Diagnose errors on photovoltaic modules.

2.1 Faults due to external causes

Apart from manufacturing defects, there were many others which are characteristically difficult to classify either as manufacture failures or even for other reasons. Some of the module failure due to external causes were, Transportation Failure, Clamping, Cable failure, connector failure, and Lightning. The effect of transportation on the PV modules were cited by.11 It was observed that the breakage of glass cover and lamination damage in some modules are due to shocks and vibrations during transportation. It is clear that this failure does not accommodate with manufacturing defect and is one of the major external causes of module failure. Most of the transportation failures can be identified neither visually nor by observing power ratings. Lock-in thermography image or an electroluminescence image can detect such damages. Among the installation issues associated with modules, clamping is the most often failure which results in glass breakage mostly for frameless PV modules. Figure 1 depicts the effects of poor clamping in PV modules. Yixian Lee et al12 conducted finite element analysis to observe the stresses faced by PV modules especially during installation stage. Various observations depicted that the sharp-edged clamp design, narrow clamps, improper positioning, and excessive tightening of screws on clamps of module causes stress on PV modules resulting in breakage. The effects of Glass breakage results in electrical safety issues and performance loss during long time operation as corrosion may occur due to moisture penetration through the cracks. The cracks developed also lead to hot spots, resulting in module overheating.13, 14

Cables and connecters are associated with PV systems for providing electrical connections to solar modules and other components of PV system including Inverter. Connectors are very important elements and play a major role in safety and reliable power generation and transmission. Of different connectors, Low-voltage DC connectors were widely discussed due to their usage in electric vehicles and in PV systems.15 Claudio Ferrara et al3 mentioned about connectors and different metals associated with them, which can show defects and cause of corrosion when exposed to atmospheric humidity alone or in combination with gases. Considering all the effects and consequences, the constraints due to cables or connectors used in a PV system were not considered as manufacturing defects. Conventionally connector failures are observed in the cases of improper cable selection or inaccurate connections between PV modules and components associated with them. These failures sometimes cause total power loss in the string and can lead to electric arcs and fires. Zaini et al16 studied the effects of lighting on a grid integrated PV system with an assumption that the lightning impulse current strikes the PV system at two different points as shown in Figure 2. The effect of lightning strike on the DC side ie at the module is observed in the form of a defective bypass diode. This effect tends to be an external cause resulting in a subsequent safety failure. Such external causes mostly result in open-circuit bypass diodes or module failure due to direct exposition to lightning. Different faults observed in a module due to both manufacturing defects and external causes were discussed in the further sections.

