A Real Time Event Detection, Classification and Localization Using Synchrophasor Data

Pandey, Shikhar; Srivastava, Anurag K.; Amidan, Brett G.

doi:10.1109/tpwrs.2020.2986019

Cited by 73 publications

(21 citation statements)

References 26 publications

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“…In this work, the synchrophasor-based event detection, classification, and localization approach proposed in [26] is used for detecting and localizing disturbance. Disturbance detection and localization module take voltage, current, real power, reactive power, and frequency measurements at all PMUs in the network as input.…”

Section: A Disturbance Detection and Localizationmentioning

confidence: 99%

Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization

Rizvi

Sadanandan

Srivastava³

2021

IEEE Access

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Short-term voltage stability of power systems is governed by load dynamics, especially the proportion of small induction motors prevalent in residential air-conditioners. It is essential to efficiently monitor short-term voltage stability in real-time by detailed data analytics on voltage measurements acquired from phasor measurement units (PMUs). It is likewise critical to identify the location of faults resulting in short-term voltage stability issues for effective remedial actions. This paper proposes a timeseries deep learning framework using 1D-convolutional neural networks (1D-CNN) for real-time shortterm voltage stability assessment (STVSA), which relies on a limited number of phasor measurement units (PMU) voltage samples. A two-stage STVSA application is proposed. The first stage comprises a 1D-CNN-based fast voltage collapse detector. The second stage comprises of 1D-CNN-based regressor to quantify the severity of the short-term voltage stability event. Two novel indices are presented, and their predicted future values are used to quantify the severity of short-term voltage stability events. This work also considers DB-SCAN clustering-based fault detection and physics-based fault localization for effective short-term voltage stability assessment and remedial actions by identifying the most critical PMUs. A bad data pre-processing technique is also included to mitigate the impact of missing data and outliers on shortterm voltage stability assessment accuracy. The proposed framework is validated using the standard IEEE test systems and compared against other machine learning models to demonstrate the superiority of 1D-CNN-based time-series deep learning for short-term voltage stability assessment.

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Section: A Disturbance Detection and Localizationmentioning

confidence: 99%

Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization

Rizvi

Sadanandan

Srivastava³

2021

IEEE Access

Self Cite

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“…A Python control process is used to facilitate workflow coordination and data exchange between PSS/E, OpenDSS and input data files. Power domain: Event localization is implemented by calculating the event signature of each PMU, where the event signature is estimated by several statistical parameters including Shannon entropy, standard deviation, range, mean difference and crest factor as introduced in the literature [47]. The PMU with the most dominant event signature indicates the location of the event.…”

Section: B Details Of Simulation Modelmentioning

confidence: 99%

A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

Zheng¹,

Xu²,

Trinh³

et al. 2021

Preprint

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The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility.

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“…The reported results indicated a classification accuracy of 98.70%, with error values ranging between 0.61% and 6.5%. The authors in [19] proposed a real-time event classification and fault localization approach for a synchrophasor dataset. Their methodology relied on three processes: 1.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Mrabet

Sugunaraj

Ranganathan

et al. 2022

Sensors

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Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

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A Real Time Event Detection, Classification and Localization Using Synchrophasor Data

Cited by 73 publications

References 26 publications

Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization

Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization

A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

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