High voltage gain DC-DC converters are widely used in various applications like low voltage sustainable sources. In traditional boost converters, the voltage gain is limited by high voltage stress, high current ripple, and low efficiency due to employing a high duty cycle ratio. In this paper, we propose converters that are a combination of four sub-structures, namely active switched inductor, passive switched inductor, switched capacitor cell, as well as an auxiliary switch with non-isolated configuration. The proposed structures have high voltage gain compared to the conventional either switched inductor-based or switched capacitor-based ones in addition to a low duty cycle ratio. By adding an auxiliary switch, the efficiency is improved, particularly for high voltage gains. The principle of operation and steady-state analysis are discussed in detail. Also, simulation results from PSCAD/EMTDC software are validated by a prototype built for experimental examination. The results are demonstrated that the voltage gain and efficiency could be improved by utilizing the auxiliary switch.

Large frequency oscillations happen when the unbalanced power is not compensated by Load Frequency Controller (LFC) system. In recent years, the using of electric vehicles (EVs) has increased with renewable generation sources such as solar cells, wind turbines, fuel cells, and so on. The large-scale integration of these new types of generation sources and demand in power grids will have significant impact on operations, planning and stability control. This work sets out to design an effective PID controller for LFC in an island Micro-grid (MG) and also proposes a model for EVs to contribute to LFC system. To this end, particle swarm optimization (PSO) based artificial neural network (ANN) technique is considered to tune the parameters of PID controller in MG structure. Simulation results demonstrate that with PSO-based ANN, the system is becoming stable in shortest time. In addition, frequency oscillation magnitude, overshoot and settling time are reduced.

Non-linear characteristics of solar cells and changes in environmental conditions, such as temperature, and in particular the intensity of daytime irradiation, make it difficult to identify faults by conventional means of protection. Therefore, a variety of machine learning techniques are proposed for fault detection in photovoltaic (PV) arrays. In this regard, classifying and identifying the location of a fault event is essential. In addition to fault recognition, selecting the method of fault correction is another issue to be addressed. However, there are scarce investigations in this field. In this paper, a comprehensive method for identifying, classifying, locating and correcting faults is introduced. The proposed method is assessed with the expansion of the diagnostic space of graph-based semi-supervised learning (GBSSL) algorithm and an increased number of class labels. After identifying the type and location of a fault, the system temporarily isolates the fault to function without interruption until it is fully corrected. The problem of overlapping cell data in normal and fault-prone modes is resolved by applying different methods of normalization. The results show that all faults including unlearned and learned in a wide range of environmental conditions, where possible PV arrays are experienced, are properly identified and corrected. Moreover, our studies demonstrate that the proposed system mitigates the output voltage variations over a fault-prone mode.