2.2 General faults in all PV modules

In general, most anticipated failure modes and degradation mechanisms were related to glass breakage, junction box failure, interconnection faults, and delamination. Juris Kalejs17 raised concern regarding robustness and durability of junction boxes. The research stated that improper design or improperly disclosed junction boxes ingress moisture which causes corrosion to connections in the junction box. This causes wiring failure which leads to internal arcing. Uichi Itoh et al18 depicted the potential risks associated due to soldering failures in junction boxes. The observations depicted two different faults, ie silver (Ag) leaching and solder joint fatigue. When solder joints come in contact with the AG electrodes of solar cell, they get dissolved with the solder electrodes (Tin–Lead (Pb–Sn)) and is observed as Ag₃Sn compound.18 This Ag leaching effect develops crack in the soldered interface due to thermal expansion resulting in connection break down. Delamination effect occurs due to adhesion contamination or due to environmental factors as shown in Figure 5, which ingress humidity, moisture, and corrosion into the laminates of PV module. The lamination failures result in optical reflection which results in subsequent power loss from the modules. Kleiss et al19 reported the quality and reliability of the PV modules and stated that 90% of modules are prone to delamination. Zhu et al20 observed the causes of delamination and stated the adhesion requirements that are to be met by the manufacturers. Issues with variation in lamination temperatures and adhesive materials were disclosed to be the major concerns for delamination. Tracy et al21 proposed a novel test procedure apart from the conventional peel test to detect delamination. The delamination phenomenon can also be observed in thin film modules where the transparent conductive oxide delaminates from the glass layer.22 In general, delamination can be relatively observed, with the help of reflectometer. Visually, delamination’s can be identified by using lock-in thermography, pulse thermography, X-Ray thermography, and an ultrasonic scanner.23, 24 The Photovoltaic (PV) back-sheets provide safe operation during high voltages and protect electronic components from being exposed to aggressive field environments.25 Back-sheets are manufactured with different materials like polymers, glass, and metal foils. Mostly back-sheets are manufactured using highly stable laminate structures and UV resistant polymers. The materials were chosen depending on the mechanical strength required, cost, and electrical isolation. Singh et al26 mentioned about using rear glass with back-sheet as the structure offer more power. There were some serious issues associated with this structure as any improper mounting or mechanical stress will lead to breakage of the glass. Irrespective of the above-mentioned problems, glass/back-sheet structure can offer 2%-3% higher power when compared with standard back sheet modules. Tang et al27 presented a double glass PV module which can withstand various environmental conditions due to the “0” moisture permeable rate and exhibits long-term stability and reliability. A composite Ageing test was conducted for performance analysis which proved to be better than the standard PV modules. Polymeric laminates are most commonly observed back-sheet construction materials. They have multiple layers which causes the effect of delamination of interfaces due to high physical and chemical stress. The only advantage that can be seen in the failure of polymer laminates is that, when delamination occurs, it will not create an immediate safety issue. Back-sheet delamination near a junction box tends to be a major issue as it results in an unconstrained junction box which leads to positioning of mechanical stress on the live components and breaking them. This breakage further results in connection failure at bypass diode which is further complicated by formation of an unmitigated arc at full system voltage.

