Imbalanced correction method for power systems transient stability assessment based on the imbalance degree of image samples

An optimized algorithm for optimal power flow based on deep learning

Traditional machine learning models are subject to changes in the data distribution. Once the topology of power grid changes, the distribution of power grid data will change, resulting in the consequence that the former transient stability assessment model based on machine learning is no longer applicable and needs to be retrained. However, the power network after the topology changes cannot provide enough data for the training of the new model. To solve the problem above, this paper proposes a method of power system transient stability assessment based on transfer learning, which fully taps the potential of past data and assists a small amount of newly acquired data in training the model. The simulation results on the CEPRI36 system show that the proposed method can effectively improve the accuracy of original classification models, especially for weak classifiers with insufficient generalization ability. The disadvantage of transfer learning lies in the long training time, which still needs to be improved.

Adaptive cost-sensitive assignment method for power system transient stability assessment

Taking full advantage of synchrophasors provided by GPS-based wide-area measurement system (WAMS), a novel VBpMKL-based transient stability assessment (TSA) method through multifeature fusion is proposed in this paper. First, a group of classification features reflecting the transient stability characteristics of power systems are extracted from synchrophasors, and according to the different stages of the disturbance process they are broken into three nonoverlapped subsets; then a VBpMKL-based TSA model is built using multifeature fusion through combining feature spaces corresponding to each feature subset; and finally application of the proposed model to the IEEE 39-bus system and a real-world power system is demonstrated. The novelty of the proposed approach is that it improves the classification accuracy and reliability of TSA using multifeature fusion with synchrophasors. The application results on the test systems verify the effectiveness of the proposal.

Transient voltage stability assessment of renewable energy grid based on residual SDE-Net

Convolutional neural network-based power system transient stability assessment and instability mode prediction

This article presents the Lyapunov functions based on the artificial intelligence (AI) method for transient stability assessment and the determination of stability region (SR). First, Lyapunov stability theory and the definition of SR are introduced. Then, the characteristics of neural networks as a general function approximator are employed as the Lyapunov function learner, and the Lyapunov function is constructed combined with stochastic gradient descent (SGD). Then, the falsifier's task is to find the state vectors that violate Lyapunov stability conditions, and the counterexamples would be added to the training set for the function learner to accelerate convergence. After obtaining the Lyapunov function of power system, the estimation of SR boundary can be represented by the maximum level set of Lyapunov function. Finally, the IEEE 9-bus 3-machine system is used as test system to demonstrate the validity and effectiveness of the proposed construction method of Lyapunov functions for power system transient stability analysis.

Machine learning is playing an increasingly important role in transient stability assessment. One of the key challenges of machine learning-based transient stability assessment in the case of dealing with large-scale and high-dimensional data samples of AC-DC power systems is to eliminate redundant variables to improve the training speed and quickly determine the optimal parameters of the adopted algorithms. Aiming at this problem, this paper proposes a Bayesian optimization-based XGBoost (B-XGBoost) method for transient stability assessment of power systems. The method uses serial integration of multiple regression trees to calculate the importance ranking of transient stability features. Combined with correlation analysis, the relationship between input data and transient stability can be explored more intuitively, and redundant variables can be eliminated; Bayesian optimization is used in training to quickly determine the best parameters of the model. Simulations are performed in a 39-node AC-DC system, and the results show that the B-XGBoost transient stability evaluation method has higher accuracy and a significant advantage in speed compared with other machine learning-based methods such as GBDT, DBN, RF, SVM.

The impact of numerous distributed generators (DGs) coupled with the implementation of virtual inertia on the transient stability of power systems has been studied extensively. Time-domain simulation is the most accurate and reliable approach to evaluate the dynamic behavior of power systems. However, the computational efficiency is restricted by their multi-time-scale property due to the combination of various DGs and synchronous generators. This paper presents a novel projective integration method (PIM) for the efficient transient stability simulation of power systems with high DG penetration. One procedure of the proposed PIM is decomposed into two stages, which adopt mixed explicit-implicit integration methods to achieve both efficiency and numerical stability. Moreover, the stability of the PIM is not affected by its parameter, which is related to the step size. Based on this property, an adaptive parameter scheme is developed based on error estimation to fit the time constants of the system dynamics and further increase the simulation speed. The presented approach is several times faster than the conventional integration methods with a similar level of accuracy. The proposed method is demonstrated using test systems with DGs and virtual synchronous generators, and the performance is verified against MATLAB/Simulink and DIgSILENT PowerFactory.

