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1. Improved Lifetime of GaN-Based Single Phase PV Inverter Using Dynamic Hardware Allocation

   https://doi.org/10.1109/ECCE47101.2021.9595850  

   Sabi, Kamal; Costinett, Daniel (October 2021, 2021 IEEE Energy Conversion Congress and Exposition (ECCE))

   Power electronic inverters for photovoltaic (PV) systems over the years have trended towards high efficiency and power density. However, reliability improvements of inverters have received less attention. Inverters are one of the lifetime-limiting elements in most PV systems. Their failures increase system operation and maintenance costs, contributing to an increased lifetime energy cost of the PV system. Opportunities exist to increase inverter reliability through design for reliability techniques and the use of new modular topologies, semiconductor devices, and energy buffering schemes. This paper presents the implementation and design for reliability for a GaN-based single-phase residential string inverter using a new topological and control scheme that allows dynamic hardware allocation (DHA). In the proposed inverter architecture, a range of identical modules and control schemes are used to dispatch hardware resources within the inverter to variably deliver power to the load or filter the second harmonic current on the DC side. This new approach more than triples the lifetime of GaN-based inverters, reducing system repair/replacement costs, and increasing the PV system lifetime energy production. 

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2. Resonant Multilevel Modular Boost Inverters for Single-Phase Transformerless Photovoltaic Systems

   https://doi.org/10.1109/ECCE.2018.8558049  

   Ni, Ze; Li, Yanchao; Johnson, Jalen; Cao, Dong (September 2018, 2018 IEEE Energy Conversion Congress and Exposition (ECCE))

   This paper presents two novel single-phase resonant multilevel modular boost inverters based on resonant switched capacitor cells and a partial power processed voltage regulator. Compared with other multilevel boost inverters applied in PV systems, one remarkable advantage of the proposed topologies is that the bulky AC filtering inductor is replaced by a smaller-size one in the partial power processed buck converter. Constant duty cycle PWM method is attractive for the multilevel inverter controller design. GaN Enhancement Mode Power Transistors help both the modular resonant switched capacitor cells and the full-bridge unfolder be realized in a small size with high power density. The clamp capacitors in the resonant switched capacitor cells effectively alleviate the switch voltage spikes. These two inverter topologies are analyzed and simulated in PLECS. Simulation results verify the validity of boost inverter function. Stress analysis shows that the inverter has relatively small total normalized switch conduction power stress and total normalized switch stress ratio. Relative total semiconductor chip area comparison results reflect that the proposed topology achieves more efficient semiconductor utilization compared with typical non-resonant multilevel modular switched capacitor boost inverters. Test results indicate that the proposed topology can be used for single-phase non-isolated PV boost inverter applications with small ground leakage current, high voltage conversion ratio, small volume and potential high efficiency. 

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3. Impact of smart photovoltaic inverter control modes on medium‐voltage grid voltage and inverter lifetime: An experimental approach

   https://doi.org/10.1049/stg2.12105  

   Mohamed, Ahmed; Saadeh, George; Kaviani, Ali_Kashefi (March 2023, IET Smart Grid)

   Abstract This study relies on an experimental approach, utilising real data from multiple photovoltaic (PV) sites located in the US Northeaster region, to inspect how different inverter reactive and active power settings impact gird voltage regulation and inverter life expectancy. These voltage regulation schemes come at a cost for the operator. Data from different solar sites with inverters running at different reactive and active power settings were analysed to compare operational trade‐offs. These trade‐offs range from production losses to shortening the lifetime of the inverters. Voltage versus reactive power plots were analysed to show production losses, while the thermal analysis was used to correlate with the inverter life expectancy. 

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4. Solar energy management as an Internet of Things (IoT) application

   https://doi.org/10.1109/IISA.2017.8316460  

   Spanias, Andreas S. (August 2017, 2017 IEEE 8th International Conference on Information, Intelligence, Systems & Applications (IISA))

   Photovoltaic (PV) array analytics and control have become necessary for remote solar farms and for intelligent fault detection and power optimization. The management of a PV array requires auxiliary electronics that are attached to each solar panel. A collaborative industry-university-government project was established to create a smart monitoring device (SMD) and establish associated algorithms and software for fault detection and solar array management. First generation smart monitoring devices (SMDs) were built in Japan. At the same time, Arizona State University initiated research in algorithms and software to monitor and control individual solar panels. Second generation SMDs were developed later and included sensors for monitoring voltage, current, temperature, and irradiance at each individual panel. The latest SMDs include a radio and relays which allow modifying solar array connection topologies. With each panel equipped with such a sophisticated SMD, solar panels in a PV array behave essentially as nodes in an Internet of Things (IoT) type of topology. This solar energy IoT system is currently programmable and can: a) provide mobile analytics, b) enable solar farm control, c) detect and remedy faults, d) optimize power under different shading conditions, and e) reduce inverter transients. A series of federal and industry grants sponsored research on statistical signal analysis, communications, and optimization of this system. A Cyber-Physical project, whose aim is to improve solar array efficiency and robustness using new machine learning and imaging methods, was launched recently 

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5. Hybrid Cyber-attack Detection in Photovoltaic Farms

   https://doi.org/10.1109/ECCE53617.2023.10362667  

   Zhang, Jinan; Ye, Jin; Song, Wenzhan; Lian, Jianming; Zhao, Dongbo; Yang, He (October 2023, IEEE Xplore)

   To address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment. 

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