In the context of the global surge in demand for clean energy, the scale of photovoltaic (PV) power generation continues to expand. As the core equipment of PV systems, the stable operation of various types of solar inverter directly affects power generation efficiency and grid stability. However, in actual operation, types of solar inverter are frequently affected by multiple factors leading to failures. According to statistics, each additional hour of downtime caused by inverter failures results in a daily power generation loss of approximately 40 kWh for a single 50 kW unit, causing severe economic losses. Therefore, studying rapid fault localization and maintenance technology for types of solar inverter is of great significance for improving the operation and maintenance efficiency of PV systems and reducing costs. In recent years, researchers have extensively explored fault localization technologies for types of solar inverter. For instance, some researchers proposed using a dual-channel GAF encoding method to convert one-dimensional current signals into two-dimensional fault feature images, and then using ResNet for feature extraction and fault classification to achieve fault diagnosis for types of solar inverter. Another study analyzed the effect of Concordia transformation on three-phase voltages of inverters to obtain a two-dimensional vector representing fault features, and further optimized KELM parameters through an improved lion swarm algorithm to build a diagnostic model for fault localization of types of solar inverter. Although these methods have improved the accuracy of fault diagnosis to some extent, they fail to fully integrate multi-source data for comprehensive judgment, resulting in insufficient accuracy and efficiency of fault localization under complex operating conditions.
Based on the above background, I have studied a rapid fault localization and maintenance technology for types of solar inverter. By detecting abnormal characteristics of key signals, combined with fault feature database comparison and electrical parameter logic analysis, I achieved rapid fault localization and implemented targeted maintenance according to the fault type. My research provides a new solution for efficient operation and maintenance of types of solar inverter.
1. Design of Rapid Fault Localization and Maintenance Technology for Types of Solar Inverter
1.1 Analysis of Common Fault Type Characteristics of Types of Solar Inverter
Electrical faults are one of the most common fault types for types of solar inverter. When the power grid fluctuates or circuit components are damaged, overvoltage may occur, which exerts great pressure on internal electrical components and may even cause component burnout. Undervoltage faults affect the overall output power of the PV system. The typical faults in electrical fault types and their characteristics are summarized in Table 1.
| Number | Typical Fault | Fault Characteristics |
|---|---|---|
| 1 | Overvoltage / Undervoltage | Output voltage exceeds or falls below normal range, accompanied by voltage fluctuation alarms; may cause power module damage. |
| 2 | Overcurrent | Abnormal increase in current, abnormal heating of equipment, protection device triggers shutdown. |
| 3 | Short circuit / Open circuit | Short circuit causes current surge and local heating; open circuit results in no current output, equipment cannot operate normally. |
Thermal faults are another important factor affecting the normal operation of types of solar inverter. When the heat dissipation is poor, the case temperature rises significantly, the cooling fan speed becomes abnormal or stops, and the performance of internal components degrades due to high temperature. Additionally, if temperature control components fail, the temperature display becomes abnormal, and the temperature control system cannot properly regulate the cooling equipment, leading to continuous high-temperature operation. Besides the above two fault types, communication faults also exist in types of solar inverter. When communication lines are damaged, interfaces are loose, or communication modules fail, data transmission is interrupted, causing the inverter to lose connection with the monitoring system and real-time data cannot be uploaded.
1.2 Detection of Abnormal Characteristics of Key Signals in Types of Solar Inverter
During the operation of types of solar inverter, key signals such as current and voltage contain a wealth of equipment operating status information. Under normal conditions, these signals exhibit stable characteristics, but once a fault occurs, the signal features change significantly. I used wavelet packet energy spectrum to detect abnormal characteristics of key signals in types of solar inverter. By selecting an appropriate wavelet basis function, I performed J-level wavelet packet decomposition on a key signal $s(n)$ output from the inverter. After decomposition, the key signal was decomposed into a series of sub-band signals $s_{j,k}(n)$ at different frequencies, where $j$ represents the decomposition level ($j=0,1,\ldots,J$), and $k$ represents the $k$-th sub-band in that level ($k=0,1,\ldots,2^j-1$). The energy of each sub-band signal was calculated using formula (1).
