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Three-phase static converters with voltage structure are widely used in many industrial systems. In order to prevent the propagation of the fault to other components of the system and ensure continuity of service in the event of a failure of the converter, efficient and rapid methods of detection and localization must be implemented. This paper work addresses a diagnostic technique based on the discrete wavelet transform (DWT) algorithm and the approach of neural network (NN), for the detection of an inverter IGBT open-circuit switch fault. To illustrate the merits of the technique and validate the results, experimental tests are conducted using a built voltage inverter fed induction motor. The inverter is controlled by the SVM control strategy.

Several researchers have carried out their investigation in relation to the field of detection and location of faults in static converters and more particularly those related to three-phase power inverters [

Others researchers used stator current as key parameter for fault diagnosis purpose because it does not require costly sensors. This technique is widely termed as Motor Current Signature Analysis (MCSA) technique [

The wavelet transform (WT) technique has perhaps been the most persistent development in recent decades. It has drawn the attention of several researchers in various fields, such as signal processing, image processing, communication, computer science, and mathematics. Numerous works describing the advancement in wavelet theory and its applications in various fields have been published. Among others, it is one of the most attractive techniques in the field of rotary machines and static converter for fault diagnosis and it particularly matches well for the nonstationary signals [

The application of artificial intelligence (AI) based techniques can be advantageous in fault diagnosis since these diagnoses have several advantages. For instance, since AI-based techniques do not require mathematical models, the development time can be significantly reduced. A literature review of recent developments in the field of AI-based diagnostic systems in power inverters has been presented [

The work proposed in this paper addresses an open-circuit fault detection of an IGBT switch of an inverter controlled by a DSPACE 1104 card based on the SVM control strategy feeding an induction motor. The analysis tools and fault diagnosis are based on the use of a combined DWT-NN approach. The DWT algorithm focuses on the investigation of the details of the stator current signal. The variation in these details for both healthy and faulty inverter cases enables us to extract useful information related to the open-circuit inverter switch faults. The NN approach is introduced in order to automate the fault diagnostic system by including a learning phase that helps in developing a very rich database storing relevant information about the open-circuit faults. To assess the effectiveness and merits of the proposed approach and validate the obtained results, experimental tests are conducted by the group diagnostic at the LDEE laboratory.

Figure

Structure of a three-phase two-level inverter.

This inverter is controlled by the SVM control strategy. For each leg of the inverter, there are two possible states:

Unfortunately during its operation, various failures can affect the inverter especially in terms of its so-called power components (IGBT in our case study) because of their fragility. Two types of faults can be reported [

Short-circuit faults affecting the IGBT switches are the most serious faults. In the presence of such a fault, the current reaches limits which can cause the fusion of its chip or its connection. If the detection of this type of fault does not occur rapidly (less than 10 microseconds), then the IGBT switch which is still active on the same leg undergoes the same phenomenon and so the whole inverter leg is shorted.

Open-circuit faults affecting the IGBT switches may occur when, for any reason, the IGBT is disconnected, is damaged, or had a problem in its grid control signal. This type of fault is very difficult to perceive directly because the motor can continue to operate but with a degradation of its performance due to the occurrence of fluctuations in the mechanical parameters (speed and torque) as well as an imbalance of the currents where the currents of the other two healthy legs take high values to maintain the average torque and the speed. The starting of the motor in the presence of this type of fault cannot always be ensured; everything will depend on the value of the torque which can be close to zero for certain rotor position.

The flowchart presented in Figure

Flowchart of the proposed DWT-NN approach.

The following two subsections tackle in more detail both the DWT algorithm and the NN approach

The wavelet transform is a mathematical tool which allows the decomposition of a temporal signal into a series of coefficients called approximation and detail; the approximations represent the slow variations of the signal, whereas the details represent the fastest [

A wavelet

Under these conditions, a signal

In the case of a discrete wavelet transform (DWT) which is the tool of our paper work, the expansion and translation parameters “

Obviously, according to the last equation, different wavelets (Haar, Daubechies, Coiflet, Meyer, Morlet, etc.) generate different wavelet classes and consequently the behavior of the decomposed signal can be very different. In fact, each wavelet has particular characteristics whose choice depends on the desired application. In this paper, the Coiflet wavelets are to be applied for the detection of the IGBT open-circuit. Figure

Coiflet wavelet families [

The DWT using the Coiflet algorithm is based on signal the decomposition using low-pass filters (LPF) and high-pass filters (HPF) followed by subsampling. Then, in each level of decomposition, the coefficients of approximation and details are computed. Figure

DWT implementation procedure.

