NPC Three-Level Inverter Open-Circuit Fault Diagnosis Based on Adaptive Electrical Period Partition and Random Forest

Liu, Shiyuan; Xu, Qian; Wan, Huawei; Ye, Zongbin; Wu, Shoupeng; Ren, Xiaozhen

doi:10.1155/2020/9206579

Cited by 21 publications

(17 citation statements)

References 26 publications

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“…This method requires a large amount of data. In [18], an approach for open‐circuit fault detection and the location of the NPC three‐level inverter was proposed. However, the process of establishing a fault tree is cumbersome.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In addition, applying this method to actual engineering accurately and efficiently is a difficult issue. In [18], a new approach for open‐circuit fault detection and location of the NPC three‐level inverter was proposed. Compared with the traditional fault diagnosis method, the speed of fault location using the proposed algorithm is quick and accurate.…”

Section: Introductionmentioning

confidence: 99%

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Open‐circuit fault diagnosis method for three‐level neutral point clamped inverter based on instantaneous frequency of phase current

Wang

2020

Energy Conversion and Economics

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Because the three‐level neutral point clamped (NPC) inverter has the advantages of a large output capacity and high output voltage, it is widely used in wind energy and solar energy power generation. Unfortunately, the three‐level NPC inverter often suffers from open‐circuit faults, which leads to system security problems and great economic losses. To guarantee the secure operation of the three‐level NPC inverter, an open‐circuit fault diagnosis method for a three‐level NPC inverter based on the instantaneous frequency (IF) of the phase current is proposed here. First, different open‐circuit fault signals of the NPC inverter are analysed. Then, the IF of the phase current is used to obtain the fault diagnosis variables. Simultaneously, the Hilbert transform (HT) is used to estimate the IF of the phase current, which carries the information of open‐circuit faults. Meanwhile, the average value of the normalised current is provided to locate faulty switches. Finally, by combining the diagnostic variables with the identification threshold, the detection and localisation of open‐circuit faults in the NPC three‐level inverter are determined. Compared to other fault diagnosis methods, the proposed method is more stable and makes it easier to diagnose open‐circuit faults. The correctness and robustness of the method are verified by simulation results.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Open‐circuit fault diagnosis method for three‐level neutral point clamped inverter based on instantaneous frequency of phase current

Wang

2020

Energy Conversion and Economics

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“…Improving the performance of mapping for mineral is the main goal of reference [ 54 ]. Liu et al [ 55 ] propose an adaptive electrical period partition algorithm for open-circuit fault detection. Software fault prediction by ensemble techniques is investigated by [ 56 ].…”

Section: Related Workmentioning

confidence: 99%

“…At each instant n, the action probability vector pi(n) is updated by the linear learning algorithm given in equation ( 13) if the chosen action ai(k) is rewarded by the environment, and it is updated according to equation ( 14) if the chosen action is penalized [104]. [11], [12], [13] Global problem [26], [27], [28] Healthcare [32], [33], [34], [35], [36], [41], [98], [37], [39], [40], [42], [43], [45], [46], [47], [48], [49], [50], [51], Industrial [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62] Network [63], [67], [68], [69], [99], [100] Physics [71], [72] Text processing …”

Section: Learning Automatamentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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“…Several methods have been investigated based on Park's vector approach and the average current [8][9][10][11][12]. In [13], a new fault detection based on the DWT-NN technique is presented.…”

Section: Introductionmentioning

confidence: 99%

PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs

Zdiri

Ammar

Salem

et al. 2021

Mathematical Problems in Engineering

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Due to the importance of the drive system reliability, several diagnostic methods have been investigated for the SSTPI-IM association in the literature. Based on the normalized currents and the current vector slope, this paper investigates a fuzzy diagnostic method for this association. The fuzzy logic technique is appealed in order to process the diagnosis variable symptoms and the faulty IGBT information. Indeed, the design, inputs, and rules of the fuzzy logic are distinct compared with the other existing diagnostic methods. The proposed fuzzy diagnostic method allows the best efficient detection and identification of the single and phase OCF of the SSTPI-IM association. Accordingly, after the fault detection and identification using this proposed FLC diagnostic method, a reconfiguration step of IGBT OCFs must be applied in order to compensate for these faults and ensure the drive system continuity. This reconfiguration is based on the change of the SSTPI-IM topology to the FSTPI-IM topology by activating or deactivating the used relays. Several simulation results utilizing a direct RFOC controlled SSTPI-IM drive system are investigated, showing the fuzzy diagnostic and reconfiguration methods’ performances, their robustness, and their fast fault detection during distinct operating conditions.

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NPC Three-Level Inverter Open-Circuit Fault Diagnosis Based on Adaptive Electrical Period Partition and Random Forest

Cited by 21 publications

References 26 publications

Open‐circuit fault diagnosis method for three‐level neutral point clamped inverter based on instantaneous frequency of phase current

Open‐circuit fault diagnosis method for three‐level neutral point clamped inverter based on instantaneous frequency of phase current

A New Random Forest Algorithm Based on Learning Automata

PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs

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