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.展开更多
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 mode...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.展开更多
Although the deployment of alternating current(AC)-busbar plug-in electric vehicle(PEV)charging station with photovoltaic(PV)is a promising alternative,the interaction among subsystems always causes the instability pr...Although the deployment of alternating current(AC)-busbar plug-in electric vehicle(PEV)charging station with photovoltaic(PV)is a promising alternative,the interaction among subsystems always causes the instability problem.Meanwhile,the conventional generalized Nyquist criterion(GNC)is complex,and it is not suitable for the design of the AC system.Therefore,this paper proposes a modified infinityone-norm(MION)stability criterion based on the impedance method to assess the stability of the foresaid charging station.Firstly,the typical structure and operation modes of the charging station are studied.Furthermore,each subsystem impedance matrix is built by small-signal method,and the MION stability criterion based on impedance method is proposed to assess the charging station stability.Compared with the previous simplified stability criteria based on the norm,the proposed criterion has lower conservatism.Furthermore,the design regulation for the controller parameters is provided,and the stability recovery way is provided by connecting the doubly-fed line and energy storage equipment,which are selected based on intermediate variable,i.e.,short-circuit ratio(SCR).Finally,the effectiveness and conservatism of the proposed stability criterion are validated through simulation and experimental results.展开更多
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 functionin...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.展开更多
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...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.展开更多
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...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.展开更多
Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicall...Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicallyversatile kagome platform with mixed low-dimensional structural framework and tunable physical properties.Ourresearch initiates with a comprehensive evaluation of the currently known Ln_(3)ScBi_(5)(Ln=La-Nd,Sm)materials,providing a robust methodology for assessing their stability frontiers within this system.Focusing on Pr_(3)ScBi_(5),we investigate the influence of the zigzag chains of quasi-one-dimensional(Q1D)motifs and the distorted kagomelayers of quasi-two-dimensional(Q2D)networks in the mixed-dimensional structure on the intricate magneticground states and unique spin fluctuations.Our study reveals that the noncollinear antiferromagnetic(AFM)moments of Pr^(3+)ions are confined within the Q2D kagome planes,displaying minimal in-plane anisotropy.Incontrast,a strong AFM coupling is observed within the Q1D zigzag chains,significantly constraining spin motion.Notably,magnetic frustration is partially a consequence of coupling to conduction electrons via Ruderman-Kittel-Kasuya-Yosida interaction,highlighting a promising framework for future investigations into mixed-dimensional frustration in Ln_(3)ScBi_(5) systems.展开更多
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 terrest...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.展开更多
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 ...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.展开更多
基金supported by the Science and Technology Project of SGCC(5100-202199558A-0-5-ZN).
文摘Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches,namely accuracy,training time,and model management complexity,a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First,the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently,K-means clustering was conducted on each line based on the similarity of their electrical features,employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally,a model effectiveness evaluation system consisting of five metrics was established,and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover,this approach allows for more flexible adjustments to line topology changes.
基金financially supported by State Key Laboratory of HVDC No.SKLHVDC-2023-KF-03.
文摘The traditional transient stability assessment(TSA)model for power systems has three disadvantages:capturing critical information during faults is difficult,aperiodic and oscillatory unstable conditions are not distinguished,and poor generalizability is exhibited by systems with high renewable energy penetration.To address these issues,a novel ResGRU architecture for TSA is proposed in this study.First,a residual neural network(ResNet)is used for deep feature extraction of transient information.Second,a bidirectional gated recurrent unit combined with a multi-attention mechanism(BiGRU-Attention)is used to establish temporal feature dependencies.Their combination constitutes a TSA framework based on the ResGRU architecture.This method predicts three transient conditions:oscillatory instability,aperiodic instability,and stability.The model was trained offline using stochastic gradient descent with a thermal restart(SGDR)optimization algorithm in the offline training phase.This significantly improves the generalizability of the model.Finally,simulation tests on IEEE 145-bus and 39-bus systems confirmed that the proposed method has higher adaptability,accuracy,scalability,and rapidity than the conventional TSA approach.The proposed model also has superior robustness for PMU incomplete configurations,PMU noisy data,and packet loss.
