Simulation Of the Conical Gravitational Water Vortex Turbine (GWVT) Design in Producing Optimum Force for Energy Production

Sustainable electricity power supply is crucial especially for less populated rural area. Micro hydropower generation in rural area is important in providing electricity especially in off-grid electricity area. This study aims to predict and harness power from micro hydropower generation through conical gravitational water vortex turbine (GWVT) via SOLIDWORKS flow simulation. Conical GWVT under study was designed as fully enclosed system with conical turbine basin. Two different turbine orientations were simulated i.e., vertical and horizontal at different blade angle designs i.e., 25°, 45°, 75°, 90°, and 120° and with different number of blades i.e., 8, 12, and 18 while forces were harnessed at tangential (z-axis) direction. The simulation results showed that it was possible to run and produce force from conical GWVT design in a fully enclosed system. It was found that vertical turbine orientation produced a slightly higher force than horizontally orientated turbine, using 12 runner blades at 90° angles where the distributed forces were 15.31N and 14.12N respectively, at tangential (z-axis) direction. The results are useful to predict turbine’s torque for small capacity micro hydropower electricity generation prior to actual conical GWVT set up, in rural area, to minimise cost implication and construction issues.

Membrane-less microbial fuel cell: effect of pH on the electricity generation powered by municipal food waste

Fossil fuels have supported the industrialization and economic growth of countries during the past centuries and it is clear that they cannot indefinitely sustain in a longer time. In this study, membrane-less microbial fuel cell (ML-MFC) with mediators-less and air cathode had potential solution to generate electricity power and at the same time could reduce the abundant of food waste (1.64 kg/daily, around 8 tonnes/year) which dumped in the landfill and it’s cost effective device. The ML-MFC operated electrochemically incorporate electrogenic bacteria (EB) acted as a biocatalyst in order to produce electricity. The performance and optimization performance of food waste was evaluated using one-factor-at-a-time (OFAT) method and it was focused to pH for power generation. To determine the generated electricity the polarization curve was used to evaluate the performance of ML-MFC. The chemical oxygen demand (COD) of food waste was studied. The optimization of pH condition in ML-MFC was ranging from 7 to 9. Results showed that pH 8 was the optimum pH for EB strain, Bacillus Subtilis, with the high voltage (807 mV), EB biomass (15.46 mg/L), and power density (373.3 mW/m2) generated. Clearly the pH environment condition affected the efficiency of ML-MFC performance. The increase in EB biomass also increased the voltage in the ML-MFC, proving that EB biomass and voltage were associated with growth.

Electricity Theft Detection in Power Consumption Data Based on Adaptive Tuning Recurrent Neural Network

Electricity theft behavior has serious influence on the normal operation of power grid and the economic benefits of power enterprises. Intelligent anti-power-theft algorithm is required for monitoring the power consumption data to recognize electricity power theft. In this paper, an adaptive time-series recurrent neural network (TSRNN) architecture was built up to detect the abnormal users (i.e., the electricity theft users) in time-series data of the power consumption. In fusion with the synthetic minority oversampling technique (SMOTE) algorithm, a batch of virtual abnormal observations were generated as the implementation for training the TSRNN model. The power consumption record was characterized with the sharp data (ARP), the peak data (PEA), and the shoulder data (SHO). In the TSRNN architectural framework, a basic network unit was formed with three input nodes linked to one hidden neuron for extracting data features from the three characteristic variables. For time-series analysis, the TSRNN structure was re-formed by circulating the basic unit. Each hidden node was designed receiving data from both the current input neurons and the time-former neuron, thus to form a combination of network linking weights for adaptive tuning. The optimization of the TSRNN model is to automatically search for the most suitable values of these linking weights driven by the collected and simulated data. The TSRNN model was trained and optimized with a high discriminant accuracy of 95.1%, and evaluated to have 89.3% accuracy. Finally, the optimized TSRNN model was used to predict the 47 real abnormal samples, resulting in having only three samples false predicted. These experimental results indicated that the proposed adaptive TSRNN architecture combined with SMOTE is feasible to identify the abnormal electricity theft behavior. It is prospective to be applied to online monitoring of distributed analysis of large-scale electricity power consumption data.

Analysis on the Use of Briquettes as an Alternative to Improve the Generation of Thermal Energy in the Locality of Aripuana-Brazil

The city of Aripuana is one of the largest wood producer in the state of Mato Grosso, Brazil. Wood residues are used in the electricity generation at three thermoelectric plants in this region. However, the plants have high costs in transporting the wood residues (due to poor road conditions). Hence, this paper compares the energy performance of wood residues in natura and compacted as briquettes by calculating the heating value and determining the influence of moisture content on the energy characteristics of wood residues. The goal is to demonstrate the viability of using briquettes in order to improve thermoelectric generation. The wood residues from this region are affected by the high humidity of the biome. An alternative to improve the use of energy contained in the wood residues is to produce briquettes with lower humidity. This allows one to maintain high levels of heat energy in a lower volume, facilitating handling and storage. The results show that the use of briquettes improved the performance of thermoelectric plants, generating 1 MW of electricity power with less than 1 ton of briquettes. This contributes to the preservation of the environment, reducing operating costs, transportation and storage of the raw materials.

Electricity Power, Democratic Governance and Economic Growth: Evidence from Nigeria

The study examines the impact of democratic governance and corruption on electricity power supply in Nigeria. To achieve this goal, an integrated regression analysis such as Dynamic Ordinary Least Squares (DOLS), Fully Modified OLS, Canonical Cointegrating Regression and OLS were utilized to estimate data spanning the period of 1986 – 2020. The result revealed a negative and significant impact of democracy and corruption on electricity power generation in Nigeria. On the other hand, economic growth has shown a positive and important impact on electricity generation, suggesting that higher GDP growth will increase the supply of electricity in Nigeria. The implication of this findings are as follows: (i) one of the dividends of democracy which is providing public good to the citizens have not been achieved, as such, policymakers need to give more attention to the provision electricity supply; (ii) the institutions in charge of fighting corruption such as the Economic and Financial Crime Commissions (EFCC) need to be further strengthened in Nigeria.

Design of Distribution Transformer Health Management System using IoT Sensors

Transformers are one of the primary device required for an AC (Alternating Current) distribution system which works on the principle of mutual induction without any rotating parts. There are two types of transformers are utilized in the distribution systems namely step up transformer and step down transformer. The step up transformers are need to be placed at some regular distances for reducing the line losses happening over the electrical transmission systems. Similarly the step down transformers are placed near to the destinations for regulating the electricity power for the commercial usage. Certain regular check-ups are must for a distribution transformer for increasing its operational life time. The proposed work is designed to regularize such health check-ups using IoT sensors for making a centralized remote monitoring system.