Abstract
Solar photovoltaic (SPV) system fault diagnostics is vital in advanced supervision because it can alert users to catastrophic failure or greater risks. To provide green and clean energy using solar, it is mandatory to analyse various faults associated with photovoltaic system which can result in energy deficit and system breakdown and may lead to fire hazards which are often difficult to avoid. Hence, as an endeavour to improve the efficiency level, more study beginning with modelling of SPV system with its parameter estimation and types of SPV faults is aimed in this work.
1. Introduction
The ever increasing energy demand of growing world population has required the use of all available energy resources. Solar energy has risen at the fastest rate of any type of electricity generation in the recent decade, spurred by concerns about climatic challenges, energy multifariousness, and supply reliability among national policy makers. Fossil fuel supplies are dwindling, while pollution is increasing, forcing humanity to reconsider the planned utilization of leftover energy wealth and the breakthrough application of various renewable energy resources. The national action plan on climate change [1] is the trigger element in writing this review paper. Regardless of the fact that solar PV systems contain stationary parts and need of little nurture, they are still susceptible to a variety of faults or malfunctions with the PV arrays, maximum power point tracking (MPPT), grounding, grid connection, batteries, and utility hook-ups [2]. PV modules are difficult to shut off totally during faults, especially on the DC side, because they are powered by sunlight throughout the day. As there are so many numbers of solar photovoltaic (SPV) modules which are coupled in a series-parallel topology, any failure among them might have an impact on the overall productivity of the SPV system. More crucially from a safety standpoint, a single failure might grow into several faults, which can lead to disaster. Furthermore, a standard series-parallel PV setup lifts the voltage and current ratings, which leads to huge defect current or DC arc risk.
Several mishaps, including fire dangers, have been observed in recent years, and the majority of these instances have took place owing to a lack of understanding of various forms of defects in SPV systems. The defect remained undiscovered and concealed in the system until the hazard caused catastrophic fire in Bakersfield’s 383 kW SPV plant, 2009 [3], and Mount Holly’s 1 MW SPV plant, 2011 [4], case studies. These fire threats demonstrate not just the flaws in traditional SPV array fault identification and safety techniques but also the immediate need for an alternative way to mitigate such problems. Fault diagnosis of SPV is useful for the technicians to detect, isolate, and troubleshoot the faults. The measured SPV parameters are checked with tolerances, and alarms are generated in case of emergency situation, as well as the monitoring function will automatically initiates counter action.
This paper does not go over the process of a SPV system or the different types of SPV systems. The rest of the paper is laid out as follows: a full examination of state-of-the-art modelling and parameter estimation of solar photovoltaic system is presented in section 2.1. This section also includes modelling of ODM and TDM. The numerous kinds of PV faults are discussed in section 2.2, followed by the conclusion in section 3.
2. Review of Modelling, Estimation, and Types of Faults in Solar Photovoltaic (SPV) System
2.1. Review of Modelling and Estimation of SPV System
Tackling the nonlinear output characteristics of a SPV system is a major challenge as it varies with solar irradiance level, geometric location of the Sun, and ambient temperature. During 12 noon to 3.00 PM, the SPV system output is normally low due to its increasing cell temperature. When the entire array receives nonuniform irradiance due to partially shadowed conditions, the characteristic I–V and P–V results become more convoluted, resulting in multiple peaks. Due to industrialization in developed and developing countries, wide experience with SPV system on buildings became available in the early 1990s. In the German 1000-Roofs-PV-Program, which began in 1990, shadowing affected 41 percentage of the installed PV systems, resulting in a ten percentage energy loss [5]. The power loss owing to poor MPPT convergence to a local peak rather than the global peak could be as high as 70%, according to [6]. [7] has suggested a microcontroller-based MPPT method with buck type dc/dc converter in SPV energy system to maximize the efficiency irrespective of weather conditions or electrical load characteristics. For a typical 60 W solar panel, which is utilized to explore the fluctuation of MPPT with varied temperature and insolation levels, the approach of parameter extraction and model evaluation using MATLAB was demonstrated in [8]. A comparison of buck and boost MPPT was performed, and the boost converter was found to have a modest edge over the buck, especially at low light levels. However, the effect of shading is not taken into account in this work.
