Real Time Monitoring and Control of Pump by Deep Reinforcement Learning

Abstract

Flood prevention is aided by rainwater pumping stations situated close to cities or agricultural regions. These stations activate a suitable number of pumps, each with a different capacity, in response to the actual amount of rainwater that is falling. Unfortunately, effective control is typically lacking when rule-based pump operations are used in isolation to monitor basin water levels. Diminishing the number of switches that is on or off for the pumps at rainfall stations is just as important as keeping the maximum water level low to avoid floods. Pump switch frequency reduction reduces maintenance expenses by lowering the chance of mechanical failure. This paper presents a method for operating rainwater pumping stations in real-time utilizing Deep Reinforcement Learning (DRL). The goal is to meet all of these operational requirements at the same time, using only data that is presently noticeable like inflow, rainfall, place of storage amount, water level basin and outflow. It was trained using simulated rainfall data created using the Huff technique with different return periods and durations. Experiments were carried out using the Storm Water Management Model (SWMM), which was set up to mimic the rainwater pumping station. Next, the suggested DRL model's efficiency was contrasted with the station's present rule-based pump operation.