• Energy Research

Modern interconnected power grid requires comprehensive and efficient on-line monitoring system to ensure the reliability and stability of the whole power system. This paper firstly introduces the structure and deficiency of traditional monitoring system. Then, relying on high precision operating parameters collected by Phase Measurement Unit (PMU), an improved on-line monitoring method is proposed, which overcomes the obstacle of existing Wide Area Monitoring System (WAMS) in global analysis of low-frequency oscillation when processing multiple PMU data. This novel method improves the traditional info-exchange and accident-reaction structure between regional and central monitoring station, which assists in the synchronization of oscillation information and coordinated recognition of an accident. Besides, the real-time global identification of inter-area oscillation and coherency grouping are as well ameliorated on considering the geographical location and distance of PMU monitoring stations. This proposed method was applied in the analysis of the Turkish blackout happened on March 2015 which resulted in serious successive degradation of frequency in European power grid. Signals measured in representative monitoring stations with PMU equipment locating Continental Europe (CE) were used to establish detailed and global analysis of the accident, including the warning of oscillation, the propagation trend and the identification of the inter-area oscillations. Based on this practical application in on-line monitoring case, the effectiveness and sensitivity of the proposed method are discussed at the end of the paper.

Abstract The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented.

To enable health monitoring and fault diagnosis of PV modules using current-voltage characteristics (I–V curves), it is generally necessary to correct the I–V curves measured under different environmental conditions to the standard condition. The most common correction methods are those from IEC 60891: 2021 standard. However, these methods can introduce significant errors when dealing with degraded PV modules due to the inability to account for changes in resistance. To address this, we propose an improved I–V curve procedure, denoted Pdynamic, which considers different types of degradation by dynamically deriving the correction coefficients from the measured I–V curves. To evaluate the performance, we simulate I–V curves across a wide range of irradiance and temperature for the healthy and degraded module, where the degradation involves increased series resistance, decreased shunt resistance, or both. The results reveal that Pdynamic can produce corrected I–V curves closer to the reference ones than Procedures 1, 2, and 4 of the IEC 60891:2021 standard. Moreover, Pdynamic exhibits resilience to both seasonal fluctuations and varying levels of degradation. These results highlight Pdynamic as a promising and robust I–V curve correction method, particularly for degraded PV modules. A Python-based open-source tool for this procedure is also available at https://github.com/DuraMAT/IVcorrection.

Abstract Correction of the I-V curve permits the comparison of curves measured under different conditions for photovoltaic (PV) panels' health monitoring purpose. IEC 60891 has defined three standard procedures named 1, 2 and 3 for the correction. They were initially designed to correct the curves of healthy PV panels. However, their performance, when applied to I-V curves measured on faulty panels, is rarely discussed. This work evaluates these correction methods on I-V curves simulated under different environmental conditions and for five types of defects of varying severity. The results show that procedure 3 has a relatively better overall performance but is not suitable for rapid application in the field as it requires the determination of reference curves. It is found that procedures 1 and 2 could introduce distortion of the curve's shape, with a relative error of up to 13.8% and 6.4%, respectively. A misestimation of 9.1% for key parameters of the curve has been observed, when using procedure 2 for maximum power. Based on the performance analysis, a new correction method is proposed to fit the corrected voltage. It can reduce the curve's average correction error by 31.3% compared to the original single curve correction method. Challenges and directions for future work are also presented.

Abstract The current–voltage characteristics (I–V curves) of photovoltaic (PV) modules contain a lot of information about their health. In the literature, only partial information from the I–V curves is used for diagnosis. In this study, a methodology is developed to make full use of I–V curves for PV fault diagnosis. In the pre-processing step, the I–V curve is first corrected and resampled. Then fault features are extracted based on the direct use of the resampled vector of current or the transformation by Gramian angular difference field or recurrence plot. Six machine learning techniques, i.e., artificial neural network, support vector machine, decision tree, random forest, k-nearest neighbors, and naive Bayesian classifier are evaluated for the classification of the eight conditions (healthy and seven faulty conditions) of PV array. Special effort is paid to find out the best performance (accuracy and processing time) when using different input features combined with each of the classifier. Besides, the robustness to environmental noise and measurement errors is also addressed. It is found out that the best classifier achieves 100% classification accuracy with both simulation and field data. The dimension reduction of features, the robustness of classifiers to disturbance, and the impact of transformation are also analyzed.

AbstractCorrection of PV modules' current–voltage characteristics (I–V curves) is essential before they can be used for performance analysis and fault diagnosis under real‐life conditions. IEC 60891 (version 2021) has updated Procedure 2 and proposed a new correction Procedure 4 compared to the 2009 version. This study aims to analyze the performance of these new procedures applied to I–V curves of faulty PV modules. The work is based on an mc‐Si PV module considering healthy and four common fault conditions with varying fault severity. The irradiance and temperature measured in the field are used to generate I–V curves. The correction procedures of IEC 60891 (version 2021) based on a single curve (Procedures 1, 2, and 4) are evaluated. Environmental factors such as measurement season and irradiation level on the correction performance are studied. The results show that Procedure 2 is relatively better with the relative root‐mean‐square error of the curve current as 2.6% compared to Procedure 1 (2.8%) and Procedure 4 (4.8%). Experimental tests using real I–V curves also show that Procedure 2 exhibits better robustness of correction. The new Procedure 4, whose correction coefficients are determined dynamically, performs poorly under partial shading and short‐circuit bypass conditions. However, it achieves similar or better performance than Procedures 1 and 2 under degraded conditions, where the PV fault is generally not easy to detect, making the I–V correction more necessary. It is, therefore, a promising alternative correction procedure when it is difficult to determine the correction coefficients in advance. Finally, the pros and cons of the procedures are discussed with the suggestion of correction procedures under different conditions. The challenges and prospects are also provided.

Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current–voltage characteristic (I-V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I-V curves of the PV system level are often not available. This paper proposes a methodology (PVPRO) to estimate these I-V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.