2.3 Review of failures found in silicon wafer-based PV modules

Crystalline silicon wafer-based PV modules share a dominant market in the world of PV modules due to their wide spread applications.28 These modules hold 95% market share as of 201729 and is the most widely used solar cell type. Despite of their widespread applications and usage, these modules are prone to very common failures like potential and light induced degradation and snail tracks. Discoloration of Ethylene Vinyl Acetate (EVA) encapsulants were initially observed at Carrizo plains installation sites in California around early 1990s and was observed to be a major problem.13, 30 Chianese et al31 discussed about ASI 16-2300 single crystalline silicon photovoltaic modules which use poly vinyl butyral encapsulant and tedlar/aluminum/tedlar back sheet. It was observed that the developed structures require a robust electrical insulation layer between the cell and the foil, which raises numerous safety concerns. Apart from the electrical insulation, metal foils also act as high voltage capacitors and any disturbance towards the electrical isolation of foil surface will charge the foil at system voltage. Pern32 investigated the various factors that affect degradation rate of EVA polymer encapsulants using noninvasive analysis. The analysis observed the field degraded, yellow to dark brown EVA to understand the chemical and physical damages. EVA is consistently drafted with additives like cross linking agent, antioxidants, UV absorber, hindered amine light stabilizers and adhesive elements. Peike et al33 observed that these additives formulate for the root cause for discoloration by originating chromophore and lumophores. The discoloration results were noticed in different patterns and were examined for complexity due to oxygen diffusion and acetic acid from additive reactions. The origin of chromophores results in a transparent EVA ring around the edges of a wafer-based cell. In some scenarios of EVA discoloration, it is observed that a single cell in the modules is gloomier than other cells. This usually defines that the temperature sensitivity of the particular cell is greater than the adjacent cells due to low photocurrent or because of the cell being placed above the junction box.34 The EVA discoloration in severe cases corresponds to EVA embrittlement, and concomitant corrosion due to dissolved oxygen.35 Even though most of the module failures cause an outright failure, discoloration and delamination does not lead to a failure, but degrade the functionality at a very slow degradation rate of ~0.5%/a.36, 37 During severe discoloration a total loss of ~10% is observed which implies that EVA discoloration is absurd for a complete failure of silicon modules. Dhimish et al38 reviewed various types of cell cracks and their effects and proposed a statistical analysis-based approach to identify them. The observations revealed about the various types of cracks namely Multi Directional Cracks, Diagonal cracks, and cracks which are parallel and perpendicular to bus bars. In case of cracks in a cell, the element might not be totally disconnected from the cell but develops a resistance between the number of cycles present in the deformed module and the cell elements. Kajari-schroder et al39 analyzed the criticality of cracks and stated that, cracks in PV modules lead to low stability in output power under artificial aging. Experimental analysis was conducted on 667 cracked cells in 27 PV modules and the results depicted that 50% of crack orientations were parallel to busbar cracks which are considered for high criticality. The variation in manufacturing process of solar modules also leads to cell cracks specifically during the stringing process.40 Issues related to transportation and installation of PV modules were also depicted as a major contribution towards cell cracks. Meyer et al41 investigated PV modules with snail tracks formed due to outdoor exposure of defected modules for several months. A detailed microscopic imaging of discoloration helps in identifying the failure. It was observed that snail trails were mostly located at cell edges or near micro cracks. It is not necessary that all the micro cracks will be developed to snail trails, but whenever a snail trail is observed in a module or cell, a micro crack is found at the same position. The optical impression of the trail is due to brownish discoloration of the grid finger position which is further imprinted to the EVA foil. The original process of discoloration due to snail tracks is not well defined in the literature. The optical impression of the snail tracks varies in different modules disturbing the cell fragments and cell edges. The formation of snail tracks in a module is termed as an electromechanical degradation process but was never deemed to be a direct cause for power loss. Fairbrother et al42 depicted that in a PV array 3% of the PV modules were subjected to burn marks at two different positions occupying 5% area of the back sheet. In the case of Back sheets, two types of burn marks are observed on the basis of temperature. One type of burn marks observed has high rupturing capacity due to high temperature, while the other types are of low rupturing capacity with lower temperatures. When burn marks were closely observed they depict physical damage through scratches, scrapes and tears. Friere et al6 stated that the relative failure rates of burn marks on a cell is ∼10%. Mohamed et al43 observed that the average annual degradation of PV modules power is 1.5% caused due to failures like burn marks, cell cracks, and delamination. It was depicted that most of PV module failures doesn’t exhibit visible burn marks but result in severe power loss.44 Pingel et al45 mentioned that Potential Induced Degradation (PID) is caused due to polarization effect or due to chemical corrosion. The level of degradation depends on the polarity and level of potential difference between PV cell and the Ground. PID is also responsible for durability issues in modules. In general, PID is observed when high voltages force sodium ions to spread out from the glass through encapsulate and accumulate on cells surface. This results in surface recombination, fill factor drop and increased local shunting.46 There are two types of PID, nonreversible and the reversible PID. The nonreversible PID occurs due to electrochemical reactions resulting in electro corrosion of transparent conducting oxide. The reversible PID also known as surface polarization accumulates positive charge on a PV cell which results in leakage current. Depending on the grounding configuration of a PV array the amount of leakage current is defined. This degrade the generation capability of the solar cell. Typical circumstances for the occurrence of PID is observed at different levels like environmental factors, module factors, system factors, and cell level. These factors are generally dependent on the temperature, humidity, system voltage, type of material used, and the refractive index of the cell. Cristaldi et al47, 48 stated that electrochemical corrosion of the string interconnects of a cell due to encapsulant results in the degradation of PV module. This phenomenon leads to high series resistance and low parallel resistance of the PV modules.49 The PV cells are generally equipped with front and rear contacts connected through bus stripes for delivering current to the external circuit. Any failure in the string ribbons result in thermal expansion—contraction and mechanical stress resulting in output power loss.50, 51 Hermanan52 and Buero53 depicted the influence of ambient temperature and temperature difference caused by flowing current on diode temperature. From the real operation, it is known that the temperature of the module is approximately about 20°C higher than the ambient temperature. As the diodes are present inside the junction box, there is an immediate vicinity of the module surface, maintaining very similar temperatures. It means that in idle state, the diode will have the ambient temperature + 20°C. In clear summer days about 55°C and in winter about 20°C.54 When the ambient temperature is low, the current flowing through the diode will be lower because of the higher value of threshold voltage. Also, the heating by the flowing current will be then limited. When the ambient temperature is high, the threshold voltage value will decrease and it will cause higher flowing currents and another additional bypass diode heating.

