A novel intelligent driver based on digital signal controller (DSC) has been put forward for magneto-rheological (MR) damper. The working principles of MR damper were described, as well as the hardware circuit scheme of signal condition and MR damper driver etc. on account of TMS320F28335 DSC. A hierarchical control algorithm was designed and the studies for the performance of the driver were conducted. The results suggest that the driver could provide accurate drive current for MR damper, and meanwhile the respond time is less than 2ms, which can meet the drive requirements of MR damper.
Introduction: With the development of artificial intelligence technology, more and more fields are applying deep learning and reinforcement learning techniques to solve practical problems. In the power system, both the direct current (DC) power system and the power grid substation are important components, and their reliability and stability are crucial for production efficiency and safety. The power grid substation is used to convert power from high-voltage transmission lines to low-voltage transmission lines, or from alternating current to direct current (or vice versa), in order to efficiently transmit and distribute power in the power system. However, diagnosing faults and designing cascaded protection strategies has always been a challenge due to the complexityand uncertainty of the DC power system.Methods: To improve the reliability and stability of the DC power system and power grid substation, this paper aims to develop an intelligent fault diagnosis system and cascaded protection strategy to reduce faults and downtime, lower maintenance costs, and increase production efficiency. We propose a method based on reinforcement learning and a convolutional neural network-long short-term memory (CNN-LSTM) model for fault diagnosis and cascaded protection strategy design in the DC power system. CNN is used to extract features from raw data, while LSTM models time-series data. In addition, we use reinforcement learning to design cascaded protection strategies to protect the power system from the impact of faults.Results: We tested our method using real 220V DC power system data in experiments. The results show that our method can effectively diagnose faults in the DC power system and formulate effective cascaded protection strategies.Discussion: Compared with traditional methods, this intelligent method can diagnose faults faster and more accurately, and formulate better cascaded protection strategies. This method helps reduce maintenance costs, increase production efficiency, and can be applied to other fields.
At present, the wind-induced response analysis of an overhead conductor is mainly based on the action of horizontal normal wind. However, for crossing hillsides or extremely strong winds, such a conductor will bear the action of updraft wind, which will change the geometry of the conductor and make its structural dynamic characteristics nonlinear to some extent. In this work, the in-plane and out-of-plane two-dimensional nonlinear equations were established under the action of self-weight and updraft wind. Furthermore, the improved equations of conductor tension and sag were obtained, and the wind-induced vibration response was further investigated. The results showed that the updraft wind caused the nonlinearity of the tension and sag of the overhead conductor, and the nonlinear geometric change significantly affected its resonance response, which exceeded 25% if the wind speed was 50 m/s. In addition, because the proportion of the resonance response in the total wind-induced response was different, the influence of the wind attack angle calculated using the gust response factor method on the gust response factor was slightly larger than that calculated using the the American society of civil engineers method.
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