Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies

© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions

Técnicas de control para el motor de corriente continua: Una revisión sistemática de literatura

La ingeniería de control se especializa en desarrollar procesos de alta calidad mediante el modelamiento matemático de diversos sistemas y el diseño de control que permite regular el comportamiento de un sistema utilizando condiciones deseadas. Las técnicas de control que se utilizan para el motor de corriente continua son de mucha utilidad al momento de llevar a cabo una estabilización de la velocidad o el par, algunas de ellas pertenecen a técnicas de control inteligente (lógica difusa y redes neuronales), pero la mayoría se centra en las técnicas de control clásicas (PI, PID) logrando resultados satisfactorios. Las técnicas de modelamiento matemático facilitan la representación de las ecuaciones diferenciales, dependiendo del tipo del motor DC se han utilizado diferentes técnicas (transformada de Laplace, espacio de estados). El software y hardware tienen una fuerte relación con lo que se refiere a las simulaciones y experimentaciones que se usan para validar el funcionamiento de un sistema complejo como lo es el motor CC. En este trabajo se presenta una revisión sistemática de literatura sobre técnicas de control, técnicas de modelamiento matemático, software y hardware que se aplican en un motor de corriente continua, para ello se analizó y resumió 75 artículos científicos de los últimos 4 años provenientes de cinco bases bibliográficas (IEEE Xplore, Digital Library, ScienceDirect, SpringerLink, ResearchGate, Preprints). Los documentos responden a tres preguntas de investigación planteadas en este estudio. Por medio de los resultados obtenidos se identificaron grandes ventajas y desventajas de las técnicas de control y modelamiento matemático, con respecto al software y hardaware se demostró su gran utilidad para la realización de sistemas automatizados

Robust Condition Monitoring and Fault Diagnosis of Variable Speed Induction Motor Drives

The main types of faults studied in the literature are commonly categorized as electrical faults and mechanical faults. In addition to well known faults, the performance of a diagnostic algorithm and its operational reliability in harsh environments has been another concern. In this work, the reliability of an electric motor diagnosis signal processing algorithm itself is studied in detail under harsh industrial conditions. Reliability and robustness of the diagnosis has especially been investigated under 1) potential motor feedback error; 2) noise interference to a diagnosis-relevant system; 3) ease of implementation; and 4) universal application of diagnostic scheme in industry. Low cost and flexible implementation strategies are also presented. 1) Signature-based diagnosis has been performed utilizing the speed feedback information which is used to determine fault characteristic frequency. Therefore, feedback information is required to maintain high accuracy for precise diagnosis which, in fact, is not the case in a practical industrial environment due to industrial noise interferences. In this dissertation, the performance under feedback error is analyzed in detail and error compensation algorithms are proposed. 2) Fault signatures are commonly small where the amplitude is continuously being interfered with motor noise. Even though a decision is based on the signature, the detection error will not be negligible if the signature amplitude is within or close to the noise floor because the boundary noise level non-linearly varies and, hence, is quite ambiguous. In this dissertation, the effect of noise interference is analyzed in detail and a threshold design strategy is presented to discriminate potential noise content in diagnosis. 3) The compensating procedure of speed feedback errors and electrical machine current noise, characteristics which are basically non-stationary random variables, requires an exhaustive tracking effort. In this dissertation, the effective diagnosis implementation strategy is precisely presented for digital signal processor (DSP) system application. 4) Most of the diagnosis algorithms in the literature are developed assuming specific detection conditions which makes application difficult for universal diagnosis purposes. In this dissertation, by assuming a sinusoidal fault signal and its Gaussian noise contents, a general diagnosis algorithm is derived which can be applied to any diagnostic scheme as a basic tool

The use of mechanical redundancy for fault detection in non-stationary machinery

The classical approach to machinery fault detection is one where a machinery’s condition is constantly compared to an established baseline with deviations indicating the occurrence of a fault. With the absence of a well-established baseline, fault detection for variable duty machinery requires the use of complex machine learning and signal processing tools. These tools require extensive data collection and expert knowledge which limits their use for industrial applications. The thesis at hand investigates the problem of fault detection for a specific class of variable duty machinery; parallel machines with simultaneously loaded subsystems. As an industrial case study, the parallel drive stations of a novel material haulage system have been instrumented to confirm the mechanical response similarity between simultaneously loaded machines. Using a table-top fault simulator, a preliminary statistical algorithm was then developed for fault detection in bearings under non-stationary operation. Unlike other state of the art fault detection techniques used in monitoring variable duty machinery, the proposed algorithm avoided the need for complex machine learning tools and required no previous training. The limitations of the initial experimental setup necessitated the development of a new machinery fault simulator to expand the investigation to include transmission systems. The design, manufacturing and setup of the various subsystems within the new simulator are covered in this manuscript including the mechanical, hydraulic and control subsystems. To ensure that the new simulator has successfully met its design objectives, extensive data collection and analysis has been completed and is presented in this thesis. The results confirmed that the developed machine truly represents the operation of a simultaneously loaded machine and as such would serve as a research tool for investigating the application of classical fault detection techniques to parallel machines in non-stationary operation.Master's These

An intelligent monitoring system for online induction motor fault diagnostics

For more than a century, the induction motor (IM) has been the powerhouse industrial applications such as machine tools, manufacturing facilities, pumping stations, and more recently, in electric vehicles. In addition, IMs account for approximately 40%- 45% of the annual global electricity consumption. Therefore it is a critical issue to improve IM operation efficiency and reliability. In applications, unexpected failures of IMs can result in extensive production loss and increased costs. The classical preventive maintenance procedures involve periodic stoppages of IMs for inspection. If such procedures result in no faults found in the machine, as is common in practice, the unnecessary downtimes will increase operational costs significantly. This inefficiency can be addressed by condition monitoring, whereby sensors relay information about the IM in real-time, allowing for incipient IM fault diagnosis. Such a process involves three general stages: • Data acquisition: A process to collect data using appropriate sensors. • Fault detection: A means to process collected data, extract representative fault features, and determine the condition of the motor components. • Fault classification: A means to automatically classify fault data to allow decision-making on whether or not the motor is healthy or damaged. However, there are challenges with the above stages that are at present, barriers to the industrial adoption of condition monitoring, such as: • Implementation limitations of traditional wired sensors in industrial plants. • The restrictive memory and range capabilities of existing commercial wireless sensors. • Challenges related to misleading representative fault signals and means to quantify the fault features. • A means to adaptively classify the data without prior knowledge given to a fault classification system. To address these challenges, the objective of this work is to develop a smart sensor-based IM fault diagnostic system targeted for real industrial applications. Specific projects pertaining to this objective include the following: Smart sensor-based wireless data acquisition systems: A smart sensor network including current and vibration sensors, which are compact, inexpensive, lowpower, and longer-range wireless transmission. • Fault detection: A new method to more reliably extract the representative fault features, applicable under all IM loading conditions. • Fault quantification: A new means to transform fault features into a monitoring fault index. • Fault classification: An evolving classification system developed to track and identify groups of fault index information for automatic IM health condition monitoring. Results show that: (1) the wireless smart sensors are able to effectively collect data from the induction motor, (2) the fault detection and quantification techniques are able to efficiently extract representative fault features, and (3) the online diagnostic classifier diagnoses the induction motor condition with an average accuracy of 99.41%