In incremental conductance method (ICM) [9], the shortcomings of the classic perturb and observe method (POM) methodology for effective power transfer from PV array to load line, especially in circumstances of rapidly altering meteorological conditions, were examined and analysed. The prior strategy, on the other hand, does not result in a seamless transition to and [10]; developed the power feedback method (PFM), which solves the previous shortage; and has the benefit of a smooth and speedy transition to the maximum power point. Furthermore, the MPPT method can be implemented without the use of a PV array’s dc current sensor. [11] has proposes a fractional–order incremental conductance approach for maximum power tracing in SPV systems, which provides a conceptual framework to modify the SPV array voltage towards the peak power. When compared to POM and PFM based on load line adjustment, this approach produces better results under shifting atmospheric and load circumstances. Enhanced incremental conductance approach was proposed in [12] that can record the maximum power of PV under a variety of operating situations with improved efficiency by about 5%.
Changes in solar irradiance levels, however, have an influence on the performance of a SPV system, as do the use of bypass diodes, individual solar cell connectivity, and solar cell characteristics. [13] has revealed a performance loss due to bypass diode degradation across each cell. In [14], bypass switches and diodes were modelled to calculate the efficiency loss in every segment of solar cell due to hot spots. [15] has solved the problem of not being able to tell the difference between a local and a global peak. Under partially shadowed conditions, the array arrangement, which is estimated by series and parallel configurations, has a serious influence on the maximum achievable power. Meanwhile, some researchers used thermal cameras to investigate the temperature features of SPV arrays under fault circumstances [16–18]. However, the relationship between electrical and thermal properties of SPV array is hardly ever discussed. In order to fix this issue, a parameter-based model [19] was proposed in which the electrical and thermodynamic characteristics of the SPV array are integrated by the law of conservation of energy.
It is fascinating to research the literature on SPV system parameter estimation. Over the years, more studies have looked into the characteristics of SPV modules under various circumstances. Due to the lumped parameter modelling of SPV system, this cannot be directly used in power generation. Unfortunately, the manufacturers of SPV panel only provides limited operational data such as open circuit voltage (), short circuit current (), maximum power point voltage (), maximum power point current (), temperature coefficient of short circuit current (), temperature coefficient of open circuit voltage (), and nominal operating cell temperature (NOCT). These data are available only at standard test condition (STC) (except NOCT), which is not matched with actual operation. Parameter estimation is the process of evaluating the parameters of a PV electrical model using sample data. The values of circuit parameters such as , , , and must be precisely determined in order to obtain accurate simulation results.
According to the literature, there are two types of traditional parameter estimation techniques: (i) analytical method, which uses several key factors in the I–V characteristic curve (, , , , and slope of I–V at axis interactions) to represent model parameters mathematically using a set of equations [20–23]. If the fundamental values are specified incorrectly, the error might be substantial. (ii) The numerical method is based on a mathematical algorithm and is considered an accurate technique because all of the I–V curve’s operating points are used [24, 25]. The numerical method’s shortcoming is that its accuracy is dependent on the type of fitting algorithm (Levenberg/Merquardt) [26], the cost function, and the initial value of the extracted parameters [27]. Modelling and simulation in photovoltaic arrays were done in [28] to find the boundary conditions of time-varying I–V equation by adjusting the curve at three significant points (, , and maximum power) with no need to quantify any other parameters other than the diode constant .
Due to the ability of handling nonlinear functions without requiring derivative information, intelligent computing techniques have gained much attention in parameter estimation of SPV systems. Few authors have proposed intelligence techniques to adjust I–V curves in an indirect manner. The benefits of intelligent controller simulation over a straight forward linear model have been discussed in [29–32], which do not demand knowledge about the internal system parameters. The technique proposed in [33] outperforms analytical methods that solve the metaphysical current-voltage relationship using the Lagrange’s method of undefined multipliers. [34] has proposed a parametric and interpolation technique to obtain array output voltage. A hybrid neurofuzzy was proposed in [35] as an alternative to pure ANN (artificial neural network), and it requires less training data, providing the potential for recently installed SPV systems with limited measured data.
