Motor load accounts for more than 50% of the total electric power load in China. Identifying the load of induction motors non-intrusively is of great importance for the design of energy-saving schemes and formulation of demand-side response strategies in industrial enterprises. Based on the transient mechanism of the induction motor, the present work first defines some motor load start-up transient feature parameters with clear physical meanings and proposes a set of non-intrusive motor load identification methods applicable to industrial settings. In addition, a case study that applied the proposed method to the industrial setting was performed to verify its effectiveness. The results showed that the proposed method can overcome the problem of misidentification caused by the fact that the start-up transient process is affected by its mechanical load characteristics and hence can identify motors with similar running power and has good anti-interference capacity despite power quality disturbances.
The motor current signal analysis (MCSA) technique is widely used as a non‐invasive method for detecting mechanical faults in induction motors by capturing characteristic components in the stator line current. However, the threshold of the characteristic component is not clear now, which makes it difficult for MCSA to judge whether the fault occurs or evaluates the mechanical fault severity. The existing model‐based evaluation methods cannot meet the requirement of online condition monitoring because of their slow calculation. To solve these problems, a simplified dynamic motor model under any type of mechanical fault is established, and a formula for the amplitude relationship between the radial vibration of the rotor and fault‐related component in the stator line current is derived. The radial vibration amplitude is related to mechanical fault severity. Using this formula, the MCSA technique can rapidly evaluate the mechanical fault severity according to the amplitude of the characteristic component in the collected stator current. The simulation study results demonstrate the accuracy of the simplified model and formula. The experimental results presented for condition monitoring in a real induction motor clearly validate the evaluation approach.
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