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A Survey on Anomalies and Faults that may Impact the Reliability of Renewable-based Power Systems

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13 May 2024

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14 May 2024

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Abstract
The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will reduce the reliability of the grid very likely, unless appropriate countermeasures are taken/implemented, hopefully at the same time the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might determine in renewable-based power systems. This paper surveys the current state-of-the-art on anomalies and faults affecting wind-PV-storage hybrid power systems, i.e., the main renewable technology ensemble that will establish the future grid, and highlights possible research directions that may help to fill the literature gaps.
Keywords: 
Subject: 
Engineering  -   Electrical and Electronic Engineering

1. Introduction

The reduction of fossil fuel usage in the future electricity grid is an important measure to alleviate global warming and strive to maintain temperature increases within acceptable thresholds in the future. This process is destined to happen through the advancement of pertinent renewable technologies and their widespread adoption, facilitated by initiatives such as new installations, the overhaul or decommissioning of antiquated fossil fuel-based power plants, and the establishment of a new grid architecture centered around renewable, distributed generation. This evolution may also usher in novel roles, such as those of prosumers, contributing to the diversification of the energy landscape.
It is widely acknowledged that the optimal functioning of renewable-based generation systems necessitates one or more renewable sources (e.g., Photo Voltaic (PV) panels, Wind Turbines (WTs)) integrated with storage solutions, efficient power conversion units, and complemented by digital technologies (including circuitry and software) for control and seamless interaction with relevant entities. In this scenario, the effectiveness of the deployed solutions in supporting or impeding the decarbonization of the electricity grid depends on their technological maturity.
Crucially, ensuring the expected quality of service from these systems is imperative, as any compromise in this regard could lead to significant systemic failures with far-reaching consequences. This risk becomes more pronounced in the context of small to mid-size installations, where larger penetration, cost constraints, and supply chain heterogeneity may introduce challenges not as prevalent in larger counterparts. As such, meticulous attention to the robustness and reliability of these renewable energy solutions is paramount for achieving a sustainable and decarbonized electricity grid.
While the existing scientific literature compiles a substantial number of publications focusing on specific components or aspects relevant to the topic, there is a notable lack of articles addressing the entire technological mix and beyond (i.e., namely, the conversion, monitoring and communication systems).
For instance, [1] addresses renewable-based energy systems and presents a review of Machine Learning (ML) techniques for health monitoring. Thus, for instance, the relevant time-scales considered by the reviewed algorithms are larger than those considered by the contribution we propose. Further, the scope addressed is quite different from that of this paper because it reviews ML techniques aiming at health monitoring, while we propose a survey on anomalies and faults in renewable-based power systems not focusing on specific algorithms. In [2] energy systems are still addressed nor the survey is focused on renewable-based systems. Rather, the Authors address Artificial Intelligence (AI) techniques for prognostic maintenance which is somehow related to anomalies and faults but it is not the main target. Power systems are addressed by the review in [3] however only related to the electric part, while the article we propose reviews anomalies and faults that affect, e.g., also the communication systems in renewable-based power systems. In this regard, [3] does not specifically consider anomalies and faults, nor renewable energy and generally targets ML applications in power systems. Renewable-based power systems are also addressed in [4], however with the scope being restricted to those with a dominance of inverter and targeting cybersecurity instead of anomalies and faults in general. A survey on fault diagnosis in micro-grids can be found in [5], thus it does not generally address power systems, renewable energy, and anomalies and faults; also it is not recent since it dates back to 2016. The same authors have already proposed a similar contribution in 2014 [6], addressing faults and fault diagnosis. However, also this case move away from the focus we propose since only the electrical part is addressed therein. In [7] a systematic review of faults that may arise in smart-grid is presented. But, the focus is not on renewable-based power systems. The paper [8] does not focus on a unique system and addresses PV and WT renewable generation. However, for instance it does not include the hydrogen-related technologies, as instead this paper does, and in particular, electrolyzers and Fuel Cells (FCs). This is a major point, since hydrogen is identified as one of the main technologies for storing renewable generation and that will strongly support its diffusion in the future power systems. Furthermore, [8] restrict its review to monitoring of fault conditions and not on the possible anomalies and faults that may instead happen. In [9], the review targets fault detection methodologies and datasets in district heating substations, in [10] the review addresses fault location and detection techniques in power distribution systems with distributed generation. In both cases the target system is more specific and the scope is not the same as with respect to what is proposed in this paper, and with the renewable generation not being considered at all.
Summarizing, the analyzed literature is either too specific, by restricting the investigation on particular instances of power systems (e.g., PV systems, district heating substations), with peculiar implemented software (e.g., AI) and hardware (e.g., inverter) technologies, and aim (e.g., fault location), and do not broadly gather the main renewable-based technologies in one self-consistent article with the focus on the possible anomalies and faults that may affect them. In particular, a substantial gap regards hydrogen technologies, monitoring and conversion systems, where reviews that considers them even within similar frameworks to that identified by this paper are basically missing.
The paper is organized according to the standard format of this journal, and the rest is as follows. Section 2 reports some clarifications regarding the terms “anomaly” and “fault” as used in the specific context addressed and more broadly in the scientific/technical community, and the survey outcomes. In particular, Section 2.2 addresses PV systems, Section 2.3 addresses WTs, Section 2.4 addresses electrolyzers, Section 2.5 addresses FCs, Section 2.6 addresses Battery Systems (BSs), Section 2.7 addresses DC/x conversion systems, Section 2.8 addresses monitoring systems, and Section 2.9 addresses communication systems. Section 3 concludes the paper.

