Search Results (18)

Recurrent catastrophic inverter failures significantly undermine the reliability and economic viability of utility-scale photovoltaic (PV) power plants. This paper presents a comprehensive investigation of severe inverter destruction incidents at the Kopli Solar Power Plant, Estonia, by integrating controlled laboratory simulations with extensive field monitoring. Initially, detailed laboratory experiments were conducted to replicate critical DC-side short-circuit scenarios, particularly focusing on negative DC input terminal faults. The results consistently showed these faults rapidly escalating into multi-phase short-circuits and sustained ground-fault arcs due to inadequate internal protection mechanisms, semiconductor breakdown, and delayed relay response. Subsequently, extensive field-based waveform analyses of multiple inverter failure events captured identical fault signatures, thereby conclusively validating laboratory-identified failure mechanisms. Critical vulnerabilities were explicitly identified, including insufficient isolation relay responsiveness, inadequate semiconductor transient ratings, and ineffective internal insulation leading to prolonged arc conditions. Based on the validated findings, the paper proposes targeted inverter design enhancements—particularly advanced DC-side protective schemes, rapid fault-isolation mechanisms, and improved internal insulation practices. Additionally, robust operational and monitoring guidelines are recommended for industry-wide adoption to proactively mitigate future inverter failures. The presented integrated methodological framework and actionable recommendations significantly contribute toward enhancing inverter reliability standards and operational stability within grid-connected photovoltaic installations. Full article

This study investigates the thermal behavior and ignition dynamics of DC series arcs in a lab-scale photovoltaic (PV) system. The impacts of current magnitude, dynamic current variations, and electrode gap on electrode surface temperatures are analyzed, while ignition characteristics of common electrical materials (PC, PVC, XLPO, PPE, etc.) are investigated by analyzing critical time thresholds during the arc-induced combustion. Results show that electrode surface temperatures rise with increased current or larger electrode gaps, driven by the enhanced DC arc energy release. Dynamic current variations (increasing/decreasing) shift the balance between heat accumulation and dissipation, resulting in the nonlinear temperature evolution. Additionally, the peak temperature of the anode is about 50% higher than that of the cathode due to the electron flow-driven heat transfer and particle collisions. Notably, general electrical materials can be ignited successfully by stable DC arcs. The anode can ignite flame-retardant materials within 3 s, while the cathode takes a relatively long time to ignite, approximately 20 to 30 s. Besides, enlarged electrode gaps can induce a mutual reinforcement between arcs and flames, resulting in further stabilized arcs and intensified flames. This highlights potential elevated fire hazards as the connector gap increases due to the DC arc erosion. Full article

With the rapid growth of photovoltaic power generation systems, fire incidents within the system have progressively increased. The lack of thorough studies on the temperature properties of direct current (DC) arc faults has resulted in an unclear ignition mechanism, significantly increasing the fire risk associated with such faults. Hence, this work presents a proposed experimental scheme for detecting photovoltaic DC series arc faults (SAFs) and the corresponding detection standards. Additionally, the temperature characteristics of the DC arc fault are further analyzed. The magnetohydrodynamic (MHD) arc fault simulation model is developed to investigate the temperature-related aspects of photovoltaic DC arc faults. Finally, our experimental validation confirms the precision of the model in simulating arc temperature. It is verified that the research presented in this paper can provide a good explanation for the rise time of DC arc temperature and the characteristic distribution of arc distance. This study elucidates the impact mechanism of line current, power supply voltage, and arc gap size on arc temperature in a photovoltaic system. Additionally, it proposes an evaluation method for assessing the arc fault ignition risk level. This method is essential for safeguarding against arc fault ignition risk in photovoltaic DC series cells. Full article

The direct current (DC) cable serves as the link for energy output in photovoltaic (PV) systems. Its degradation can cause arcs, which easily lead to fire accidents. Locating arc faults, however, is challenging. To cope with it, this paper proposes an arc location method based on time reversal. The method has been tried to locate system fault. However, its application in the arc fault location of photovoltaic systems is seldom discussed and needs further research. For this purpose, the voltage waveforms of an arc fault collected at one of the cable ends is reversed. This transformation derives a symmetrical arc fault signal. Afterwards, the reversed signal is injected back into the cable to trace the fault location, which is a symmetrical process of the arc fault signal travelling from its origin to the detection point. Utilizing the energy-focusing characteristics of time reversal, the position with the highest energy in the derived waveform corresponds to the actual fault location. To verify the proposed method, a DC arc fault test is performed to obtain the wave characteristics. The Paukert arc model is chosen based on the tested result. A PV system containing a DC cable with an arc fault is simulated with Simulink with the affecting factors, i.e., grounded resistance, cable length, fault location and sampling frequency. The simulated results demonstrate that the localization error is within 5% in the worst case. Full article