2.4 Faults in thin-film modules

In order to save materials (and thus costs), thin-film PV modules have been developed. These modules, against conventional crystalline modules, have the lower conversion efficiency. But this is offsets by lower prices (production is less materially and usually less technologically demanding) and improved properties at low irradiance levels. From the viewpoint of the material and manufacturing process, the modules are classified as, CuInSe2 (CIS), Cu (In, Ga) Se2 (CIGS) a CuGaSe2 (CGS) modules, CdTe modules, amorphous and micromorphous silicon modules, and many other thin-film cells like multijunction cells, cells using nanostructures, and organic cells. The design process for all the above-mentioned modules varies as they deal with multiple compounds and compositions. But irrespective of the design procedure the cause and effect of any fault on a given module has a potential effect which degrades the module. Some potential cause and effects that were observed for thin film modules are glued connectors micro arcs, hot spots shunts,55 front glass breakage, and back contact degradation.56, 57 Recently, first solar published an introduction to the subject58 and interesting degradation kinetics. Depending on climate and system interconnection factors, one may expect an initial degradation of 4%-7%, over the first 1 to 3 years. Various faults that can be seen without the help of any instrument are depicted in Table 1:

Table 1. Visual faults associated with a PV module23, 41

Fault type	Power loss	Safety issue	Visual fault
Shorting of module wires and diodes	<3% of power loss	Fire failure may be caused
Laminated cell fragment	<3% of power loss	Fire failure, electric shock, and physical danger
Cell cracks damaging 10% of cell area	Degradation of power loss which saturates over time	No effect on safety
Bubbles or delamination	Degradation of power loss in steps over time	Electric shock resulting in major safety problem
Burn marks on back sheet	Degradation of power loss in steps over time	Failure may cause fire, electric shock, and physical danger
Front panel discoloration due to metallic interconnections overheating	Degradation of power loss in steps over time	Failure may cause fire, electric shock, and physical danger
Multicrystalline Si module delamination	Degradation of power loss in steps over time	Failure may cause physical danger
Thin film module delamination	Degradation of power loss in steps over time	Failure may cause physical danger
Glass breakage in thin film modules	Degradation of power loss in steps over time	Failure may cause physical danger
EVA browning	Degradation of power loss linearly over time	No effect on safety for slightly browned condition, but as the browning grows faster fire failure may be caused
Snail trails	Degradation of power loss linearly over time	Fire failure may be caused
Back sheet delamination	Degradation of power loss linearly over time	Fire failure may be caused

 

