Open data IEEE test systems implemented in SimPowerSystems for education and research in power grid dynamics and control

Moeini, Ali; Kamwa, Innocent; Brunelle, P.; Sybille, G.

doi:10.1109/upec.2015.7339813

Cited by 120 publications

(34 citation statements)

References 13 publications

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“…To further illustrate the effectiveness of the proposed method, the Australian simplified 14 generators system (ASS14) [36] is employed in this subsection. The Australian simplified 14 generators system models the southern and eastern Australian power system (refer to [36] for more details). This system includes 14 generators, 59 buses, 29 loads and 114 transmissions.…”

Section: Feature Reduction Methodsmentioning

confidence: 99%

Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

Xiao

Wang

2018

Energies

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In machine learning-based transient stability assessment (TSA) problems, the characteristics of the selected features have a significant impact on the performance of classifiers. Due to the high dimensionality of TSA problems, redundancies usually exist in the original feature space, which will deteriorate the performance of classification. To effectively eliminate redundancies and obtain the optimal feature set, a new feature reduction method based on neighborhood rough set and discernibility matrix is proposed in this paper. First, 32 features are selected to structure the initial feature set based on system principle. An evaluation index based on neighborhood rough set theory is used to characterize the separability of classification problems in the specified feature space. By constructing the discernibility matrix of input features, a feature selection strategy is designed to find the optimal feature set. Finally, comparative experiments based on the proposed feature reduction method and several common feature reduction techniques used in TSA are applied to the New England 39 bus system and Australian simplified 14 generators system. The experimental results illustrate the effectiveness of the proposed feature reduction method.

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Section: Feature Reduction Methodsmentioning

confidence: 99%

Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

Xiao

Wang

2018

Energies

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“…10). This large dynamic benchmark power system with load case 4 [31,32] was used as the base case of the system load, which is approximately 14.5GW. this an opportunity to increase the realistic of the response behaviour than work presented in the literature.…”

Section: Demonstration Of the Proposed Control Of Besssmentioning

confidence: 99%

“…The results are stored as vectors to visualise and compare the results. This model was used for testing new control techniques in a power system; further details were presented in [31,32]. The control of a population of BESSs was implemented using a dynamic controllable load.…”

Section: Demonstration Of the Proposed Control Of Besssmentioning

confidence: 99%

Control of a population of battery energy storage systems for frequency response

Obaid

Cipcigan

Muhssin

et al. 2020

International Journal of Electrical Power & Energy Systems

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The control of multiple battery energy Storage systems (BESSs) to provide frequency response will be a challenge in future smart grids. This paper proposes a hierarchical control of BESSs with two decision layers: the aggregator layer and the BESS control layer. The aggregator layer receives the states of charge (SoC) of BESSs and sends a command signal to enable/disable the BESS control layer. The BESS controller was developed to enable the BESSs to respond from the highest to lowest SoC when the frequency drops, and from lowest to highest when it rises. Hence, the BESS's response is prioritised to reduce the impact on the power system and end-users during the service. The BESS controller works independently when a failure occurs in the communication with the aggregator. The dynamic behaviour of the population of the controllable BESSs was modelled based on a Markov chain. The model demonstrates the value of aggregation of BESSs for providing frequency response and evaluates the effective capacity of the service. The model was demonstrated on the 14-machine SouthEast Australian power system with a 14.5GW load. 254MW of responsive capacity of aggregated batteries was effective in reducing the system frequency deviation below 0.2 Hz following a sequence of disturbances.

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“…We generate the data using MATLAB power system toolbox (Simscape Power Systems), based on the New England Power System IEEE benchmark topology [19]. Each disturbance scenario is a load increase at one of 21 bus locations.…”

Section: Simulationmentioning

confidence: 99%

Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

Lee

Zhang

Modiano

2018

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data.

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Open data IEEE test systems implemented in SimPowerSystems for education and research in power grid dynamics and control

Cited by 120 publications

References 13 publications

Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

Control of a population of battery energy storage systems for frequency response

Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

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