76 research outputs found
House Bill 3289 (2019) report
No full textby: Michael Weinerman (Senior Research Analyst), Katherine Tallan (Research Analyst), Akinbosade Adedayo (Programs Analyst).Title from PDF title page (viewed on October 30, 2020)."During the 2019 legislative session, the Oregon Legislature passed and the Governor signed House Bill 3289. HB 3289 tasked the Criminal Justice Commission with creating a report examining several topics... Further, the bill also created a Jail Advisory Committee consisting of practitioners, subject matter experts, and advocates from a variety of organizations"--Page i.Text in English.Includes bibliographical references.Mode of access: Internet from the Oregon Government Publications Collection
Investigation and analysis of premature failure of valve regulated lead acid batteries
No full textOver the past fifty years Valve Regulated Lead Acid (VRLA) battery has been deployed with Uninterruptable Power Supplies (UPS) backup systems as a last line of defense against primary power outages, due to its affordability and maintainability. Although other newer battery technologies such as Lithium-Ion and Sodium Nickel Chloride are starting to take more space in the market, most of the safety-critical systems still depend on VRLA batteries for energy storage. The usage of VRLA batteries with UPS backup systems continues to increase, although up to 40% of VRLA cells fail prematurely. This high failure rate compromises safety and it is also a substantial loss of revenue. The charging and discharging method on a battery is very crucial because it can affect the life span of a battery. When a lead-acid battery is discharging or charging, lead-sulfate crystals build up gradually on the electrodes, this could result in total loss of capacity if a battery is not charged properly.
In this study, a collection of ninety-six VRLA batteries based on GEL electrolyte and Absorbent Glass Matt (AGM) type were tested under float and cyclic applications. The battery strings were arranged in a series connection of 32 VRLA batteries at three different test sites. The specification of battery strings used is based on ampere-hour and voltage ratings of 170 AH and 12 V respectively. The batteries were continuously monitored for more than 600 days using Sentinel Battery Monitoring System (BMS) and the performance data were analyzed to identify the rate of premature failure. The result obtained shows that VRLA batteries that are based on GEL electrolyte are more robust than those that are based on AGM technology. One battery out of thirty-two GEL electrolyte type batteries failed as compared to thirty-two batteries out of sixty-four AGM type batteries that failed within twenty-four months. It was also observed that the GEL type is more suitable for cyclic application, whereas AGM is more suitable for float application. In addition, it was also observed that the equalization charge proved to improve the life cycle of both types of VRLA batteries.M. Tech. (Engineering Electrical)Electrical and Mining Engineerin
Detection and identification of faulty modules in a large-scale photovoltaic installation
No full textThe global warming and realisation that fossil fuel is limited, has been a drive over the years to move to renewable energy sources (RES). Consequently, solar energy and other renewable energy sources are currently being exploited. The fast development of photovoltaic technologies makes the systems one of the leading methods to effectively exploit the RES. In recent times, the Photo voltaic (PV) system are used at large to augment the classical energy supply. However, large-scale photovoltaic installations possess challenges of being susceptible to faults. Therefore, it is imperative that faulty modules are detected and isolated to preserve the efficiency of the overall PV system.
Fault detection is crucial to ensure operational reliability of a large-scale photo-voltaic installations (LSPVI). However, faults detection and identification are still a key challenge in LSPVI. In a large-scale photovoltaic installation, there are large quantity of photovoltaic arrays. Because of these arrays and their complex configuration, various types of faults are produced frequently which directly affect reliability and safety of PV installation. One of the goals of any PV installation is to retain power generation with the desired operation efficiency. Therefore, it is imperative that faulty modules are detected to preserve an efficiency of PV system. In this study, a fault detection and identification technique which involves excitation of both normal and faulty modules with signals that have various frequencies was implemented for detection of faulty modules in LSPVI. The results obtained show that the proposed techniques can diagnose and identify faulty solar panels in a large-scale photovoltaic installation.M. Tech. (Electrical Engineering)Electrical and Mining Engineerin
Voltage stability index based on standard deviation-mean ratio for identification of weak nodes
No full textIdentification of Weak Nodes in Power System Based on Standard Deviation of Voltage Magnitude
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Forecasting of Residential Energy Utilisation Based on Regression Machine Learning Schemes
No full textEnergy utilisation in residential dwellings is stochastic and can worsen the issue of operational planning for energy provisioning. Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest and decision tree) are used to train a forecasting model. The model is based on a yearly dataset and its subset seasonal partitions. The dataset is first preprocessed to remove inconsistencies and outliers. The performance measures of mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to evaluate the accuracy of the model. The results show that the performance of the model can be enhanced with hyperparameter tuning. This is shown with an observed improvement of about 44% in accuracy after tuning the hyperparameters of the decision tree regressor. The results further show that the decision tree model can be more suitable for utilisation in forecasting the partitioned dataset
Investigating seasonal wind energy potential in Vredendal, South Africa
No full textGlobal warming and the energy crisis have necessitated an urgent exploitation and utilisation of renewable energy. Wind energy has gained popularity over the years because of vast availability of its resource. A study was carried out to investigate the stochastic characteristics of the available wind energy at installation sites. Data for a ten-minute interval wind speed collected over a period of five years and measured at a height of 10, 40 and 62 m in Vredendal was considered. Wind speed data was arranged in seasonal format and its statistical distribution investigated based on Weibull, lognormal and gamma distributions. The Anderson-Darling test and Akaike information criterion were used to evaluate the goodness of fit. The results showed that wind power at different heights and time stamps exhibited different statistical distribution. It was found that wind turbines in Vredendal must be installed as high as possible to harness wind power effectively. During summer and spring, there was a high potential for wind power availability compared with that of winter
Enhancement of modeling environment for power distribution network in Gridlab-D on Emacs
No full textTechniques for the Identification of Critical Nodes Leading to Voltage Collapse in a Power System
No full textAbstractThis paper proposes two techniques for the identification of critical buses in a power system. The technique of Network Structural Theory Participation Factor (NSTPF) depends on the network structural interconnection of buses as captured by the admittance matrix of the system and is formulated based on the fundamental circuit theory law using eigenvalue decomposition method. Another power flow based technique which depends on the system maximum loadability, the system step size among other factors is also proposed. Traditional power flow based techniques are used as benchmarks to determine the significance of the proposed methods. To ensure voltage stability enhancement, STATCOM FACTS device is installed at the selected weak load buses of the practical Nigerian 24 bus and IEEE 30 bus test systems. The results of the simulation obtained show that, the suggested approach of NSTPF is more suitable in the identification of weak buses that are liable to voltage instability in power systems as it requires less computational burden and also saves time compared to techniques based on power flow solutions.</jats:p
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