The evolutionary algorithm is a dynamical optimization approach for enhancing real-valued multimodel objective functions that appears to be very efficient. In order to avoid estimating any unrevealed parameters of SPV model, the authors [36] advocated using field test data and particle swarm optimization (PSO) technique to find , , and . However, if the temperature changes, the accuracy will suffer. To address these flaws, [37, 38] suggested a SPV model identification method based on PSO (particle swarm optimization) with an inverse threshold condition for determining the unrevealed parameters of one diode model (ODM). In [37], the assumption of ideality factor was likewise removed. A rigorous analysis and efficiency comparison of bacterial foraging algorithm (BFA) with genetic algorithm (GA) [39, 40] was conducted, and the optimum computational technique was determined related to performance parameters such as precision, stability, duration of settlement, and absolute error [41]. Because there is no direct general analytical solution to the mystical function that determines the I–V relationship of a SPV cell, the pattern search (PS) technique was proposed in [42]. In [43], data analyses were performed to assess the accuracy of computed parameters and model compatibility using simulated annealing (SA). Penalty-based differential evolution (P-DE) surpasses other metaheuristic algorithm methods such as SA, GA, and PSO and persistently converges to global optimum values very quickly, according to test carried out using the engineered I–V data set in [44]. With a low quadratic mean error value, the cuckoo search [45] method will retrieve all parameters with higher end precision. [46] has provided a modified PSO for estimating the inherent parameters of PV panels premised on I–V characteristics with increased flexibility, adaptability, and remote sensing capabilities without altering current infrastructure.
The mathematical modelling of a SPV system is described below based on the knowledge gained from the aforementioned literature review. The fault identification and rectification of a SPV array relies heavily on the modelling of SPV modules. The first sign of any faults found in PV modules is a drop in output power. The basic output parameters such as , , , and are clearly shown in the characteristic I–V and P–V curve analysis to understand the fault scenario among SPV arrays [15, 47]. In order to examine different types of faults, it is necessary to study the I–V characteristics of SPV modules which are nonlinear in nature [11, 48].
2.1.1. Ideal PV Cell
When semiconductor materials are exposed to light (Sun), the photon energy from the Sun’s irradiation is absorbed by the p–n junction and forms significant number of free electrons in the crystal which causes potential difference to establish all over the junction. This charge carrier starts to flow through the external circuit. This is referred to as the photovoltaic effect, and the resulting electrical current is referred to as the photocurrent, [49]. Figure 1 depicts the circuit of equivalence of the ideal SPV cell.
The mathematical equation which describes the SPV cell’s I–V characteristics based on the study from theory of semi-conductors is given by
Here, the magnitude of is proportional to the flux of insolation as well as the absorption capability of the semiconductor material. As evident from (), the model needs three parameters (, , and ) to fully characterize the I–V characteristic curve. Figure 2 shows the characteristic curve of I–V and P–V with three remarkable points (, 0), (0, ), and (, ).
SPV cell has unique feature that it will act as constant current source and constant voltage source. The small portion of constant current line at (0, ) in V–I characteristic curve gives an idea of slope for the constant current portion, and therefore, it implies the existence of a high value of shunt resistance () across a constant current source. Similarly, the small portion of constant voltage line at (, 0) gives an idea of slope for the constant voltage portion, and therefore, it implies the existence of series impedance () with the terminals.
2.1.2. One–Diode Model (ODM)
The basic equation (1) of the elementary SPV cell does not portray the I–V characteristics of a practical SPV array. Lumped parameter equivalent circuit model of practical array is shown in Figure 3.
ODM is the commonly used model for predicting SPV cell energy production. This solar cell is represented as a light-generated current source that is shunted with a reverse-polarity bypass diode to prevent hotspot emergence during open circuit or other module faults. SPV cells can be connected in series to create modules, which can then be established in a parallel-series connection to form arrays.
