Hierarchical Task Planning for Power Line Flow Regulation

The complexity and uncertainty in power systems cause great challenges to controlling power grids.As a popular data-driven technique,deep reinforcement learning(DRL)attracts attention in the control of power grids.However,DRL has some inherent drawbacks in terms of data efficiency and explainability.This paper presents a novel hierarchical task planning(HTP)approach,bridging planning and DRL,to the task of power line flow regulation.First,we introduce a threelevel task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes(TP-MDPs).Second,we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units.In addition,we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP.Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization,a state-of-the-art deep reinforcement learning(DRL)approach,improving efficiency by 26.16%and 6.86%on both systems.

Knowledge Model for Electric Power Big Data Based on Ontology and Semantic Web

It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data.Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data.Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method.Based on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is demonstrated.Different forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and reduction.Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data.With the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.

Framework for Artificial Intelligence Analysis in Large-scale Power Grids Based on Digital Simulation

The planning,design,operation,control and scientific research of power systems all require a variety of simulation analysis.Thus power grid simulation analysis is a fundamental supporting technology of large-scale power grids.In power grid simulation analysis,in addition to simulation calculations,there are many links for analysis and decision-making,relying on specialists.The introduction of advanced artificial intelligence technology provides a new method to improve the efficiency and accuracy of power grid simulation analysis.Nevertheless,the research of the related artificial intelligence technologies face a great deal of new challenges due to the complexity of the largescale power grid simulation data,including massive volumes,high dimensionality,strong coupling and complex correlations.Also a great deal of knowledge and experience need to be integrated in the process of analysis.In order to deal with these challenges,based on the existing works,this paper focuses on the core scientific problem of artificial intelligence analysis and decision making related to the massive simulation results of large-scale power grids,and proposes an artificial intelligence analysis method framework for large-scale power grids based on digital simulation,which includes the power grid simulation analysis knowledge model with application method,the power grid simulation knowledge mining method and the artificial intelligence models with transfer learning ability of diversified grids as well as analyzing and calculation adjusting for largescale power grid simulation results,etc.This work is expected to open up a new technical approach for large-scale power grid simulation analysis and provide strong technical support for the safe and stable operation of large-scale power grids.

Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition

Analyzing network robustness under various circumstances is generally regarded as a challenging problem.Robustness against failure is one of the essential properties of large-scale dynamic network systems such as power grids,transportation systems,communication systems,and computer networks.Due to the network diversity and complexity,many topological features have been proposed to capture specific system properties.For power grids,a popular process for improving a network’s structural robustness is via the topology design.However,most of existing methods focus on localized network metrics,such as node connectivity and edge connectivity,which do not encompass a global perspective of cascading propagation in a power grid.In this paper,we use an informative global metric algebraic connectivity because it is sensitive to the connectedness in a broader spectrum of graphs.Our process involves decreasing the average propagation in a power grid by minimizing the increase in its algebraic connectivity.We propose a topology-based greedy strategy to optimize the robustness of the power grid.To evaluate the network robustness,we calculate the average propagation using MATCASC to simulate cascading line outages in power grids.Experimental results illustrate that our proposed method outperforms existing techniques.

WaveLines:towards effective visualization and analysis of stability in power grid simulation

Closely related to the safety and stability of power grids,stability analysis has long been a core topic in the electric industry.Conventional approaches employ computational simulation to make the quantitative judgement of the grid stability under distinctive conditions.The lack of in-depth data analysis tools has led to the difficulty in analytical tasks such as situation-aware analysis,instability reasoning and pattern recognition.To facilitate visual exploration and reasoning on the simulation data,we introduce WaveLines,a visual analysis approach which supports the supervisory control of multivariate simulation time series of power grids.We design and implement an interactive system that supports a set of analytical tasks proposed by domain experts and experienced operators.Experiments have been conducted with domain experts to illustrate the usability and effectiveness of WaveLines.