This work presents a sensorless brushless DC motor (BLDCM) drive control, optimized for solar photovoltaic (PV)- and battery-fed light electric vehicles (LEVs). A back-electromotive force (EMF) observer integrated with an enhanced quadrature-phase-locked-loop (QPLL) structure is proposed for accurate rotor position estimation, addressing limitations of existing control methods at low speeds and under dynamic conditions. The study replaces the conventional arc-tangent technique with a QPLL-based approach, eliminating low-pass filters to enhance system adaptability and reduce delays. The experimental results demonstrate a significant reduction in commutation error, with a nearly flat value at 0 degrees during steady-state and less than 8 degrees under dynamic conditions. Furthermore, the performance of a modified single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) in solar-powered LEVs is verified, minimizing current ripple and ensuring smooth motor operation. The system also incorporates a regenerative braking mechanism, extending the vehicle’s range by efficiently recovering kinetic energy through the battery with 30.60% efficiency. The improved performance of the proposed method and system over conventional approaches contributes to the advancement of efficient and sustainable solar-powered BLDC motor-based EV technologies. Full article

The ecofriendly tin selenide (SnSe) is expected to find multiple applications in optoelectronic, photovoltaic, and thermoelectric systems. This work is focused on the thermoelectric properties of thin films. SnSe single crystals exhibit excellent thermoelectric properties, but it is not so in the case of polycrystalline bulk materials. The investigations were motivated by the fact that nanostructuring may lead to an improvement in thermoelectric efficiency, which is evaluated through a dimensionless figure of merit, ZT = S² σ T/λ, where S is the Seebeck coefficient (V/K), σ is the electrical conductivity (S/m), λ is the thermal conductivity (W/mK), and T is the absolute temperature (K). The main objective of this work was to obtain SnSe films via magnetron sputtering of a single target. Instead of common radiofrequency (RF) magnetron sputtering with a high voltage alternating current (AC) power source, a modified direct current (DC) power supply was employed. This technique in the classical version is not suitable for sputtering targets with relatively low thermal and electrical conductivity, such as SnSe. The proposed solution enabled stable sputtering of this target without detrimental cracking and arcing and resulted in high-quality polycrystalline SnSe films with unprecedented high values of ZT equal to 0.5 at a relatively low temperature of 530 K. All parameters included in ZT were measured in one setup, i.e., Linseis Thin Film Analyzer (TFA). The SnSe films were deposited at sputtering powers of 120, 140, and 170 W. They had the same orthorhombic structure, as determined by X-ray diffraction (XRD), but the thickness and microstructure examined by scanning electron microscopy (SEM) were dependent on the sputtering power. It was demonstrated that thermoelectric efficiency improved with increasing sputtering power and stable values were attained after two heating–cooling cycles. This research additionally provides further insights into the DC sputtering process and opens up new possibilities for magnetron sputtering technology. Full article

This paper presents a review of research progress on photovoltaic direct current arc detection based on VOSviewer bibliometric analysis. This study begins by introducing the basic concept and hazards of photovoltaic DC arcing faults, followed by a summary of commonly used arc detection techniques. Utilizing VOSviewer, the relevant literature is subjected to clustering and visualization analysis, offering insights into research hotspots, trends, and interconnections among different fields. Based on the bibliometric analysis method of VOSviewer software, this paper analyzes the articles published in the last 10 years (2014–2023) on photovoltaic DC fault diagnosis. We analyzed the specific characteristics of 2195 articles on arc failures, including year of publication, author, institution, country, references, and keywords. This study reveals the development trend, global cooperation model, basic knowledge, research hotspots, and emerging frontier of PV DC arc. Future research directions and development trends for photovoltaic DC arc detection are proposed which provides valuable references for further studies and applications in this domain. This comprehensive analysis indicates that photovoltaic DC arc detection technology is expected to find broader applications and greater promotion in the future. Full article

With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model. Full article

The DC arc is the main cause of fire in photovoltaic (PV) systems. This is due to the fact that the DC arc has no zero-crossing point and is prone to stable combustion. Failure to detect it in a timely manner can seriously endanger the PV system. This study analyzes the influences of the series arc and the maximum power point tracking (MPPT) algorithm on the PV output characteristics based on the PV equivalent circuit module. The PV voltage and current variation characteristics are obtained when the series arc occurs. The findings indicate that the input voltage of the converter remains unchanged due to the MPPT algorithm before and after the series arc occurs. Furthermore, the PV faulty string output current will drastically decrease when the series arc fault occurs. On this basis, a series arc detection method based on the current change is proposed, suppressing the combustion of the series arc by increasing the target voltage of the MPPT algorithm. The experimental results show that the proposed method can effectively detect and extinguish the series arc in the PV system within 0.6 s. Compared to the other methods, the proposed method can be integrated into the PV system without additional hardware. Full article

