Flexible AC Transmission Systems (FACTS) play an important role in minimizing power losses and voltage deviations while increasing the real power transfer capacity of transmission lines. The extent to which these devices can provide benefits to the transmission network depend on their optimal location and sizing. However, finding appropriate locations and sizes of these devices in an electrical network is difficult since it is a nonlinear problem. This paper proposes a technique for the optimal placement and sizing of FACTS, namely the Thyristor-Controlled Series Compensators (TCSCs), Shunt VARs Compensators (SVCs), and Unified Power Flows Controllers (UPFCs). To find the optimal locations of these devices in a network, weak buses and lines are determined by constructing PV curves of load buses, and through the line stability index. Then, the whale optimization algorithm (WOA) is employed not only to find an ideal ratings for these devices but also the optimal coordination of SVC, TCSC, and UPFC with the reactive power sources already present in the network (tap settings of transformers and reactive power from generators). The objective here is the minimization of the operating cost of the system that consists of active power losses and FACTS devices cost. The proposed method is applied to the IEEE 14 and 30 bus systems. The presented technique is also compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The findings showed that total system operating costs and transmission line losses were considerably reduced by WOA as compared to existing metaheuristic optimization techniques.

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Power restoring time in power distribution systems (PDS) can be minimized by using efficient fault localization techniques. This paper proposes a novel, robust and scalable cloud based internet of things (IoT) solution for identification and localization of faults in PDS. For this purpose, a new algorithm is developed that can detect single and multiple simultaneous faults in the presence of single and multiple device or sensor failures. The algorithm has utilized a zone based approach that divides a PDS into different zones. A current sensing device (CSD) was deployed at the boundary of a zone. The function of CSD is to provide time synchronized current measurements and communicate with a cloud server through an edge device (ED). Another contribution of this research work is the unique implementation of context aware policy (CAP) in ED. Due to CAP, only those measurements are transmitted to cloud server that differ from the previously transmitted measurements. The cloud server performed calculations at regular intervals to detect faults in PDS. A relational database model was utilized to log various fault events that occur in PDS. An IEEE 37 node test feeder was selected as PDS to observe the performance of our solution. Two test cases were designed to simulate individual and multiple simultaneous faults in PDS. A third test case was implemented to demonstrate the robustness and scalability of proposed solution to detect multiple simultaneous faults in PDS when single and multiple sensor failures were encountered. It was observed that the new algorithm successfully localized the faults for all the three cases. Consequently, significant reductions were noticed in the amount of data that was sent to the cloud server. In the end, a comparison study of a proposed solution was performed with existing methods to further highlight the benefits of our technique.

Nanosized inorganic oxides have the trends to improve many characteristics of solid polymer insulation. In this work, the characteristics of improved poly (vinyl chloride) (PVC)/ZnO are evaluated using 0, 2, 4 and 6 phr of ZnO nanoparticles dispersed in polymer matrix using internal mixer and finally compressed into circular disk with 80 mm diameter using compression molding technique. Dispersion properties are studied by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffractometry (XRD), and optical microscopy (OM). The effect of filler on the electrical, optical, thermal, and dielectric properties of the PVC are also analyzed. Hydrophobicity of nano-composites is evaluated by measuring contact angle and recording hydrophobicity class using Swedish transmission research institute (STRI) classification method. Hydrophobic behavior decreases with the increase in filler content; contact angle increases up to 86°, and STRI class of HC3 for PZ4 is observed. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) are employed to evaluate the thermal properties of the samples. Also, continuous decrease of optical band gap energy from 4.04 eV for PZ0 to 2.57 eV for PZ6 is observed. In the meantime, an enhancement in the melting temperature, T_m, is observed from 172 to 215 °C. To check the stability of materials against hydrothermal stresses, all the fabricated materials are then subjected to a hydrothermal aging process for 1000 h and their structural stability is analyzed using optical microscopy and FTIR analyses.

High conversion gain is often required for the grid integration of renewable energy resources such as PV, fuel cells, and wind. It is desired that the stress across switches is lower when higher voltage gain is attained. Similarly, it is also preferred that the converter can achieve high voltage gain without operating at higher duty cycle values. This article presents a novel high-gain CUK converter (HGCC) that uses voltage-multiplier units. The HGCC is a combination of a modified CUK converter and voltage-multiplier units (VMUs). The converter utilizes a boost converter as an input to the modified CUK converter, resulting in an increase in the gain value. The voltage gain of HGCC is increased further by placing VMUs. Based on its overall design, the HGCC inherits various advantages of the CUK converter, such as continuous input and output current, resulting in low input and output current ripples. A mathematical model is developed for the HGCC, which helps calculate its voltage gain at different stages. The model is developed considering ideal elements without conduction and switching losses. Generalized equations for output voltage and gain are derived for $n$ level converter. A simulation study was performed in MATLAB/Simulink that further highlights the advantages of the HGCC. Voltage stresses across different components and the switching of MOSFET and diodes are studied in simulations. An experimental setup is established for hardware prototyping of the converter and validation with the simulation and Mathematical models.