Real-time voltage stability assessment using phasor measurement units: Influence of synchrophasor estimation algorithms

Thilakarathne, C.R.; Meegahapola, Lasantha; Fernando, Nuwantha

doi:10.1016/j.ijepes.2020.105933

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…Typically, PMU data channels are being used for real-time voltage stability monitoring and analysis [91,92]. Thevenin equivalent and Lyapunov exponent are the most commonly used methods in real-time voltage stability analysis [93]. In [90].…”

Section: Real-time Voltage Stability Analysismentioning

confidence: 99%

Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends

et al. 2020

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The energy sector is currently undergoing a rapid transformation with the integration of power electronic converter (PEC)-interfaced renewable energy sources (RES), such as wind and solar photovoltaic (PV) systems, at both the transmission and distribution networks. Power system stability has been significantly influenced by this power grid transformation. This paper comprehensively reviews major power system stability issues affected due to large-scale integration of PEC-interfaced RES in power grids, with some example case studies relevant for each stability category. According to the review, stability issues are mainly originating from reduction in synchronous inertia, reduction in reactive power reserve, low short-circuit strength of the power network, and fault ride-through (FRT) strategy/capability of the PEC-interfaced RES. Decrease in synchronous inertia could affect both the rotor angle stability and the frequency stability, while decrease in short-circuit strength and reactive power reserve could cause voltage stability and rotor angle stability issues in power networks. Sub-synchronous control interactions are also receiving a lot of attention by the power industry due to increasing oscillatory stability incidents reported in power networks with PEC-interfaced RES. FRT capabilities/strategies of PEC-interfaced RES are also playing a pivotal role in power grid stability due to its influence on active and reactive power, hence more emphasis should be placed on FRT schemes of PEC-interfaced RES, since future power grids are expected to operate with 100% PEC-interfaced generation sources. Stability improvement strategies could be implemented to address multiple stability issues in PEC-interfaced power networks; however, rigorous stability studies are required to identify the optimal conditions to implement these improvement strategies. Furthermore, ongoing structural changes in power grids to accommodate remotely sited PEC-interfaced RES are also influencing the stability of power grids. Therefore, all these factors must be carefully considered by system operators when planning and operating power grids in a secure and stable manner with high penetration levels of PEC-interfaced RES.

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Section: Real-time Voltage Stability Analysismentioning

confidence: 99%

Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends

et al. 2020

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“…The maximum Lyapunov exponent represents the convergence of the voltage amplitude trajectory using positive and negative values of an index, and a method to improve its accuracy by phase correction was proposed in [17]. In [18], the Lyapunov exponent algorithm was improved to enhance the robustness of its stability assessment. However, obtaining accurate assessment results requires substantial processing if the voltage trajectory changes gradually.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The sensitivity of the voltage to the active and reactive powers can be calculated using real-time response data. The sensitivity derived from the model and that obtained from the response data should satisfy equation (18).…”

Section: Correction Of Thevenin Parametersmentioning

confidence: 99%

Online Assessment of Short-Term Voltage Stability Based on Hybrid Model and Data-Driven Approach

Cai,

Cao,

Liu

et al. 2023

Preprint

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“…The measurement of synchro phasors by PMUs plays a significant role in dynamically monitoring transient processes within energy supply systems, making a valuable contribution in this regard. The integration of PMUs in power systems greatly enhances the opportunities for monitoring and analyzing the dynamics of the power system [10][11][12]. Compared to traditional SCADA measurements, PMUs have a higher sampling rate, allowing for signals to be collected at up to 60 samples per second and FDEs to be captured with higher precision and speed seconds [13].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to traditional SCADA measurements, PMUs have a higher sampling rate, allowing for signals to be collected at up to 60 samples per second and FDEs to be captured with higher precision and speed seconds [13]. This is attributed to the higher sampling rate of PMUs, which typically ranges from 30 to 120 samples per second, surpassing the sampling rate of traditional SCADA measurements taken at intervals of 2 to 4 s [10][11][12]14]. Despite the fact that there are many types of faults, such as unsymmetrical faults (L-G,LL-G,LL) and symmetrical faults (LLL-LLLG) [15], deep learning algorithms can detect fault type, identify fault location and determine faulty parts, learn incipient failure and their causes, and predict the pattern of faults.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks

Alhanaf,

Balik,

Farsadi

2023

Energies

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Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements.

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Real-time voltage stability assessment using phasor measurement units: Influence of synchrophasor estimation algorithms

Cited by 10 publications

References 27 publications

Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends

Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends

Online Assessment of Short-Term Voltage Stability Based on Hybrid Model and Data-Driven Approach

Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks

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