3.1 Diagnosis using radiation

3.1.1 Thermography

When assessing parameters gained through electric measuring, it is necessary to disconnect the cells and especially the modules from the rest of the device and to measure them separately, usually in a laboratory. In case of PV modules, this method can be quite expensive as well as rather time consuming with less accessible installations. If only for this reason, the use of any method, enabling defect detection without the need of disassembly is necessary. Thermography is one such method which is frequently used in diagnostics of faults of PV installations.59 The method works at a principle of detection of thermal irradiation using an appropriate detector. The cells in the module are connected serially. Therefore, when an ideal state is considered where all the cells are identical, the same current flows through them while having the same voltage, ie they have the same short circuit current. When the short circuit current flows through all the cells, the voltage is zero. In case of one faulty cell, the current and voltage proportions in the circuit change as given in.60 Given that the overall voltage in short circuit state has to stay zero, the damaged cell is reverse polarized toward other cells and the voltage in it is the total sum of voltage of other cells in forward direction. In case of the real operation, the cells function on a level of the highest output, but the mechanism of heating of the damaged spots is identical.61 This effect can be caused either by the defect in the cell or by shading. For a reduction in this influence so called bypass diodes are used. These diodes open in case the voltage drop in a reverse direction exceeding their threshold voltage V_B. The bypass diode is not used for every single cell in the actual modules but it has an antiparallel connection to a group of serially connected cells (usually 20-24 cells).62 The functionality of the bypass diode is also apparent from the I-V curve, where the “stairs” will occur, which can complicate the detection of MPP, because more than one local extreme is then present at the curve (Figure 3).63

The Shading on one submodule string where shaded irradiance is approximately 150 W/m² and nonshaded irradiance is approximately 1000 W/m². The distribution of temperatures can be detected by the use of appropriate equipment. Reading of thermal field used to be done by thermometer (whether they were contact or contactless). These days, thermal cameras are broadly used thanks to the drop in their price. Detection with the thermal camera allows uncovering a wide range of defects as depicted in Table 2.64 However, the disadvantage is that the thermogram does not provide a quantitative assessment of the examined module.65

Table 2. Thermography based fault identification in PV modules64

Observation	Detail	Reason for failure	Electrical characteristics	Power loss	Safety issue
	Some cells of a module are warmer than other cells due to random distribution of individual cells- (patch work pattern)	Incorrectly connected cells	Complete module short circuit	Constant power loss	Fire may occur due to short circuits
	One module in the array is warmer than others	Open circuited module	Fully functional and normal module	Considered as a system failure	No effect on safety
	A particular cell in the module is warmer than the other cells	Effect of shadowing, defect in cells, delaminated cell	Power loss is observed, but it is not necessary for every instant of this failure	Power loss increases with mechanical load, thermal cycling, and humidity	Extreme conditions may lead to fire
	A particular part of the cell in a module is warmer than the other cells	This may be due to broken cell, or interconnection failure	Power and form factor reduces drastically	Power loss increases with mechanical load, thermal cycling	Fire may occur due to failure
	The substring part of the module is remarkably hot	Short circuit in cell string because of faulty bypass diode	Very high short circuit current and reduction in power	<3% of power loss, linearly over time	Extreme conditions may lead to fire

 

3.2 Static characteristics measurement—current-voltage characteristics

Current-Voltage characteristics (I-V curves) measurement method, sometimes also called I-V analysis or Voltammetry, is the most widespread method for PV cells and modules diagnostics. It allows determination of basic PV components parameters. The measurement can be performed either in a lab or directly at the installation, but it is used mostly for the precise measurement under Standard Test Conditions which are specified in international standards like irradiance G = 1000 W/m², and cell temperature 25°C. This can be achieved by using continual solar simulator or by flash solar simulator. Usually, the measurement is performed under flash solar simulator—flash tester, because it is much less complicated and cheaper solar simulator than the continual one and the pulse duration is sufficient for most technologies. During PV component irradiation, the whole I-V curve through the electronic load control is measured. The continual solar simulator is necessary only in the special cases like concentrator modules or solar thermal collectors. For outside measurement, the solar analyzers, which usually allow also connection of external sensors for ambient conditions measurement are used.72 The most problematic part is testing thin films due to season annealing effect and high capacitance.

