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Direct Axis Current Compensated MTPA Control Algorithm of IPMSM Based on Direct Criterion Calculation |
Fu Xinghe1, Chen Rui1, Yin Kaixuan2, Jiang Zhenglong1 |
1. College of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu Province, China; 2. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shanxi Province, China |
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Abstract Due to the asymmetry of the magnetic circuit of the quadrature axis and direct axis, there are both permanent magnet torque and reluctance torque in the electromagnetic torque of interior permanent magnet synchronous motor (IPMSM). In order to improve the utilization rate of reluctance torque, the maximum torque per ampere (MTPA) control has become the preferred control strategy of IPMSM. Corresponding to a certain load torque, the MTPA control minimizes the armature current required to maintain the operation of the motor by distributing the d-q axis current, thereby reducing copper consumption and improving efficiency. Some of the existing MTPA control methods have the problems of large amount of computation, high complexity and low practicability. An improved MTPA control algorithm to realize the high-performance operation of IPMSM is proposed in this paper, which aims to simplify the control structure, speed up the dynamic response and enhance the robustness of the system. The proposed algorithm directly calculates the differential term of electromagnetic torque according to d-q axis currents, which is taken as the decision criterion of MTPA state. According to the convergence relationship between the MTPA criterion and the dθ/dt term, when the motor is in a non-MTPA state, the ∂Te/∂θ is not zero, and the current vector angle θ needs to be adjusted. Then, with the criterion value equal to zero as the control target, an error-driven closed-loop control structure is constructed. Finally, the given value of the current vector angle θref is adjusted in real time on the basis of the initial value of the current vector angle θini. The criterion is utilized to compensate the reference value of d-axis current to realize the tracking and maintenance of MTPA state. The proposed method does not need actual or virtual signal injection and demodulation, and the dynamic performance of the system is improved. The d-axis current compensation control solves the problem that the convergence speed of conventional algorithm is sensitive to load conditions, and improves the robustness of control system. This paper has compared the proposed algorithms, including fixed-gain current vector angle compensation (FGAC), adaptive-gain current vector angle compensation (AGAC) and d-axis current compensation (DCC), with the existing virtual signal injection (VSI) and multiple virtual signals injection (MVSI). The 1N.m load test show that time required for the d-q axis current corresponding to converge to the MTPA state is about 6s (FGAC), 1.8s (AGAC), and 1.8s (DCC). When the load is changed to 4N.m, the response time of three methods is about 2.2s. The convergence speed of the proposed algorithm for MTPA state is 4~5 times faster than that of the VSI algorithm. Experiments show that the convergence speed of the DCC algorithm is basically the same (about 2s) under different working conditions of 2N.m and 4N.m. The dynamic convergence speed of VSI is slower than that of DCC algorithm under light load and heavy load. Compared with MVSI, the time complexity (online computation) of DCC is reduced by about 85%, and the space complexity (memory footprint) is reduced by about 55%. The following conclusions can be drawn from simulation and experimental analysis: 1) The proposed algorithm establishes the MTPA state direct decision criterion, which shortens the solution convergence time. Taking the VSI dynamic convergence process duration as the benchmark value (1pu), under different working conditions, the convergence time of the proposed algorithm is only 0.2pu, which is faster than most of the existing methods. 2) The proposed algorithm extracts the MTPA state decision criterion by means of direct calculation, without injecting real/virtual signals into the system and without the signal demodulation process. Both time complexity and space complexity are reduced. 3) By setting the adaptive integral gain and optimizing the structure of the MTPA control system, the sensitivity of the dynamic convergence speed of the algorithm to the load, speed and other working conditions is reduced, and the applicability of the algorithm is enhanced.
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