2. Materials and Methods

The present article elucidates the primary causes of anomalous behaviors and malfunctions in specific systems, along with the accessibility of data for their characterization. To achieve this objective, technical-scientific literature and technical documentation (e.g., datasheets provided by manufacturers) for each relevant component has been systematically examined. This process aimed to identify the most prevalent types of malfunctions and the availability of simulated and/or experimental data for their characterization. In instances where such data was not readily available, a thorough exploration of mathematical models and/or empirical/simulative methodologies suitable for fault characterization has been conducted. To enhance the usability of the obtained results, they have been organized into tables.

2.1. Caveats

Before presenting the outcomes of the survey, some clarifications are needed regarding the terms “anomaly” and “fault”. In the scientific/technical literature, their meaning is debated and there is no unique understanding about. This is well reflected in how the subject is addressed by, e.g., IEEE and NASA, two prominent institution in the technical field. In the first case, IEEE Std 1044-2009 [11] reports that <<[...] The word `anomaly’ may be used to refer to any abnormality, irregularity, inconsistency, or variance from expectations. It may be used to refer to a condition or an event, to an appearance or a behavior, to a form or a function. The 1993 version of IEEE Std 1044 characterized the term `anomaly’ as a synonym for error, fault, failure, incident, flaw, problem, gripe, glitch, defect, or bug, essentially deemphasizing any distinction among those words. Such usage may be common practice in everyday conversation where the inherent ambiguity is mitigated by the richness of direct person-to-person communication, but it is not conducive to effective communication by other (especially asynchronous) methods [...]>>.
On the contrary, in the second case, NASA SP-2016-6105 [12] reports that an anomaly is <<[...] The unexpected performance of intended function.>> while a fault is <<[...] A physical or logical cause, which explains a failure[...]>> and relies on how the question is addressed in [13].
The existence of heterogeneous positions regarding the meaning attributed to the terms “anomaly” and “fault” is an element of ambiguity that, e.g., affects the definition of possible taxonomies aiming at the establishment of pertaining structured knowledge.
However, by considering that the surveyed literature makes no difference about the two terms and use both interchangeably [14], in what follows, the same convention is kept without any further discussion. Yet, in a broad sense, this identifies a possible gap in the literature that could be filled by a specific contribution on the topic from interested fellows.

2.2. Anomalies and Faults in PV Systems

The survey focused on articles related to PV systems (as shown in Figure 1), composed by several PV modules organized in strings in diverse configurations. The DC power generated by the photovoltaic array is directed towards the DC/DC converter. The voltage and current readings are continuously monitored and adjusted within the Maximum Power Point Tracking (MPPT) to maximize the output power. Subsequently, the output from the DC/DC converter is sent to the DC/AC inverter and then to the grid (for grid-connected PV systems). Prior to entering the grid, the inverter’s output is directed through a low-pass filter leaving only the fundamental frequency of the utility grid (typically 50 Hz or 60 Hz). Filtered signal then passes through a step-up transformer before being injected into the utility grid. Sometimes, an electricity storage is also paired, whilst this is neglected since it is separately addressed in the following sections.

