Optimal deep learning based aggregation of TCLs in an inverter fed stand-alone microgrid for voltage unbalance mitigation

Bera Souvik; Chakraborty Sourav; Kumar DeepakAli NadaLehtonen Matti

首页> 外文期刊>Electric Power Systems Research >Optimal deep learning based aggregation of TCLs in an inverter fed stand-alone microgrid for voltage unbalance mitigation

【24h】

Optimal deep learning based aggregation of TCLs in an inverter fed stand-alone microgrid for voltage unbalance mitigation

机译：Optimal deep learning based aggregation of TCLs in an inverter fed stand-alone microgrid for voltage unbalance mitigation

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

The rapid growth of distributed generating (DG) units due to its advancement in modern day power systems has put a major effect in the operation of low inertial stand-alone Microgrids (MG). Moreover, the limited availability of ancillary services pose a severe challenge in power quality issues especially during unbalanced loading condition. To perpetuate its resiliency, this paper presents an inverter fed Space Vector based Droop controlled Positive-Negative sequence compensation along with deep learning-based aggregation of Thermostatic loads for incorporating Demand Side Management (DSM) to mitigate voltage unbalance (VU) adhering to IEC-61000-3-13 and without compromising the thermal comfort of customers. The major portion of Thermostatic Control Loads (TCLs) comprise of air conditioners that occupy a wide sector in residential and commercial areas; hence the aggregation and scheduling of such loads re-establishes dynamic reactive power of DG by alleviating VU during peak hours. The novel one-time registration of loads is also introduced in this paper by incorporating Artificial Neural Network (ANN) for easy access to customer's newly connected loads to participate in DSM. The test system comprises of eight bus system connected with Solar Photovoltaic, Battery unit, Slow responding Diesel Generator (DZ) along with single and three phase loads constituting of TCLs. The performance of the proposed VU mitigation control strategy is evaluated by considering various case studies and the results are validated through simulations performed in MATLAB / Simulink environment.

著录项

来源
《Electric Power Systems Research》 |2022年第9期|1-14|共14页
作者
Bera Souvik; Chakraborty Sourav; Kumar DeepakAli NadaLehtonen Matti;
展开▼
作者单位

Birla Inst Technol;

Natl Inst Technol;

Aalto Univ;

展开▼
收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
Deep learning; Demand side management; Droop control strategy; Voltage unbalance; Positive-negative sequence compensation; Thermostatically controlled loads;

Optimal deep learning based aggregation of TCLs in an inverter fed stand-alone microgrid for voltage unbalance mitigation

摘要

著录项

相关主题

期刊订阅