###### Abstract  
To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, deep forest is applied to reinforcement learning. Therefore, deep forest reinforcement learning (DFRL) as a preventive strategy for AGC is proposed in this paper. The DFRL method consists of deep forest and multiple subsidiary reinforcement learning. The deep forest component of the DFRL is applied to predict the next systemic state of a power system, including emergency states and normal states. The multiple subsidiary reinforcement learning component, which includes reinforcement learning for emergency states and reinforcement learning for normal states, is applied to learn the features of the power system. The performance of the DFRL algorithm was compared to that of 10 other conventional AGC algorithms on a two-area load frequency control power system, a three-area power system, and the _China Southern Power Grid_. The DFRL method achieved the highest control performance. With this new method, both the occurrences of emergency situations and the curse of dimensionality can be simultaneously reduced. deep forest reinforcement learning; preventive strategy; automatic generation control; deep forest; reinforcement learning +
Footnote †: journal: Applied Soft  
controller was proposed for relaxed AGC [12; 13]; a Q(\(\lambda\)) learning-based controller was applied to smart generation control in multi-agent systems (MASs) [14]; and an R(\(\lambda\)) imitation learning-based controller was designed for AGC [15]. These reinforcement learning algorithms can achieve high control performances with small system loads [12], and the convergence of reinforcement learning was proved by [PERSON] [16]. However, these reinforcement learning algorithms have two major weaknesses, including that (i) they may not balance active power between generators and large system loads, and (ii) they may lead to a systemic frequency deviation that is larger than the frequency deviation limitation (0.2 Hz) [17]. Moreover, a low-level reserve capacity in an electric power system leads to emergency situations during generation control in large-scale interconnected power systems [18; 19; 20; 21]. For example, an frequency emergency control strategy was considered for high-capacity generators and a low-load grid [19]; the emergency conditions were applied to the LFC of power systems [20]; and regional frequency-based emergency control plans were addressed in [21]. Therefore, a preventive strategy for an AGC controller with the aim to prevent emergency situations should be considered. Preventive strategies have been established in various fields. For instance, preventive replacement and preventive maintenance were designed for offshore wind turbines [22]; both preventive and emergency states were accounted for using a two-stage robust mixed-integer optimization model [23]; and preventive actions were applied to increase the transient stability margin in order to re-dispatch generators [24]. Therefore, a preventive strategy for averting emergency situations involving an AGC controller is considered in this paper. The conventional reinforcement learning-based AGC controller can achieve a high control performance in large-scale interconnected power systems in normal situations [12; 13; 15], but it experiences a low control performance in emergency situations.