3.2.2 Capacitance effects

The modules with higher capacitance, eg thin-film modules, can be easily wrongly evaluated when measured by the flash tester. This is caused by charging or discharging the capacity. When the module is measured from the short circuit state, the measured performance value can be lower than the real one—the capacity is charged. During the reverse measurement—from open circuit state to short circuit state, this capacity is discharged and it can cause the virtual increase in the measured performance. If there are no visible differences between forward and reverse regime, the pulse duration can be considered as sufficiently long.75 The pulse duration influence can be eliminated either by a sufficiently long pulse duration or using multiflash measurement or also by the so called “dragon back pulse” tester of company PASAN who developed the special measurement method of high-capacitance modules using one single controlled 10 ms pulse.10 It is observed that most literature on the topic of I-V curves measurements stated that capacitance is the major concern for distortions in output characteristics of solar cell as in.76–79 The problem with this behavior is that if it is modeled like the common capacitor, it does nt work. With larger amount of PV cells, the resulting capacitance should be lower, but the opposite situation happens. These effects can be relatively easily simulated using SPICE (parameters of the diode can be edited to achieve PV cell behavior). Unlike the classical diode model, SPICE diode model covers these effects by “borrowing” the Transition Time (TT) parameter from the transistor’s theory. It is very useful, because this explains a lot in the connection with the “strange” behavior of the capacitance inside the structure. During irradiation, the capacitance is in ideal case infinite—the electrodes of the virtual capacitor, it means grounds of P and N type area are infinitely close to each other. Whole area is then full of nonequilibrium carriers. These carriers need some time to leave and create the depletion region again to recover the previous equilibrium state, if the module is put into the dark again. This time, it can be characterized just by the transition time parameter, so the resulting value of serial connection of capacitors inside the module can be imagined more as the connection of batteries and some kind of transition charge. The similar effect can be achieved by changing the capacitance values, but the problem is, that this capacitance is then necessary to change for every pulse duration change, when proper values should be obtained. This is caused just by the fact that the capacitance in this case cannot be represented by the simple capacitor as mentioned above. The different faults that can be identified using deviation of I-V characteristics both at cell level and Module level can be observed in Tables 3 and 4.

Table 3. Cell failure detection using I-V characteristics

	Failure	Broken cell interconnect ribbons	Cracked cells	Short- circuited cells
	Power loss	Degradation of power loss in steps over time	Degradation of power loss in steps over time	Degradation of power loss in steps over time
Characteristic	Safety issue	Failure may cause fire, electric shock, and physical danger	No effect on safety	No effect on safety
P max		√	√	√
I SC			√
V OC				√
R OC		√		√
R SC

• F, failure condition; NF, no failure.

 

Table 4. Module failure detection using I-V characteristics

	Failure	Bypass diode (short-circuit)	Delamination		Induced degradation		Solder corrosion
			Homogenous	Heterogenous	Potential	Light
	Power loss	Degradation of Power Loss in steps over Time	Degradation of Power Loss which saturates over time	Degradation of Power Loss which saturates over time	Degradation of power loss linearly over time	Degradation of Power Loss in steps over time	Degradation of power loss linearly over time
Characteristic	Safety issue	Failure may cause fire, Electric Shock, and Physical Danger	Failure may cause fire, Electric Shock, and Physical Danger	Failure may cause fire, Electric Shock, and Physical Danger	No effect on safety	No effect on safety	No effect on safety
P max		√	√	√	√	√	√
I SC			√	√		√
V OC		√			√	√
R OC							√
R SC				√

• F, fault condition; NF, no fault.

