This paper focuses on the design optimization of a Hydraulic Energy Storage and Conversion (HESC) system for WECs.
A hydraulic energy storage generation system (HESGS) can transform hydraulic energy stored in the hydraulic accumulator into stable and constant electrical energy by controlling the variable motor
This paper focuses on the design optimization of a Hydraulic Energy Storage and Conversion (HESC) system for WECs. The structure of the HESC system and the mathematical models of its key components are presented.
The work of hydraulic-mechanical abnormal data acquisition is conducted based on the micro pumped storage system. Initially, acceleration sensors, swing sensors, pressure pulsation sensors, and other sensors are deployed to monitor the operational status.
As a high energy consumption machine, there is plenty of abnormal energy consumption in the operation of a hydraulic press, which leads to energy loss and reduces energy efficiency.
There is growing interest in developing technology to store energy in deep hydraulic fractures, as this has the potential to offer numerous benefits over other forms of energy storage.
This paper proposes a novel hydraulic energy storage component (NHESC) that integrates hybrid energy storage through the use of compressed air and electric energy. The system configuration of the NHESC is
Therefore, a universal hydraulic-mechanical diagnostic framework integrating signal acquisition, feature extraction and fault recognition is proposed in this paper.
This paper proposes a novel hydraulic energy storage component (NHESC) that integrates hybrid energy storage through the use of compressed air and electric energy. The system configuration of the NHESC is first designed, followed by the modeling of key components and analysis of working states.
Existing data-driven methods for identifying abnormal flow patterns in hydraulic turbines primarily focus on classification accuracy, disregarding the fact that abnormal flow regimes are rare events.
Abstract Hydraulic machinery systems are a widely used machine found in hydropower stations. As a result, it is vital that such machinery is monitored, diagnosed, maintained, or replaced prior to failing to reduce downtime and labor costs.
We design a universal hydraulic-mechanical diagnostic framework integrating signal acquisition, feature extraction and fault detection. The abnormal on-field measurements of micro pumped storage system demonstrate the effectiveness and superiority of framework.
Precise identification of abnormal flow patterns in the draft tube is an effective measure to improve the energy conversion efficiency of PSUs. Existing data-driven methods for identifying abnormal flow patterns in hydraulic turbines primarily focus on classification accuracy, disregarding the fact that abnormal flow regimes are rare events.
The testing of external characteristics of hydraulic machinery includes performance testing (head, flow, power, etc.) and pressure pulsation testing. The corresponding parameters can be directly or indirectly measured by pressure sensors, power sensors, flow meters, and so on.
Constructing a hydraulic-mechanical diagnostic benchmark set that reflects the operation scenarios of real PSUs is meaningful work. (2) In terms of fault diagnostic methods, existing feature extraction or fault detection models lack adaptability.
Based on the energy conversion relationship between water and machinery, and the hydraulic characteristics, dynamic characteristics and structural characteristics of hydraulic machinery are the main research objects, and the main task is to ensure high efficiency and safe and stable operation of various hydraulic machines.
The three-phase comprehensive comparative experiments are conducted to validate the hydraulic-mechanical fault diagnosis framework against existing widely-used methods. Firstly, the feature extraction methods such as MCSLZC, RCMLZC, MDLZC, MPLZC, and MLZC are introduced to conduct comparative experiments.
