Pattern Analysis of Future HVDC Grid Development

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

Today, Function-as-a-Service (FaaS), as an emerging path to implementing serverless computing paradigm, seems quite promising in responding to the growing increase in requests for microservices. However, the serverless computing model also brings new challenges. Although the execution time of the functions is very short in FaaS, the initialization of containers and cold start is time-consuming and leads to a decrease in system performance. Cold start latency reduces the attractiveness of FaaS with the cost it imposes on the system. This is the most serious challenge in the majority of serverless computing platforms. Despite the importance of the subject, to the best of our knowledge, there is no comprehensive systematic review of cold start latency mitigation mechanisms in serverless computing platforms. This study aims to provide a systematic literature review on cold start latency mitigation mechanisms in serverless computing environment. In our proposed taxonomy, the mechanisms are classified into four categories: application-based, checkpoint-based, invocation time prediction-based, and cache-based; then, every category is introduced and reviewed. Finally, we propose open issues and conclude. 

Cold Start Latency Mitigation Mechanisms in Serverless Computing: Taxonomy, Review, and Future Directions

In recent years, serverless computing has garnered significant attention owing to its high scalability, pay-as-you-go billing model, and efficient resource management provided by cloud service providers. Optimal resource scheduling of serverless computing has become imperative to reduce energy consumption and enable sustainable computing. However, existing serverless platforms encounter two significant challenges: the cold-start problem of containers and the absence of an effective resource allocation strategy for serverless workflows. Existing pre-warm strategies are associated with high computational overhead, while current resource scheduling techniques inadequately account for the intricate structure of serverless workflows. To address these challenges, we present SSC, a pre-warming and automatic resource allocation framework designed explicitly for serverless workflows. We introduce an innovative gradient-based algorithm for pre-warming containers, significantly reducing cold start hit rates. Moreover, leveraging a critical path and priority queue-based algorithm, SSC enables efficient allocation of resources for serverless workflows. In our experimental evaluation, SSC reduces the cold start hit rate by nearly 50% and achieves substantial cost savings of approximately 30%.

Sustainable Serverless Computing with Cold-start Optimization and Automatic Workflow Resource Scheduling

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

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

faaShark: an End-to-End Network Traffic Analysis System atop Serverless Computing Platforms

The development of power simulation artificial intelligence technology needs massive open sample data. It is a trend to use the characteristics of online data to construct sample data. In order to solve the problem that the online data of power grid is rich in information, but the utilization rate of features is not high, aiming at the information features of generators, the LTTB dimension reduction and DBSCAN + L2 clustering methods are proposed, which reduce the complexity of feature extraction of time series data. The method is verified by the actual power grid data, and has achieved certain results.

Research on feature extraction method of power grid online data based on big data

Renewable energy will play an important role in the future energy development of West China. Voltage source converter based on high voltage direct current (VSC-HVDC) has a strong regulation capability of voltage and power, and can be interconnected with weak AC grid or even passive grid. A ± 1000kV large west sending terminal DC power grid based on VSC-HVDC is constructed, by which with rich distributed resources in West China can be centrally dispatched to East China, avoiding the uncertainty impact of renewable energy power generation on power system stability. Steady and transient characteristics of the sending terminal DC grid are simulated and analyzed.

Scheme and Operation Characteristic Analysis of Large Sending Terminal DC Grid Based on Flexible DC in West China

This paper presents a methodology for the systematic generation of synthetic power grid test examples. The synthetic power grids have the same characteristics with actual power systems and do not correspond to any real grid. thus, the generated synthetic power grids are free from confidentiality requirements and available for public use. The examples constructed by this method have small world effect and their characteristics are consistent with that of China’s power grid. Meanwhile, the evaluation method of the electrical characteristic parameters of the generated network is studied, and finally the test examples that can be used for various researches are generated. The statistical characteristics of the generated examples are basically consistent with the existing typical examples. The generated examples can provide help for the study of various kinds of power grid operation control methods and power flow calculation methods.

Generation Method of Power System Test Examples Based on Complex Network Theory

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