The mathematical equation which describes the I–V characteristics of practical SPV array is given by,
where
Equation (2) represents one–diode model, which require four parameters (, , , and ) to characterize the I–V characteristic curve [50]. When the SPV cell encounters drastic temperature variation due to , however, the accuracy of the model declines. The temperature sensitivity can be enhanced by embedding the shunt resistance, , in the circuit [28, 51, 52]. Equation (3) illustrates the model with shunt resistance () where , is the thermodynamic voltage of the array with a serial cell connection. SPV cells coupled in series give higher output voltages, whereas cells connected in parallel enhance current. The addition of to ODM expanded the number of parameters that could be retrieved to five (, , , , and ).
2.1.3. Two–Diode Model (TDM)
Some authors have developed more complex models that are more accurate and can be used for a myriad of purposes [20, 53, 54]. The impact of recombination current loss is inherently neglected in the one-diode models presented thus far, especially at low voltage. As a result, a two-diode model was developed, and its equivalent circuit is depicted in Figure 4. The junction recombination is revealed here by connecting a second diode to the first and setting the diode ideality constant to two.
The characteristic equation of the two-diode model is given by Where
For two-diode circuit modelling, the parameters to be retrieved are totally seven (). Some of the factors in equation (4) are affected by operating conditions, such as the irradiance on the module’s surface and the temperature of the SPV module . These relationships are represented in the following expressions.
A better equation to explain the saturation current for a TDM that takes temperature change into account [55] is as follows: where and .
From equations (6) to (8), the variables and parameters have been measured at STC. Several studies have used iterations to derive the values of Io1 and Io2 in the TDM. The iterative strategy will increase the computation time due to nonsuitable initial assumptions [55]. In [28], it was proposed that the magnitudes of both reverse saturation currents and be equal, equations (7) and (8) can be combined as,
The combined equation represented in (9) does not require iteration; the result can be analytically evaluated. According to Shockley’s diffusion theory [56], diffusion current component, must be unity. Recombination current component, , can be derived from , where can be set to a value larger than 2.2. The simplified equation (10) will eliminate the ambiguity in selecting and values.
The TDM can be simplified by neglecting () without affecting accuracy. This reduces the number of parameters from seven to six (). Then, equation (4) can be simplified as
Several researchers have worked on estimation of SPV parameter over the past decade, and their references for estimating parameter of solar PV is given in Table 1.
In general, the ODM is the simplest technique for developing a SPV cell model, especially in quickly changing weather situations [76]; however, in typical weather settings, the TDM is favoured since it delivers better results than ODM. In the next section, different classifications of PV faults were reviewed.
2.2. Review of Faults in SPV System
Faults are the key sources for efficiency reduction and self-degradation of SPV devices. The faults may be natural or man-made due to faulty engineering components. The majority of published SPV-related articles focus on increased cell level utilization, electrical system modelling, MPPT, panel engineering, and circuit level optimization, with only a few studies focusing on various SPV system faults. In this section, different types of faults that have occurred in both DC and AC side of solar photovoltaic system are discussed. Occurrence of DC faults starts at string connection and ends before inverter, whereas in AC side, it starts from inverter to grid connectivity.
2.2.1. Faults in DC Side
(1)Open–Circuit Faults
An open circuit fault can occur when one of the current carrying routes in series with the load is breached or opened [77]. Open circuit fault may not be a fault, but it can be treated as down time of the system, which reduces the overall efficiency of the system. These faults can be identified by using standard DC shunt which accepts any amount of current as an input and delivers 0–75 mV as an output for measurement purpose. Shunt is connected with each string to monitor the string contribution, so that no string will be unused. DC shunts are current measuring device in DC loads made up of hard copper bar finds in high DC current applications. If the output current is zero, then the shunt delivers 0 mV (i.e. the system is open circuited).
