The worldwide electricity supply network has recently experienced a huge rate of solar photovoltaic penetration. Grid-connected photovoltaic (PV) systems range from smaller custom built-in arrays to larger utility power plants. When the size and share of PV systems in the energy mix increases, the operational complexity and reliability of grid stability also increase. The growing concern about PV plants compared to traditional power plants is the dispersed existence of PV plants with millions of generators (PV panels) spread over kilometers, which increases the possibility of faults occurring and associated risk. As a result, a robust fault diagnosis and mitigation framework remain a key component of PV plants. Various fault monitoring and diagnostic systems are currently being used, defined by calculation of electrical parameters, extracted electrical parameters, artificial intelligence, and thermography. This article explores existing PV fault diagnostic systems in a detailed way and addresses their possible merits and demerits.
The fluctuating nature of power produced by renewable energy sources results in a substantial supply and demand mismatch. To curb the imbalance, energy storage systems comprising batteries and supercapacitors are widely employed. However, due to the variety of operational conditions, the performance prediction of the energy storage systems entails a substantial complexity that leads to capacity utilization issues. The current article attempts to precisely predict the performance of a lithium-ion battery and capacitor/supercapacitor under dynamic conditions to utilize the storage capacity to a fuller extent. The grey box modeling approach involving the chemical and electrical energy transfers/interactions governed by ordinary differential equations was developed in MATLAB. The model parameters were extracted from experimental data employing regression techniques. The state-of-charge (SoC) of the battery was predicted by employing the extended Kalman (EK) estimator and the unscented Kalman (UK) estimator. The model was eventually validated via loading profile tests. As a performance indicator, the extended Kalman estimator indicated the strong competitiveness of the developed model with regard to tracking of the internal states (e.g., SoC) which have first-order nonlinearities.
The share of photovoltaic (PV) power generation in the energy mix is increasing at a rapid pace with dramatically increasing capacity addition through utility-scale PV power plants globally. As PV plants are forecasted to be a major energy generator in the future, their reliable operation remains of primary concern due to a possibility of faults in a tremendously huge number of PV panels involved in power generation in larger plants. The precise detection of nature and the location of the faults along with a prompt remedial mechanism is deemed crucial for smoother power plant operation. The existing fault diagnostic methodologies based on thermal imaging of the panels as well as electrical parameters through inverter possess certain limitations. The current article deals with a novel fault diagnostic technique based on PV panel electrical parameters and junction temperatures that can precisely locate and categorize the faults. The proposed scheme has been tested on a 1.6 kW photovoltaic system for short circuit, open circuit, grounding, and partial shading faults. The proposed method showed improved accuracy compared to thermal imaging on panel scale fault detection, offering a possibility to adapt to the PV plant scale.
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