The utilization rate of permanent magnet synchronous motors (PMSM) is increasing in the industry today. Due to this fact, the high efficiency ratio of PMSMs has reached IE5 premium class efficiency. Therefore, the efficiency coefficient of the PMSM varies from 92% to 97%. As a result, this type of motor is replacing traditional asynchronous motor by falling into efficiency classes of IE1, IE2, IE3, and IE4, which range from 75% to 92% in the industry. Thus, the object of the research was to develop a method to determine the efficiency of permanent magnet synchronous motor applications in order to identify and verify the variating parameters. In this study, an innovative and safe method of PMSM testing when the nominal parameters of the motor are unknown was presented through research. Also, the comparison of PMSM oscillograms with different types of load types and phase shift oscillograms, generated using operation amplifier, were analyzed and is scrutinized. During the design process, the PMSM was projected for the IE5 premium efficiency class. However, after production, the PMSM sometimes does not match the nameplate parameters, which are declared by the factory. As a result, during the testing procedures, the PMSM nameplate parameters did not match the projected parameters. Facing the problem of the projected and tested efficiency mismatch, the PMSM highest efficiency determination experiments were performed in a laboratory in order to prove the highest efficiency of the PMSM. The results showed different PMSM input parameters. Furthermore, the experimental results of the PMSM testing were confirmed with electrical machines theory, and simulation results were performed with electrical circuits. The theory of PMSM operating in different values of input voltage is represented in graphical abstract.
a b s t r a c tThe increased number of renewable power plants pose threat to power system balance. Their intermittent nature makes it very difficult to predict power output, thus either additional reserve power plants or new storage and control technologies are required. Traditional spinning reserve cannot fully compensate sudden changes in renewable energy power generation. Using new storage technologies such as flow batteries, it is feasible to balance the variations in power and voltage within very short period of time. This paper summarises the controlled use of hybrid flow battery, thermal and hydro power plant system, to support wind power plants to reach near perfect balance, i.e. make the total power output as close as possible to the predicted value. It also investigates the possibility of such technology to take part in the balance of the Lithuanian power system. A dynamic model of flow battery is demonstrated where it evaluates the main parameters such as power, energy, reaction time and efficiency. The required battery size is tested based on range of thermal and hydro power plant reaction times. This work suggests that power and energy of a reasonable size flow battery is sufficient to correct the load and wind power imbalance. Crown
In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.
This paper presents methodology of identification of dynamic models of steam turbine, governor, boiler and its regulators required for investigation and dynamics analysis of stability of power systems. According to the methodology, structure and parameters of dynamic model of generating units are estimated taking into consideration operating mode of turbine and boiler. Measured and simulated regime parameters' curves are presented. Correlation factors of measured and calculated regime parameters for evaluation of identification accuracy are estimated. Expressions for recalculation of model parameters to one apparent generating unit's power are presented, too.
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