2.2.1. Research Highlights

The analyzed literature encompasses a diverse array of instances, wherein anomalies/faults can be attributed to varied and heterogeneous origins. Broadly, an absence of uniform taxonomy emerges among different authors, potentially stemming from the intricate interplay of contributing factors, more realistically manifesting in a domino effect. Authors of [16] groups PV anomalies and faults in three categories: internal, external, and electrical.
The internal faults are localized inside the PV module (e.g. under the protective glass, on the strings, on PV cells, etc.). The main internal faults are short circuit, bridging, fault to bypass diode and open circuit [17,18,19]. The main causes of these type of faults are manufacturer’s defects, subpar fabrication quality, packaging inadequacies, and improper wiring and have a dramatic impact on PV system. In particular: short circuit entails the failure to supply power to the DC load or the power conditioning unit; bridging results in a complete absence of power output; bypass diode fault manifests as an incapacity to mitigate hotspot events; open circuit fault leads to the incapability of delivering power to the DC load or the power conditioning unit leading to a partial blackout or to a not homogeneity in the power production [16].
The external anomalies and faults are located outside the PV module and usually are due to the environmental condition, natural disasters but also to wrong packaging, installation, etc. Since PV systems are located outdoors, they frequently encounter environmental stress as high temperatures, rain, snow, and the PV system does not operate under Standard Test Conditions (STCs), thus failing to achieve their nominal power. Since variations in solar irradiation directly impact the power generation of PV systems [20], with the consequent uncertainties that must be carefully considered [21], certain areas of PV arrays could yield higher power output compared to others (mismatch) due to non-uniform shading from physical obstructions like trees, buildings, and overhead power lines, etc. [22,23]. Additionally, environmental factors such as dust accumulation, bird and leaf droppings could lead to a partial shading condition [24,25]. Furthermore, natural disasters like lightning and storms [26] can have dramatic consequences on the PV modules. Some of the faults listed before are temporary because they are reversible (e.g., partial shading, dust accumulation, etc.). Permanent mismatch faults, instead, are irreversible and can be related to poor soldering, module degradation, glass breakage, and structural defects due to improper manufacturing processes or environmental conditions like heavy snow loads or frequent temperature fluctuations [27,28]. Since the external anomalies and faults are very diverse the severity of the related damages vary as well, going from a non homogeneous power production to a complete blackout [16].
The electrical faults are related to the perturbations to variables as voltage, current, power, etc. The main electrical faults are the ground faults, line-to-line fault, and arc fault [29,30]. These faults can have dramatic consequences as electrocution of operators or great damage to the equipment as a fire [16]. Finally, other faults can affect other parts of the PV system as MPPT [31], inverter [32].
The results of the survey are presented in Table 1, where the first column specifies the target component, of the system at hand, subject to anomaly/fault, the second column reports a description of the anomaly/fault considered, the third column specifies causal factors, and the fourth column compiles bibliographic references. All similar tables, i.e., related to literature findings about the other systems addressed by this paper, are organized with equal column names.
The literature also offers several datasets, summarized in Table 2, obtained from experimental measurements of real plants or simulated through mathematical models and, in some cases, the anomalies are also simulated. The first column reports the dataset name as specified in the referred online resource, the second column allows to specify, e.g., whether the provided data are from real plants/systems, simulations, lab-scale installation or others, the third column describes the dataset, the fourth column reports the bibliografic reference and the fifth column reports possible other references of papers that the authors of the dataset ask to cite. Also in this case, all other tables related to the datased of the other systems addressed by this paper, are organized with equal column names.

2.3. Anomalies and Faults in Wind Turbines

The surveyed literature targets WTs as depicted in Figure 2. The blades transform the kinetic energy of the wind in a rotation movement applied to the rotor and, through a generator, in electricity. There are two main types of WTs, Vertical-Axis Wind Turbines (VAWTs) and Horizontal-Axis Wind Turbines (HAWTs) as the one described in Figure 2. HAWTs are the most common and usually consist of two or three blades, or a disc containing several blades. On the other hand, VAWTs are designed with blades that rotate around a vertical axis, thus being able to harness wind blowing from any direction.

