Submodular Load Clustering with Robust Principal Component Analysis

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.Comment: Accepted by 2019 IEEE PES General Meeting, Atlanta, G

Photovoltaic fault diagnosis algorithm using fuzzy logic controller based on calculating distortion ratio of values

Introduction. The efficiency of solar energy systems in producing electricity in a clean way. Reliance on it in industrial and domestic systems has led to the emergence of malfunctions in its facilities. During the operating period, these systems deteriorate, and this requires the development of a diagnostic system aimed at maintaining energy production at a maximum rate by detecting faults as soon as possible and addressing them. Goal. This work proposes the development of an algorithm to detect faults in the photovoltaic system, which based on fuzzy logic. Novelty. Calculate the distortion ratio of the voltage and current values resulting from each element in the photovoltaic system and processing it by the fuzzy logic controller, which leads to determining the nature of the fault. Results. As show in results using fuzzy logic control by calculating the distortion ratio of the voltage and current detect 12 faults in photovoltaic array, converter DC-DC and battery

Evaluating Power Loss and Performance Ratio of Hot-Spotted Photovoltaic Modules

The impact of photovoltaic (PV) hot-spots is assessed through the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. PV hot-spots were categorized into eight different groups using the percentage of power loss. All hot-spots groups were modeled using the cumulative density function, state-of-the-art geographical mapping, and performance ratio (PR) analysis. Significantly, it was found that 92.15% of the PV modules affected by hot-spotted PV string are located in northern U.K., where the effect of low-temperature levels, heavy snow, and hoarfrost are more significant. Finally, it was found that the distribution of PV modules affected by only one hot-spotted solar cell are likely (82.41%) located in coastal locations. Hence, coastal locations expect to have lower risks for causing multiple hot-spotted solar cells in PV modules, compared to central and colder locations. The PR of all examined PV modules was analyzed. It was evident that the mean PR is significantly reduced due to the existence of hot-spots in the PV modules. The least difference in the PR between healthy and hot-spotted PV modules is equal to -0.83%, whereas the most difference is calculated at -15.47%