Radio Frequency Energy Harvesting for Low Power Sensors

Wireless sensor networks and the internet of things are benefiting from recent advances in power consumption to implement intelligent control entities. Similar advances in battery technology have enabled these systems to become autonomous. Nevertheless, this approach is insufficient for modern applications. An alternative solution to power these sensors is to use the energy available in their environment, such as thermal, mechanical vibration, light or radio frequencies. However, sensors are frequently placed in an environment where power density is low. This study investigates energy harvesting from radio frequencies compared to other sources. After demonstrating the potential for collecting energy over a wide frequency band, a statistical study was carried out to determine the RF power density present in the urban environment and in rural areas. Multi-band RF harvester systems were designed to harvest energy in several frequency bands to show when multiple RF sources are available. The amount of energy harvested can be increased when the system is designed to operate over a wide frequency band. In this study, multiband RF energy harvester to power wireless sensors is produced using Advanced Design Software (ADS). According to the design outcomes the proposed energy harvesting scheme works better on the GSM900 and GSM1800 bands

PCA-ANN Based Algorithm for the Determination of Asymmetrical Network Failures of Network-Connected Induction Generators

Presented in this study is a principal component analysis - artificial neural network based hybrid failure determination system that can make failure determination selectively and rapidly in asymmetrical external failures that might occur on the network side of a grid-connected induction generator. By creating asymmetrical external failures in the developed simulation model, analysis of noisy and unbalanced fluctuations that carry effects of positive, negative and zero sequence in currents were realized. The suggested model depends on entering data taken from the simulation into the artificial neural network model as a training data by being simplified with principal component analysis, in phase-phase, phase-ground and two phase-ground failures. The protection model makes correct classification with acceptable errors in case of above stated failures. However, in current fluctuations caused by sudden load changes and operation under an unbalanced load, it may remain insensitive by behaving selectively

The Design and Implementation of Adsorptive Removal of Cu(II) from Leachate Using ANFIS

Clinoptilolite was investigated for the removal of Cu(II) ions from industrial leachate. Adaptive neural fuzzy interface system (ANFIS) was used for modeling the batch experimental system and predicting the optimal input values, that is, initial pH, adsorbent dosage, and contact time. Experiments were studied under laboratory batch and fixed bed conditions. The outcomes of suggested ANFIS modeling were then compared to a full factorial experimental design (23), which was utilized to assess the effect of three factors on the adsorption of Cu(II) ions in aqueous leachate of industrial waste. It was observed that the optimized parameters are almost close to each other. The highest removal efficiency was found as about 93.65% at pH 6, adsorbent dosage 11.4 g/L, and contact time 33 min for batch conditions of 23 experimental design and about 90.43% at pH 5, adsorbent dosage 15 g/L and contact time 35 min for batch conditions of ANFIS. The results show that clinoptilolite is an efficient sorbent and ANFIS, which is easy to implement and is able to model the batch experimental system

OPTIMIZATION OF PROCESS PARAMETERS FOR COMPOSTING OF PULP/PAPER MILL SLUDGE WITH HAZELNUT KERNEL USING A STATISTICAL METHOD

An effective way to remove ammonium from compost using hazelnut kernels (HK) has been presented. The role of experimental factors on the removal of ammonium was examined by using the full factor experimental design (FFED). The experimental factors and their related levels were selected as time of 1-6 weeks, moisture of 50-70%, and HK amendment ratio of 5-25. The results were then evaluated by the ANOVA test to examine importance of the process variables (inputs) and their levels. A regression model taking into account main significant and interaction effects was suggested. According to the optimization algorithm, time of 5 weeks, moisture of 50%, and HK amendment ratio of 25 with the removal capacity of 60% were selected as optimum levels. The proposed analyzing procedure is simple to implement and cost-effective

Probabilistic Load Flow of Unbalanced Distribution Systems with Wind Farm

This paper examines the practical application of the Unscented Transform (UT) method in carrying out load flow studies in distributions systems with embedded generation and unbalanced loading. A hybrid ladder network based backward/forward sweep technique was employed in solving the distribution system load flow. The performance of the load flow technique plus the UT method is demonstrated using a real 44-Bus unbalanced distribution system with a proposed wind farm which is located in Samsun, Northern Turkey. Results obtained were compared with those from the Monte Carlo Simulation method while the effect of the wind farm on the existing network was also evaluated

An Eco-Friendly Gas Insulated Transformer Design

Electricity companies around the world are constantly seeking ways to provide electricity more safely and efficiently while reducing the negative impact on the environment. Mineral oils have been the most popular transformer insulation, having excellent electrical insulating properties, but have many problems such as high flammability, significant cleaning problems, and are toxic to fish and wildlife. This paper presents an alternative approach to mineral oil: a transformer design that is clean and provides better performance and environmental benefits. A 50 kVA, 34.5/0.4 kV gas insulated distribution transformer was designed and evaluated using the COMSOL Multiphysics environment. R410A was used as insulation material. R410A is a near-azeotropic mixture of difluoromethane (CH2F2, called R-32) and pentafluoro ethane (C2HF5, called R-125), which is used as a refrigerant in air conditioning applications. It has excellent properties including environmentally friendly, no-ozone depletion, low greenhouse effect, non-explosive and non-flammable, First, the breakdown voltage of the selected gas was determined. The electrostatic and thermal properties of the R410A gas insulated transformer were investigated in the COMSOL environment. The simulation results for the performance of oil and SF6 gas insulated transformers using the same model were compared. The gas-insulated transformer is believed to have equivalent performance and is an environmentally friendly alternative to current oil-based transformers

A novel cycle counting perspective for energy management of grid integrated battery energy storage systems

Battery energy storage systems (BESS) are essential for flexible and reliable grid performance as the number of renewable energy sources in grids rises. The operational life of the batteries in BESS should be taken into account for maximum cost savings, despite the fact that they are beneficial for economical grid operation. In this context, this paper present a new battery cycle counting perspective for energy management of grid-connected BESS. For this purpose battery’s one full charge–discharge cycle characteristic is compared with the operating battery charge–discharge cycle every time step. This comparison was explained mathematically and graphically in detail. The results are compared with the rain flow counting method which is the most popular cycle counting algorithm. Consequently, this cycle counting approach successfully counts the battery charge/discharge cycles and it has shown that has an advantage for BESSs due to being specifically developed just for batteries