Figure 5 shows the real-time open circuit fault. The location where open circuit fault may occur is marked as X in the above figure. The open circuit fault may occur inside the string between SPVs and in string to string bus connectivity. If anyone of the shunt value goes to zero, it is clear that the string is not contributing to the load. Furthermore, which SPV is exactly disconnected can be found by using string monitoring circuits. (2)Line-Line Fault
Short circuit or double ground faults in SPV arrays can cause line-line faults. (In an electrical system, an unintentional low-resistance contact is established between two potential differences.) Figure 6 shows line-line fault circuit.
Line-to-line faults can be analysed by monitoring each string voltage independently. In case of any discrepancies between strings, the amount of difference between normal and an abnormal string must be analysed and can be concluded how many internal PV arrays are bypassed and made line-to-line faults. Locating the exact point of line-to-line fault is more difficult, and this can be investigated using indirect measurements in both grounded and ungrounded systems using a short circuit fault and a double ground fault, respectively. Further, protection of SPV system from over current damages was also studied in [78]. [2] has investigated line-line faults under low sunlight incidence with 40% position mismatch as well as faults during the night-day transition. (3)Bypass Diode Faults
Diodes are a semiconductor device which works on forward bias from PV to application circuits. In case of diode open circuit failure, the contribution become zero and become a safe-failure when compared with diode short circuit failure. If diode gets short circuited, isolation becomes questionable among the strings and one string may become load to another and both will not be contributing to their maximum capacity. This can be detected by comparing the values of shunt output which is already used in open circuit problems.
Figure 7 shows bypass diode fault. If anyone of the shunt output becomes zero, it may be considered as diode open circuit failure (), and if current is unbalanced among the shunts, it may be a short circuit failure of the diodes ().
By using a bypass diode, the dangerous effects of hot-spot heating can be avoided. Bypass diodes allow current to flow around shaded cells, lowering voltage losses in the module [77]. As a result of long-term energy dissipation in the bypass diode due to partial shading or position mismatch, the thermal effect of the bypass diode and its adjacent components rises, and this phenomenon can lead to bypass diode failure [79]. (4)Charge Controller Faults.
Charge controller is an essential system of a SPV which increases the efficiency by employing various tracking techniques and high-frequency switching systems. The main causes of low conversion efficiency are undesirable load current and increased operating temperature due to nonlinear solar radiation levels. To solve these issues, online or offline algorithms are used to track the PV system’s maximum power operating point, and the system’s operating point is pushed towards this optimal condition. In SPV systems, MPPT is used to continuously tune the system so that it tries to draw maximum SPV power irrespective of load and weather conditions.
Charge controller is the electronic device connected in series to obtain input from multiple strings, and it will be coupled with inverter unit. Figure 8 shows charge controller fault monitoring system. Charge controller is monitored by a redundancy controller for high level safety and safe data acquiring. Redundancy controllers are a supervisory system to monitor the activities of charge controllers and activate the standby controller if the active controller fails [80]. Literature survey on [81, 82] discussed about hardware-based MPPT methods for nonuniform irradiance operation using dynamic reconfiguration of SPV modules. Failure of charge controller was addressed in [83, 84] such that if the charge controller fails to work, this can be bypassed with simple high-frequency charge controller which works almost equal to charge controller. (5)Ground Faults
It is an accidental electrical short circuit created due to various hostile environment conditions such as high wind, high temperature, heavy rain, and fog. Ground faults are the most prevalent fault in PV, and if they are not kept safe, they can result in huge fault currents which lead to fire [78, 85]. This (lower and upper ground faults) can be analysed by using megger-based instrumentation. (6)Arc Faults
Loose shunting of electrical wires causes arc fault which may result in smoke and fire. It is one type of discontinuity or disconductivity between joining of PV to PV or string to string or string to charge controller. Both serial and parallel arc faults are vulnerable to the SPV system, and it must be de-energized to protect SPV systems from fire hazards [86, 87]. Perfect springed connectors may solve this issue provided that the contacts are hermitically sealed. (7)Mismatch Faults
