Fuzzy Control System for Voltage Regulation In Power Transformers

Spatti, Danilo Hernane; Silva, I N da; Usida, Wesley F.; Flauzino, Rogério Andrade

doi:10.1109/tla.2010.5453946

Cited by 19 publications

(3 citation statements)

References 16 publications

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“…And machine learning has been proposed to provide ideas to solve this aspect of the challenge. However, although the existing machine learning-based voltage estimation methods have solved the problems of computational difficulties and complexity in these areas, they still require part of the station areas data, such as the precise topology of the station areas, operating parameters, or the electricity usage habits of users under the influence of time and climate [3][4][7] [8], which not only makes this type of method have certain prerequisite difficulties in data acquisition but also reduces the advantage in prediction time advantage and cannot meet the real-time requirements of voltage prediction. In addition, as the user voltages are coupled with each other, the electricity loads of any user in the station areas will affect the voltage quality of other users, and at the same time there is a certain randomness in the users' electricity consumption characteristics, so direct user LV prediction will lead to an increase in the accumulation of errors.…”

Section: Feature Selectionmentioning

confidence: 99%

A Low Voltage Prediction Based on LSTM-BP Combined Model for Distribution Station Areas

Yi,

Zhou,

Huang

2023

J. Phys.: Conf. Ser.

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The timely management of low voltage issues in the distribution network relies heavily on accurate predictions of voltage in the station areas. Many current methods for voltage prediction require complex data collection involving power grid topology parameters and electricity information. However, these methods suffer from drawbacks such as the need for extensive data, limited real-time performance, and significant prediction errors. Hence, this study proposes an approach to voltage prediction utilizing a combined LSTM-BP model. Initially, an analysis of the mechanism of low voltage formation in the station areas reveals that the primary factor influencing node points voltage is the power consumption of users. Subsequently, the LSTM neural network is employed to forecast short-term load curves in the distribution station areas, while the self-learning capability of the BP neural network is utilized to establish the correlation between users’ power and their corresponding voltage levels. By effectively combining the above two neural network models, the historical load’s data can be used to accurately and quickly predict the future low voltage situation in the station areas. Finally, taking station areas as the research object and comparing the actual voltage data with the voltage data predicted in this paper, the results show that within the 1V error range, the prediction accuracy is 99.8%. In contrast to the conventional voltage prediction approach, the method put forward in this research paper enables instantaneous and precise anticipation of low voltage levels without relying on details such as station configuration, line characteristics, or user voltage information.

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Section: Feature Selectionmentioning

confidence: 99%

A Low Voltage Prediction Based on LSTM-BP Combined Model for Distribution Station Areas

Yi,

Zhou,

Huang

2023

J. Phys.: Conf. Ser.

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“…These intelligent systems are robust to the uncertainty of system dynamics and can function without an accurate model of the power grid [24]. In [25,26], voltage regulation of a power distribution system is accomplished via fuzzy logic control (FLC). While [27] uses FLC and gain scheduling for microgrid-scale distributed voltage control.…”

Section: Introductionmentioning

confidence: 99%

A Coordinated Voltage Regulation Algorithm of a Power Distribution Grid with Multiple Photovoltaic Distributed Generators Based on Active Power Curtailment and On-Line Tap Changer

et al. 2023

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The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method.

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“…In [23], a gain-scheduling control scheme combining with FL control is performed for the voltage regulation in a microgrid including distributed generations (DGs). A FL control approach is introduced for the voltage regulation of power transformers in [24]. The application of the FL control systems requires proper adjusting for the membership functions to provide the good performance.…”

Section: Introductionmentioning

confidence: 99%

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

et al. 2021

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This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

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Fuzzy Control System for Voltage Regulation In Power Transformers

Cited by 19 publications

References 16 publications

A Low Voltage Prediction Based on LSTM-BP Combined Model for Distribution Station Areas

A Low Voltage Prediction Based on LSTM-BP Combined Model for Distribution Station Areas

A Coordinated Voltage Regulation Algorithm of a Power Distribution Grid with Multiple Photovoltaic Distributed Generators Based on Active Power Curtailment and On-Line Tap Changer

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

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