arXiv subjects

Ruisheng Diao

Publications and source records attributed to Ruisheng Diao.

At least 19 recordsLinked to original sources

arXiv2025

MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution

Research on long-term time series prediction has primarily relied on Transformer and MLP models, while the potential of convolutional networks in this domain remains underexplored. To address this, we propose a novel multi-scale time series reshape module that effectively captures cross-period patch interactions and variable dependencies. Building on this, we develop MS-DFTVNet, the multi-scale 3D deformable convolutional framework tailored for long-term forecasting. Moreover, to handle the inherently uneven distribution of temporal features, we introduce a context-aware dynamic deformable convolution mechanism, which further enhances the model's ability to capture complex temporal patterns. Extensive experiments demonstrate that MS-DFTVNet not only significantly outperforms strong baselines but also achieves an average improvement of about 7.5% across six public datasets, setting new state-of-the-art results.

cs.LG↗

arXiv2025

Transient Stability Analysis of a Hybrid Grid-Forming and Grid-Following RES System Considering Multi-Mode Control Switching

The inherent control switching of renewable energy sources (RESs) during intricate transient processes introduces complexity to the dynamic behavior of modern power systems. This paper reveals the dynamic coupling between grid-forming (GFM)/grid-following (GFL)-based RES and dominant instability modes of the hybrid system. First, six control combinations are systematically investigated by pairing the two GFM-RES modes, normal control (NC) and current saturation (CS), with the three GFL-RES modes: normal control, low voltage ride-through (LVRT), and high voltage ride-through (HVRT). Based on switching system theory, the coupled power flow and dynamic motion models are developed considering multi-mode switching characteristics. It is revealed that the hybrid system exhibits two distinct instability modes when the GFM-RES and GFL-RES exceed their P-f and V-f desynchronization boundaries, respectively. The two-dimensional spatiotemporal damping characteristics of GFL-RES induced by GFM-RES are also uncovered for the first time. A novel criterion is proposed to quantify the impact of GFM-RES on GFL-RES dynamics, capturing both its stabilizing and destabilizing effects under different control combinations. High-fidelity electromagnetic transient simulations validate the correctness of the analysis framework.

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arXiv2025

A Novel Decoupled LVRT Control Strategy for Transient Voltage Stability Enhancement of IBRs Using Voltage-Angle Coupling Analysis

With the fast-increasing penetration of inverter-based resources (IBRs), the voltage support capability of the grid following (GFL) IBRs under low voltage ride through (LVRT) control significantly influences the transient voltage stability of the power system. The existing LVRT adjusts the q-axis current to regulate reactive power injection. However, under a large disturbance, the phase-locked loop (PLL) error invalidates the proportional relationship between the q-axis current and reactive power, consequently causing deviation in the actual reactive power injection of the IBR. Besides, the variation of IBR current, determined by the PLL phase and LVRT, also directly influences the transient voltage. To address this issue, the specific influence of PLL error on active and reactive power injection is first analyzed under LVRT control. In addition, by combining the LVRT and PLL dynamics, the mechanisms of three voltage problems caused by voltage angle coupling are revealed. overvoltage, low voltage, and DC-side overvoltage. The specific scenarios in which these voltage stability problems occur are also obtained by the voltage-vector-triangle graphic. Furthermore, a power angle decoupled LVRT control is proposed to eliminate the influence of voltage angle coupling. Finally, the mechanism analysis and effectiveness of the decoupled LVRT are verified in the case study.

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arXiv2025

Improved PLL Design for Transient Stability Enhancement of Grid Following Converters Based on Lyapunov Method

Fluctuations in phase angle and frequency under large disturbances can lead to loss of synchronism (LOS) in grid-following (GFL) converters. The power angle and frequency of synchronous generators (SGs) correspond to rotor position and speed, whereas those of converters lack a direct physical counterpart in the real world and can thus be directly adjusted by control methods to prevent loss of synchronization. In this paper, an improved phase-locked loop (PLL) design with reset control for GFL converters is proposed to enhance transient stability. The stability domain (SD) of a GFL converter is first analyzed, and three forms of SD are identified under different short circuit ratios. Secondly, based on the characteristics of the three SD forms, two PLL-reset methods are proposed, including omega reset and omega&delta reset. Thirdly, to provide the triggering conditions for the PLL-reset control, the Lyapunov function of the GFL converter is constructed based on three methods: the approximation-based Lyapunov method, the Zubov method, and the analytical trajectory reversing method. All these methods are immune to the negative damping problem of PLL dynamics, which makes traditional energy-perspective Lyapunov functions invalid. Finally, the estimation accuracy of the three Lyapunov-based methods is analyzed, and the effectiveness of the PLL-reset control is verified in single-machine and multi-machine case studies.