2.3.1. Research Highlights

In [62] authors present several statistics of anomalies of different constituting components of the WT. Some of them have a higher median failure rate (electrical components, control system, pitch system, blades, hub), while others have a higher median downtime (transmission, system, shafts, bearings, structure). Failure rates for offshore installations are generally higher than those for onshore installations, even because they are under more critical operating conditions (e.g., higher wind speed, corrosive action of sea salt, etc.). Downtime in offshore installations, given logistical difficulties, is generally higher than that in onshore installations.
In general, technical-scientific literature provides numerous works on WT diagnostic systems [63,64] but provides few details about the different type of faults and anomalies that can occur in WTs, except for the already mentioned work on the statistics [62]. Moreover, there are several datasets containing the real and simulated data related to the WTs and are listed in Table 3.

2.4. Anomalies and Faults in Electrolysers

In the recent years, the water electrolysis is the most considered way for the eco-friendly hydrogen production, in particular, whereas energy input for the process is achieved by renewable sources. The basic reaction of water electrolysis is expressed in (1) [80].
H 2 O + E l e c t r i c i t y ( 237.0 k J m o l − 1 ) + H e a t ( 48.6 k J m o l − 1 ) ↔ H 2 + 1 2 O 2
The electrolyser is the device in which the process is host, the main part of which is the electrolytic cell, in which the electro-chemical reaction takes place. A typical electrolytic cell representation is reported in Figure 3. From an overall point of view, the cell is composed by two bipolar plates (anodic and cathodic), in which the water is fed and at which the electrical potentials are applied. The crucial component of the cell, that characterizes the cell typology, is the electrolytic membrane, that separates the anodic zone from cathodic zone, allowing the selective cross-over of a specific ion through it. Moreover, the Gas Distribution Layer (GDL) aims to allow an uniform access to the gas from the anodic or cathodic plates towards the membrane. The GDLs terminate with a catalytic layer, devoted to promote the chemical reactions hosted at anodic or chatodic sides. The nature of the catalyst depends on the typology of reaction to be promoted: for example, in a Polymeric Electrolyte Membrane (PEM) electrolyzer, at the anodic side, catalysts based on ruthenium and iridium are widely used [81] to promote the water splitting in H+ protons and OH- anions, while at cathodic side platinum nanoparticles (dispersed on carbon supports) are mainly employed to promote the protons reduction to hydrogen [82].

2.4.1. Research Highlights

The survey’s outcome is summarized in Table 4. Notably, the analysis reveals that predominant failure causes are associated with the membrane and catalyst, with occurrences of failures in bipolar plates and current collectors being comparatively infrequent. Membranes failures are typically associated to aging and cracking mainly due to fabrication defects or due to thermal, mechanical and chemical stresses in normal and severe operating conditions. Mechanical failures, including cracking, perforation or pinholes, are due to abnormal stresses or other mechanical factors, such as temperature, humidity, start-up and shut-down cycles, operating conditions fluctuation and warm-up/cool-down procedures [83]. Temperature anomalies could increase membrane failure rate up to 2 order of magnitude when operating T increases from 55°C to 150°C [80]. Impurities could also result in membrane degradation [84,85], often due to catalyst corrosion [86]. Moreover, radical attacks are responsible for membrane degradation [80]: the phenomenon is more promoted for low current density [87,88], since a faster membrane thinning could be observed [89]. It is however worth noting that temperature effect is more severe with respect to the operative current density [88]. Catalyst degradation is a very slow process, thus is not responsible for sudden cell failure. Among typical catalyst deactivation mechanisms, most common are particles dissolution and migration, sintering, catalytic layer detachment and support passivation [90]. A more common phenomenon is the catalytic particle dissolution and the consequent penetration in the membrane lattice, affecting its functionality [80,86]. Another mechanism is the catalyst passivation, due to the oxidation of the catalytic support at the anodic side, thus reducing the electron flux between support and the anodic plate. One of the most common deactivation mechanism is the catalyst sintering, since high temperature could cause the catalytic particle agglomeration, resulting in a reduced catalytic activity [91]. Finally, catalytic poisoning due to impurities in the water or metallic dissolution in bipolar plates are responsible for a (more or less) transitory catalytic deactivation, since impurities occupy active sites [92]. Diverse diagnostic approaches are deployed, with the most cutting-edge methodologies involving statistical techniques grounded in neural networks. These, however, necessitate extensive historical or synthetic device data, leading to prolonged characterization times. In contrast, conventional methods relying on electrical and electrochemical measurements, while more practicable, exhibit a more confined capacity for fault identification.
The investigation on possible empirical dataset pertaining to electrolyser failures highlighted a consistent lack in this regard. For this reason, Table 5 actually compiles only mathematical models that can be used to achieve synthetic datasets anyway.

