Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Abstract

This work proposes an automated reconfiguration system to manage two types of faults in any position inside the solar arrays. The faults studied are the short-circuit to ground and the open wires in the string. These faults were selected because they severely affect power production. By identifying the affected panels and isolating the faulty one, it is possible to recover part of the power loss. Among other types of faults that the system can detect and locate are: diode short-circuit, internal open-circuit, and the degradation of the internal parasitic serial resistance. The reconfiguration system can detect, locate the above faults, and switch the distributed commutators to recover most of the power loss. Moreover, the system can return automatically to the previous state when the fault has been repaired. A SIMULINK model has been built to prove this automatic system, and a simulated numerical experiment has been executed to test the system response to the faults mentioned. The results show that the recovery of power is more than 90%, and the diagnosis accuracy and sensitivity are both 100% for this numerical experiment.

1. Introduction

Several investigations about dynamic reconfiguration systems (DRSs) in photovoltaic (PV) arrays focus on reducing the electrical incompatibilities or mismatches among the solar panels. Studies such as [1,2,3] aim to compensate the losses in the delivered power by the solar installation as fast as possible when mismatching or partial shading occurs. Typically, these mismatches are caused by nonuniform irradiation over the solar array due to temporal events, such as partial shadows over the modules, and other reasons as stated in [4,5].

The cost of adding a dynamic reconfiguration system can negatively affect the return of investment of the PV plant if the power gain obtained with the DRS is not large enough. As mentioned in [6], the aforementioned situation is most probable in the cases in which the DRSs focus solely on solving irradiance mismatches. Nevertheless, a reconfiguration system can be easily justified if it addresses and solves severe faults that affects the PV plant production and/or can generate a security risk [7].

A DRS that mitigates severe faults in a PV installation requires a fault detection subsystem. In this regards, several authors have developed fault detection methods, e.g., [8,9,10,11,12,13,14]. Fault detection methods may use images such as the ones taken by unmanned aerial systems (UASs) that have been proved useful for large PV plants given that the visual data about the site can be taken in relatively short time and does not require additional measurement circuitry [15,16]. Nevertheless, image-based fault detection techniques are not suitable for real-time reconfiguration given that it requires a complex data communication network with a relatively large bandwidth. Fault detection techniques based on measurement of electrical variables such as [17,18] are most suitable for a mitigating faults with DRS.

In this work, we propose a system that reconfigures the PV generators to minimize the power losses when a short-circuit to the ground or a wire open-circuit fault occurs in series-parallel photovoltaic arrays. These two faults cause the disconnection of the entire PV string generating an significant decrease of the yield plant. Other faults such as diode short-circuit, internal open-circuit inside PV modules, and parasitic serial resistance degradation are only located and classified because the power losses caused by each one of these three faults are minimal, affecting mainly the faulty panel.

A similar approach to this paper can be found in [7] where the DRS is proved in a total cross-tied PV array to reconfigure the array when electrical faults happen. Nevertheless, there are substantial differences between our work and others. Our contribution focuses on the following aspects:

• The presented switch matrix is built in a modular and distributed way as close as possible to the PV panel. This approach is opposite to the centralize switching matrix presented in the literature, e.g., [7,19].

• Our work focuses on detecting, locating, and isolating the electrical faults in the PV array. Almost all the DRSs are oriented to reduce mismatching or partial shading problems, except for [7]. When our system isolates the faulty panel, is easy to observe the increment of output power.

• Our systems reconfigure the PV array in simulation time when a critical electrical fault occurs, and when the electrical faults are repaired. This means that the Diagnosis Algorithm has to detect the faults, the position, and classify them, also when the fault is fixed. Then a second algorithm made the reconfiguration of the faulty panel or repaired panel. Our reconfiguration solution is based on a multiple finite state machine and is far different from other solutions reported in the literature as is shown later, because it receives the diagnosis information to take a control action.

• Our diagnostic algorithm is computationally lightweight when compared to other approaches from artificial intelligence, such as neural networks or metaheuristic solutions.

• Our simulated PV array has panel string with commercial size reaching voltages close to 600 volts; this shows the applicability of the solution for actual commercial installations.

• Our numerical experiment is quite different because we simulate an operative point of the PV array, and then we apply 19 fault events in simulation time, one at a time, to see the behavior of the produced power. This way, we prove our solution is more realistic to study current–voltage ($i-v$) curves characteristics.