In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cramér Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving.

Deep Neural Network (DNN) has been introduced to power system transient stability assessment (TSA) and achieved quite remarkable success. However, since the DNNs are ‘blackbox’ models, it is very hard to reveal misbehaviors of the DNNs and repair them to get better performance. To address these challenges, this paper proposes to use Arachne, a fresh program repair technique for DNNs, to repair TSA DNNs. Arachne can fix DNN's specific misclassifications at little cost to overall accuracy. In TSA, missed detections is more severe to the system than false alarms due to the conservation requirements. Case studies have demonstrated the usefulness of the proposed method.

In this paper, an online power system transient stability assessment (TSA) problem is mapped as a two-class classification problem and a novel data mining algorithm the core vector machine (CVM) is proposed to solve the problem based on phasor measurement units (PMUs) big data. First of all, an offline training, online application framework is proposed, which contained four sub-steps, namely features selection, offline training, online application, and assessment evaluation. First, 24 features are selected to present the system status. Then in the offline training procedure, the PMU big data is generated by time domain simulation, and a CVM model is trained and tested. In the online application procedure, an interface between PMU data center and feature calculation program is set up to collect real time specific PMU big data and the CVM trained is applied to the TSA problem. Last but not least, the evaluation indices are calculated. Compared with other support vector machines, the proposed CVM based assessment algorithm has the higher precision, meanwhile, it has the least time consumption and space complexity. As long as online PMU big data are received, TSA can be done simultaneously. Case studies on the IEEE New England 39-bus system, and real systems in China and the U.S., exhibit the speed and effectiveness of the proposed algorithm.

The power transient stability analysis is one of the basis for determining the control strategy of power system security and stability. Considering the influence of power grid topology on the transient stability of power system, the transient stability evaluation model is constructed based on the graph attention neural network. The electrical components and their transient operation data are mapped to the graph data with the spatial topology characteristics of power system for model training, so as to improve the topological generalization performance of the model. The marginal contribution of input characteristics to the output of transient power angle stability evaluation model is quantitatively calculated based on Shapley additive explanation (SHAP), so as to improve the interpretability of data-driven method for transient power angle stability evaluation. The effectiveness of the proposed method is verified by the IEEE 39-bus system.

Consideration of transient stability constraints in optimal power flow (OPF) problems is increasingly important because modern power systems tend to operate closer to stability boundaries due to the rapid increase of electricity demand and the deregulation of electricity markets. Transient stability constrained OPF (TSCOPF) is however a nonlinear optimization problem with both algebraic and differential equations, which is difficult to be solved even for small power systems. This paper develops a robust and efficient method for solving TSCOPF problems based on differential evolution (DE), which is a new branch of evolutionary algorithms with strong ability in searching global optimal solutions of highly nonlinear and nonconvex problems. Due to the flexible properties of DE mechanism, the hybrid method for transient stability assessment, which combines time-domain simulation and transient energy function method, can be employed in DE so that the detailed dynamic models of the system can be incorporated. To reduce the computational burden, several strategies are proposed for the initialization, assessment and selection of solution individuals in evolution process of DE. Numerical tests on the WSCC three-generator, nine-bus system and New England ten-generator, 39-bus system have demonstrated the robustness and effectiveness of the proposed approach. Finally, in order to deal with the large-scale system and speed up the computation, DE is parallelized and implemented on a Beowulf PC-cluster. The effectiveness of the parallel DE approach is demonstrated by simulations on the 17-generator, 162-bus system.

This paper proposes an improved LSSVM power system transient stability evaluation method. According to the Bayesian nonlinear hierarchical framework, the automatic selection of parameters is implemented to improve the sensitivity of LSSVM classifier model to parameter and improve the accuracy of transient stability assessment Firstly, the original feature set related to the system stability is calculated by the wide area measurement information. The feature selection method is used to compute the feature, and the optimal feature set which is strongly related to the stability of the power system is determined. The training set and the test set are divided into Mapped to high-dimensional space, so that the nonlinear and linear classification of the transformation. Then, the optimal parameters of LSSVM are determined by Bayesian nonlinear hierarchical model, and the fast transient stability is determined. Finally, the validity and accuracy of the evaluation model are verified by IEEE-39 node system and actual system.