基金the National Natural Science Foundation of China(5227-7084).
文摘With the continuous expansion and increasing complexity of power system scales,the binary classifica-tion for transient stability assessment in power systems can no longer meet the safety requirements of power system control and regulation.Therefore,this paper proposes a multi-class transient stability assessment model based on an improved Transformer.The model is designed with a dual-tower encoder structure:one encoder focuses on the time dependency of data,while the other focuses on the dynamic correlations between variables.Feature extraction is conducted from both time and variable perspectives to ensure the completeness of the feature extraction process,thereby enhancing the accuracy of multi-class evaluation in power systems.Additionally,this paper introduces a hybrid sampling strategy based on sample boundaries,which addresses the issue of sample imbalance by increasing the number of boundary samples in the minority class and reducing the number of non-boundary samples in the majority class.Considering the frequent changes in power grid topology or operation modes,this paper proposes a two-stage updating scheme based on self-supervised learning:In the first stage,self-supervised learning is employed to mine the structural information from unlabeled data in the target domain,enhancing the model’s generalization capability in new scenarios.In the second stage,a sample screening mechanism is used to select key samples,which are labeled through long-term simulation techniques for fine-tuning the model parameters.This allows for rapid model updates without relying on many labeled samples.This paper’s proposed model and update scheme have been simulated and verified on two node systems,the IEEE New England 10-machine 39-bus system and the IEEE 47-machine 140-bus system,demonstrating their effectiveness and reliability.
基金Project(2014QNA50) supported by Fundamental Research Funds for the Central Universities,ChinaProject(51404248) supported by the National Natural Science Foundation of ChinaProject supported by the Priority Academic Program Development(PAPD) of Jiangsu Higher Education Institutions,China
文摘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.
文摘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.
基金supported by the National Science Foundation under Grant No.ITE-2134840This work relates to the Department of Navy award N00014-23-1-2124 issued by the Office of Naval Research.The United States Government has a royalty-free license worldwide for all copyrightable material contained herein。
文摘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.
基金This work is supported by the State Grid Shanxi Electric Power Company Technology Project(52053023000B).
文摘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.
文摘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.
基金supported by the Technology Project from China Electric Power Planning&Engineering Institute(No.K202312)。
文摘Deep learning technology is identified as a valid tool for transient stability assessment(TSA).Moreover,the superior performance of the TSA model depends on generously labeled samples.However,the power grid is dynamic,and some topologies or operation conditions change substantially.The traditional method generates a significant quantity of samples for each specific topology.Nonetheless,generating these labeled samples and establishing TSA models is very time-consuming.This paper proposes a high-quality sample generation framework based on data-driven methods to build a high-quality offline samples database for TSA model training and updating.Firstly,the representative topologies provided by the system operator are clustered into four different categories by density-based spatial clustering of applications with noise(DBSCAN).Thus the corresponding samples are collected.Then,when a new topology is encountered in the online application,scenario matching is used to match the most similar topology category.After that,instance-based transfer learning is implemented from a database of the best-matched topology category.Finally,a deep convolutional generative adversarial network(DCGAN)is constructed to mitigate the class imbalance problem.That is,unstable scenarios occur far more rarely than stable scenarios.Consequently,a high-quality and balanced TSA model training and updating database is constructed.The comprehensive test results on the Central China Power Grid illustrate that the proposed framework can generate high-quality and balanced TSA samples.Furthermore,the sample generation time is dramatically shortened.In addition,the metrics of accuracy,reliability and adaptability of the TSA model are significantly enhanced.
文摘Weighted least-square support vector machine(WLS-SVM)is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units(PMUs).This method considers the directional overcurrent relays(DOCRs)for the transmission system,whereas in previous studies,the effect of protective mechanisms on the transient stability was largely ignored.When protective relays are activated in power system,the configuration of the power system is altered to mitigate the risk of the power system becoming unstable.The present study considers the operation of DOCRs in transmission lines for the transient stability so that the proposed method can respond to changes in the configuration of the case study system.In addition,WLS-SVM is employed for an online assessment of the transient stability.WLS-SVM not only is effective in response due to its faster speed,but also is resistant to noise and has excellent performance against the measurement errors of PMUs.To extract the characteristics of the vectors that are fed into the WLS-SVM algorithm,principal component analysis is used.The findings of the suggested technique reveal that it has higher accuracy and optimum performance,as compared to the extreme learning machine method,the adaptive neuro-fuzzy inference system method,and the back-propagation neural network method.The proposed technique is validated in the New England 39-bus system and the IEEE 118-bus system.
