Research on power quality prediction for DG integrated smart grid based on neural network

Weng, Guirong; Zhu, Shuangshuang; Gong, Yangguang; Ma, Tai-Yi; Xie, Fangrui; Fang, Mingjia

doi:10.1109/ei2.2017.8245370

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…With the worsening of environmental pollution and the depletion of traditional fossil fuels, the development and utilization of new energy sources have become a global trend [3][4][5][6]. To achieve the high penetration of renewable energy and enhance the performance of power systems, researchers have focused on the safety, stability, and energy quality benefits of power systems.…”

Section: Related Workmentioning

confidence: 99%

Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm

Xu,

Han,

Yin

et al. 2024

Computers

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To address the challenges associated with wind power integration, this paper analyzes the impact of distributed renewable energy on the voltage of the distribution network. Taking into account the fast control of photovoltaic inverters and the unique characteristics of photovoltaic arrays, we establish an active distribution network voltage reactive power-optimization model for planning the active distribution network. The model involves solving the original non-convex and non-linear power-flow-optimization problem. By introducing the second-order cone relaxation algorithm, we transform the model into a second-order cone programming model, making it easier to solve and yielding good results. The optimized parameters are then applied to the IEEE 33-node distribution system, where the phase angle of the node voltage is adjusted to optimize the reactive power of the entire power system, thereby demonstrating the effectiveness of utilizing a second-order cone programming algorithm for reactive power optimization in a comprehensive manner. Subsequently, active distribution network power quality control is implemented, resulting in a reduction in network loss from 0.41 MW to 0.02 MW. This reduces power loss rates, increases utilization efficiency by approximately 94%, optimizes power quality management, and ensures that users receive high-quality electrical energy.

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Section: Related Workmentioning

confidence: 99%

Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm

Xu,

Han,

Yin

et al. 2024

Computers

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“…Although the interconnection of power generating systems based on renewable energy resources (RES) into the current transmission grid presents a number of power quality difficulties [5][6][7], the potential future advantages of using RES as a source of electricity generation are promising. Owing to the fact that the quality of the electricity produced by RES such as PV or wind power is influenced by the unpredictability of the surrounding environment [8]. As a result, the utilization of renewable generating systems such as PV and wind power, as well as these systems frequent integration with the grid, has resulted in major power quality challenges in the networks that are now in place.…”

Section: Introductionmentioning

confidence: 99%

A Brief Review of Power Quality Issues Emerged due to Modernization of Power System Infrastructure

Bajaj

2022

International Transactions on Electrical Engineering and Comput

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The architecture of power systems is evolving daily due to technological advancements aimed at reducing energy consumption and improving the environment. However, this improvement in the architecture of the network has affected the quality of the electricity and is producing several problems associated with the quality of power supplied. In a broad sense, the recent standard procedures for power generation, transmission and distribution are the root cause of the recently discovered problems with power quality. Investigating these problems is essential to accurately forecast the stochastic impacts on today network's functioning and locate the best remedy for these problems. This study includes a discussion on quality challenges that evolved due to modernization in the infrastructure of power network and an evaluation of the latest solutions presented in the literature to overcome those difficulties. This paper also makes recommendations for the future scope of research that should be done to address these new power quality problems.

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A Machine-Learning Pipeline for Large-Scale Power-Quality Forecasting in the Mexican Distribution Grid

et al. 2022

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Electric power distribution networks face increasing factors for power-quality (PQ) deterioration, such as distributed, renewable-energy generation units and countless high-end electronic devices loaded as controllers or in standalone mode. Consequently, government regulations are issued worldwide to set up strict PQ distribution standards; the distribution grids must comply with those regulations. This situation drives research towards PQ forecasting as a crucial part of early-warning systems. However, most of the approaches in the literature disregard the big-data nature of the problem by working on small datasets. These datasets come from short-scale off-grid configurations or selected portions of a larger power grid. This article addresses a study case from a region-sized state-owned Mexican distribution grid, where the company must preserve essential PQ standards in approximately 700 distribution circuits and 150 quality-control nodes. We implemented a machine-learning pipeline with nearly 4000 univariate forecasting models to address this challenge. The system executes a weekly forecasting pipeline and daily data ingestion and preprocessing pipeline, processing massive amounts of data ingested. The implemented system, MIRD (an acronym for Monitoreo Inteligente de Redes de Distribution—Intelligent Monitoring of Distribution Networks), is an unprecedented effort in the production, deployment, and continuous use of forecasting models for PQ indices monitoring. To the extent of the authors’ best knowledge, there is no similar work of this type in any other Latin-American distribution grid.

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Research on power quality prediction for DG integrated smart grid based on neural network

Cited by 3 publications

References 14 publications

Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm

Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm

A Brief Review of Power Quality Issues Emerged due to Modernization of Power System Infrastructure

A Machine-Learning Pipeline for Large-Scale Power-Quality Forecasting in the Mexican Distribution Grid

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