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Multimachine Stability01:25

Multimachine Stability

227
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:
227
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

153
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...
153
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

328
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
328
Generator Voltage Control01:21

Generator Voltage Control

243
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
243
Secondary Distribution01:25

Secondary Distribution

130
Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
In residential areas, 120/240 V single-phase, three-wire service is commonly used for lighting, outlets, and large appliances. Urban areas with high-density loads...
130
Load-frequency control01:28

Load-frequency control

254
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...
254

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Updated: Sep 9, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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電気配電システムの電圧安定性評価のための機械学習アルゴリズム

Molla Addisu Mossie1, Tefera Terefe Yetayew2, Girmaw Teshager Bitew3

  • 1Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia. mollaaddisu2@gmail.com.

Scientific reports
|August 30, 2025
PubMed
まとめ

機械学習モデルはエチオピアの 電力網の電圧の安定性を 正確に予測します グラデント・ブースティングとランダム・フォレストは ネットワークの回復力を高めるために 重要なバスを迅速に評価し 特定します

キーワード:
配送システム高速電圧安定性指数機械学習アルゴリズム電圧安定性の評価

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科学分野:

  • 電気工学
  • コンピューター・インテリジェンス

背景:

  • 電圧の不安定性は,電力システムにおける重要な問題であり,運用能力と伝送を制限します.
  • 従来の電圧安全性評価方法は計算が密集しており,リアルタイムでの応用が困難です.
  • 機械学習は 効率的で正確な電圧安定性分析のための有望な代替手段です

研究 の 目的:

  • エチオピアの配電ネットワークにおける高速電圧安定性指数 (FVSI) の予測のための機械学習モデルを開発し評価する.
  • 電圧安定性評価のための線形回帰,ランダムフォレスト,グラデントブースト,およびサポートベクトルマシンの性能を比較する.
  • 研究された電力システムにおける不安定性の高いリスクを持つ重要なバスを特定する.

主な方法:

  • 応用線形回帰,ランダムフォレスト,グラデーションブースト,サポートベクトルマシンモデル.
  • 35台のバスと53台のエチオピアの配送ネットワークで,名目および異なる負荷条件 (10-150%) でFVSIが予測されています.
  • R2とルーツ・ミーン・スクエア・エラー (RMSE) メトリクスを用いてモデルの精度を分析し,FVSIの値分析を行った.

主要な成果:

  • グラデーション・ブースト (GB) とランダム・フォレスト (RF) のモデルは,優れた精度を示した (R2はそれぞれ0.9998と0.999).
  • GBモデルは,低RMSE値 (例えば,53バスシステムでは0.0002) で最高精度を達成した.
  • 両システムの特定のバスは,緊急の監視を必要とする重要な不安定なリスクポイントとして特定されました.

結論:

  • 集成的な機械学習方法,特にGBとRFは,迅速な電圧安定性の評価に非常に有効です.
  • この研究は,ネットワークの回復力を向上させるための標的型介入の重要な分野を成功裏に特定しました.
  • 電力配電ネットワークの電圧崩壊を防ぐには,リアルタイムで電圧安定性を正確に予測することが重要です.