2.5. Anomalies and Faults in Fuel Cells

A FC is a device able to generate electricity by exploiting electrochemical potential of oxidation-reduction reactions. In a general overview, reactants are basically a fuel and an oxidant: in particular in the case the fuel is the hydrogen, and the oxidant is oxygen (or air), the reaction, summarized in (2), is able to generate electrical power and heat, by resulting in water as the only side-product.
H 2 + 1 2 O 2 ↔ H 2 O + E l e c t r i c i t y + H e a t
Of course, depending on the employed FC typology, methane, ethanol, carbon monoxide or other hydrocarbons can be used as fuels, and carbon dioxide can be used as oxidant. In a global point of view, a FC is an electrolytical cell (similar to cells used in electrolysis) able to intercept electrons involved in the oxidation-reduction reactions, thus forcing electrons flux in an electrical circuit, thus generating electrical power. FC elements are reported in Figure 4. Main components of the cell are the same already described for the electrolyzer: fuel is fed to the cathodic plate, while oxidant is fed to anodic plate: bipolar plates also act as electrical collector. Reagents are delivered to the catalytic layers through dedicated gas distribution layer; on the catalytic surface, the chemical reactions take place, which mechanism strictly depends on the cell typology. The membrane separating anodic and cathodic sides act as a selective barrier, aiming the cross-over of only a selected ion depending on the hosted process: in the case of PEM-FC, membrane only allow the proton (H+) crossing.

2.5.1. Research Highlights

The survey’s outcome is summarized in Table 6 and shows that the most fragile components are the membrane and the catalyst, accounting for 95% of malfunctions. As mentioned for the electrolyzers, membranes can suffer for cracking or perforation due to uncontrolled humidity or temperature in the process, that originates to tensile, mechanical and thermal stresses responsible for the failure of the component [96,97], causing the reactants crossover and in turn the uncontrolled fuel combustion [98]. It is worth to underline that such events are more frequent in the early period of the cell lifetime [99]. Membrane degradation can moreover be originated by peroxyl and hydroxyl radicals attack, particularly in low current conditions: under such conditions, PEM membrane could release fluorides, thus undergoing to a weakening that leads to the membrane failure [100,101]. The second reason for FC failure is catalyst degradation: it could occur for particle sintering [102], carbon monoxide poisoning [103] or carbon support oxidation [104]: these failure mechanisms are responsible for a more or less severe activity reduction of the device, rather than a real cell service interruption. Some phenomena such as corrosion or mechanical stresses could occur also at the GDL [103] and bipolar plates [101] causing conductivity loss and structure deformation or fracture. GDL can also suffer for embrittlement of the support material due to severe operating conditions as well as to the contact with hydrogen. Finally, non-adequate operating conditions, in terms of temperature or pressure, as well as factory defects, can be responsible for sealing failure originates by mechanical fractures [101].
Unfortunately, it was not possible to obtain experimental data on faults associated with FCs. Conversely, several methods for FC failure prediction were explored in the available literature, both stochastic [110,111] and neural network based [112,113]. Their usage is strictly connected to the achieving of instrument typical data through a long training phase. Analytic methods require the knowledge of specific FC parameters, not easy to achieve [113]. Several techniques for on-line failure analysis and characterization are available in the literature: among them, the most viable are the Electrochemical Impedance Spectrometry (EIS) [108], the V/P characteristic curve analysis [114], and the cell voltage measuring [115]. Several mathematical models able to describe the degradation mechanism are reported in Table 7.

2.6. Anomalies and Faults in Battery Systems

The survey focused on the currently most widespread BSs technology [121] that is Lithium-ion (Li-ion) technology. The Figure 5 shows a sketch of a Li-Ion battery cell. It has four main components: positive electrode, negative electrode, electrolyte and separator. The separator aims to isolate the electrodes to avoid internal short circuit. Moreover, it is made up of porous material to allow, with the electrolyte, the ions movement between the electrodes.

