Dynamic Modeling of HVDC for Power System Stability Assessment: A Review, Issues, and Recommendations

Abedin, Tarek; Lipu, Molla Shahadat Hossain; Hannan, M. A.; Ker, Pin Jern; Rahman, S A; Yaw, Chong Tak; Tiong, Sieh Kiong; Muttaqi, Kashem M.

doi:10.3390/en14164829

Cited by 29 publications

(9 citation statements)

References 122 publications

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“…Although a recent review of HVDC modeling for power system stability assessment [144] made no mention of ML model applications, one example has recently been presented in [145]. Furthermore, the use of data-driven modeling techniques and machine learning methods for the development of dynamic models of microgrids is advocated in [146].…”

Section: Component Modelsmentioning

confidence: 99%

Review of Data-Driven Techniques for On-Line Static and Dynamic Security Assessment of Modern Power Systems

De Caro,

Collin,

Giannuzzi

et al. 2023

IEEE Access

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The secure operation of the transmission grid is of primary importance for any power system operator. However, the introduction of new technologies, market deregulation, and increasing levels of interconnectivity have made the analysis and assessment of power system security both more challenging and essential than ever. In this context, data-driven-based methodologies are being increasingly employed to classify and anticipate insecure future states, and make inferences on potential triggers of undesired operational conditions. This paper provides a comprehensive and systematic review of this fast-moving research area and covers data-driven-based methodologies deployed in both static and dynamic security assessment. Particular attention is paid to recent trends, such as the use of spatiotemporal feature selection algorithms and the increasing research activity in short-term voltage stability and frequency stability, which are not yet widely assessed as a collective in the existing literature.

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Section: Component Modelsmentioning

confidence: 99%

Review of Data-Driven Techniques for On-Line Static and Dynamic Security Assessment of Modern Power Systems

De Caro,

Collin,

Giannuzzi

et al. 2023

IEEE Access

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“…In the domain of dataset building, several open challenges facing the research community can be identified: (1) open sourcing datasets from power system WAMS measurements, (2) addressing potential security concerns associated with WAMS data [11], (3) open sourcing existing benchmark test cases, (4) providing a unified and consistent set of benchmark test cases featuring power systems of varying sizes and levels of RES penetration, (5) providing benchmark test cases featuring hybrid AC/DC power grids, and (6) providing standardized simulated environments of power systems for the reinforcement learning. These points deal with standardization of benchmark test cases and bringing them closer to the expected future levels of RES penetration, as well as introducing the hybrid AC/DC power grids [82,83]. It also deals with bringing standard simulated environments for RL (i.e., something such as OpenAI Gym (https://gym.openai.com, accessed on 18 November 2021) for power systems).…”

Section: Challenges and Future Research Opportunitiesmentioning

confidence: 99%

Artificial Intelligence Techniques for Power System Transient Stability Assessment

et al. 2022

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The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.

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“…In many applications, the 2-level diode/thyristor-based line-commutated rectifiers (LCRs) and voltage-source converters (VSCs) are used as basic building blocks due to the simplicity and reliability of this technology [1], [2]. Such applications include the classic HVDC transmission systems [3]- [5], power systems of vehicles [6], [7], ships and aircraft, induction furnaces [8], excitators of synchronous generators [9]- [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Detailed Parametric Modeling of AC–DC Converters for EMT Simulators

Hosseinian,

Ebrahimi,

Jatskevich

2023

IEEE Open J. Power Electron.

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Detailed switching models (DSM) of ac-dc converters are available in many offline and real-time electromagnetic transient (EMT) simulation programs. However, such discrete models typically require small simulation time steps for accurately handling the switching events without interpolation. This paper proposes a new parametric modeling approach for line-commutated and 2-level voltage-source ac-dc converters. The proposed methodology is based on parametric functions that relate ac and dc variables in the instantaneous sense, which allows the reconstruction of the waveforms of voltages and currents (with the same of details as the switching models of converters) without topological changes in the converter circuit. Thus, it can operate at larger time steps without requiring interpolation, thereby enhancing simulation efficiency. The computational advantages of the proposed models over the conventional switching models are demonstrated in offline PSCAD and real-time RTDS NovaCor simulators in terms of maximum possible time step size and accuracy. It is shown that the proposed models can accurately run with much larger time steps (up to ~200 μs for LCRs, and up to ~50 μs for VSCs) compared to detailed switching models, which are limited to relatively small time steps of ~10 μs.

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Dynamic Modeling of HVDC for Power System Stability Assessment: A Review, Issues, and Recommendations

Cited by 29 publications

References 122 publications

Review of Data-Driven Techniques for On-Line Static and Dynamic Security Assessment of Modern Power Systems

Review of Data-Driven Techniques for On-Line Static and Dynamic Security Assessment of Modern Power Systems

Artificial Intelligence Techniques for Power System Transient Stability Assessment

Detailed Parametric Modeling of AC–DC Converters for EMT Simulators

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