DC arc faults are dangerous to photovoltaic (PV) systems and can cause serious electric fire hazards and property damage. Because the PV inverter works in a high−frequency pulse width modulation (PWM) control mode, the arc fault detection is prone to nuisance tripping due to PV inverter noises. An arc fault detection method based on the autoregressive (AR) model is proposed. A test platform collects the database of this research according to the UL1699B standard, in which three different types of PV inverters are taken into consideration to make it more generalized. The arc current can be considered a nonstationary random signal while the noise of the PV inverter is not. According to the difference in randomness features between an arc and the noise, a detection method based on the AR model is proposed. The Burg algorithm is used to determine model coefficients, while the Akaike Information Criterion (AIC) is applied to explore the best order of the proposed model. The correlation coefficient difference of the model coefficients plays a role as a criterion to identify if there is an arc fault. Moreover, a prototype circuit based on the TMS320F28033 MCU is built for algorithm verification. Test results show that the proposed algorithm can identify an arc fault without a false positive under different PV inverter conditions. The fault clearing time is between 60 ms to 80 ms, which can meet the requirement of 200 ms specified by the standard. Full article

DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions (PFs) representing information from different frequency bands, then selects the PFs that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%. Full article

Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.16% for arc fault detection. Furthermore, an attention mechanism is combined with the Efficientnet-B1 to make the algorithm more focused on arc features, which can help the algorithm reduce unnecessary computation. The test results show that the detection accuracy of the proposed method can be up to 98.81% under all test conditions, which is higher than that of general networks. Full article

Wind and solar energy are examples of clean energy that are widely developed and utilized in order to achieve the goal of carbon neutrality. Higher requirements for the safety and reliability of the power grid are put forward after they are connected to it. In the case of disconnectors, as the power system’s protection equipment, their arc interruption characteristics are closely tied to the safety and reliability of the power system. In addition, a disconnector is required to be able to break the DC arc in the photovoltaic power generation system. Therefore, this paper focuses on the arc evolution characteristics in disconnectors. A magnetohydrodynamics (MHD) model of disconnectors was built. In this model, not only are the coupling of the electromagnetic field and the airflow field considered, but also the characteristics of the external circuit. Therefore, not only can arc evolution characteristics be obtained through this simulation model, but the breaking performance will also be directly obtained. The temperature, pressure and velocity distribution are obtained to analyze the evolution process. The curve of current versus time is calculated to analyze the breaking performance. The evolution characteristics of AC and DC arcs in the disconnector are analyzed by calculation and comparison. This provides theoretical guidance for the optimal design of DC disconnectors through simulation analysis. Full article

The constant increase in electrical energy consumption has led to a growth of photovoltaic installations (PV) along with the corresponding power converters for proper operation. Power electronics converters represent a challenge to maintain the system’s performance and safety; one such problem is series DC Arc Fault (AF). DC AFs lead to fire risk, damaging the main bus and the loads when not detected and interrupted in time. Therefore, research about DC AFs in power electronics converters must be carried out to predict the behavior and help avoid damage to the system. In this work, an innovative hybrid multilevel inverter for PV applications is used to explore the effect of series DC AFs in the converters’ internal signals, with the aims of setting the bases for the development of a detection system for power electronics. Both stages of conversion (DC/DC and DC/AC) are presented. In addition, the placement of the MPPT converter was considered for the tests. The AF experimental tests were performed with a generator based on the UL1699B specifications. The measurements of signals were performed in strategic points of the DC side, and changes and how to exploit them are discussed. This study contributes to a better understanding of the DC AF phenomenon and provides new insights for the development of new PV system protections. Full article

In this study, the frequency characteristics of series DC arcs are analyzed according to the types of frequency fluctuations caused by inverters in photovoltaic (PV) systems. These frequency fluctuation types used in analysis include centralized frequency fluctuations by three-phase inverter, spread frequency fluctuations by three-phase inverter, and centralized frequency fluctuations by single-phase inverter. To collect arc current data, the frequency fluctuations are generated by inverters in the arc-generating circuit, designed by referring to UL1699B, and the arcs are generated by separating the arc rods of the arc generator. The frequency analysis of the arc current data, collected using an oscilloscope, is conducted using MATLAB. From the results of the frequency characteristics analysis, it is confirmed that the frequencies in the range from 5 to 40 kHz increase after arc generation regardless of the type of frequency fluctuation. In addition, the smaller the current, the greater the increase in frequencies between 5 and 40 kHz after arc generation. Further, in case of arc currents with centralized frequency fluctuations, for larger switching frequencies, the 5 to 40 kHz components increase to a greater extent after arcing. Full article