Fault Detection and Location in DC Microgrids by Recurrent Neural Networks and Decision Tree Classifier
Authors
Abstract:
Microgrids have played an important role in distribution networks during recent years. DC microgrids are very popular among researchers because of their benefits. Protection is one of the significant challenges in the way of microgrids progress. As a result, in this paper, a fault detection and location scheme for DC microgrids is proposed. Due to advances in Artificial Intelligence (AI) and suitable performance of smart protection methods in AC microgrids, Recurrent Neural Networks (RNNs) are used in the proposed method for fault location in DC microgrids. In this method, the fault detection and location are done by measuring feeders current and main bus voltage. Further, the performance of the proposed method is assessed in grid-connected, and islanded operation modes of the microgrid. The result will confirm the efficiency of the proposed scheme. In this paper, MATLAB and DIgSILENT are used to design RNNs and DC microgrid simulation respectively.
similar resources
fault location in power distribution networks using matching algorithm
چکیده رساله/پایان نامه : تاکنون روشهای متعددی در ارتباط با مکان یابی خطا در شبکه انتقال ارائه شده است. استفاده مستقیم از این روشها در شبکه توزیع به دلایلی همچون وجود انشعابهای متعدد، غیر یکنواختی فیدرها (خطوط کابلی، خطوط هوایی، سطح مقطع متفاوت انشعاب ها و تنه اصلی فیدر)، نامتعادلی (عدم جابجا شدگی خطوط، بارهای تکفاز و سه فاز)، ثابت نبودن بار و اندازه گیری مقادیر ولتاژ و جریان فقط در ابتدای...
Integrated Fault-detection and Control of DC Microgrids Using SDRE Observer-controller
In this paper, using the state-dependent Riccati equation (SDRE) technique, a suboptimal fault-tolerant control scheme is designed for a DC microgrid in the islanded mode. The objectives are the voltages control of the photo-voltaic cell, the battery, the capacitor bank, and the DC bus as well as on time fault detection. In the design procedure of the SDRE observer-controller, a nonlinear mathe...
full textAnomaly Detection Using SVM as Classifier and Decision Tree for Optimizing Feature Vectors
Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...
full texthydrochlorothiazide detection in urine samples by hplc-dad and experimental design dispersive l-l microextraction
hydrochlorothiazide (hct) is a diuretic agent which is shown to be effective in the treatment of hypertension. literature reports have demonstrated that urinary excretion data may be used to assess the bioavailability of various formulations containing this thiazide. also hct consumption by the athletes is one of the drugs which should be regulated by world anti-doping agency (wada), because of...
Identification of Crack Location and Depth in a Structure by GMDH- type Neural Networks and ANFIS
The Existence of crack in a structure leads to local flexibility and changes the stiffness and dynamic behavior of the structure. The dynamic behavior of the cracked structure depends on the depth and the location of the crack. Hence, the changes in the dynamic behavior in the structure due to the crack can be used for identifying the location and depth of the crack. In this study the first th...
full textMy Resources
Journal title
volume 11 issue 4
pages 0- 0
publication date 2022-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023