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相关概念视频

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

178
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
178
Energy and Power Signals01:17

Energy and Power Signals

272
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:
272
Multimachine Stability01:25

Multimachine Stability

150
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
150
Electrical Power01:07

Electrical Power

3.0K
Electric power is the product of current and voltage, represented in units of joules per second, or watts. For example, cars often have one or more auxiliary power outlets with which you can charge a cell phone or other electronic devices. These outlets may be rated at 20 amps and 12 volts, so that the circuit can deliver a maximum power of 240 watts. Consider a 25 Watt bulb and a 60 Watt bulb. The conversion of electrical energy produces heat and light, while the kinetic energy lost by the...
3.0K
Load-frequency control01:28

Load-frequency control

137
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...
137
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

180
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
180

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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

487

改进的综合指数平滑基于CNN-LSTM算法,预测未来一天的电价.

Kunal Shejul1, R Harikrishnan1, Harshita Gupta1

  • 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

MethodsX
|September 12, 2024
PubMed
概括

准确的前一天电价预测对于短期市场参与者来说至关重要. 一种新的指数式光滑-CNN-LSTM模型显著提高了预测准确性,超过了现有的方法.

科学领域:

  • 能源经济学 能源经济学
  • 人工智能的人工智能
  • 时间序列分析时间序列分析

背景情况:

  • 电力市场的放松管制刺激了短期交易的增长,需要准确的前一天价格预测,以便发电厂和消费者有效投标.
  • 由于消费者竞标模式的动态,电价在很大程度上波动,这凸显了对强大的预测模型的需求.

研究的目的:

  • 提出和评估一种修改后的指数式平滑-CNN-LSTM (卷积神经网络长期短期记忆) 方法,用于改进前一天电价预测.
  • 通过印度能源交易所 (IEX) 的现实数据评估拟议的预测模型的性能.

主要方法:

  • 该研究整合了指数式光滑来捕捉趋势和季节性,并与CNN-LSTM集成,用于模拟时间序列数据中的复杂空间和时间依赖性.
  • 一种混合指数光滑-CNN-LSTM模型被开发和测试在前一天的电力市场数据上.

主要成果:

  • 拟议的指数式光滑-CNN-LSTM模型实现了优异的预测性能,其平均绝对误差 (MAE) 为0.11,根平均平方误差 (RMSE) 为0.17,平均绝对百分比误差 (MAPE) 为1.53%.
  • 混合模型与单个指数式光滑,LSTM和CNN-LSTM技术相比,显示出更高的准确性.

结论:

  • 开发的指数式平滑-CNN-LSTM方法为短期市场参与者在前一天电价预测方面取得了重大进展.
关键词:
在美国,CNN是CNN.动态电力定价 动态电力定价基于CNN-LSTM的指数式光滑指数式光滑是一种指数式光滑.预测 预测 预测 预测这是LSTM的LSTM.

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  • 该模型的有效性表明,它可能适用于金融,零售,医疗保健和制造业等各个行业的时间序列预测挑战.