 

4.1 Working with thermal images

Primarily there are two ways to do the thermal analysis: Right on the camera and Information based. Some sought of thermal imaging cameras that almost look like camcorders and behave in the similar ways except for the thing that these thermal cameras have thermal sensors and visual sensors. Whenever, a module is tested for thermal image, functions like spot tools or area tools can be applied and temperature information can be extracted right on the camera. The disadvantage of right on the camera thermal analysis is that it is only limited only with spot tools, area tools. In order to overcome these disadvantages information based thermal analysis can be adapted. This procedure is capable of performing all kinds of analysis like spot, area, polygon, free form, and segmentation analysis. The major advantage of information based thermal analysis is that it can work on matrix level and cover up to a large are of PV systems. This can also be implemented in real time monitoring of PV plant and predictive assessment of aging phenomenon. To check the possibility of quantification of thermograms there were many measurements performed at the real system and in a laboratory. PV modules with defects were detected and were subsequently tested by a flash tester (a device for accurate measuring of I-V curves of modules—establishing nameplate parameters of PV modules). To meet standard condition, the flash test time must be <50 ms.94 All acquired results can be found in.95 The obtained I-V characteristics can be utilized for training of intelligent systems which classify the fault based on the type, nature and effect of fault on the power output of the system.

4.2 I-V characteristics-based fault detections

In this section Wavelet-Based Fault Detection technique96–99 is proposed which aims at finding the optimum combination of mother wavelets and the number of wavelet decomposition levels that help extracting the most important attributes from the signal, which are needed for fault diagnosis in a PV module. Figure 5 shows the hierarchical structure of the selection of wavelet transform parameters in which three main parameters need to be determined.100 The first parameter is the wavelet functions, the second parameter is the number of decomposition levels, including the approximations and the details as shown in Table 5, and the third parameter is the signal type, including both voltage and/or current signals and their features to be extracted. In general, the fault location in a PV module will be attempted to diagnose from its output voltage waveforms because the output voltages are normally independent from the load and correspond with fault types and locations.

where N = wavelet order, r = reconstruction, and d = decomposition. Once the required features of the resultant signals were achieved, trained data are obtained by classifying them. In MATLAB there are various classifier system which provide efficient trained data. In our system Multilayer Perceptron Neural Network (MLPNN) is adapted to classify the data obtained for various faults of PV modules. MLPNN is a hybrid system which is used in training the given set of data for multiple outputs to display the type of fault.101 MLPNN deals with the extracted features of the data obtained from the wavelet transform decomposed and filtered signal.102, 103 In MLPNN, the data are adapted in the tabular from the workspace which is obtained from the wavelet transforms, thermography, and flash tests as shown in Figure 6.

By applying DWT to the output voltages of the PV modules under normal and fault operating conditions, the energy and power of the signals is obtained. The DWT contains an extensive library of the wavelet basic functions, which makes this transform suitable for transient analysis and, hence, provides time-frequency spectrum at different resolutions. The advantage of computational time for DWT as shown in Table 6, makes it more adaptable. It is observed that for various faults, the features of the system vary and the energy of signal is more in comparison with normal condition. These constraints for output of faulted modules help us in detecting the type of fault and localizing it in a stipulated time.

The developed diagnosis and localizing method are implemented in three steps namely, Identifying the characteristics of fault, extraction of features, and classification action. In classification process the neural networks are trained with the operating data of PV modules under various faults and working conditions; there by indicating them with predefined binary codes. These binary codes can be used with the intelligent fault diagnosis to observe the fault type and its location. The output I-V characteristics of PV modules bearing different faults are illustrated in Figures 7–11. It can be observed that the signals cannot be directly adapted for fault classification hypothesis, due to correlation with each other. In order to differentiate these signals, a signal transformation technique is adapted. By selecting an appropriate feature extractor, the neural network is provided with adequate yet significant details in the pattern set so that the highest degree of accuracy and performance is achieved. Momoh et al105 explained various signal transformation techniques suitable for training neural network for fault diagnosis. Prior to application of wavelet transform, the VI signal was filtered by applying median filter (a nonlinear digital filter) in order to remove any noise present inside the signal. A Daubechies 3 level (db3) wavelet decomposition was applied for sampling the input signal and captures both the location and frequency information. The output decomposition structure consists of the wavelet decomposition vector c and the bookkeeping vector l, which contains the number of coefficients by level. Figure 12 below depicts the three-level wavelet decomposition of the I-V characteristics of a normal operating condition PV module. Once the signal is filtered and decomposed, in order to reduce the feature dimension, the feature extraction methods are generally implemented at each decomposition level. Various features like energy, Shannon-entropy, power, Signal to noise ratio, Total harmonic distortion, peaks analysis, frequency analysis, and spectrum analysis were used as the feature’s extractors.

