Frequency Converter Encoderless Control via DFT Characteristic Identification
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Solution Overview
Problem
Electric machines, such as permanent-magnet synchronous machines and synchronous reluctance machines, require precise knowledge of current-dependent and rotational-angle-position-dependent characteristic variables for cost-effective encoderless operation, as these variables change due to saturation-dependent properties, posing a challenge in determining them accurately.
Innovation Solution
A method involving setting a rotational angle position, locking the rotor, forming periodic current setpoint values, regulating currents, applying discrete Fourier transform or Goertzel algorithms to determine manipulated-voltage and current coefficients, and calculating characteristic variables, which is implemented by a frequency converter with a control unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If encoderless operation is used to reduce cost, then device complexity is reduced, but measurement precision of characteristic variables deteriorates
Solution Approach 1:
The patent applies preliminary action by performing identification of characteristic variables before actual operation. The method determines current-dependent and rotational-angle-position-dependent characteristic variables in advance through a structured identification process, storing them for subsequent use in encoderless control, thereby ensuring precision is established beforehand without requiring sensors during operation
Solution Approach 2:
The patent employs periodic action by using periodic AC signals to excite the electric machine during identification. Direct-axis and quadrature-axis current setpoint AC signals are applied periodically to generate corresponding voltage signals, enabling the determination of characteristic variables through analysis of these periodic responses, which improves measurement precision without requiring physical sensors
2Reliability
If characteristic variables are determined accurately to enable encoderless operation, then reliability is improved, but device complexity increases due to identification requirements
Solution Approach 1:
The patent applies self-service by enabling the electric machine to identify its own characteristic variables without external sensing equipment. The machine is excited with known AC signals, and its own voltage and current responses are measured and analyzed to automatically determine its characteristic variables, making the system self-characterizing and improving reliability without adding complex external identification devices
Solution Approach 2:
The patent replaces mechanical sensing systems (encoders) with signal processing methods. Instead of using physical position sensors to obtain rotor position and characteristic variable information, the invention uses mathematical analysis of electrical signals (DFT algorithms) to extract the same information, thereby improving reliability through software-based solutions while avoiding mechanical sensor complexity
3Measurement precision
If periodic AC signals are applied to identify characteristic variables, then measurement precision is improved, but loss of time increases due to identification process
Solution Approach 1:
The patent applies preliminary action by performing the time-consuming identification process during manufacturing or initial setup, before the machine enters production operation. The characteristic variables are determined in advance and stored, allowing rapid encoderless control during actual operation without repeating the identification process, thus achieving high measurement precision without time loss during operational phases
Solution Approach 2:
The patent uses periodic AC signals with specific frequencies to excite the machine during identification. By using periodic signals rather than random or continuous excitation, the identification process can be completed efficiently within a limited number of cycles, reducing the time required while maintaining measurement precision through the well-defined spectral content of periodic signals
Data Source
AI summary
A method for determining current-dependent and/or rotational angle position-dependent characteristic variables of an electrical machine by setting a rotational angle position of blocking a rotor, forming a series current desired value using a series current desired value alternating signal which changes periodically and/or forming a parallel current desired value using a parallel current desired value alternating signal which changes periodically, regulating a series current using the series current desired value and/or a parallel current using the parallel current desired value, measuring phase currents of the electrical machine and determining an established series current and/or an established parallel current, producing series setting voltage coefficients and series current coefficients and/or parallel setting voltage coefficients and parallel current coefficients using a discrete Fourier transform algorithm, and calculating characteristic variables on the basis of series setting voltage coefficients and series current coefficients and/or parallel setting voltage coefficients and parallel current coefficients.