基金supported by National Key R&D Program of China(No.2018YFB0904500)State Grid Corporation of China(No.SGLNDK00KJJS1800236)
文摘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.
基金supported by National Key R&D Program of China (No.2018YFB0904500)State Grid Corporation of China。
文摘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.
基金supported by the National Key R&D Program of China(2018AAA0101500).
文摘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.
基金supported by National Key Research and Development Program of China(No.2018YFA0702200)National Natural Science Foundation of China(No.61773109)Major Program of National Natural Foundation of China(No.61573094)
文摘Although the deployment of alternating current(AC)-busbar plug-in electric vehicle(PEV)charging station with photovoltaic(PV)is a promising alternative,the interaction among subsystems always causes the instability problem.Meanwhile,the conventional generalized Nyquist criterion(GNC)is complex,and it is not suitable for the design of the AC system.Therefore,this paper proposes a modified infinityone-norm(MION)stability criterion based on the impedance method to assess the stability of the foresaid charging station.Firstly,the typical structure and operation modes of the charging station are studied.Furthermore,each subsystem impedance matrix is built by small-signal method,and the MION stability criterion based on impedance method is proposed to assess the charging station stability.Compared with the previous simplified stability criteria based on the norm,the proposed criterion has lower conservatism.Furthermore,the design regulation for the controller parameters is provided,and the stability recovery way is provided by connecting the doubly-fed line and energy storage equipment,which are selected based on intermediate variable,i.e.,short-circuit ratio(SCR).Finally,the effectiveness and conservatism of the proposed stability criterion are validated through simulation and experimental results.
基金supported by Central China Branch of State Grid Corporation of China(Characteristics Analysis and Operation Control Technology Research on Power Grid Adapting to Large-scale and Strong Sparse New Energy)。
文摘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.
文摘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.
基金funded by the National Natural Science Foundation of China under Grant No.51607105.
文摘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.
基金supported by the National Key R&D Program of China(Grant Nos.2024YFA1408400 and 2021YFA1400401)the National Natural Science Foundation of China(Grant Nos.U22A6005 and 52271238)+2 种基金the China Postdoctoral Science Foundation(Grant No.2025M770186)the Center for Materials Genome,and the Synergetic Extreme Condition User Facility(SECUF)supported by the AI-driven experiments,simulations and model training on the robotic AI-Scientist platform from Chinese Academy of Sciences and the Research Funds for the Central Universities(Grant No.N25ZLE007).
文摘Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicallyversatile kagome platform with mixed low-dimensional structural framework and tunable physical properties.Ourresearch initiates with a comprehensive evaluation of the currently known Ln_(3)ScBi_(5)(Ln=La-Nd,Sm)materials,providing a robust methodology for assessing their stability frontiers within this system.Focusing on Pr_(3)ScBi_(5),we investigate the influence of the zigzag chains of quasi-one-dimensional(Q1D)motifs and the distorted kagomelayers of quasi-two-dimensional(Q2D)networks in the mixed-dimensional structure on the intricate magneticground states and unique spin fluctuations.Our study reveals that the noncollinear antiferromagnetic(AFM)moments of Pr^(3+)ions are confined within the Q2D kagome planes,displaying minimal in-plane anisotropy.Incontrast,a strong AFM coupling is observed within the Q1D zigzag chains,significantly constraining spin motion.Notably,magnetic frustration is partially a consequence of coupling to conduction electrons via Ruderman-Kittel-Kasuya-Yosida interaction,highlighting a promising framework for future investigations into mixed-dimensional frustration in Ln_(3)ScBi_(5) systems.
基金support of the National Natural Science Foundation of China(Grant No.42102316)the Open Project of the Technology Innovation Center for Geological Environment Monitoring of Ministry of Natural Resources of China(Grant No.2022KFK1212005).
文摘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.
文摘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.