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arXiv2025

Transient Stability Analysis for Grid Following Converters in Low-Inertia Power Systems by Direct Method

With the increased penetration of renewable energy and reduced proportion of synchronous generators, the low-inertia characteristics of todays power system become prominent, and the transient stability issue of grid following converter (GFLC) under low inertia system (LIS) condition becomes critical. There are two prominent problems in the transient stability analysis of GFLC-LIS. The angular dynamic of LIS increases the complexity of transient stability analysis, and the nonlinear, possibly negative damping of GFLC makes it difficult to guarantee the conservative of the traditional methods. These problems make the traditional methods inapplicable. In this paper, the transient stability analysis of GFLC LIS is investigated to provide an accurate estimation of the attraction boundary and critical clearance time (CCT). Firstly, a dynamic model of GFLC-LIS is constructed, considering the phase-locked loop (PLL)-based GFLC dynamics and swing equation-based LIS dynamics. The frequency mutation of PLL at fault occurrence and clearing time is also considered. Secondly, a Zubov based transient stability analysis method is proposed, which can construct the energy function in a way that is different from the traditional conservation of energy perspective and can address the negative damping issue. Moreover, the accuracy of the CCT estimation is analyzed, and the influences of LIS parameters on transient stability are illustrated. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method

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arXiv2025

TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation

With the recent development and advancement of Transformer and MLP architectures, significant strides have been made in time series analysis. Conversely, the performance of Convolutional Neural Networks (CNNs) in time series analysis has fallen short of expectations, diminishing their potential for future applications. Our research aims to enhance the representational capacity of Convolutional Neural Networks (CNNs) in time series analysis by introducing novel perspectives and design innovations. To be specific, We introduce a novel time series reshaping technique that considers the inter-patch, intra-patch, and cross-variable dimensions. Consequently, we propose TVNet, a dynamic convolutional network leveraging a 3D perspective to employ time series analysis. TVNet retains the computational efficiency of CNNs and achieves state-of-the-art results in five key time series analysis tasks, offering a superior balance of efficiency and performance over the state-of-the-art Transformer-based and MLP-based models. Additionally, our findings suggest that TVNet exhibits enhanced transferability and robustness. Therefore, it provides a new perspective for applying CNN in advanced time series analysis tasks.

cs.LG↗

arXiv2023

Robust Adaptive Control of STATCOMs to Mitigate Inverter-Based-Resource (IBR)-Induced Oscillations

The interaction among inverter-based resources (IBRs) and power network may cause small-signal stability issues, especially in low short-circuit-level grids. Besides, integrating static synchronous compensators (STATCOMs) in a multi-IBR system for voltage support can deteriorate small-signal stability. However, it is still challenging to fully understand the impact mechanism of STATCOMs on IBR-induced oscillation issues and to design optimal STATCOMs' control for dampening these oscillation issues in a multi-IBR system due to complex system dynamics and varying operating conditions. To tackle these challenges, this paper proposes a novel method to reveal how STATCOMs influence IBR-induced oscillation issues in a multi-IBR system from the viewpoint of grid strength, which can consider varying operating conditions. Based on this proposed method, critical operating conditions are identified, wherein the system tends to be most unstable. Moreover, we demonstrate that robust small-signal stability issues of the multi-IBR system with STATCOMs can be simplified as that of multiple subsystems under critical operating conditions, which avoids exhaustive studies on many operating conditions with detailed system models. On this basis, an adaptive control-parameter design method is proposed for STATCOMs to ensure system robust stability under varying operating conditions. The proposed methods are validated by a modified IEEE39-node test system.

