To meet the power demands, grid integration of distributed generation systems (DGs) is fast growing in modern power systems. Islanding scenarios are taken into consideration as a concern for the stable functioning of utilities in the grid-integrated (GI) functioning of these systems. In this work, a method for classifying islanding scenarios and quickly identifying the grid anomalies is developed. Data sets that correspond to different grid anomalies are generated in the classification process and trained the data sets by using a machine learning algorithm. To understand this research, a 3.5 kW transformerless single-phase grid-integrated photovoltaic system (GIPV) is simulated and analyzed in Typhoon hardware-in-loop (HIL) software to obtain various data sets for grid anomalies. By using the k-nearest neighbor (kNN) classifier, the data sets are trained for islanding classification mechanism with the efficiency of 98.4%. The kNN classifier has capability to perform classification effectively at a rate of about 26000 observations per second. 

Intelligent Islanding Classification with Optimal k-Nearest Neighbors Technique for Single Phase Grid Integrated PV System

To meet the power demands, grid integration of distributed generation systems (DGs) is fast growing in modern power systems. Islanding scenarios are taken into consideration as a concern for the stable functioning of utilities in the grid-integrated (GI) functioning of these systems. In this work, a method for classifying islanding scenarios and quickly identifying the grid anomalies is developed. Data sets that correspond to different grid anomalies are generated in the classification process and trained the data sets by using a machine learning algorithm. To understand this research, a 3.5 kW transformerless single-phase grid-integrated photovoltaic system (GIPV) is simulated and analyzed in Typhoon hardware-in-loop (HIL) software to obtain various data sets for grid anomalies. By using the k-nearest neighbor (kNN) classifier, the data sets are trained for islanding classification mechanism with the efficiency of 98.4%. The kNN classifier has capability to perform classification effectively at a rate of about 26000 observations per second.

Renewable Energy Sources (RES) using PV arrays are considered and extensively employed in today’s world. Islanding is an issue that happens when The RES is connected to the grid and unexpected circuit breakers are connected to the grid trip. It is necessary to notice the islanding condition in two seconds according to IEEE standards. This manuscript proposed an effectual hybrid system for islanding detection of Solar PhotoVoltaic (SPV) based distributed generation system. The proposed technique is a hybrid combination of Gradient Boosting Decision Tree (GBDT) and Jelly Fish Search algorithm (JS) known as GBDT-JS Techniques. The main concept of this paper is to diminish the Non‐Detection Zone (NDZ) and maintain output power quality. These objectives are achieved by the proposed hybrid technique considering the Rate Of Change Of Frequency (ROCOF) at the target DG position is employed by the input assigned for the RF system in intelligent islanding detection. Here, Discrete Wavelet Transform (DWT) is employed for extracting intrinsic features among islanding and grid disturbance along GBDT. Also, the JS algorithm is used in the classification of islanding and grid disturbance. To find the feasibility of the proposed system various conditions such as different loads, switch operation, and network conditions are tested. In the validation of the proposed system MATLAB/Simulink working platform is utilized.

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Islanding detection in distributed generators using GBDT-JS techniques

The rapid development of economic globalization increases the process of urbanization, which leads to high resource consumption and labor intensity. Relieving of the non-capital function is a policy guideline proposed by the Chinese Government to promote the production upgrade and the transformation of economic development mode of Beijing. As the foundation of Beijing economy, manufacturing industry transfer is given increasingly attention by academic circles. By using District X of Beijing as example, this paper tests current policies with empirical methods. According to the analysis results, strategies for improving transfer policies are offered. As a new research perspective, it can be a good solution.

An Empirical Test of Beijing Industry Transfer Policy

With the widely application of the distributed generation technology, micro grid islanding detection technology has been greatly developed. A new type of islanding detection technique is proposed in this paper. The new detection technology has realized the rapid, efficient, stable and reliable islanding detection, and achieved the zero non detection zone. At the same time, which put money into limit in minimum range. This paper presents the principle of islanding comprehensive detection technology. This paper designs and describes the method to realize the new islanding detection technology. The new technology makes up for a series of shortcomings,such as the non-detection zone (NDZ) is larger of the local testing technology. And it also overcomes a great many shortcomings of the remote detection technology, such as a big investment and too single.

http://www.sersc.org/journals/IJHIT/vol9_no6_2016/1.pdf

Linlin Gao

Papers

A New Technology of Comprehensive Islanding Detection