A PSO-MMA Method for the Parameters Estimation of Interarea Oscillations in Electrical Grids

Bonavolontà, Francesco; Noia, Luigi Pio Di; Liccardo, Annalisa; Tessitore, Salvatore; Lauria, D.

doi:10.1109/tim.2020.2998909

Cited by 19 publications

(9 citation statements)

References 39 publications

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“…The research unit of the electrical systems group has carried out an intense activity on the estimation and control of interconnected electrical systems [10][11][12][13][14]. Particularly relevant results have been achieved in the problem of estimating inertia in the face of the impact of the growing penetration of renewable sources.…”

Section: Main Research Programsmentioning

confidence: 99%

Electrical Power Systems

Lauria,

Villacci

2024

Springer Aerospace Technology

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Section: Main Research Programsmentioning

confidence: 99%

Electrical Power Systems

Lauria,

Villacci

2024

Springer Aerospace Technology

Self Cite

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“…This requirement can be satisfied if optimal hyperparameter values of the DNN models are singled out. This optimization stage was carried out by means of meta-heuristic optimization techniques; in particular, the Particle Swarm Optimization (PSO) [52][53][54] algorithm has been used to finely tune the dense model hyperparameters, thus combining its inherent performance in prediction with a lower memory footprint with respect to the original implementation.…”

Section: Dense Model Optimization Through Meta-heuristic Optimization...mentioning

confidence: 99%

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

Alteriis

Ruggiero

Prete

et al. 2023

Sensors

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In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters.

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“…This power mismatch is caused by quite common events such as increase of large loads, loss of tie lines or loss of generating units. As a result, a transient phenomenon characterized by Low-Frequency Oscillations (LFOs) may start [27].…”

Section: A Literature Reviewmentioning

confidence: 99%

“…For what concerns the LFOs, their frequency can vary in the 1 Hz to 2 Hz range if they are related to a single generator or in the range 0.1 Hz to 1 Hz if they are associated to a group of generators [27]- [29].…”

Section: A Literature Reviewmentioning

confidence: 99%

Measuring Harmonics With Inductive Voltage Transformers in Presence of Subharmonics

Crotti¹,

D’Avanzo²,

Letizia³

et al. 2021

IEEE Trans. Instrum. Meas.

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The measurement of harmonics is one of the key tasks in the modern power systems, performed basically for power quality assessment, verification of planning levels compliance, immunity and compatibility purposes and so on. Especially at Medium Voltage (MV) and High Voltage (HV) levels, harmonics are always measured through Voltage and Current Transformers (VTs and CTs), which very often are inductive. Recent papers show that inductive VTs and CTs, due to the intrinsic iron core nonlinearity, can introduce errors up to some percent in harmonic measurements. The paper aims to investigate in-depth this phenomenon by analyzing how the accuracy of harmonic measurements performed through an inductive MV VT is affected by the presence subharmonics in its input voltage. Subharmonics are spectral components having frequencies lower than the power frequency, that is 50 Hz for European countries. Some theoretical considerations are carried out and the performance of two different inductive MV VTs are tested in various operating conditions. Experimental results show that subharmonics can worse the harmonic measurements done through inductive VTs up to some tens of percent.

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A PSO-MMA Method for the Parameters Estimation of Interarea Oscillations in Electrical Grids

Cited by 19 publications

References 39 publications

Electrical Power Systems

Electrical Power Systems

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

Measuring Harmonics With Inductive Voltage Transformers in Presence of Subharmonics

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