When the electrical parameters of one cell in a SPV module differ significantly from those of other cells, mismatches occur. Mismatch faults can cause irreversible damage to PV modules as well as a significant loss of power. They are, however, difficult to detect using traditional protection devices because they rarely result in large fault currents. Different categories of mismatch faults are given below. (a)Partial Shading
Trees, passing clouds, the shadow of one panel on another, nearby buildings, mobile towers, and smokestacks can all cause partial shading on SPV module surfaces, drastically altering the P–V curve and resulting in multiple local peaks. [15, 47, 54, 81–83, 88]. (b)Nonuniform Irradiance Distribution
Due to various irradiance intensity of sun during day time, some regions of the PV cell are excessively illuminated that generate huge currents and get heated. Similarly, due to nonuniform illumination, some areas are rarely illuminated which will reduce the electrical output of the PV system [14, 89–91]. (c)Snow Covering and Hotspot
A hot-spot or localized heating in a SPV module may be formed due to cell failure, partial shading, interconnectivity failure, and variation in the insolation from cell to cell due to mismatch in parallel connected strings. Snow cover on PV arrays is caused by extreme temperatures, which depends on weather conditions and geographical location [79, 92, 93]. (d)Soiling
Dust particle sedimentation on the surface of a SPV module and bird droppings can have a serious influence on the performance of SPV systems which is discussed in [94]. Relative humidity and wind speed are also relevant climatological factors to the soiling. (e)Degradation Faults
Moisture infringement, colour tarnish, bubbles in the solar module, fissures in the cells, defects in the antireflective coating, loss of binding, delamination over cells, and interconnections all contribute to degradation and an increase in internal series resistance. [95]. (8)DC-DC Converter Fault
DC-DC converters are the most important circuit of any PV system which reduces cable thickness, minimizes the losses, and leads to safe wiring connections. In general, each string voltage will be boosted to high voltage so that current reduces and the overall power remains same. The reduced current leads to low thickness wires with high degree of flexibility in wiring. In this case, many electronic components like diodes, MOSFET switches, and a controller arc are employed which often gets failed because of the nonfavourable environmental situation of the instrument. This can be eliminated by using standard enclosures to avoid fire and moisture-based failure which often exists in PV installation [96, 97].
2.2.2. Faults in AC Side
(1) Inverter Faults. Inverter inverts DC voltage into appropriate AC voltage and can be tuned according to the output requirement in all aspects like voltage, current, and frequency. It is an instrument which possesses all the quality required for newer power generation using appropriate feedback technology, and it enables fine tuning to minimize constant AC voltage dispatch. This instrument consists of multileg MOSFET switches, waveform generators, small transformers, and other electronic controlled systems. There is remote possibility of failure because these instruments provide direct output to the grid, and often an uncertainty in the load causes this instrument to go into faulty condition [98, 99].
(2) Synchronizer Faults. Most of the three phase inverter outputs must be used for on-grid applications. In such a case, the inverter output standard should meet the requirement of existing grid power line. The voltage, frequency, and phase sequence of the inverter should be same as that of existing grid power line. So these inverters must be tuned in accordance to the parameter acquired from the power line for perfect synchronization, or else the generation of power will not be supported to the grid. There should be live comparator which compares line voltage and inverter voltage, line frequency and inverter frequency, phase sequence of the line, and phase sequence of the inverter. If all the values come true, the synchronizer relay activates and connects both the source as single source. Current is the only desynchronization parameter which desynchronizes the bus using zero current supply by any one of the source [100–102]. Figure 9 shows the control scheme for inverter and synchronizer fault.
(3) Unbalanced Voltage, Current, and Frequency. Improper loading methods may lead to unbalanced errors, and it is essential that three phase voltage should be equal to reduce unbalanced errors. The primary objective of any power system is to have higher stability of output voltage called good power quality (PQ). Unbalanced voltage is created because of unbalanced current on the loading part. So the loads must be equally distributed among the phases. Otherwise, unbalanced load may create unbalanced current and unbalanced current creates unbalanced voltage. When voltage and current become unbalance, unbalanced frequency cannot be avoided. Due to this effect, current part will abnormally oscillate. Lower frequency will increase the current proportionally, and this can be avoided by using uniform distribution method [103, 104].