The mathematical assessment for the feature’s extractors were given described as follows:

Energy of the signal E is defined as:

(1)

Where x(n) is the signal whose energy feature is to be extracted.

The Power feature of signal is defined as:

(2)

Usually the limits are taken over an infinite time interval.

In addition to power and energy of signal there were various other features that vary from signal to signal. It was obvious that every signal is prone to some sought of noise which may make the signal undetectable. This Noise varies from signal to signal and hence providing a wide scope for differentiating the signals. The ratio of signal to noise power is defined as Signal to Noise Ratio and is calculated from:

(3)

*SNR in decibels (dB)

(4)

In order to calculate the SNR, root mean square (RMS) of the noise is to be measured. Considering a digitized signal, the RMS can be calculated by squaring each value of the signal, deriving the arithmetic mean of the squared values, and applying square root to the result. Generally, the RMS of a signal represents the average “power” of a signal. To Identify spectral features in signals using wavelet techniques, initially the signal is preprocessed to remove artifacts and Perform wavelet-based time-frequency analysis to identify features. The instantaneous frequencies associated with time series are represented with numerical solutions of the obtained signals, producing the frequency evolution. This allows us to differentiate between normal and faulted modules. Since for faulted modules the frequencies are constant and coincide with the Fourier frequency, resonance channels are identified due to the good accuracy in the assignments of frequencies. Furthermore, the time evolution of the frequencies allows us to detect temporary resonance trapping of faulted modules and their implications in system performance. The Time Frequency analysis, Spectrum analysis, and Spectrogram of I-V characteristics of PV module operating under normal conditions were depicted in Figure 13.

Extracting all the mentioned features for different signals obtained for normal and faulted modules, MLPNN technique is used to classify them, to obtain the trained data which can be used for fault identification as shown in Figure 14.

The observations reveal that the output voltages are related to the fault locations and fault types. Once all the fault signals were classified, a suitable intelligent technique such as neural network is applied to classify the fault features. The application of neural networks (multilayer perceptron) especially for fault classification as depicted in Figure 14 is observed to be the most advantageous solution. The other advantages with neural networks are that, for a single neuron failure the performance of the network is partially degraded but can still decide by using the remaining neurons hence provide an extra degree of freedom to solve nonlinear problems. The trained data which is obtained by classifying the extracted features is used to detect the fault for a given set of a data. The prediction of fault for a trained data set is shown in Figure 15.

The major advantage with the algorithm is the classification accuracy and time taken to detect the fault. The classification accuracy (C_acc) of the system depends on the incorrectly predicted cases (I_pc) and number of test cases (T_c) given by (5).

(5)

The total accuracy for a given classification model C_acct for N trails is given by (6):

(6)

Where N_f determines the number of folds trails. The fault detection time for a given algorithm is defined as the time elapsed between occurrence of fault and the classification of the fault. Average Fault Detection times were significantly shorter for the modules in the Alarm condition, with an average Fault Detection time of 9 seconds.

The fault detection time is given by (7):

(7)

Where, FDT is Fault Detection Time, AFT = Alarm and Flash test time (The flash test time should be <50 ms),94 SPT = Signal processing time and CDT = Classification and Decision time. Times for I-V characteristic processing (SPT) and classification and decision (CDT) were 3 seconds, and 5 seconds respectively. The alarm time depends on the area and number of PV modules installed in a particular area.94, 99, 106, 107 Average fault detection time and efficiency of different wavelets along with various classifiers were presented in the Table 7.