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.
Highlights
Over the past few decades, there has been a growing trend of connecting new and renewable resources to large-scale interconnected power systems [1,2]
Where EAVE-1min is the clock-1-min average of the area control error (ACE); EAVE-10min is the the clock-10-min average of the ACE; ∆FAVE-1min is the clock-1-min average of the frequency deviation; ε 1min is the targeted frequency bound for the CPS1 index with clock-1-min; ε 10min is the targeted frequency bound for the CPS1 index with clock-10-min; B represents the frequency bias of the control area, expressed in MW/0.1 Hz; BS represents the frequency bias of the power system, expressed in MW/0.1 Hz
Two cases of three power systems with the deep forest reinforcement learning (DFRL) and 10 conventional Automatic generation control (AGC) algorithms were simulated in this work
Summary
Over the past few decades, there has been a growing trend of connecting new and renewable resources to large-scale interconnected power systems [1,2]. As an efficient algorithm for classification with low-dimensional data, deep forest can be applied to predict the systemic state in a large-scale interconnected power system. To reduce occurrences of emergency situations and simultaneously mitigate the curse of dimensionality, deep forest reinforcement learning (DFRL) applied as a preventive strategy for AGC is proposed in this paper. The multiple subsidiary reinforcement learning component of DFRL is applied to provide the generation command to the AGC unit of the large-scale interconnected power system, while the deep forest of DFRL is used to predict the systemic state. The systemic states of a power system, including emergency states and normal states, can be predicted by the deep forest of DFRL using low-dimensional data Since both the Q-value matrix and the action set of reinforcement learning are split into those of an emergency situation and a normal situation, calculation memory is reduced.
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