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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Energy and Power Signals01:17

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
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在能源行业,ESG指导和人工智能支持电力系统分析.

Qingjiang Li1, Guilin Zou2, Wenlong Zeng3

  • 1China Southern Power Grid Co., Ltd., Guangzhou, 510000, People's Republic of China.

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这项研究整合了人工智能 (AI) 和环境社会治理 (ESG) 以进行先进的电力系统分析. 它引入了新的负载需求预测和故障诊断模型,提高了能源部门的效率和准确性.

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科学领域:

  • 电力系统工程 电力系统工程
  • 人工智能的人工智能
  • 环境社会治理 (ESG)

背景情况:

  • 电力系统分析和故障诊断需要提高精度和效率.
  • 整合环境社会治理 (ESG) 对于评估电力系统影响至关重要.
  • 现有的方法可能缺乏负载需求预测和故障预测的准确性.

研究的目的:

  • 评估使用人工智能和ESG来改进分析和故障诊断的电力系统.
  • 为准确的电力负载需求预测开发CNN-BiLSTM模型.
  • 实现和优化一个深度信念网络 (DBN) 与粒子群优化 (PSO) 用于电网故障诊断.

主要方法:

  • 介绍一个ESG框架来评估环境,社会和治理影响.
  • 开发一个卷积神经网络-双向长期短期记忆 (CNN-BiLSTM) 模型,用于负载需求预测.
  • 优化深信网络 (DBN) 使用粒子群优化 (PSO) 进行故障诊断.

主要成果:

  • 在CNN-BiLSTM模型显著提高了预测准确度,低RMSE (0.054),MAE (0.076),和MAPE (0.102).
  • 在129.94秒内,PSO优化的DBN在电网故障诊断中实现了96.22%的准确性.
  • 拟议的模型在预测和故障预测效率方面都超过了现有的算法.

结论:

  • 综合人工智能和ESG方法增强了电力系统分析和故障诊断.
  • 该研究为能源行业的可持续和智能增长提供了强有力的框架.
  • 开发的模型为电力系统操作的准确性和效率提供了显著的改进.