Parallel relaxation‐based joint dynamic state estimation of large‐scale power systems

Karimipour, Hadis; Dinavahi, Venkata

doi:10.1049/iet-gtd.2015.0808

Cited by 39 publications

(11 citation statements)

References 51 publications

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“…To further accelerate EMT simulations, massive-thread parallelization of device-level simulation implemented on GPUs was presented in [119]. Moreover, the contribution of GPUs was also seen in dynamic state estimation, transient stability, and simulation [120]- [122].…”

Section: Hardware-based Simulation Toolsmentioning

confidence: 99%

Review of Methods to Accelerate Electromagnetic Transient Simulation of Power Systems

et al. 2021

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Converter-based generators are increasingly replacing classical synchronous generation, resulting in significant challenges to the operation and planning of modern power systems. Power electronics (PE)-based equipment, along with non-linear PE-driven loads, introduce time-varying characteristics and fast switching behavior that increases the complexity of the power system model. Faster control actions are needed to overcome the fast switching dynamics to ensure the reliability and stability of future power systems. Thus, this requires advanced and detailed simulation methods and tools with highly accurate equivalent models to embody the relatively slower electromechanical to faster electromagnetic transient (EMT) phenomena. Conventional transient stability analysis using positive-sequence simulators has become inadequate for representing converter-dominated power systems, while EMT simulators suffer from the high computational burden. This review paper presents accelerated EMT simulation methods and tools that are categorized and discussed in three topics: system equivalents, simulation methods, and accelerating tools. Dynamic system equivalent techniques are discussed to model small to large interconnected external systems of the grid network. Moreover, a systematic review is made for existing EMT simulation methods, along with advanced co-simulation methods, for addressing simulation speed and accuracy issues in large power system networks. Emerging hardware-based simulation tools are reviewed that reduce the computational burden and increase the simulation efficiency of the power system model. Challenges and trends in EMT simulation are also presented and concluded by providing perspectives on this research topic.

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Section: Hardware-based Simulation Toolsmentioning

confidence: 99%

Review of Methods to Accelerate Electromagnetic Transient Simulation of Power Systems

et al. 2021

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“…W is the covariance matrix. To make sure about the accuracy of the estimation, measurement data will be checked to remove bad data [11].Traditionally, bad data is detected through following 2-norm residual test:…”

Section: A State Estimation Of Power Systemsmentioning

confidence: 99%

Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection

Sakhnini

Karimipour

2019

2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE)

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100

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False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods proposed to detect FDI attacks. This paper analyzes three various supervised learning techniques, each to be used with three different feature selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus, and 118-bus systems for evaluation of versatility. Accuracy of the classification is used as the main evaluation method for each detection technique. Simulation study clarify the supervised learning combined with heuristic FS methods result in an improved performance of the classification algorithms for FDI attack detection.

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“…It is a two‐level domain decomposition which decomposes the system based on the coherency characteristic in the first level and uses load balancing in the second level. It is independent of the system size, level of complexity, and location of disturbance [26]. Fig.…”

Section: Proposed Estimator and Fdi Attack Detectormentioning

confidence: 99%

Relaxation‐based anomaly detection in cyber‐physical systems using ensemble kalman filter

Karimipour

Leung

2019

IET cyber-phys. syst.

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Parallel relaxation‐based joint dynamic state estimation of large‐scale power systems

Cited by 39 publications

References 51 publications

Review of Methods to Accelerate Electromagnetic Transient Simulation of Power Systems

Review of Methods to Accelerate Electromagnetic Transient Simulation of Power Systems

Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection

Relaxation‐based anomaly detection in cyber‐physical systems using ensemble kalman filter

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