A. Zare; M. Simab; M. Nafar
Abstract
Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity ...
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Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity of photovoltaic (PV) arrays, several cells are arranged in parallel or in series to form a panel in order to obtain the expected power. Short circuit (SC) and open circuit (OC) faults in the solar PV systems are the main factors that reduce the amount of solar power generation, which has different types. Partial shadow, cable rot, un-achieved maximum power point tracking (MPPT) and ground faults are some of these malfunctions that should be detected and located as soon as possible. Therefore, effective fault detection strategy is very essential to maintain the proper performance of PV systems to minimize network interruptions. The detection method must also be able to detect, locate and differentiate between SC and OC modules in irradiated PV arrays and non-uniform temperature distributions. In this paper, based on artificial intelligence (AI) and neural networks (NN), neutrons can be utilized, as they have been trained in machine learning process, to detect various types of faults in PV networks. The proposed technique is faster than other artificial neural networks (ANN) methods, since it uses an additional hidden layer that can also increase processing accuracy. The output results prove the superiority of this claim.
M. Afshari; Seyed M. Mahdi Moosavi; M. B. Abadi; S.M.A. Cruz
Abstract
Doubly-fed induction generators (DFIG) have been widely used in wind turbines installed in the last decades. These generators are prone to some faults that could deteriorate their performance and even lead to their outage from the network. Stator inter-turn short-circuits (SITSC) and high resistance ...
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Doubly-fed induction generators (DFIG) have been widely used in wind turbines installed in the last decades. These generators are prone to some faults that could deteriorate their performance and even lead to their outage from the network. Stator inter-turn short-circuits (SITSC) and high resistance connections (HRC) in the stator are two major types of faults that cause electrical asymmetry in the stator circuit. Yet, SITSC are more noticeable and require immediate scrutiny. Hence, if an HRC can be distinguished from a SITSC fault, the immediate outage of the WT can be avoided in the case of an HRC. In this paper, both types of faults are studied and compared, being their detection performed using appropriate fault indices obtained from the stator current, rotor current, and rotor modulating voltage signals, all available in the control system of the DFIG. Several fault severity indices are proposed for a better evaluation of the fault extension, and the discrimination between SITSC and HRC is discussed. The performance of the defined fault indices is verified using a magnetic equivalent circuit model of the DFIG and an experimental setup with the DFIG running at several operating conditions.