2.6.1. Research Highlights

The survey’s findings are gathered in Table 8. The outcomes shown in Table 8 highlight that the BS faults can be classified into two types [123]: cell fault and system fault. The cell faults are mainly caused by Battery degradation phenomena and include the following anomalies/faults: loss of active material; electrolyte consumption; increase in internal resistance; lithium deposition; gas generation; Solid Electrolyte Interphase (SEI) thickening. A passivation layer called SEI is formed on electrode surfaces from decomposition products of electrolytes. The SEI allows Li+ transport and blocks electrons in order to prevent further electrolyte decomposition and ensure continued electrochemical reactions [124]; current collector corrosion; internal short circuit (can cause explosion and is mainly caused by overload); thermal runaway; capacity diving; liquid leakage. The system faults are mainly caused by battery management system anomaly/fault [125,126], sensory system anomaly/fault, cables and connections anomaly/fault. They can be classified as: overcharge (can provoke the reaction of the positive electrode with the electrolyte, resulting in heat generation, pressure increase, and subsequent fire); overdischarge; reduced battery life; thermal runaway; reduced battery performance; equalization errors; thermal runaway accident; increase in internal resistance; thermal runaway safety accident.
The Table 9 highlights the main dataset on faults in Li-ion BSs.

2.7. Anomalies and Faults in DC/x Conversion Systems

A power electronics converter is a circuit adequately interfacing a power source with an electricity absorbing system, such as a load, a storage or a sinking busbar of the main grid. They are constituted by different stages (Figure 6), including an input and an output filter, a switching stage and a magnetic section. The switching section represents the converter core which can be realized by Metal–Oxide–Semiconductor Field-Effect Transistor (MOSFET) or IGBT devices. A gate driver circuit turns on/off the converter switching devices according to Pulse-Width Modulation/Modulated (PWM) technique assuring its functioning mode.
These converters can be employed as interfaces between a DC voltage source to obtain a DC output voltage of different magnitude or as a DC/AC conversion system. They can be designed and realized to assure unidirectional or bidirectional power flows.

2.7.1. Research Highlights

The conducted study highlights the converter is subjected to operating conditions that can vary over time, its devices are subjected to electrical, thermal, mechanical or combined (electro-thermal, etc.) stresses. In addition, aging phenomena can impact the converter performances. These factors can cause anomalies and/or failures to the power stage devices, the control stage, switching components driving circuit, and at the converter inputs and outputs terminals.
The sector literature focused the attention mainly on the physics of the failure and/or anomaly of the individual components, also reporting the converters most damaged/fault devices.
In switching devices, overcurrent and overvoltage conditions can cause the component overtemperature. In particular, the action of thermal stress can have significant effects on secondary breakdown phenomena which lead to the destruction of the switching device. During design phases of such equipment it is essential to consider the safe operating area of these components and appropriate heat sinks. It must be kept in mind that the value of the junction temperature depends on the ambient temperature as reported in (3):
T j = T a + P a R T j − a ,
where T j is the switching device junction temperature, T a is the ambient temperature, P a is the losses power, and R T j − a is the thermal ambient-junction resistance.
Also the switch Drain-Source resistance, and Gate-Source voltage depend on the junction temperature. The Drain-Source resistance increases to the junction temperature growth (Figure 7(a)) while, in Figure 7(b), the Gate-Source voltage decreases for rising junction temperature values.
The graph reported in Figure 8 underlines that the switch threshold voltage ( V t h ) can decrease to the ambient temperature increase. Given the temperatures that have characterized the latest heat wave phenomena, the threshold voltage V t h could drop causing the unwanted switching on of the switching device with harmful consequences for the individual component and also for the interface converter of which it is part.
Furthermore, it must be underlined that temperature also represents a stressful agent for the materials that make up the switching devices. The use of materials characterized by different behavior in terms of thermal expansion and compression can, in fact, cause cracks with the consequent failure of the component.
A further cause of anomalies and failures in switching devices are electrostatic discharges. In detail, their action can cause the gate oxide to break without obvious malfunctions for the component immediately, but leading to breakage after a period of time from the event. The adoption of protections and the possibility of monitoring the gate charge can avoid the failure.
With reference to capacitive components it is necessary to underline that electrolytic, ceramic and film capacitors are used in interface converters. The capacity of these devices varies with temperature as shown in Figure 9.
The graphs represented in Figure 10 also highlight the halving of the operational life (expressed in hours) of a condenser for every 10°C increase in temperature.
The converter output voltage is obtained by appropriately driving the switching on and off of the switching devices present in the circuit. This function is carried out by appropriate integrated circuits (drivers). They may exhibit anomalous behavior when negative voltages are applied to their inputs or outputs.
In particular, the switching on and off phases (Figure 11) of mosfet and igbt are characterized by voltage and current transients which, in the presence of parasitic components (capacitances and inductances), can cause the presence of negative voltages at the driver terminals.
The analysis of anomalies and faults occurring in power converters has made it possible to identify the most delicate devices in the power and circuit control stage and the main causes of damages and malfunctions. They are schematically synthesized in Table 10.
It is appropriate, in any case, to highlight that it was not possible to find datasets relating to the anomalies and failures of either the interface converters or the switching, capacitive and inductive devices of the power stage, nor of the components of the control stage.

