Real-Time Voltage Stability Monitoring Using Machine Learning-Based PMU Measurements

Amroune, Mohammed; Bourzami, Arif; Zellagui, Mohamed; Musirin, Ismail

doi:10.1007/978-3-030-54275-7_16

Cited by 3 publications

(5 citation statements)

References 30 publications

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“…The SCADA system has some limitations such as relatively lower accuracy of measurement and low refresh rate [ 42 ] which makes it impractical for online security analysis [ 33 ]. To resolve these drawbacks, new monitoring and communication infrastructure called Wide Area Measurement System (WAMS) with Phasor Measurement Unit (PMU) becomes practical to monitor voltage and current phasors at higher resolution (sampling rate) and fidelity [ 32 , 43 ].…”

Section: Functions Of Power System Securitymentioning

confidence: 99%

“…Data-driven artificial intelligence (AI) techniques such as fuzzy inference systems [ 29 , 33 ], Artificial Neural Network ( ), support vector machine (SVM) [ 14 ], decision tree (DT), graph convolutional neural network [ 34 ], cascaded convolutional network [ 35 ] and long short-term memory network ( ) [ 30 ] have been widely employed to overcome the limitations of classical model-driven simulation approaches in DSA [ 3 ]. Due to their capacity to tackle nonlinear problems with remarkable speed and accuracy, these and other AI approaches have attracted the researchers’ attention [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Techniques of power system static security assessment and improvement: A literature survey

Hailu¹,

Nyakoe²,

Muriithi³

2023

Heliyon

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Section: Functions Of Power System Securitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Techniques of power system static security assessment and improvement: A literature survey

Hailu¹,

Nyakoe²,

Muriithi³

2023

Heliyon

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“…From survey, Pan et al (2020) need to correct voltage stability, Jahan et al (2021) require greater efficacy in the device, You et al (2021) requires full power device calculations, and Amroune et al (2020) is more resilient to tiny fluctuations in load levels by analyzing previous research. Manivasagam and Prabakaran (2020) should concentrate on voltage interference, reactive power loss, harmonic current and noise to improve power efficiency.…”

Section: Literature Surveymentioning

confidence: 99%

“…Amroune et al (2020) proposed a data-driven methodology for gaining insight into changes in voltage magnitudes through synchronous PMU data based on the moment-based spectrum estimation process, as well as taking appropriate steps to prevent voltage collapse. To map the motion of the system, an inverse Jacobian dependent predictor, i.e.…”

Section: Literature Surveymentioning

confidence: 99%

“…Since MEMD is a measurement-based technique, the oscillation mode information of that region, such as the oscillation amplitude and phase angle, cannot be reconstructed when the calculation in one field is completely inaccessible. Amroune et al (2020) proposed a data-driven methodology for gaining insight into changes in voltage magnitudes through synchronous PMU data based on the moment-based spectrum estimation process, as well as taking appropriate steps to prevent voltage collapse. To map the motion of the system, an inverse Jacobian dependent predictor, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Voltage instability curtailment and quality improvement with phasor measurement unit in power system

Preeti

Rani²

2022

COMPEL

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Purpose The earlier methods are more resilient to improvements such as load shift and path change. This results in problems such as a voltage drop and a high reactive flux. In addition, due to the delay, congestion or interruption of the transmission, the system cannot receive all phasor measurement unit (PMU) measurements at the relevant time as well as the presence of noise in the received data. Design/methodology/approach With the development of wide area measurement system technologies, it seems to be possible to track voltage stability online via time-stamped PMUs. As the voltage instability causes a voltage decomposition, voltage instability is one of the most important problems when monitoring the power supply. Findings This harmonic distortion significantly decreases the data quality in the grid. As a result, instability ascertainment based on PMU has been suggested as a method for detecting voltage instability in power systems monitored with PMU. In addition, a technique called instability amendment via load dropping has been proposed to keep the device from collapsing due to voltage failure. Originality/value To improve the power output, the power prominence melioration technique was developed. This proposed system has been implemented in MATLAB Simulink and compared with the recent researches.

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Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization

et al. 2022

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In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical boundary index (CBI) approach using the machine learning technique. Prediction models are based on an adaptive neuro-fuzzy inference system (ANFIS) and its enhanced model with particle swarm optimization (PSO). Standalone ANFIS and PSO-ANFIS models are implemented using the fuzzy ‘c-means’ clustering method (FCM) to predict the expected values of CBI as a veritable tool for measuring the VSM of power systems under different loading conditions. Six vital power system parameters, including the transmission line and bus parameters, the power injection, and the system voltage derived from load flow analysis, are used as the ANFIS model implementation input. The performances of the two ANFIS models on the standard IEEE 30-bus and the Nigerian 28-bus systems are evaluated using error and regression analysis metrics. The performance metrics are the root mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (R) analyses. For the IEEE 30-bus system, RMSE is estimated to be 0.5833 for standalone ANFIS and 0.1795 for PSO-ANFIS; MAPE is estimated to be 13.6002% for ANFIS and 5.5876% for PSO-ANFIS; and R is estimated to be 0.9518 and 0.9829 for ANFIS and PSO-ANFIS, respectively. For the NIGERIAN 28-bus system, the RMSE values for ANFIS and PSO-ANFIS are 5.5024 and 2.3247, respectively; MAPE is 19.9504% and 8.1705% for both ANFIS and PSO-ANFIS variants, respectively, and the R is estimated to be 0.9277 for ANFIS and 0.9519 for ANFIS-PSO, respectively. Thus, the PSO-ANFIS model shows a superior performance for both test cases, as indicated by the percentage reduction in prediction error, although at the cost of a higher simulation time.

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Real-Time Voltage Stability Monitoring Using Machine Learning-Based PMU Measurements

Cited by 3 publications

References 30 publications

Techniques of power system static security assessment and improvement: A literature survey

Techniques of power system static security assessment and improvement: A literature survey

Voltage instability curtailment and quality improvement with phasor measurement unit in power system

Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization

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