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Condition monitoring of insulated gate bipolar transistor (IGBT) modules is an effective way to improve the transient performance and reliability of modular multilevel converters (MMC). This article proposes a novel bond wire failure monitoring method for the multichip IGBT modules in the MMC half-bridge submodule (SM) structure. The collector voltage undershoot VCA(np) of the complementary IGBT switch is measured during the turn-off switching transition of the controlled IGBT switch. VCA(np) is sensitive to the induced voltage over the stray inductance of the IGBT bond wires and provides high sensitivity as a health indicator. The non-intrusive measurement technique is demonstrated using a half-bridge circuit during the turn-off transition of the controlled IGBT, while its complementary switch (i.e., the device under test) is in the off state. Theoretical analysis and experimental results show that the parametric drift due to wire bonding failures can be effectively monitored with high specific relative sensitivity and granularity. To ensure its effectiveness in practical applications, the influence of load current, SM capacitor voltage, and junction temperature is discussed. In addition, a readout circuit is designed featuring the integrated desaturation detection and voltage peak detection, which offers both the short-term overcurrent fault protection and long-term bond wire aging monitoring functions. [ABSTRACT FROM AUTHOR]

Operation and maintenance costs of wind power plants represent a significant share of the total expenditure of a wind farm project. It is therefore vital to optimize maintenance activities in order to reduce costs while improving wind turbine reliability and availability. The induction generator is a major contributor to failure rates and downtime of wind turbines, where doubly fed induction generators (DFIGs) are the dominant variable-speed technology employed. Actual data from operating wind turbines is seldom presented in the scientific literature. This paper analyzes two in-service DFIGs from wind turbines operating in a Spanish wind power plant. One of the generators under study reported excessive temperature on the rotor windings, while the other did not. In order to achieve a diagnosis for the reported excess of temperature, current signature analyses were performed on both machines. Fault-related frequency components were identified in the current spectra of the faulty machine, and were compared against the healthy one. A diagnosis was achieved for the faulty machine: Dynamic eccentricity caused by a rotor fault was identified as the cause of the excessive temperature in the rotor windings. [ABSTRACT FROM AUTHOR]

The key function of rotating machine condition monitoring (CM) is to detect structural changes during machine operations. This paper presents a novel statistical time-frequency analysis method for this purpose. In particular, frequency spectrum is extracted from the machine condition signals based on periodogram estimation. Undirected weighted graph is then constructed from the resulting periodograms, where the so-called median graph is introduced and adopted to describe the normal machine status. Statistical analysis is performed to investigate newly observed data with respect to the median graph for change decision making. The proposed method has been applied to three different engineering applications to evaluate its effectiveness: load CM; early bearing failure detection; and speed CM. The results were compared with some benchmark methods reported in the literature, where significant improvements of the proposed method were demonstrated, indicating its good potentials in engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents data-driven approaches to improve the active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust the active power output of individual turbines according to their health condition and can thus optimize the total energy output of a wind farm. In the paper, extreme learning machine algorithm and Bonferroni interval are applied to estimate fault degree, while an analytic hierarchy process is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reducing the fatigue loading on the faulty wind turbines. [ABSTRACT FROM AUTHOR]

Condition monitoring (CM) has been considered as a promising technique to improve the reliability of insulated gate bipolar transistors (IGBTs). Among various condition parameters, switching time is a good health status indicator to detect IGBT failures. However, on-line monitoring of the IGBT high-speed switching time is still difficult in practice due to the requirement of the extremely high sampling rate for signal acquisition. To overcome the technical difficulty, this paper provides an innovative compressed sensing (CS) method to achieve equivalent sampling performance for high-speed IGBT switching time monitoring with a lower sampling rate. By utilizing the sparse characteristics of an IGBT switching signal, the sampling rate in the CS method could be far less than the traditional Nyquist sampling rate. To clarify the method, the CM mechanism using IGBT switching time is first analyzed. Then, three key points in the CS method are studied, i.e., the selection of sparsifying basis, the design of a measurement matrix, and the implementation of a reconstruction algorithm. Finally, experiments are carried out to investigate the performance of the CS method for on-line CM. Experimental results show that the IGBT turn-offtime at different temperatures can be accurately monitored with a highly compressed sampling rate, which validates the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An electrical signature analysis (ESA)-based method to detect bearing defects in electromechanical drivetrains (e.g., wind turbines) is proposed in this paper. The novelty of the proposed technique is justified by its capability to detect a defect that occurs in a drivetrain bearing, external to the machine. Unlike the traditional vibration-based method, the electrical measurements of the terminal machine are utilized in this case to perform the fault detection. The proposed ESA-based method is developed utilizing a new electrical signal model of bearing defect, proposed in this paper, based on the excitation of torsional resonances. The proposed ESA-based method and the signal model are validated through experimental tests. The proposed technique demonstrates excellent fault detection capability even at low operating speed, which is advantageous over the traditional vibration-based method in terms of sensor cost, installation complexity, and detection reliability. [ABSTRACT FROM PUBLISHER]