2.8. Anomalies and Faults in Monitoring Systems

A monitoring system can be seen as a set of sensors, wiring and user interface as shown in the following Figure 12.
A software layer is used to manage the data transmitted by the sensors and to give a suitable visualization to the user. The main component that is affected by failures is the sensor so the survey is focused on this.

2.8.1. Research Highlights

The surveyed literature is organized in Table 7. It is highlighted that the main anomalies and faults associated with monitoring system can be listed [159] as mechanical faults regarding to the mechanical structure of the apparatus (e.g. degradation of materials [160], vibrations, external shocks [161]), electrical faults that affect the electrical properties of the device (e.g. loss of insulation [162,163], anomalous measurement residual [164] due to blackout or overloading of the device) and other faults, that can affect the measurement due for example to noise [165], reading errors (value read by the device different from the actual one due to a change in gain) [166], calibration losses or performance degradation [167].
The study highlighted that the failures of monitoring equipment strongly depend on the operating conditions of the environment in which they are inserted. The most common are reading errors due to incorrect sensor calibration, performance degradation, or electrical faults; mechanical failures are less frequent.
For all type of faults, it was not possible to find simulated and/or experimental data or information regarding faults and anomalies relating to measurement systems for the specific sector of electricity networks (smart meters).
Table 11. Main contributions on anomalies/faults in monitoring systems.
Table 11. Main contributions on anomalies/faults in monitoring systems.
Preprints 106372 i011

2.9. Anomalies and Faults in Communication Systems

The main aim of a communication system is to send and receive data. This system consists of a sender, a receiver and a communication support in order to obtain the data transmission between them. The following Figure 13 shows the principle scheme of a communication system.
The data are packaged in a message by means of a suitable protocol. Therefore, the causes of failure are attributable to multiple subsystems as described in the following paragraph.

2.9.1. Research Highlights

The surveyed literature is organized in Table 12. The study has highlighted that the main anomalies and faults associated with communication systems can be classified [168] into failures of the communication support regarding to the support and transmission medium (e.g. fiber breakage, excessive bending, connectors or splice breakage [169,170]), receiver failure involving a malfunction of the receiver, such as a high data packet reception time [171,172] and data integrity that affect the integrity of the transferred data, degrading the accuracy and reliability of the transmission, and caused by alteration or loss of part of the transmitted data packet [173]. These last are, generally, recognized by the receiver using checksum [174]. The survey has highlighted that failures due to the support medium are more frequent than the other two types.
For all type of faults it was not possible to find simulated/experimental datasets but only methods for fault diagnostics (Table 12).

3. Conclusions

This paper presents a survey on anomalies and faults in renewable-based power systems and may impact their reliability, by addressing several heterogeneous technologies that are therein deployed. Tables related to the literature findings and possible datasets are also reported for reference. In both cases, the survey shows that some aspects are covered by very old articles/references and it is not possible to find up-to-date material. This is a gap that should be filled maybe orienting the study to the specific scope this paper addresses. Furthermore, the survey also highlights a lack of datasets for some technologies, namely electrolyzers, FCs, DC/x conversion systems, monitoring and communication systems. In the case of electrolyzers and FCs, suitable tables report mathematical models that can reproduce the target anomaly/fault phoenomenon and can output synthetic data for further analysis/investigation. And this is another gap that should be filled.
In general, with regards to similar paper, this paper includes many technologies and does not restrict itself to specific ones. For instance, beyond technologies specific to the power domain, also monitoring and communication systems in renewable-based power systems are surveyed. This can help other fellows in orienting their research effort via a self-consistent reference.