Automated Fault Management System Approach

The traditional dynamic reconfiguration systems can be transformed into an automated fault management system when capabilities are added to detect and diagnose faults inside the array. This concept is presented in Figure 1, where the block diagram has three main subsystems: the data acquisition system, the control unit, and the switching matrix. In the context of reconfigurable solar arrays, each part has its particular complexity.

The data acquisition system (DAQ) captures signals such as currents, voltages, irradiances, and temperatures to estimate the array’s performance. The proposed reconfiguration system’s approach acquires each panel’s differential voltage, the string currents, and the array’s operative voltage. The differential voltage could be measured by simple methods as is proposed in [20,21].

The control unit is responsible for finding whether the solar array has an abnormal issue and for controlling the switches. These tasks are achieved with the following submodules:

• The first submodule aims to determine the condition and fault severity of each panel within the array. In order to do this, signal processing and mathematical modeling techniques are used. An overview of the online, offline techniques for fault detection in solar panels is presented in [22]. Another approach is given by Mellit in [23], where detection and classification techniques are divided in (1) image analysis and (2) electrical characterization. Imaging methods are currently expensive and time consuming, whereas electrical characterization is cheaper and more flexible [24]. The latter methods also can be further split into several branches: signal processing and statistical methods (e.g., [8,25,26]), $i-v$ curve characteristics analysis (e.g., [27,28]), power losses analysis or efficiency analysis (e.g., [17,29,30]), current and/or voltage measurements (e.g., [31,32]), and artificial intelligent methods (e.g., [9,33,34]). The diagnosis algorithm implemented in this work is based on the IF-THEN rules presented by the authors in [35] and follows the diagnosis branch based on current–voltage measurements.

• Once the fault has been determined and located, a second algorithm establishes the configuration that produces an improvement of the output power for the given conditions. In this regard, there are plenty of proposals trying to maximize the output power in the array. For instance, there are approaches based on electric measurements using sorting algorithms [4,36], or based on the equalization of the irradiance [37]. Moreover, there are more elaborated solutions using branches of artificial intelligence, such as algorithms based on metaheuristics techniques [38,39,40,41], algorithms based on diffuse logic [42], procedures based on neural networks [43], algorithms based on rough set [44], or based on the study of the inflection points of the curve I-V [45]. This work uses a simple approach based on a multiple finite state machine that has not been reported in previous articles.

Finally, the switching matrix performs the electrical re-connection according to the results of the reconfiguration algorithm. This matrix is usually implemented with relays, and according to [1] the selected topology defines the complexity of the switching matrix. The proposed reconfiguration system focuses on series-parallel (SP) topologies, however this is not the only array topology but is indeed the most used from the commercial viewpoint [46].

In summary, this article proposes an automated fault management (AFM) system capable of dealing with electrical faults in the solar array. The primary objective is recovering part of the energy loss caused by severe faults. Hence, the modeling process for the photovoltaic generators, fault types description, the switching matrix logic, and the diagnosis/reconfiguration algorithms are presented in Section 2. The planned numerical experiment is shown in Section 2.5, while the results and analysis are given in Section 3. The primary conclusions of this study are pointed out in the last section.

2. Materials and Methods

2.1. Modeling the PV Array

The electrical behavior of the crystalline-silicon solar modules is modeled with the single-diode model (SDM) presented in [47], and according to [48] the errors generated by this model are equivalent to errors produced by the double-diode model at standard test conditions. Specifically, we are using SDM because we are not working under low irradiance conditions in which the carrier-recombination losses in the depletion region are important to consider. Therefore, SDM is accurate enough and is as follows, [Formula omitted. See PDF]

The planned sequence of fault events mixes faults with different severity levels; for instance, the short-circuit to the ground or an open-wire fault causes much more power losses in the PV array than minor faults such as a short-circuiting diode, internal degradation, or internal open-circuit. In this sense, the presented AFM system locates and diagnoses the PV modules with a slight time delay of just one sample.

Additionally, notice in Figure 8 that events {E2, E4, E5, E8, E9, E11, E12, E14} are the ones which affect several modules in one string. This event list is associated only with short-circuit-to-ground faults or open wire in the string. When these two fault types happen, they generate one faulty module and $m-1$ affected modules. For example, the event E4 over module 22 is an open wire between the module and the switching box; the figure shows that $m-1$ PV modules are affected and are classified as modules with open-circuit voltage, and the faulty panel 22 is the one with the defect. Moreover, other events like E11, which is the short-circuit to ground over module 11, generate $m-1$ affected modules. In this case, modules located above module 11 are in open-circuit voltage (this means a tag number less than eleven). In contrast, modules below module 11 are classified as short-circuited to the ground (this means a tag number higher than eleven).

