Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs) is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.

Transient stability assessment (TSA) based on deep learning has recently attracted broad attention. The offline training of deep learning model typically requires a large number of labeled samples, and the training process is very time-consuming. Moreover, it is difficult to ensure that the trained model is suitable for all the operation conditions of power systems. In order to dramatically reduce the computation cost, and further enhance the adaptability of the model, an innovative method is proposed based on the active transfer learning (TL). First, the deep belief network (DBN) is designed as the base model and trained with a large number of labeled samples to obtain better evaluation performance for TSA. Second, when the topologies or operation conditions change substantially, active learning with information entropy is exploited to select the minimum number of the most informative samples, which effectively reduces the time of samples generation. Third, Maximum Mean Discrepancy (MMD) is calculated to select different transfer schemes. Only the last BP layer needs to be fine-tuned for the scenarios with a low MMD, but for those with high MMD, the entire network needs to be fine-tuned. The transfer time is greatly shortened on the premise of ensuring the transfer effect. The comprehensive experimental results of three power systems illustrate the effectiveness of the proposed method. Note to Practitioners—This research is motivated by automatic adaptive assessment of power system transient stability. The main objective is to realize online fast and continuous prediction with limited samples. This kind of demand is very normal in the industrial systems where it is expensive, time-consuming and difficult to obtain labeled samples. This paper proposes an active transfer learning method to select critical samples and update the deep learning model, which greatly reduces the time of samples generation and improves the updating efficiency of model. The proposed method is also applicable to make classification prediction or to address regression problems of industrial scenarios based on deep learning models.

In order to maintain the stable operation of power systems, quick and accurate transient stability assessment (TSA) after the fault clearance is important. Machine learning methods have been widely used in the transient stability analysis of power systems. However, how to make good use of time series data of PMUs and effectively balance the contradiction between rapidity and accuracy brings new challenges to TSA. To address this problem, we propose an anti-jitter dynamic evaluation method based on long-short term memory (LSTM) network. In this model, the trajectory cluster characteristics of generators power angles after fault clearance are taken as inputs, and an improved LSTM is used to learn the nonlinear mapping relationship between the input characteristics and the transient stability. Meanwhile, by the use of sliding time windows and anti-jitter mechanism, a hierarchical real-time prediction framework is constructed to effectively utilize the time series data of PMUs. The case studies on two systems indicate that the proposed method has superior evaluation accuracy and general performance. In addition, the proposed method can effectively evaluate the stability margin or instability degree of samples, which provides reliable reference information for emergency control.

This paper proposes a real-time pricing scheme for the demand response management between one energy provider and multiple energy hub operators. A promising energy trading scenario has been designed for the near future integrated energy system. The Stackelberg game approach was employed to capture the interactions between the energy provider (leader) and energy consumers (follower). A distributed algorithm was proposed to derive the Stackelberg equilibrium, then, the best strategies for the energy provider and each energy hub operator were explored in order to maximize their benefits. Simulation results showed that the proposed method can balance the energy supply and demand, improve the payoffs for all players, as well as smooth the aggregated load profiles of all energy consumers.

To achieve rapid real-time transient stability prediction, a power system transient stability prediction method based on the extraction of the post-fault trajectory cluster features of generators is proposed. This approach is conducted using data-mining techniques and support vector machine (SVM) models. First, the post-fault rotor angles and generator terminal voltage magnitudes are considered as the input vectors. Second, we construct a high-confidence dataset by extracting the 27 trajectory cluster features obtained from the chosen databases. Then, by applying a filter–wrapper algorithm for feature selection, we obtain the final feature set composed of the eight most relevant features for transient stability prediction, called the global trajectory clusters feature subset (GTCFS), which are validated by receiver operating characteristic (ROC) analysis. Comprehensive simulations are conducted on a New England 39-bus system under various operating conditions, load levels and topologies, and the transient stability predicting capability of the SVM model based on the GTCFS is extensively tested. The experimental results show that the selected GTCFS features improve the prediction accuracy with high computational efficiency. The proposed method has distinct advantages for transient stability prediction when faced with incomplete Wide Area Measurement System (WAMS) information, unknown operating conditions and unknown topologies and significantly improves the robustness of the transient stability prediction system.

The authors propose a novel sliding‐mode variable structure controller for cascade STATic var COMpensator (STATCOM), which integrates the direct feedback linearisation control for the balanced system and the individual phase instantaneous current tracking control for the unbalanced system, to achieve a versatile STATCOM controller. It can adapt automatically to various distribution system conditions. Based on the degree of system unbalance, the authors design a switching function to make the STATCOM work reliably with satisfactory performance, when the serious voltage sag occurs or the distribution system enters serious asymmetrical condition. The authors also propose a hierarchical DC‐link voltages balance control strategy. The authors build a detailed ± 10 MVar STATCOM model with power system CAD and electro magnetic transient in DC system (PSCAD/EMTDC) and validate the effectiveness and advantage of the proposed control strategy with the simulation study.