Search Results (4)

Power converters (PCs) are vital elements of critical applications, making their reliable operation crucial. Enhancing PCs’ reliability can be achieved by adding intelligence to the system, enabling it to predict failures and generate early warnings before a failure occurs. In this context, intelligence is integrated into the system through preventive diagnostic algorithms (PDAs) that assess the converter condition. This article introduces a PDA designed to determine the optimal replacement timing for aluminum electrolytic capacitors (AECs) within power converters. AECs, in addition to being a fundamental component of PCs, also represent the most vulnerable element of the PCs’ power section. The aging of AECs is characterized by a decrease in capacitance (C) and an increase in the equivalent series resistance (ESR). Therefore, ESR and C serve as key indicators for assessing the AECs’ health status. One of the most critical functional requirements for designing a PDA is its accuracy, which can be significantly affected by transients. The solution proposed in this paper is resilient to transients, overcoming a common problem in implementing AECs’ PDAs. The proposed algorithm employs discrete wavelet transform (DWT) to extract the converter signal modes. Subsequently, key characteristics of these modes are extracted, enabling the calculation of both ESR and C. Finally, by using the estimated ESR and C values, two fault indicators can be obtained that are resilient to transients. Employing a Kalman filter reduces noise and ensures the indicators’ resilience to transients, making them highly effective for evaluating the AECs’ health status. The proposed PDA was validated through multiple computer simulations conducted in MATLAB/Simulink for a three-phase interleaved boost converter (3ϕIBC), which includes a proportional-integral (PI) controller with anti-windup capability. Full article

In recent years, significant technological advances have emerged in renewable power generation systems (RPGS), making them more economical and competitive. On the other hand, for the RPGS to achieve the highest level of performance possible, it is important to ensure the healthy operation of their main building blocks. Power electronic converters (PEC), which are one of the main building blocks of RPGS, have some vulnerable components, such as capacitors, which are responsible for more than a quarter of the failures in these converters. Therefore, it is of paramount importance that the design of fault diagnosis techniques (FDT) assess the capacitor’s state of health so that it is possible to implement predictive and preventive maintenance plans in order to reduce unexpected stoppage of these systems. One of the most commonly used capacitors in power converters is the aluminum electrolytic capacitor (AEC) whose aging manifests itself through an increase in its equivalent series resistance (ESR). Several advanced intelligent techniques have been proposed for assessing AEC health status, many of which require the use of a current sensor in the capacitor branch. However, the introduction of a current sensor in the capacitor branch imposes practical restrictions; in addition, it introduces unwanted resistive and inductive effects. This paper presents an FDT based on the random forest classifier (RFC), which triggers an alert mechanism when the DC-link AEC reaches its ESR threshold value. The great advantage of the proposed solution is that it is non-invasive; therefore, it is not necessary to introduce any sensor inside the converter. The validation of the proposed FDT will be carried out using several computer simulations carried out in Matlab/Simulink. Full article

As technology advances, the utilization of lighting systems based on light-emitting diode (LED) technology is becoming increasingly essential, given its benefits in terms of efficiency, reliability, and lifespan. Unfortunately, the power electronic components required to drive LEDs are unable to compete with LED devices in terms of lifetime. Aluminum electrolytic capacitor (AEC) failures represent the root cause of power electronic equipment breakdown, mainly through both aging and temperature effects. This highlights the importance of designing robust power converter architectures and control methods that allow the evaluation of the condition of electrolytic capacitors while maintaining the performance of converter controllers, even in the presence of capacitor failure. On this basis, this work proposes a novel condition-monitoring system for the diagnosis of capacitor faults on a fault-tolerant LED driver, which is able to deal with the specific architecture and low ratings of the most recent LED lighting systems. The fault-detection task applies the short time least square Prony’s (STLSP) approach to perform an online estimation of the ESR and C parameters, allowing the continuous evaluation of the electrolytic capacitor’s condition and, as a result, the prevention of total system failure. With regard to capacitor failure, the experimental results suggest that the condition-monitoring task is extremely effective, even when considering a limited number of data samples. Full article

The aluminum electrolytic capacitor (AEC) is one of the most vulnerable parts in power electronic converters and its reliability is crucial to the whole system. With the growth of service time, the equivalent series resistance (ESR) increases and the capacitance (C) decreases due to the loss of electrolytes, which will result in extra power loss and even damage to transistors. To prevent significant damages, the AEC must be replaced at an optimal period and online health monitoring is indispensable. Through the analysis of degradation parameters (ESR and C), ESR is proved to be a better health indicator and therefore is determined as the monitoring parameter for AEC. From the capacitor perspective, ESR estimation schemes of output capacitors for a Buck converter are studied. Based on the voltage–current characteristics, two ESR calculation models are proposed, which are applicable for both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). From the point of implementation view, the advantages and disadvantages of the two estimation schemes are pointed out, respectively. A Buck prototype is built and tested, and simulation and experimental results are provided to validate the proposed ESR estimation schemes. Full article