Programmable protective device for LV distribution system protection

Naik, Sahil; Ravi, Vibin; Arshiya, Ramya

doi:10.1186/s41601-018-0101-5

Cited by 8 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The distribution network serves as the crucial link in the power delivery chain to endusers. It's safe and stable operation has a direct impact on the reliability and quality of power supply to customers (Hock et al, 2018;Naik et al, 2018). Furthermore, an accurate representation of the distribution network topology is essential for various activities such as line loss calculations, tide analysis, grid transformation, and outage restoration.…”

Section: Introductionmentioning

confidence: 99%

A model for identifying the feeder-transformer relationship in distribution grids using a data-driven machine-learning algorithm

et al. 2023

View full text Add to dashboard Cite

With the increasing demand for reliable power supply and the widespread integration of distributed energy sources, the topology of distribution networks is subject to frequent changes. Consequently, the dynamic alterations in the connection relationships between distribution transformers and feeders occur frequently, and these changes are not accurately monitored by grid companies in real-time. In this paper, we present a data-driven machine learning approach for identifying the feeder-transformer relationship in distribution networks. Initially, we preprocess the collected three-phase voltage magnitude data of distribution transformers, addressing data quality and enhancing usability through three-phase voltage normalization. Subsequently, we derive the correlation coefficient calculations between distribution transformers, as well as between distribution transformers and feeders. To tackle the challenging task of determining the correlation coefficient threshold, we propose a multi-feature fusion approach. We extracted additional features from the feeders and combined them with the correlation coefficients to create a feature matrix. Machine learning algorithms were then applied to calculate the results. Through experimentation on a real distribution network in Jiangxi province, we demonstrated the effectiveness of the proposed method. When compared to other approaches, our method achieved outstanding results with an F1 score of 0.977, indicating high precision and recall. The precision value was 0.973 and the recall value was 0.981. Importantly, our method eliminates the need for additional measurement installations, as the required data can be obtained using existing collection devices. This significantly reduces the application cost associated with implementing our approach.

show abstract

Section: Introductionmentioning

confidence: 99%

A model for identifying the feeder-transformer relationship in distribution grids using a data-driven machine-learning algorithm

et al. 2023

View full text Add to dashboard Cite

show abstract

“…To detect electricity theft more effectively, in the past decades, researchers from various countries have proposed different detection methods, which can be divided into three types: state estimation-based, game theory-based, and machine learning algorithm-based ones [5]. For the state estimation-based method, the operation data in the distribution network collected by advanced measurement infrastructure (AMI) [6], [7], wireless sensor, and other equipment are used to detect the operation status of the system to determine the abnormality in the power systems [8]- [13]. In [8], a detection method of electricity theft is proposed based on illegal branch impedance identification.…”

Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection Based on Stacked Autoencoder and the Undersampling and Resampling Based Random Forest Algorithm

et al. 2021

View full text Add to dashboard Cite

Electricity theft has been a major concern to the secure operation of power systems and the interests of power companies. Due to the different methods and types of electricity theft behaviors, it is difficult to determine the suspicion levels of consumers in the research of electricity theft detection. An electricity theft detection method based on stacked autoencoder (SAE) and the undersampling and resampling based random forest (UaRe-RF) algorithm is proposed in this work to formulate appropriate strategies for the practical electricity theft detection requirements of the power company. In the proposed method, the supervised SAE is first trained to extract electricity consumption features that are more adaptable to the classification algorithm for electricity theft detection. Then, the UaRe-RF algorithm is used to establish the class-balanced subsets and determine the suspicion level of each electricity theft user. Finally, two cases of different datasets of electricity consumers are studied for demonstrating the effectiveness of the proposed method, and the results show that higher classification accuracy and more targeted detection strategies can be achieved through the proposed method.

show abstract

“…Provide protection of home appliances in low voltage (LV) distribution system then to avoid their failure due to abnormal operating conditions. This by monitoring over voltage, under voltage and over current conditions [8]. Malfunctions and failures in the supply of electrical energy cause disturbance to the reliability of the quality and safety of the power system.…”

Section: Introductionmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Jahlool

2021

View full text Add to dashboard Cite

In this research, a multi-tasking protection and control device has been designed and implemented has the ability to control and manage automatically the supply of electrical power from various sources to electric appliances connected to the light loading line (3L). At the same time, providing comprehensive protection for the appliances by delaying the supply of power for short period of time during the moment of conversion from one source to other in order to protect it from damage. The mechanism working of the proposed device depends on detecting the problem, diagnosing it and choosing the appropriate solution for it, and then organizing the supply from the appropriate source, and as a result ensure power supply to home appliances.

show abstract

Programmable protective device for LV distribution system protection

Cited by 8 publications

References 1 publication

A model for identifying the feeder-transformer relationship in distribution grids using a data-driven machine-learning algorithm

A model for identifying the feeder-transformer relationship in distribution grids using a data-driven machine-learning algorithm

Electricity Theft Detection Based on Stacked Autoencoder and the Undersampling and Resampling Based Random Forest Algorithm

International Journal of Electronics and Telecommunications

Contact Info

Product

Resources

About