High-speed directional protection without voltage sensors for distribution feeders with distributed generation integration based on the correlation of signals and machine learning

Morales, John; Orduña, Eduardo; Villarroel, H.; Quispe, Juan Carlos

doi:10.1016/j.epsr.2020.106295

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…In Hassan et al (2020) the authors suggest a methodology directed at addressing the concern of siting and determining the dimensions of distributed generation units fueled by sustainable energy sources. Morales et al (2020) A procedure is described to identify the current flow along the distribution feeder (DF) while integrating distributed generation (DG). This method incorporates decision trees (DT), empirical decomposition (ED), and support vector machines (SVM) to achieve the intended objective, delineating the steps of the methodology.…”

Section: Related Workmentioning

confidence: 99%

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Jain,

Gupta

2024

Front. Energy Res.

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As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

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Section: Related Workmentioning

confidence: 99%

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Jain,

Gupta

2024

Front. Energy Res.

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“…This method is particularly effective for spot load systems, but cannot be used if there is a load or distributed power source in the section between protective devices, such as distribution systems, due to the underreach effect. Recently, methods for automatically estimating the fault direction of complex grids by adding machine learningbased techniques were investigated [36]- [38]. however, these approaches are in the early stage in the research and the applied power distribution network is limited to RDS or CLS.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based Fault Direction Estimation and Protection Coordination for Networked Distribution System

Kim

Chae

et al. 2022

IEEE Access

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While the world's power distribution system resembles an intricate web-like structure, the most conventionally implemented distribution mechanism is the radial distribution system (RDS) where connection points for each distribution line are normally kept open. However, disadvantages regarding the traditional system have led to active research on establishing the networked distribution system (NDS), in which multiple circuits are interconnected with electricity as well as high-speed communication systems. The NDS offers multiple advantages including increased facility utilization, increased hosting capacity, and higher terminal voltage. Conversely, an unsolved issue prevails as the existing protection coordination method designed for the RDS is inadequate for fault occurrences in the NDS due to inaccurate fault direction identification. Hence, there is an urgent need for an alternative protection coordination method that allows high precision fault direction identification through communication. Moreover, the application of various existing technologies is hindered as the distance relay protection coordination algorithm malfunctions in situations where distribution lines are of short length, loads are dispersed onto multiple lines, and integration of distributed generation (DG) is frequent. Therefore, this paper presents a fault direction identification method that uses the waveform of the fault current based on long short-term memory (LSTM) neural network and a communication-based protection coordination scheme that can be applied in fault situations within an NDS.

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“…Therefore, the research methods based on data‐driven and AI become hotspots in the planning, operation, maintenance, and marketing of power grid. Meanwhile, the protection based on data driven and AI has also attracted rising attention [1–10].…”

Section: Introductionmentioning

confidence: 99%

Knowledge‐based artificial neural network for power transformer protection

Jiao

2020

IET Generation, Transmission & Distribution

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High-speed directional protection without voltage sensors for distribution feeders with distributed generation integration based on the correlation of signals and machine learning

Cited by 7 publications

References 33 publications

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

LSTM-Based Fault Direction Estimation and Protection Coordination for Networked Distribution System

Knowledge‐based artificial neural network for power transformer protection

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