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Power system state forecasting using machine learning techniques

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Abstract

Modern power sector requires grid observability under all scenarios for its ideal functioning. This enforces the operator to incorporate state estimation solutions based on a priori measurements to deduce the corresponding operating states of the grid. The key principle for such aforementioned algorithms lies on an occurrence of an over determined class of system having an ample redundancy in the measurements. Operators employ state forecasting solutions to counter the loss of real-time measurements. This work encompasses a critical comparison between several machine learning models along with ARIMA and time delayed neural network architecture for proper forecasting of operating states under normal as well as contingency scenarios. To showcase the efficacy of the proposed approach, this work incorporates a comprehensive comparison between them based on RMSE, MSE and MAE index. Copula-based synthetic data generation based on Gaussian multivariate distribution of the a priori measurements and operating states along with optimal hyper-parameter tuning of the models have shown the effectiveness of such algorithms in predicting future state estimates. The proposed machine learning models can be also seen to showcase an effective forecasting strategy under varying noise scenarios. This work also showcases the implementation of the models for real-time state forecasting strategy having computational times in the order of micro seconds. All the simulations have been carried out on the standard IEEE 14 bus test bench to support the former proposals.

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Availability of data and materials

The data sets are available from the corresponding author on request.

Code availability

The custom code is available from the corresponding author on request.

Abbreviations

AHFASE:

Asynchronous hierarchical forecasting-aided state estimator

ARIMA:

Autoregressive integrated moving average

DSE:

Dynamic state estimator

EGPR:

Exponential Gaussian process regression

FGSVM:

Fine Gaussian support vector machine

GPR:

Gaussian process regression

L-G:

Line-to-ground

LL-G:

Double line-to-ground

LLL-G:

Three phase-to-ground

MAE:

Mean absolute error

MGSVM:

Medium Gaussian support vector machine

MSE:

Mean squared error

PDC:

Phasor data concentrator

PDF:

Probability distribution function

PMUs:

Phasor measurement units

PSSE:

Power system state estimator

RLR:

Robust linear regression

RMSE:

Root mean squared error

RSE:

Robust state estimator

RTDS:

Real-time digital simulator

SEGPR:

Squared exponential Gaussian process regression

SVM:

Support vector machine

SCADA:

Supervisory control and data acquisition

TDNN:

Time delayed neural network

WLS:

Weighted least squares

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, 221005, India

    Debottam Mukherjee & Sandip Ghosh

  2. Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Arunachal Pradesh, 791112, India

    Samrat Chakraborty

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  1. Debottam Mukherjee
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  2. Samrat Chakraborty
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  3. Sandip Ghosh
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All authors have equally contributed for analysis and interpretation of the results and data, drafting the article or revising it critically and preparing the final version.

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Correspondence to Debottam Mukherjee.

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Mukherjee, D., Chakraborty, S. & Ghosh, S. Power system state forecasting using machine learning techniques. Electr Eng 104, 283–305 (2022). https://doi.org/10.1007/s00202-021-01328-z

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