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arXiv2021

Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents

Planning future operational scenarios of bulk power systems that meet security and economic constraints typically requires intensive labor efforts in performing massive simulations. To automate this process and relieve engineers' burden, a novel multi-stage control approach is presented in this paper to train centralized and decentralized reinforcement learning agents that can automatically adjust grid controllers for regulating transmission line flows at normal condition and under contingencies. The power grid flow control problem is formulated as Markov Decision Process (MDP). At stage one, centralized soft actor-critic (SAC) agent is trained to control generator active power outputs in a wide area to control transmission line flows against specified security limits. If line overloading issues remain unresolved, stage two is used to train decentralized SAC agent via load throw-over at local substations. The effectiveness of the proposed approach is verified on a series of actual planning cases used for operating the power grid of SGCC Zhejiang Electric Power Company.

cs.LG↗

arXiv2021

The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms

In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods. However, it is often difficult to directly compare these studies due to subtle differences in the AC-OPF problem formulation as well as the network, generation, and loading data that are used for evaluation. To help address these challenges, this IEEE PES Task Force report proposes a standardized AC-OPF mathematical formulation and the PGLib-OPF networks for benchmarking AC-OPF algorithms. A motivating study demonstrates some limitations of the established network datasets in the context of benchmarking AC-OPF algorithms and a validation study demonstrates the efficacy of using the PGLib-OPF networks for this purpose. In the interest of scientific discourse and future additions, the PGLib-OPF benchmark library is open-access and all the of network data is provided under a creative commons license.

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arXiv2020

Rethink AI-based Power Grid Control: Diving Into Algorithm Design

Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain.In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering.To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process. The performance results demonstrate that theproposed approach has strong generalization ability with much less training time.The agent trained by imitation learning is effective and robust to solve voltagecontrol problem and outperforms the former RL agents.

cs.AI↗

arXiv2020

On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control

Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presents a novel artificial intelligence (AI) based methodology to achieve multi-objective real-time power grid control for real-world implementation. State-of-the-art off-policy reinforcement learning (RL) algorithm, soft actor-critic (SAC) is adopted to train AI agents with multi-thread offline training and periodic online training for regulating voltages and transmission losses without violating thermal constraints of lines. A software prototype was developed and deployed in the control center of SGCC Jiangsu Electric Power Company that interacts with their Energy Management System (EMS) every 5 minutes. Massive numerical studies using actual power grid snapshots in the real-time environment verify the effectiveness of the proposed approach. Well-trained SAC agents can learn to provide effective and subsecond control actions in regulating voltage profiles and reducing transmission losses.

math.OC↗

arXiv2020

Evaluating Load Models and Their Impacts on Power Transfer Limits

Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line thermal limits, and dynamic constraints, such as transient stability limits, voltage stability limits and small-signal stability limits. Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits. Currently, multiple load models have been proposed and adopted in the industry and academia, including the ZIP model, ZIP plus induction motor composite model (ZIP + IM) and WECC composite load model (WECC CLM). Each of them has its unique advantages, but their impacts on the power transfer limits are not yet adequately addressed. One existing challenge is fitting the high-order nonlinear models such as WECC CLM. In this study, we innovatively adopt double deep Q-learning Network (DDQN) agent as a general load modeling tool in the dynamic assessment procedure and fit the same transient field measurements into different load models. A comprehensive evaluation is then conducted to quantify the load models' impacts on the power transfer limits. The simulation environment is the IEEE-39 bus system constructed in Transient Security Assessment Tool (TSAT).

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arXiv2020

Deriving AC OPF Solutions via Proximal Policy Optimization for Secure and Economic Grid Operation

Optimal power flow (OPF) is a very fundamental but vital optimization problem in the power system, which aims at solving a specific objective function (ex.: generator costs) while maintaining the system in the stable and safe operations. In this paper, we adopted the start-of-the-art artificial intelligence (AI) techniques to train an agent aiming at solving the AC OPF problem, where the nonlinear power balance equations are considered. The modified IEEE-14 bus system were utilized to validate the proposed approach. The testing results showed a great potential of adopting AI techniques in the power system operations.

