Copper Indium Gallium Selenide- (CIGS-) based solar cells have become one of the most promising candidates among the thin film technologies for solar power generation. The current record efficiency of CIGS has reached 22.6% which is comparable to the crystalline silicon- (c-Si-) based solar cells. However, material properties and efficiency on small area devices are crucial aspects to be considered before manufacturing into large scale. The process for each layer of the CIGS solar cells, including the type of substrate used and deposition condition for the molybdenum back contact, will give a direct impact to the efficiency of the fabricated device. In this paper, brief introduction on the production, efficiency, etc. of a-Si, CdTe, and CIGS thin film solar cells and c-Si solar cells are first reviewed, followed by the recent progress of substrates. Different deposition techniques’ influence on the properties of molybdenum back contact for CIGS are discussed. Then, the formation and thickness influence factors of the interfacial MoSe₂ layer are reviewed; its role in forming ohmic contact, possible detrimental effects, and characterization of the barrier layers are specified. Scale-up challenges/issues of CIGS module production are also presented to give an insight into commercializing CIGS solar cells.

Transformer failures have a significant cost impact on the operation of an electrical network. In many utilities, transformers have been operating for many years past their expected usable life. As power demand has surged, transformers in some areas are being loaded beyond their rated capacity to meet the demand. One of the vital components in a transformer is the on-load tap changer (OLTC), which regulates the voltage in the distribution network. This study aims to review several condition-monitoring techniques (online and offline) that can monitor the health of the OLTC and assure the safety of the transformer’s OLTC from irreparable damage by detecting the defect at an earlier stage, which is preceded by the specification of typical faults. This paper also discussed the common faults of the OLTC and the root causes of these faults. The OLTC is prone to mechanical faults due to its frequently changing mechanism in the tap operation. The OLTC are also prone to oil as well as thermal faults. As a result, it is critical to monitor OLTC conditions while they are in use. Proper management of condition monitoring (CM) for the OLTC is useful and necessary to increase availability and achieve optimised operating. Condition monitoring (CM) and diagnostics methods (DM) have been developing since the 1950s. CM and DM have been implemented to diagnose and detect an incipient fault, especially for the OLTC. Many techniques, online and offline, are being used to monitor the condition of the OLTC to prevent failure and minimize outages. These DM and CM will prolong the operational cycle and avoid a major disaster for the OLTC, which is an unfavorable scenario.

The protection of intertie zone between wind farm and grid line is critical for stable and safe operation of both the grid line and wind farm in the event of fault within or outside the intertie zone. As a reliable source of renewable energy doubly fed induction generator (DFIG) based wind farms have been increasingly integrated to the power grid over the last two decades. Nowadays with the enormous penetration of large-scale DFIG wind farms, the commonly used distance relays are no longer reliable due to their incapability of providing accurate impedance measurement during internal and external faults. Thus, it results in maloperation, false tripping and/or delayed operation. Therefore, in this paper a digital differential-based protective relay (DBPR) scheme is designed and developed to provide reliable protection for wind farm intertie zone. Additionally, a new Bayesian-based optimized Support Vector Machine (SVM) as a supervised machine learning classifier approach is developed to take into account both the dynamic behaviors of wind speed and the current measured by the current transformers. Thus, the proposed hybrid SVM-DBPR scheme can distinguish among the normal operation, internal and external faults correctly that helps to avoid any false tripping. In a laboratory environment the proposed DBPR is implemented in real-time using FPGA DE2-115 board equipped with Cyclone IV-E device (EP4CE115F29C7). It is found from both simulation and experimental results that the proposed hybrid SVM-DBPR is able to provide reliable, efficient and robust protection for the intertie zone of wind farms with 97.5% accuracy rate.

Electric vehicles (EVs) could be used to address the issues of environmental pollution and the depletion of non-renewable energy resources. EVs, which are energized by a battery storage system, are becoming attractive because they keep the environment clean. Furthermore, the cost of EVs is becoming cheaper. Thus, EVs will become a significant load on utility distribution system in the future. EV chargers play a significant role in the expansion of EVs. The input current of an EV charger with a high total harmonic distortion (THD) and a high ripple distortion of the output voltage can impact battery life and battery charging time. Furthermore, the high cost and large size of the chargers are considered other issues in EV development. This work presents the complete design process of a universal EV charger with a special focus on its control algorithms. In this regard, a novel control algorithm based on the integration of voltage-oriented control (VOC) and the sinusoidal pulse-width modulation (SPWM) technique is proposed to ensure effective Levels 1, 2, and 3 battery charging. A simulation of the universal EV charger was conducted and assessed in MATLAB–Simulink. Moreover, a laboratory prototype was constructed with a TMS320F28335 digital signal processor (DSP) programmed as the controller to validate its operation and performance. The findings show that the proposed charger is able to provide a controllable and constant charging voltage for a variety of EVs, with an input current of low total harmonic distortion (THD) and an almost unity power factor.

Battery energy storage systems (BESSs) can support microgrid's economic operation. In this paper, the optimal capacity of BESS is determined for economic operation of microgrid. The BESS sizing problem is solved simultaneously with “mix-mode energy management system” (MM-EMS). Here, the MM-EMS is solved using linear programming (LP), and mixed integer linear programming (MILP) optimization techniques. Metaheuristic optimization techniques such as grey wolf optimizer (GWO), particle swarm optimization (PSO), artificial bee colony (ABC), gravitational search algorithm (GSA) and genetic algorithm (GA) are used to solve the BESS sizing problem and a comparison of its performance is also carried out. It was found that GWO produces the most optimal solution than other optimization techniques. With this, the performance of the proposed BESS sizing method is validated with traditional tradeoff method. Moreover, a comparison in microgrid's operating cost with and without BESS is carried out. It was also found that, 70% savings in microgrid's operating cost can be achieved when microgrid is operated with BESS.