Where the LPF frequency band is defined as

Prior knowledge of

Knowing

Decomposition of stator current signal

The proposed NN is a multilayer network of (6-6-1) whose adopted architecture is illustrated in Figures

Proposed neural network architecture.

Evaluation of the quadratic error as a function of the number of learning iterations (using the method of retrogradient propagation).

Each neuron is connected to all the neurons of the next layer by connections whose weights are any real numbers.

The neuron network study used in this paper is carried out through the three main steps:

The construction of the network NN block using the Levenberg algorithm

The data acquisition (learning base)

The network test

Figure

An input layer composed of six neurons, whose role is to transmit the values of the inputs that correspond to the maxima of details (max(

A hidden layer with six neurons with selected sigmoid activation functions

An output layer, which is composed of a neuron, where output of each neuron is 0 or 1

Before building the NN block system, one must first access the learning phase. This can be in the form of a table. The latter consists of vectors (which represent the input layer of the NN), where each vector consists of 2 parameters.

A very rich database for healthy and faulty (open-circuit) cases can be developed, which has a lot of information about the open-circuit fault. During this phase, the maxima details of the healthy case are taken as the references; then the maxima of details for the faulty case are extracted and compared with the healthy case. From this comparison, it can be deduced as either state 0 (i.e., no variation in detail) or state 1 (i.e., variation in detail). Table

Fault classification.

State | Fault type | Symbol | Code |
---|---|---|---|

1 | Healthy state | HS | 0 |

2 | Open-circuit at | OC | 1 |

An automatic learning was performed using the MATLAB software. The learning is reached once a small quadratic error of value

The experimental test-rig used in this paper work includes a three-phase induction squirrel-cage motor fed by a three-phase two-level voltage source inverter. The detailed characteristics of the motor are given in the Appendix. Furthermore, the motor is mechanically coupled to a DC generator supplying resistors which allows varying the load torque. Moreover, the measuring system includes three current Hall effect sensors and three voltage sensors and a DSPACE 1104 acquisition card to generate pulses for triggering the IGBTs gates. The whole set is connected to a computer for visualizing the processed sensed signal as shown in the photo of Figure

Photo of the experimental test-rig.

Figure

Currents waveforms of an induction motor for healthy and faulty open-circuit

From the experimental results depicting the currents waveforms of the motor in Figure

Figure

Details signal decomposition for healthy and faulty open-circuit at

Figure

Details of one stator current phase for both healthy and faulty open-circuit at

Max details | max | max | max | max | max | max |
---|---|---|---|---|---|---|

Input | ||||||

Healthy state | 2.568 | 3.126 | 1.928 | 10.67 | 7.014 | 1.228 |

Open-circuit state | 2.261 | 2.412 | 3.206 | 7.632 | 6.502 | 0.909 |

Output | | 0.0000 | 1.0000 | 1.0000 | 0.0000 | 0.0000 |

Variation of the details maxima.

Table

By comparing the details for the case of the healthy inverter state and that of the open-circuit switch fault state as depicted in Figure

Figure

FFT of stator current signal for healthy and faulty open-circuit at

Comparing both spectra in Figure

Amplitudes and frequencies of harmonics.

Harmonic (Hz) | | | |
---|---|---|---|

Healthy state (db) | | | 0 db |

Open-circuit state (db) | | | |

A comparative analysis between both healthy and faulty states shows with more clarity a particular frequency signature around 100 Hz for the spectrum level

In this paper, a research area dealing with the technique of diagnosis and detection of open-circuit fault in a three-phase two-level voltage source inverter fed induction motor is investigated. The paper proposes a diagnosis approach based on the association of both the discrete wavelet transform (DWT) and the neural network (NN) for the detection of the IGBT open-circuit fault of an inverter.

The study focuses first on the extraction of the details for the cases of the healthy and the open-circuit faulty IGBT by using the DWT algorithm. The investigation of the harmonics related to the obtained details particularly

The various obtained results are validated by several experimental tests conducted in the LDEE laboratory by the group diagnostic to assess the effectiveness and merits of the combined DWT-NN proposed approach.

Rated power: 3 KW

Supply frequency: 50 Hz

Rated voltage: 380 V

Rated current: 7 A

Rotor speed: 1440 rev/min

Number of rotor bars: 28

Number of stator slots: 36

Power factor: 0.83

Number of pair of poles: 2

The authors declare that there are no conflicts of interest regarding the publication of this paper.