Author Contributions

Conceptualization, A. Buonanno, A. Ricca, R. Ciavarella, G. Adinolfi, V. Sorrentino, M. Valenti; methodology, A. Buonanno, A. Ricca, R. Ciavarella, G. Adinolfi, V. Sorrentino, M. Valenti; investigation, V. Mariani, A. Buonanno, A. Ricca, R. Ciavarella, G. Adinolfi, V. Sorrentino, M. Valenti; data curation, A. Buonanno, A. Ricca, R. Ciavarella, G. Adinolfi, V. Sorrentino; writing—original draft preparation, V. Mariani, A. Buonanno, A. Ricca, R. Ciavarella, G. Adinolfi, V. Sorrentino; writing—review and editing, V. Mariani, A. Buonanno, A. Ricca, R. Ciavarella, G. Adinolfi, V. Sorrentino; visualization, V. Mariani, A. Buonanno, A. Ricca, R. Ciavarella, G. Adinolfi, V. Sorrentino; supervision, M. Valenti; project administration, M. Valenti, G. Graditi; funding acquisition, M. Valenti, G. Graditi. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Research Fund for the Italian Electrical System through the project “Accordo di Programma 2022–2024—Project 2.3” between ENEA and the Ministry of the Environment and Energetic Safety (MASE).

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
Preprints 106372 i013aPreprints 106372 i013b

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Figure 1. Sketch of a PV system (adapted from [15]).
Figure 1. Sketch of a PV system (adapted from [15]).
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Figure 2. Sketch of a WT (adapted from [61]).
Figure 2. Sketch of a WT (adapted from [61]).
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Figure 3. Sketch of a PEM electrolyzer.
Figure 3. Sketch of a PEM electrolyzer.
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Figure 4. Sketch of a PEM-FC.
Figure 4. Sketch of a PEM-FC.
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Figure 5. Sketch of a Li-ion battery cell (adapted from [122]).
Figure 5. Sketch of a Li-ion battery cell (adapted from [122]).
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Figure 6. Sketch of a DC/x conversion system.
Figure 6. Sketch of a DC/x conversion system.
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Figure 7. (a) Drain-Source resistance vs Junction temperature; (b) Drain-Source for different Junction temperature values [149].
Figure 7. (a) Drain-Source resistance vs Junction temperature; (b) Drain-Source for different Junction temperature values [149].
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Figure 8. Typical threshold voltage vs ambient temperature in mosfet [150].
Figure 8. Typical threshold voltage vs ambient temperature in mosfet [150].
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Figure 9. Typical capacitance (normalized against values at 20°C, 100Hz) vs. temperature characteristics in electrolytic capacitors [151].
Figure 9. Typical capacitance (normalized against values at 20°C, 100Hz) vs. temperature characteristics in electrolytic capacitors [151].
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Figure 10. Typical ripple current (normalized against the maximum value) and operative life vs. temperature characteristics in electrolytic capacitors [152].
Figure 10. Typical ripple current (normalized against the maximum value) and operative life vs. temperature characteristics in electrolytic capacitors [152].
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Figure 11. Typical mosfet/igbt turn on (left) and turn off (right) characteristics [153].
Figure 11. Typical mosfet/igbt turn on (left) and turn off (right) characteristics [153].
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Figure 12. Sketch of a monitoring system.
Figure 12. Sketch of a monitoring system.
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Figure 13. Sketch of a communication system.
Figure 13. Sketch of a communication system.
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Table 1. Main contributions on anomalies/faults in PV systems (adapted from [15]).
Table 1. Main contributions on anomalies/faults in PV systems (adapted from [15]).
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Table 2. Anomalies/faults datasets for PV systems.
Table 2. Anomalies/faults datasets for PV systems.
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Table 3. Anomalies/faults datasets for WTs.
Table 3. Anomalies/faults datasets for WTs.
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Table 4. Main contributions on anomalies/faults in electrolysers.
Table 4. Main contributions on anomalies/faults in electrolysers.
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Table 5. Anomalies/faults models for electrolysers.
Table 5. Anomalies/faults models for electrolysers.
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Table 6. Main contributions on anomalies/faults in FCs.
Table 6. Main contributions on anomalies/faults in FCs.
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Table 7. Anomalies/faults models for FCs.
Table 7. Anomalies/faults models for FCs.
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Table 8. Anomalies/faults for BSs.
Table 8. Anomalies/faults for BSs.
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Table 9. Anomalies/faults datasets for BSs (adapted from [137]).
Table 9. Anomalies/faults datasets for BSs (adapted from [137]).
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Table 10. Main contributions on anomalies/faults in DC/x conversion systems.
Table 10. Main contributions on anomalies/faults in DC/x conversion systems.
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Table 12. Main contributions on anomalies/faults in communication systems.
Table 12. Main contributions on anomalies/faults in communication systems.
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