基於動態熱容量之超高壓輸電線最佳感測器部署

隨著電力需求與日俱增，長距離之超高壓電網所面臨的負載也越來越重，然而建造新電網曠日費時且所費不貲。近年來動態熱容量(Dynamic thermal rating, DTR)技術被認為可能解決這個問題。DTR是協助智慧電網進行規劃與決策的有效工具，它利用即時天氣資訊來估算架空輸電線導體溫度與安培容量，而透過這些關鍵資訊可以在不犧牲輸電安全的情況下提升輸電效益。由於DTR仰賴即時且準確的氣象資料，所以部署感測器在輸電線上至關重要。然而，應用於超高壓輸電線之感測器成本高昂，部署感測器於輸電線每一段跨距可能不是可行的做法。因此本研究提出一種改良的二元粒子群最佳化法(Modified binary particle swarm optimization, MBPSO)來解決這個多目標最佳化問題，目標是部署最少量的感測器以達到理想的感測效能。本研究以345 kV全興~南投一路為例，利用2013至2017年間中央氣象局之每小時氣象資料計算導體溫度與安培容量來進行最佳化。結果表明，所提出的方法僅需要部署7.9 %的感測器即可監測96 %以上之全線高溫事件，而且透過降維重建導線溫度分布與原始導體溫度分布之均方誤差小於0.8 °C。這種方法可以提供電力公司作為操作電網系統時增加輸電量和評估過載風險的可靠技術。

關鍵字

智慧電網；動態熱容量；二元粒子群最佳化法；感測器部署問題

並列摘要

With the increasing demand for electricity, the load on the long-distance extra high voltage (EHV) power grid is getting heavier. However, the construction of a new power grid is time-consuming and costly. In recent years, dynamic thermal rating (DTR) technology is considered to be able to solve this problem. DTR is an effective tool for assisting smart grids in planning and making decisions. It uses real-time weather information to estimate the conductor temperature and ampacity of the overhead transmission lines. Through these crucial information, transmission efficiency can be improved without sacrificing safety. Because DTR relies on real-time and accurate meteorological data, deploying sensors is necessary on transmission lines. However, sensors used in EHV transmission lines are costly, and deploying sensors on each span of the transmission line may not be feasible. Therefore, this study proposes a modified binary particle swarm optimization (MBPSO) to solve this multi-objective optimization problem. The goal is to deploy a minimum number of sensors to achieve ideal sensing performance. This study adopts Quanxing ~ Nantou first line of 345 kV power grid as an example, and uses the hourly meteorological data of the Central Weather Bureau (CWB) from 2013 to 2017 to calculate conductor temperature and ampacity for optimization. The results show that the proposed method only needs to deploy 7.9 % of sensors to effectively monitor more than 96 % of high conductor temperature events occurring across the entire transmission line, and the mean square error in the reconstructed conductor temperature distribution is lesser than 0.8 °C. This method can provide the power company as a reliable technology for increasing transmission current and assessing the risk of overload when operating the power grid system.

並列關鍵字

smart grid ； dynamic thermal rating ； binary particle swarm optimization ； sensor placement problem

參考文獻

台灣電力公司。2018。歷年停限電次數。台北：台灣電力公司。網址：https://www.taipower.com.tw/tc/chart_m/d01_%E7%94%A8%E6%88%B6%E8%B3%87%E8%A8%8A_%E9%99%90%E9%9B%BB%E7%9F%A5%E8%AD%98%E8%88%87%E8%B3%87%E8%A8%8A_%E6%AD%B7%E5%B9%B4%E5%81%9C%E9%99%90%E9%9B%BB%E6%AC%A1%E6%95%B8.html。上網日期：2018-06-25。