4. Discussion

The AFM system’s results are entirely satisfactory for several reasons, among them: real-time diagnosis without false positives or false negatives, real-time reconfiguration sensible to critical faults, and high recovery of power loss after reconfiguration.

In Figure 8, it is easy to observe that the number of false positive (FP) or false negative (FN) detections are zero. For example, capturing the FP could be understood when there is no AFM system detection while the programmed fault occurs. On the other hand, capturing the FN could be appreciated when detection results appear in time periods without programmed faults. In both cases, the counting of FP and FN indicates that the accuracy and sensitivity are 100% for this experiment. This means that the diagnosis algorithm detects only the positive cases and detects only the negative ones.

As mentioned before, the Figure 9 shows the output power behavior with and without the AFM system. When the AFM system is not working and the fault sequence is applied to the array, the produced power drops depending on the fault type. Minor faults events like module short-circuit, internal open-circuit, or internal degradation, generate small losses around $1.4\%$ of the total produced power, i.e., less than 2.1%. However, when the fault type is a short-circuit to the ground or an open-wire in one string, the harm in the production power is notorious. For instance, when the events E2, E4, E5, E8, E9, E11, E12, and E14 happen, the power production drops, as it is shown in the third column on Table 6. Although, when the AFM system works properly, the power loss recovery is more than 90% for all the severe cases; therefore, the effect in the total power is minimum, as shown in the fifth column in the Table 6.

In addition, a positive aspect of this diagnosis and reconfiguration system is the automatic detection of the recovered panels; this means that the system can return to the original array configuration when the fault is repaired. This can be observed in Figure 9.

An important characteristic of the diagnosis algorithm is the low number of operations required to classify the $m\times n$ elements in the PV array. Moreover, the algorithm complexity is $O\left(mn\right(log\left(m\right)+1\left)\right)$, where the logarithmic term comes from the ordering required in line 9 in Algorithm 1. Notice that in other research papers such as [7,32] or specialize reviews such as [8,23], the computational complexity of the solutions are not addressed.

As a future work, the impact of the variability of the PV array irradiances and temperatures in the algorithm will be analyzed. It is of special interest to tune the proposed algorithm in such a way that fast changing irradiances do not produce false positive or false negatives. In addition, the effect of external faults outside the PV array has to considered; for instance, faults in the inverters or power transformer are topics interesting to incorporate for future work.

5. Conclusions

We have presented an automated fault management system composed of three main parts: the diagnosis algorithm, the reconfiguration algorithm, and the distributed switching matrix. The AFM system was tested using a solar array composed of $16\times 3$ PV modules and 19 events that use 5 electrical faults. The simulated faults have different severity levels, and for the short-circuit to the ground or an open-wire, the AFM system recovers more than 90% of the power loss with a diagnosis accuracy and sensitivity of 100% for the planned experiments.

The diagnosis algorithm is lightweight because it is based on IF-THEN rules derived from circuit analysis theory applied to the PV array. This is a vital aspect against other classification techniques like artificial neural networks, support vector machines, k-nearest neighbor, etc., because our diagnosis method based on behavioral rules processes the data with only a sample delay. No data training is required, which is suitable to be implemented using micro-controllers or IoT devices.

Author Contributions

Conceptualization, C.M. and L.D.M.-S.; methodology, L.D.M.-S.; software, L.D.M.-S.; validation, L.D.M.-S. and C.M.; formal analysis, L.D.M.-S.; investigation, L.D.M.-S. and C.M.; resources C.M.; data curation, L.D.M.-S.; writing—original draft preparation, L.D.M.-S.; writing—review and editing, C.M.; visualization, L.D.M.-S.; supervision, C.M.; project administration, C.M.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by scholarship program of the Costa Rica Institute of Technology and the VIE project 5402-1341-1701.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

Special thanks to all SESLab members for apportioning support and ideas http://www.ie.tec.ac.cr/seslab/pmwiki/pmwiki.php/Main/Team accessed on 16 April 2021.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:

MDPI	Multidisciplinary Digital Publishing Institute
DOAJ	Directory of open access journals
PV	Photo-voltaic
SDM	Single-Diode Model
STC	Standard Test Conditions
DRS	Dynamic Reconfiguration Systems
AFM	Automated Fault Management
SC	Short Circuit
OC	Open Circuit
NC	Normally-close
NO	Normally-open
DAQ	Data acquisition system
SPST	Single-Pole Single-Throw
SPDT	Single-Pole Double-Throw
FP	False Positive
FN	False Negative

Appendix A

Figure A1

Test bench for the numerical experiment.