(4) Overload on AC Side. Due to overload on AC side, all the parameters of the AC part will get disturb. The word overload means increase in current beyond the set point and decrease in voltage below the desired level, but the power remains constant. So this leads to spoiling of cable lines and power electronic devices creates abnormal power factor (lag). A minimum overload can be compensated using mitigating system, but huge overload should be sensed and partial/entire load to be disconnected to keep the system stable [100, 104, 105].
3. Conclusion
Solar power generation will not only solve the power recession but it will also lessen the detrimental effects of greenhouse gases produced by fossil fuel-based energy generation. Solar energy is required in countries like India, where a large portion of the population is concentrated in rural areas and requires access to electricity. The effective power generation system should ensure the optimal amount of energy is transmitted to the storage system or load [106]. But the existence of above said faults will reduce the power generation efficiency. The comprehensive study of [107] literature papers in the field of modelling, estimation, and types of faults in photovoltaic energy system gives a wide knowledge in this thrust area.
In the first section, modelling and parameter estimation of SPV cells were systematically reviewed. Solar cell parameters like , and depend on the irradiance level and temperature, which are nonlinear in nature and will affect the magnitude of global peak. Followed by modelling, different types of faults in SPV system have been investigated. Faulty conditions such as open and short circuit faults, line faults, bypass diode faults, bridging faults, MPPT faults, ground faults, arc faults, mismatch faults, inverter faults, synchronizer faults, and faults due to unbalanced voltage, current, and frequency were discussed. This review of the literature can be used as a guide for SPV system modelling and fault analysis, as well as a comprehensive reference for researchers and practitioners.
Nomenclature
| ODM: | One-diode model |
| TDM: | Two-diode model |
| MPPT: | Maximum power point tracking |
| SPV: | Solar photovoltaic |
| STC: | Standard test condition |
| NOCT: | Nominal operating cell temperature |
| ICM: | Incremental conductance method |
| POM: | Perturb and observe method |
| PFM: | Power feedback method |
| ANN: | Artificial neural network |
| PSO: | Particle swarm optimization |
| BFA: | Bacterial foraging algorithm |
| GA: | Genetic algorithm |
| PS: | Pattern search |
| P-DE: | Penalty-based differential evolution |
| SA: | Simulated annealing |
| : | Solar irradiance (W/m2) |
| : | Solar irradiance at STC (1000 W/m2; AM 1.5; ) |
| : | Temperature at STC (25°C) |
| : | Boltzmann constant () |
| : | Electron charge () |
| : | Temperature of module in Kelvin (K) |
| : | Diode ideality constant |
| : | Open circuit voltage (V) |
| : | Short circuit current (A) |
| : | Maximum power point voltage (V) |
| : | Maximum power point current (A) |
| : | Current generated by the incident light (A) (it is directly proportional to the Sun’s irradiation and area of the cell) |
| : | PV output voltage (V) |
| : | PV output current (A) |
| : | Series resistance (Ω) |
| : | Shunt resistance (Ω) |
| : | Number of cells connected in series |
| : | Number of cells connected in parallel |
| : | Current through diode in ODM (A) |
| : | Saturation current in ODM (A) |
| : | Current through diode 1 in TDM (A) |
| : | Current through diode 2 in TDM (A) |
| : | Saturation current of diode 1 in TDM (A) |
| : | Saturation current of diode 2 in TDM (A) |
| : | Thermodynamic voltage of the array with cells (V) |
| : | Thermodynamic voltage of diode 1 in TDM with array of cells (V) |
| : | Thermodynamic voltage of diode 2 in TDM with array of cells (V) |
| : | Temperature coefficient of short circuit current (%/°C) |
| : | Temperature coefficient of open circuit voltage (mV/°C) |
| : | Ideality constant of diode 1 in TDM represents diffusion current component |
| : | Ideality constant of diode 2 in TDM represents recombination current component |
| : | Short circuit current at STC (A) |
| : | Open circuit voltage at STC (V) |
| : | Current generated by incident light at STC (A). |
Data Availability
Data will be available on request. For the data-related queries, kindly contact Baseem Khan at [email protected]
Conflicts of Interest
The authors declare that they have no conflicts of interest.