Abstract

In most of the automatic generation control (AGC) studies proposed so far, area control error (ACE) signal is derived considering fixed step size load disturbance which does not represent the real time operating condition of power system adequately and may cause sometimes the over regulation of the power system. Therefore, a very short-term load forecasting (STLF) using artificial neural network (ANN) is proposed to obtain a load disturbance pattern to derive an effective AGC scheme. Further, real time load data of a particular month are collected from a 220 kV substation and are used to perform STLF. The predicted hourly load is used to determine future load estimates considering a 10 minute interval basis. The ACE signal is derived accordingly. The model predictive control (MPC) based AGC scheme is designed to counter the upcoming load variations very effectively. A two-area power system having thermal power plants and interconnected via parallel AC/DC tie-lines is considered for the investigations. Furthermore, the dynamic performance of the designed control strategy is also evaluated considering the governor dead-band and generation rate constraint (GRC).

摘要

到目前为止，在大多数自动发电控制（AGC）研究中，面积控制误差（ACE）信号都是在考虑固定步长负载扰动的情况下得出的，该扰动不能充分代表电力系统的实时运行状况，有时可能会导致调节过度电力系统。因此，提出了一种使用人工神经网络（ANN）的非常短期负荷预测（STLF）来获得负荷扰动模式，以得出有效的AGC方案。此外，从220 kV变电站收集特定月份的实时负载数据，并将其用于执行STLF。每小时的预计负荷用于确定未来的负荷估算（以10分钟间隔为基础）。ACE信号相应地得出。基于模型预测控制（MPC）的AGC方案旨在非常有效地应对即将到来的负载变化。考虑使用具有火力发电厂并通过并行AC / DC联络线互连的两区域电力系统。此外，还考虑了调速器死区和发电率约束（GRC）来评估设计的控制策略的动态性能。