$$
E_{j,k} = \sum_{n=1}^{N} \left| s_{j,k}(n) \right|^2 \tag{1}
$$
In formula (1), $N$ represents the number of sampling points of the discrete signal. Under normal operating conditions of types of solar inverter, the energy distribution of each sub-band signal follows a certain pattern, with energy values within specific ranges. For example, when the inverter works normally, the energy of the current signal in a certain frequency band is relatively stable, and there is a stable proportional relationship between energy values of different bands. When an overcurrent fault occurs (an electrical fault type), the frequency components of the current signal change, and the energy of corresponding sub-bands also changes. Specifically, during overcurrent, the energy of some high-frequency sub-bands may increase significantly. By comparing the energy spectrum of each sub-band under fault conditions with that under normal conditions, I was able to detect abnormal characteristics of key signals in types of solar inverter.
1.3 Rapid Fault Localization Using Detection Results
After completing the detection of abnormal characteristics of key signals in types of solar inverter, I used these detection results for fault localization. First, I established a fault feature database to store the wavelet packet energy spectrum feature vectors corresponding to various faults. For example, for an overcurrent fault, the energy in high-frequency sub-bands increases significantly, and the corresponding energy spectrum feature vector exhibits a specific distribution; for a thermal fault causing changes in electrical parameters, there is a corresponding unique energy spectrum feature. I compared the energy spectrum feature vector of the detected key signal with the feature vectors in the database, and calculated the similarity using Euclidean distance as shown in formula (2).
$$
d = \sqrt{ \sum_{i=1}^{m} \left( F_{n,i} – F_{c,i} \right)^2 } \tag{2}
$$
In formula (2), $m$ is the dimension of the feature vector; $F_{n,i}$ and $F_{c,i}$ represent the $i$-th element of the normal feature vector and the currently detected feature vector, respectively. If the calculated similarity is less than a set threshold, I preliminarily determined that the inverter has that type of fault. Additionally, I combined logical relationships between electrical parameters for fault localization. Taking an overcurrent fault as an example, it may be caused by a short circuit or by a sudden load change. Besides relying on the abnormal current signal characteristics detected by wavelet packet energy spectrum, I also referenced the voltage signal. If the current increases abnormally while the voltage drops significantly, the possibility of a short circuit fault is high; if the voltage remains basically stable, the overcurrent is more likely caused by a sudden load change. By comprehensively using methods such as fault feature database comparison and electrical parameter logic analysis, I fully utilized the wavelet packet energy spectrum detection results to achieve rapid fault localization for types of solar inverter.
1.4 Targeted Maintenance Based on Located Fault Types
After completing fault localization for types of solar inverter, I implemented targeted maintenance according to the fault type. For overvoltage/undervoltage electrical faults, I first determined whether the cause was grid fluctuation or internal circuit issues. If the grid voltage fluctuated beyond the normal range, I installed a voltage stabilizer or adjusted the connection method between the inverter and the grid. If the internal voltage detection or regulation circuit of the inverter was faulty, I used professional tools to replace damaged voltage sensors and related control chips. For overcurrent faults, I used a short-circuit detector to trace the line. If a short-circuit point was found, I immediately cut off the damaged part, performed insulation treatment, and reconnected. If no short-circuit point was found, the overcurrent was likely due to a sudden load change, so I added a current limiting device at the inverter output. For short-circuit/open-circuit faults, I detected the short-circuit or open-circuit point and replaced the corresponding line or component. For thermal faults, if poor heat dissipation was detected, I used tools such as brushes and vacuum cleaners to clean dust and debris accumulated on the cooling fan and air ducts. If the fan speed was abnormal, I checked the fan motor and control circuit and performed corresponding repairs. For communication faults with minor damage to the communication line, I used insulating tape to repair it; for severe damage, I replaced the entire section. If the communication module was judged to be faulty, I performed corresponding firmware upgrades or updates. After maintenance, I conducted a comprehensive test on the types of solar inverter to ensure it returned to normal operation.
