Comprehensive current amplitude ratio based pilot protection for line with converter-interfaced sources

Fault behaviours of converter-interfaced renewable energy sources (CIRESs) are greatly diverse from those of synchronous generators (SGs), so the traditional proportional restraint differential protection may fail to be activated. In order to deal with this issue, a comprehensive current amplitude ratio-based pilot relay is proposed. Since the fault current from CIRESs is much smaller than that of SGs, so phase current amplitude ratio on both end is lower than 1. In order to improve protection sensitivity for high resistance faults and grounding faults, sequence current ratio is also introduced to constitute a comprehensive protection criterion. The proposed method only uses the amplitude feature, so it has a lower time synchronization requirement for the currents on both terminals. Meanwhile, it can be applied for different fault ride through (FRT) strategies because the current limiting is always satisfied. Furthermore, the proposed method is easy to be deployed in protection devices after a small revision is done for the original protection algorithm. PSCAD simulation demonstrates that the proposed method is effective for different fault scenarios

Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks

Scenario generations of cooling, heating, and power loads are of great significance for the economic operation and stability analysis of integrated energy systems. In this paper, a novel deep generative network is proposed to model cooling, heating, and power load curves based on a generative moment matching networks (GMMN) where an auto-encoder transforms high-dimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples. After training the model, the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN. Unlike the explicit density models, the proposed GMMN does not need to artificially assume the probability distribution of the load curves, which leads to stronger universality. The simulation results show that the GMMN not only fits the probability distribution of multi-class load curves well, but also accurately captures the shape (e.g., large peaks, fast ramps, and fluctuation), frequency-domain characteristics, and temporal-spatial correlations of cooling, heating, and power loads. Furthermore, the energy consumption of generated samples closely resembles that of real samples.Comment: This paper has been accepted by CSEE Journal of Power and Energy System

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed

Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach

Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems. However, the short-term power prediction of renewable energy has always been considered a complex regression problem, owing to the fluctuation and intermittence of output powers and the law of dynamic change with time due to local weather conditions, i.e. spatio-temporal correlation. To capture the spatio-temporal features simultaneously, this paper proposes a new graph neural network-based short-term power forecasting approach, which combines the graph convolutional network (GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to learn complex spatial correlations between adjacent renewable energies, and the LSTM is used to learn dynamic changes of power generation curves. The simulation results show that the proposed hybrid approach can model the spatio-temporal correlation of renewable energies, and its performance outperforms popular baselines on real-world datasets.Comment: This paper was accepted the 22nd Power Systems Computation Conference (PSCC 2022