Development of an Ensemble Decision Tree-Based Power System Dynamic Security State Predictor

Mukherjee, Rituparna; De, Abhinandan

doi:10.1109/jsyst.2020.2978504

Cited by 26 publications

(11 citation statements)

References 19 publications

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“…Training RF involves a significant number of hyperparameters. Many approaches to power system TSA, involving decision trees or random forests, have been proposed, e.g., [22,[41][42][43][44][45].…”

Section: Machine Learningmentioning

confidence: 99%

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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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Section: Machine Learningmentioning

confidence: 99%

“…Ensemble learning has been a very popular approach to power system TSA, e.g., see [17,28,40,46,[49][50][51][52]. Furthermore, several authors have used different tree-based ensembles [22,41,43], or special bagging ensembles [16,32,53]. This is still an active area of research.…”

Section: Ensemblesmentioning

confidence: 99%

Artificial Intelligence Techniques for Power System Transient Stability Assessment

et al. 2022

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“…In order to solve the limitations of the SVM-based PGPM in the large-sample condition, a lightweight PGPM based on ensemble decision tree haswas proposed in ref. [11], which can predict a power system's operating states in a real-time and in an on-line environment. In the proposed solution, an ensemble security predictor (ENSP) was developed and trained to predict and classify power system's dynamic operating states into secure, insecure, and intermediate transitional classes.…”

Section: Introductionmentioning

confidence: 99%

An Improved Generating Energy Prediction Method Based on Bi-LSTM and Attention Mechanism

Zhang

et al. 2022

Electronics

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The energy generated by a photovoltaic power station is affected by environmental factors, and the prediction of the generating energy would be helpful for power grid scheduling. Recently, many power generation prediction models (PGPM) based on machine learning have been proposed, but few existing methods use the attention mechanism to improve the prediction accuracy of generating energy. In the paper, a PGPM based on the Bi-LSTM model and attention mechanism was proposed. Firstly, the environmental factors with respect to the generating energy were selected through the Pearson coefficient, and then the principle and implementation of the proposed PGPM were detailed. Finally, the performance of the proposed PGPM was evaluated through an actual data set collected from a photovoltaic power station in Suzhou, China. The experimental results showed that the prediction error of proposed PGPM was only 8.6 kWh, and the fitting accuracy was more than 0.99, which is better than existing methods.

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“…The adoption of DT for performing the classification task is motivated by its ability to attain improved generalisation (inputoutput mapping) for high dimensional complex datasets. DT-based classifiers follow a structure in which all possible outcomes are taken into consideration while assigning an input to a particular class [25,26]. The simple structure allows for easy implementation with a reduced computational cost [27,28].…”

Section: Introductionmentioning

confidence: 99%

Protection of six-phase transmission line using recursive estimation of non-linear autoregression model coefficients and decision tree

Althi

Koley

Ghosh

2020

IET Science, Measurement &Amp; Technology

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Ever-increasing energy demands and limited right of way over land motivates the adoption of six-phase power transmission. However, the wider recognition of six-phase transmission necessitates the development of a reliable protection system. Soaring assimilation of non-linear loads has complicated the protection task due to the intrusion of harmonics. This work aims at imparting necessary protection to six-phase transmission systems with immunity against harmonics. The issue pertaining to harmonics and sensor noise-induced non-linearity in the signal being fed to the protective relays has been addressed by modelling the current-voltage profile using a non-linear autoregression approach. Conventional relays are known to malfunction during non-linear loading and noisy measurement. In this context, an adaptive window-based approach has been employed to capture the non-linear dynamics, followed by mapping the model coefficients with the state of the six-phase system using decision trees. The inclusion of parameters in the non-linear auto regressive model for representing harmonic intrusion and noise imparts necessary robustness to the proposed protection scheme against non-linear loading and noisy sensor measurement. The appropriateness of the non-linear autoregression-based scheme has been tested for varying fault scenarios by considering wide variation in the fault attributes, non-linear loading, and noise level in the sensor measurement.

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Development of an Ensemble Decision Tree-Based Power System Dynamic Security State Predictor

Cited by 26 publications

References 19 publications

Artificial Intelligence Techniques for Power System Transient Stability Assessment

Artificial Intelligence Techniques for Power System Transient Stability Assessment

An Improved Generating Energy Prediction Method Based on Bi-LSTM and Attention Mechanism

Protection of six-phase transmission line using recursive estimation of non-linear autoregression model coefficients and decision tree

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