Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs (μ-PMUs), and smart meters. However, large amount of data collected by these devices bring several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications. 

Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward

Knowledge-enhanced deep reinforcement learning for intelligent event-based load shedding

A Novel Probabilistic Framework with Interpretability for Generator Coherency Identification

Intelligent and rapid event-based load shedding pre-determination for large-scale power systems: Knowledge-enhanced parallel branching dueling Q-network approach

Increasing scale of power grids induces higher computational complexity of static contingency analysis, which is in contradiction with the real-time requirements. Towards fast credible contingency analysis, a power flow calculation method based on graph neural network was proposed. This method can realize fast fitting of nonlinear relationship between new energy output, load data and branch power flow, node voltage in multiple scenarios. To avoid the disappearance of edge graph nodes caused by branch disconnection and to ensure the robustness of the model, a matrix reflecting the topology changes is designed in response to the network structure changes caused by various anticipated accidents. Besides, the load-source data is separated and the input eigenvector considering the change of the load-source data is constructed. The test of IEEE 30-bus and 118-bus system shows that the proposed model can adapt to changes of network topology brought by N-1 faults and the fluctuations of new energy, realize fast fitting of power flow, and provide a new tool for online fast credible contingency analysis.

Switching Convolution of Node Graph and Line Graph-driven Method for Fast Static Security Analysis

In order to solve the problem of manpower and time cost consumption caused by strict non-convergence of power flow in large-scale power grid calculation, an automatic adjustment method of power flow convergence based on knowledge experience and deep reinforcement learning is proposed. Firstly, the knowledge experience of power flow convergence adjustment and the basic concepts and principles of deep reinforcement learning are introduced. Then we design state space, action space and multiple rewards of reinforcement learning, as well as the framework of deep neural network. Next, by adding knowledge and experience into reinforcement learning, the search space is narrowed. And the process of human adjustment is simulated, which is balancing active power and then balancing reactive power. So that the search has directivity, and the power flow adjustment strategy is constructed. When balancing reactive power, Dijkstra algorithm is used to locate capacitors and reactors near the nodes with potential reactive power deficiency by means of optimal path. Finally, the CEPRI(China Electric Power Research Institute) 36-node system and the northeast power grid are used to verify the effectiveness of the method.

Automatic Adjustment Method of Power Flow Calculation Convergence for Large-scale Power Grid Based on Knowledge Experience and Deep Reinforcement Learning

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Automatic Adjustment Method of Power Flow Calculation Convergence for Large-scale Power Grid Based on Knowledge Experience and Deep Reinforcement Learning