[Figure omitted. See PDF]

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Figures and Tables

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Figure 1. Block diagram for the automated fault management system.

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Figure 2. Model of the photovoltaic module implemented in Simulink.

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Figure 3. Photovoltaic array schematic. The electrical measurements are indicated in red color, and the distributed switch boxes for fault reconfiguration are next to the PV panels.

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Figure 4. Safe states for the switching box. (a) State A, panel in the main circuit; (b) State B, panel in the testing circuit; (c) State C, short-circuited panel; (d) State D, panel with open connection.

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Figure 5. Modular design of the m×3 switching box.

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Figure 7. Location of the fault events inside the PV array.

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Figure 8. Plotted results of the diagnosis algorithm.

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Figure 9. The behavior of the output power with different electrical faults in the 16×3 PV array.

Table 1

Control vector for reachable safe states.

	Figure 4	K1	K2	K3	K4
State A	(a)	0	0	0	0
State B	(b)	1	1	1	0
State C	(c)	x	1	0	1
State D	(d)	x	1	0	0

Table 2

Tags for fault classification.

Fault Type	Tag
Normal module	0
Recover module	1
Short-circuit to ground	2
Short-circuit module	3
Open-circuit module	4
Open wire in the string	5
Bypass diode working	6
Internal degradation	7

Table 3

Values of the five-parameter model of the KC200GR [49].

Parameter	Value
Saturation current $\left(I_s\right)$	$9.825\times {10}^{-8}$ A
Photo current $\left(I_{ph,n}\right)$	$8.214$ A
Series resistance $\left(R_s\right)$	$0.221\Omega$
Parallel resistance $\left(R_p\right)$	$415.405\Omega$
Ideally factor $\left(A_q\right)$	$1.3$

Table 4

Electrical performance at standard test conditions (STC).

Specification for KC200GT	Value
Maximum Power $\left(Pmax\right)$	$200.0$ W
Maximum Power Voltage $\left(Vmpp\right)$	$26.3$ V
Maximum Power Current $\left(Impp\right)$	$7.61$ A
Open Circuit Voltage $(Voc,n)$	$32.9$ V
Short Circuit Current $(Isc,n)$	$8.21$ A
Temperature Coefficient of Voc $\left(K_V\right)$	$-1.23\times {10}^{-1}$ V/${}^{\circ }$C
Temperature Coefficient of Isc $\left(K_I\right)$	$3.18\times {10}^{-3}$ A/${}^{\circ }$C
Number of series cell $\left(N_s\right)$	54

Table 5

Programmed events applied to the PV plant and the expected diagnosis results.

	Fault Type	Interval (s)	PV Label	Diagnosis
E1	Short Circuit	0.5–1.0	3	3
E2	SC to ground	1.5–2.0	3	2
	Open Circuit module	1.5–2.0	18	6
E3	OC module	2.5–3.0	3	6
E4	Open wire	3.5–4.0	22	5
E5	SC to ground	4.5–5.0	22	2
E6	SC	5.5–6.0	22	3
E7	SC	6.5–7.0	46	3
E8	SC to ground	7.5–8.0	46	2
E9	Open wire	8.5–9.0	46	5
E10	SC	9.5–10.0	11	3
E11	SC to ground	10.5–11.0	11	2
E12	Open wire	11.5–12.0	11	5
E13	SC	12.5–13.0	41	3
E14	SC to ground	13.5–14.0	41	2
E15	OC module	14.5–15.0	41	6
E16	OC module	15.5–16.0	5	6
E17	OC module	16.5–17.0	29	6
E18	OC module	17.5–18.0	35	6
E19	OC module	18.5–19.0	9	6
	Internal degradation	18.5–19.0	26	7

Table 6

Power loss and recovered power with the system.

Event	Fault Type	LostPower (%)	RecoveredPower (%)	RealLoss (%)
E2	SC to ground	34.78	31.96	2.82
E4	Open wire	33.33	31.95	1.38
E5	SC to ground	33.33	31.95	1.38
E8	SC to ground	14.28	12.90	1.38
E9	Open wire	33.33	31.95	1.38
E11	SC to ground	33.33	31.95	1.38
E12	Open wire	33.33	31.95	1.38
E14	SC to ground	33.33	31.95	1.38