A Classifier-Guided Diffusion Model-Based Key Sample Augmentation Method for Power System Transient Stability

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

Deep learning-based transient stability assessment framework for large-scale modern power system

Transient stability assessment (TSA) is a cornerstone for resilient operations of today’s interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. We devise a quantum TSA (QTSA) method to enable efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth quantum circuit (HELD) is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD training and 3) A systematical analysis on QTSA’s performance under various quantum factors is performed. QTSA underpins a foundation of quantum machine learning-enabled power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

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.

Modern power systems are large in size and complex in features; the data collected by Phasor Measurement Units (PMUs) are often noisy and contaminated; and the machine learning models that have been applied to the transient stability assessment (TSA) of power systems are not sufficiently capable of capturing long-distance dependencies. All these issues make it difficult for data mining-based power system TSA methods to have sufficient accuracy, timeliness, and robustness. To solve this problem, this paper proposes a power system TSA model based on the transformer and neighborhood rough set. The model first uses the neighborhood rough set to deal with the redundant features of the power system trend data and then uses the transformer model to train the TSA model, in which various normalization methods such as Batch Normalization and Layer Normalization are introduced in the process to obtain better evaluation performance and speed up the convergence rate of the model. Finally, the model is evaluated by two evaluation indicators, F1−measure and accuracy, with values of 99.61% for accuracy and 0.9972 for F1−measure, as soon as the tests on noise contamination and missing data test results on the IEEE39 system show that the NRS-Transformer model proposed in this paper is superior in terms of prediction accuracy, training speed, and robustness.

It is becoming increasingly difficult to conduct standard stability studies on modern power systems due to the growing use of power electronic devices. Power electronic converters, regardless of their power or voltage, must contend with a rising challenge known as electromagnetic transient stability. For power system stability analysis and control, transient stability assessment (TSA) is essential. On the basis of numerical integration of the dynamics equations for an EPS under consideration at a particular disturbance, a method for assessing transient stability reserves is described. In order to improve the transient stability of hybrid AC/DC power systems, a multi-HVDC emergency coordinated modulating method has been suggested in this study. Multi-DC lines are used to build and test the AC/DC hybrid power system. Machine learning models that forecast power system transient stability are more reliable, versatile, and robust if they incorporate knowledge of power system dynamics into their data gathering process. Fault location and how disturbance effects spread over the network are critical for determining unstable regions of operation, thus this determination is made using domain knowledge. That’s why traditional machine learning models may now be used in an instance transfer learning framework thanks to a whole new data gathering technique. Therefore, the suggested solution does not rely primarily on the collected data, but also incorporates system insights.

Online transient stability assessment and preventive control are important measures to prevent power system blackouts. Based on the deep cross network (DCN), an online assessment and preventive control method for power system transient stability is proposed. The deep cross network is developed to estimate the critical clearing time (CCT) of the credible fault contingencies. Based on the deep cross network model, the sensitivity of the active power of the generator set to the fault critical clearing time is calculated and then the DCN model-embedded transient stability constrained optimal power flow (TSCOPF) model is proposed to compute the preventive generation re-dispatch strategy. This TSCOPF model is computed by successive linear programming assisted by the DCN. The simulation results of the IEEE 39-bus test system show that this method can give early warning to the operation mode with instability risk and generate preventive control strategies, which can effectively improve the safety level of power grid operation.

Transient stability assessment (TSA) plays an essential role in the safe and stable operation of power system. The traditional time-domain simulation method and direct method have great limitation in modeling and computing, which can not meet the need of fast and accurate assessment of power system stability. Therefore, combined physics model with data-driven algorithm, this paper proposes a physics-informed deep learning paradigm to solve this problem. In the data-driven algorithm, recurrent neural networks are trained to predict the rotor speed and power angle with the guiding of the power system’s physical information. Finally, the simulation on IEEE14 system is carried out by using PSS/E software, which proved that the proposed method can predict the power angle trajectory quickly and accurately.

Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

Real-time localized control of transient stability by the very recently proposed Localized Transient Stability (LTS) Method with Static Var Compensator (SVC) as the local control means is presented here. However, since the technique is based on the LTS method, transient stability in this technique is viewed as the interaction of each individual generator with its respective remaining generators. Therefore, in this technique, the post-fault power system is represented by a two-generator localized power system at the site of each individual generator. Each of these localized power systems is then driven to its respective stable equilibrium by local control actions with local computations using the locally measured data to drive the full (entire) power system to its stable equilibrium. New investigative results are presented here to demonstrate the potential of the real-time localized control of transient stability by the LTS method.

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.

This paper presents the study of using a new hybrid simulator for transient stability assessment. This new hybrid simulator is developed to extend the traditional transient stability program to include detailed modelling of modern power electronics devices such as SVC, FACTS, etc. It possesses the capability of representing extensive power systems and modelling the particular devices in details using transient stability and electromagnetic transient models respectively. In the context of transient stability assessment, the hybrid simulator has the merit that it can analyze the influences of internal faults and malfunctions within devices such as FACTS and HVDC on systemwide stability. Case studies on three-generator nine-bus system are given to show the advantages of the hybrid simulator over traditional transient stability program.

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.

Transient stability greatly challenges the safe and stable operation of the power system. Therefore, it is extremely necessary to assessment transient stability quickly and accurately. This paper proposes an intelligent model for transient stability assessment (TSA) based on the separation feature and deep belief networks (DBN) for large-scale AC/DC hybrid power grid. With this model, a training method which is suitable for large-scale AC/DC hybrid power grids was proposed. According to the correlation degrees between different electrical characteristics and power system transient stability, the input features are separated into operation mode and disturbance mode, and then, using DBN to extract the features of the two types of input data respectively. In order to adapt the large-scale AC/DC hybrid power grid, different pre-training sample sets are applied for different DBN feature extraction networks to improve the generalization ability and reduce the complexity of model training. Test results with the proposed model on the 265-bus AC/DC hybrid power grid designed by the China Electric Power Research Institute indicated that the new model is easy to train, and it can effectively reduce the influence of imbalanced samples, with good generalization ability.

Node breaker model based transient stability simulations including protective devices modeling and time coordination