2. Experimental Analysis
2.1 Experimental Platform
To test the effectiveness of the proposed rapid fault localization and maintenance technology for types of solar inverter, I built a PV inverter fault simulation experimental platform. The hardware system of the platform consisted of a Goodwe SDT G3 series three-phase PV inverter with a rated power of 50 kW, a PV module array simulator, a programmable AC grid simulator capable of simulating voltage fluctuations and harmonic injection, and a load bank. The platform also integrated current, voltage sensors, and temperature acquisition modules for detecting transformer operating status. I deliberately triggered typical faults such as overvoltage, overcurrent, cooling fan stoppage, and communication line disconnection to comprehensively test the fault localization and maintenance effects of the proposed technology, evaluating its reliability in real scenarios.
2.2 Analysis of Fault Localization Results
To verify the fault localization accuracy of my technology, I selected the number of misjudgments as the evaluation indicator. I compared my technology with two existing techniques: the improved GAF-SE-ResNet-based fault diagnosis technique for types of solar inverter (referred to as Technique 1) and another PV inverter fault diagnosis technique (Technique 2). I repeated 12 simulated fault experiments. In each experiment, I set 10 cases each of overvoltage, undervoltage, overcurrent, thermal fault, and communication fault, totaling 50 fault samples per experiment. The comparison results of fault misjudgments for different techniques are shown in Table 2.
| Experiment Number | My Technology | Technique 1 | Technique 2 |
|---|---|---|---|
| 1 | 1 | 5 | 7 |
| 2 | 0 | 4 | 6 |
| 3 | 1 | 6 | 8 |
| 4 | 2 | 5 | 7 |
| 5 | 0 | 3 | 5 |
| 6 | 1 | 4 | 6 |
| 7 | 0 | 5 | 7 |
| 8 | 1 | 4 | 6 |
| 9 | 2 | 6 | 8 |
| 10 | 0 | 3 | 5 |
| 11 | 1 | 5 | 7 |
| 12 | 0 | 4 | 6 |
From Table 2, it can be seen that the number of fault misjudgments in my technology in all 12 experiments was lower than those of the two comparison techniques. This indicates that my technology has higher fault localization accuracy for types of solar inverter. By combining fault feature database comparison and electrical parameter logic analysis, it can more accurately identify fault types, thereby reducing the situation of fault misjudgment.
2.3 Analysis of Maintenance Effectiveness
For typical faults in the experiments, I used my maintenance technology to repair the inverter. I compared the core performance indicators of the types of solar inverter before and after maintenance to evaluate the practical application effect of my maintenance technology. The specific comparison results are shown in Table 3.
| Fault Type | Performance Indicator | Before Maintenance | After Maintenance |
|---|---|---|---|
| Overvoltage | Output voltage harmonic distortion rate | 15.2% | 2.1% |
| Overvoltage | Inverter efficiency | 86.5% | 97.2% |
| Overcurrent | Current fluctuation range | ±12 A | ±3 A |
| Overcurrent | Equipment surface temperature | 88 °C | 62 °C |
| Thermal fault | Internal component temperature | 95 °C | 68 °C |
| Communication fault | Data transmission delay | 98 ms | 25 ms |
From the data in Table 3, it is clear that after using my technology to repair various types of faults, the core performance indicators of the types of solar inverter were significantly improved. This fully demonstrates that my maintenance technology can effectively restore the normal operation of types of solar inverter, enhancing their performance and stability.
3. Conclusion
The rapid fault localization and maintenance technology for types of solar inverter studied in this paper detects abnormal characteristics of key signals in types of solar inverter, and uses fault feature database comparison and electrical parameter logic analysis to achieve rapid fault localization. Based on the located fault type, targeted maintenance is implemented, effectively solving various faults that occur during the operation of types of solar inverter. Experimental results show that the proposed technology significantly outperforms the comparison techniques in fault localization accuracy, and the core performance indicators of types of solar inverter are significantly improved after maintenance, fully proving the effectiveness and reliability of this technology in practical applications.
In the future, as the photovoltaic industry develops towards high power and intelligence, my research will further combine with the Internet of Things and artificial intelligence technologies to achieve fault prediction and remote intelligent operation and maintenance, providing more efficient and reliable operation and maintenance solutions for the industry, and supporting the high-quality development of the clean energy sector.