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arXiv2020

Online Low Frequency Oscillation Detection and Analysis System with an Ensemble Filter

The widespread deployment of phasor measurement unit (PMU) overpower systems makes it possible to monitor and analyze grid dynamics in real-time. Low-frequency oscillation is harmful to power system equipment and operation, and in the worst-case scenario may lead to cascading failures. Therefore, it is critical to detect and identify them as soon as they appear. This paper presents an online low-frequency oscillation detection and analysis (LFODA) system, which has the merit of significantly reducing the chance of false alarm via a voting schema and a time-serial filter. A novel algorithm based on density-based spatial clustering of applications with noise (DBSCAN) is proposed to classify oscillation modes as well as to group their corresponding buses/monitoring sites. Performance of the LFODA system is evaluated through experiments using both simulated and real-world PMU data.

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arXiv2019

AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs

This paper presents a novel AI-based approach for maximizing time-series available transfer capabilities (ATCs) via autonomous topology control considering various practical constraints and uncertainties. Several AI techniques including supervised learning and deep reinforcement learning (DRL) are adopted and improved to train effective AI agents for achieving the desired performance. First, imitation learning (IL) is used to provide a good initial policy for the AI agent. Then, the agent is trained by DRL algorithms with a novel guided exploration technique, which significantly improves the training efficiency. Finally, an Early Warning (EW) mechanism is designed to help the agent find good topology control strategies for long testing periods, which helps the agent to determine action timing using power system domain knowledge; thus, effectively increases the system error-tolerance and robustness. Effectiveness of the proposed approach is demonstrated in the "2019 Learn to Run a Power Network (L2RPN)" global competition, where the developed AI agents can continuously and safely control a power grid to maximize ATCs without operator's intervention for up to 1-month's operation data and eventually won the first place in both development and final phases of the competition. The winning agent has been open-sourced on GitHub.

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arXiv2019

A Deep Reinforcement Learning Based Approach for Optimal Active Power Dispatch

The stochastic and dynamic nature of renewable energy sources and power electronic devices are creating unique challenges for modern power systems. One such challenge is that the conventional mathematical systems models-based optimal active power dispatch (OAPD) method is limited in its ability to handle uncertainties caused by renewables and other system contingencies. In this paper, a deep reinforcement learning-based (DRL) method is presented to provide a near-optimal solution to the OAPD problem without system modeling. The DRL agent undergoes offline training, based on which, it is able to obtain the OAPD points under unseen scenarios, e.g., different load patterns. The DRL-based OAPD method is tested on the IEEE 14-bus system, thereby validating its feasibility to solve the OAPD problem. Its utility is further confirmed in that it can be leveraged as a key component for solving future model-free AC-OPF problems.

math.OC↗

arXiv2019

Mathematical representation of the WECC composite load model

The Western Electricity Coordinating Council (WECC) composite load model is a newly developed load model that has drawn great interest from the industry. To analyze its dynamic characteristics with both mathematical and engineering rigor, a detailed mathematical model is needed. Although WECC composite load model is available in commercial software as a module and its detailed block diagrams can be found in several public reports, there is no complete mathematical representation of the full model in literature. This paper addresses a challenging problem of deriving detailed mathematical representation of WECC composite load model from its block diagrams. In particular, for the first time, we have derived the mathematical representation of the new DER_A model. The developed mathematical model is verified using both Matlab and PSS/E to show its effectiveness in representing WECC composite load model. The derived mathematical representation serves as an important foundation for parameter identification, order reduction and other dynamic analysis.

math.OC↗

arXiv2019

Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning

Modern power grids are experiencing grand challenges caused by the stochastic and dynamic nature of growing renewable energy and demand response. Traditional theoretical assumptions and operational rules may be violated, which are difficult to be adapted by existing control systems due to the lack of computational power and accurate grid models for use in real time, leading to growing concerns in the secure and economic operation of the power grid. Existing operational control actions are typically determined offline, which are less optimized. This paper presents a novel paradigm, Grid Mind, for autonomous grid operational controls using deep reinforcement learning. The proposed AI agent for voltage control can learn its control policy through interactions with massive offline simulations, and adapts its behavior to new changes including not only load/generation variations but also topological changes. A properly trained agent is tested on the IEEE 14-bus system with tens of thousands of scenarios, and promising performance is demonstrated in applying autonomous voltage controls for secure grid operation.

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