Wide Range Reactive Power Compensation for Voltage Unbalance Mitigation in Electrical Power Systems

Ghaeb, Jasim A.; Alkayyali, Malek; Tutunji, Tarek A.

doi:10.1080/15325008.2021.2002474

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…There is also a known relationship between Mvar and voltage. The work in [15] discusses ways to improve the VUF of a power system different methods of injecting Mvar. Also, the fast decoupled power flow method can separate MW and phase angle from Mvar and voltage magnitude [16].…”

Section: B Results: Second Data Setmentioning

confidence: 99%

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

Boyd,

Reising,

Murphy

et al. 2024

IEEE Open J. Ind. Applicat.

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Voltage unbalance is a growing issue that, among other things, can impact three-phase motor and drive loads, result in nuisance tripping of generation units and capacitor banks, and prevent optimization of conservative voltage regulation strategies. This difference between the three phases of voltage delivered to customers can damage the equipment of these customers as well as negatively impact the power system itself. This work presents an approach for predicting voltage unbalance using machine learning. Historical megawatt and megavar data-obtained through a Supervisory Control And Data Acquisition (SCADA) system-are used to train an Artificial Neural Network model as a binary classifier with a portion of the data serving to validate the trained model. Voltage unbalance is predicted at an accuracy above 95% for eight substations within the power utility's Extra-High Voltage transmission network and over 91% for all forty-two substations. The trained model is tested in a manner that would be employed using simulated data generated by state estimation software. This simulated data validates the model's capacity to predict the substation buses that would experience voltage unbalance.

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Section: B Results: Second Data Setmentioning

confidence: 99%

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

Boyd,

Reising,

Murphy

et al. 2024

IEEE Open J. Ind. Applicat.

View full text Add to dashboard Cite

show abstract

“…There is also a known relationship between Mvar and voltage. The work in [11] discusses ways to improve the VUF of a power system through different methods of injecting Mvar. Also, the fast decoupled power flow method can separate MW and phase angle from Mvar and voltage magnitude [12].…”

Section: B Results: Second Data Setmentioning

confidence: 99%

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

Boyd¹,

Reising²,

Murphy³

et al. 2023

Preprint

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<p>Voltage unbalance is a growing issue that, among other things, can impact three-phase motor and drive loads, result in nuisance tripping of generation units and capacitor banks, and prevent optimization of conservative voltage regulation strategies. This difference between the three phases of voltage delivered to customers can damage the equipment of these customers as well as negatively impact the power system itself. This work presents an approach for predicting voltage unbalance using machine learning. Historical megawatt and megavar data–obtained through a Supervisory Control And Data Acquisition (SCADA) system–are used to train an Artificial Neural Network model as a binary classifier with a portion of the data serving to validate the trained model. Voltage unbalance is predicted at an accuracy above 95% for eight substations within the power utility’s Extra-High Voltage transmission network and over 91% for all forty-two substations. The trained model is tested in a manner in which it would be used through the use of using simulated data generated by state estimation software. Using this simulated data validates the model’s capacity to predict the substation buses that would experience voltage unbalance.</p>

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“…Te authors in [19] have proposed an algorithm for voltage unbalance detection based on Clark transformation, which converts the three-phase voltages into complex zplane. Te static volt-ampere-reactor compensative (SVC) has the ability to balance the loads and stabilize the electrical power system [20][21][22]. Te authors in [23] used the SVC to balance the load voltages and improve the power factor.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

Ghaeb,

Al-Naimi,

Alkayyali

2023

Journal of Electrical and Computer Engineering

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In this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%).

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Wide Range Reactive Power Compensation for Voltage Unbalance Mitigation in Electrical Power Systems

Cited by 11 publications

References 26 publications

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System

Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

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