Assessment of induction machine efficiency with comments on new standard IEC 60034-2-1

The paper assesses the efficiency of induction machines and measurement uncertainties arising from various input-output testing methods used in industry. Existing testing standards vary in methodology, procedure and required instrumentation accuracy, thus leading to significant differences in the experimentally determined efficiency for the same induction machine tested to the different standards as well as by different testing personnel. This paper focuses on the recently published IEC standard 60034-2-1, with comparisons of its previous version (IEC 34-2), and IEEE 112-B. Five induction machines with ratings between 7.5 and 150 kW are carefully tested using these methods and power loss results are validated by a separate calorimeter. Through theoretical analysis of measurement uncertainty using realistic perturbation-based estimation (RPBE) on these results, IEC 60034-2-1 is assessed in terms of its effectiveness and improvements over its previous version. Attention is paid particularly to these factors significantly impacting the machine efficiency such as determinations of stray load losses (SLL), stator winding resistance, stator winding temperature, and detailed specifications of testing procedures

Theoretical Analysis and Implementation of Photovoltaic Fault Diagnosis

The utilization of solar energy by photovoltaics (PVs) is seen in increase across the world since the technologies are getting mature and the material costs are being driven down. However, their operating costs are still very high, owing to their vulnerability to harsh outdoor environments they are working. Currently, the reliability of PV systems is the bottle-neck issue and is becoming a heated research topic. This chapter presents the state-of-the-art technologies for photovoltaic fault diagnosis, based on an intensive literature review and theoretical analysis. The chapter evaluates the fault mechanisms of photovoltaics at the cell, module, string and array levels. Analytical models are developed to understand the PV’s terminal characteristics for diagnostic purposes. Offline and online fault diagnosis technologies are reviewed and compared based on the use of electrical sensors and thermal cameras. The aim of this chapter is to illustrate the PV faulty characteristics, to develop offline and online fault diagnosis, and to use the fault diagnosis information to achieve optimal operation (maximum power point tracking) under various PV faulty conditions, by using multi-disciplinary analytical, empirical and experimental methods

Reinforcement-Learning-Based Parameter Look-Up Table Generating Method for Optimal Torque Control of Induction Motors

In the optimal control of induction motors, it is a challenging task to maintain the optimal torque over the varying operation conditions. This paper proposes a parameter look-up table generating method, that can achieve an optimal torque over a wide range of currents and speeds, even though the commands of current are not set correctly. Based on the motor’s testing data, this method uses a reinforcement-learning algorithm to generate parameter look-up tables iteratively. Experimental results show that the proposed method can learn appropriate parameters from the running data to output an optimal torque. The comparative studies show that the proposed method can generate 5%-25% more torque than traditional model-based parameter estimation methods, over a wide range of currents and speeds. Furthermore, the proposed method has a faster convergence feature and a higher identification resolution than many conventional search-based methods.Post-print / Final draf

Active disturbance rejection control of a longitudinal tunnel ventilation system

This paper proposes an innovative approach for controlling pollutant release in a long-distance tunnel via longitudinal ventilation. Enhanced by an active disturbance rejection control (ADRC) method, a ventilation controller is developed to regulate the forced air ventilation in a road tunnel. As a result, the pollutants (particulate matter and carbon monoxide) are reduced by actively regulating the air flow rate through the tunnel. The key contribution of this study lies in the development of an extended state observer that can track the system disturbance and provide the system with compensation via a nonlinear state feedback controller equipped by the ADRC. The proposed method enhances the disturbance attenuation capability in the ventilation system and keeps the pollutant concentration within the legitimate limit in the tunnel. In addition to providing a safe and clean environment for passengers, the improved tunnel ventilation can also achieve better energy saving as the air flow rate is optimized

An Improved LPTN Method for Determining the Maximum Winding Temperature of a U-Core Motor

In a traditional lumped-parameter thermal network, no distinction is made between the heat and non-heat sources, resulting in both larger heat flux and temperature drop in the uniform heat source. In this paper, an improved lumped-parameter thermal network is proposed to deal with such problems. The innovative aspect of this proposed method is that it considers the influence of heat flux change in the heat source, and then gives a half-resistance theory for the heat source to achieve the temperature drop balance. In addition, the coupling relationship between the boundary temperature and loading position of the heat generator is also added in the lumped-parameter thermal network, so as to amend the loading position and nodes’ temperature through iterations. This approach breaks the limitation of the traditional lumped-parameter thermal network: that the heat generator can only be loaded at the midpoint, which is critical to determining the maximum temperature in asymmetric heat dissipation. By adjusting the location of heat generator and thermal resistances of each branch, the accuracy of temperature prediction is further improved. A simulation and an experiment on a U-core motor show that the improved lumped-parameter thermal network not only achieves higher accuracy than the traditional one, but also determines the loading position of the heat generator well

Electromagnetic loss modeling and demagnetization analysis for high speed permanent magnet machine

This paper presents a research work on the electromagnetic loss modeling and demagnetization analysis for a high-speed permanent magnet machine (HSPMM). The iron loss is estimated by improved modeling considering harmonics and rotational magnetic field effects to achieve high precision; rotor eddy current loss is researched and comprehensively investigated using finite-element method (FEM). The auxiliary slot and PM beveling are also proposed to reduce the rotor eddy current loss for machine at high-speed operation. Temperature-dependent PM demagnetization modeling is utilized in HSPMM FEM analysis to investigate machine performance due to temperature variation, while optimized rotor structures are proposed and comparatively researched by FEM to improve the machine anti-demagnetization capability in harsh conditions. The HSPMM temperature is estimated based on the calculated loss results and machine computational fluid dynamic modeling. Experimental measurements on the prototype machine verify the effectiveness of the machine electromagnetic and thermal modeling in the paper