182 research outputs found
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
Rescheduling of Generators with Pumped Hydro Storage Units to Relieve Congestion Incorporating Flower Pollination Optimization
No full textIn this paper, a Flower Pollination Algorithm (FPA) has been proposed for relieving congestion in the deregulated power electricity industry. Congestion in the power market is one the contemplative challenges to be overcome in the era of deregulation. The primary cause of congestion is due to the loss of the transmission line, an increase in load, or loss of generator(s). Hence, managing congestion is one of the issues which have to be tackled in the present scenario. There are several techniques to relieve congestion. It is quite well-known that the thermal limits of transmission lines in a power system are fixed. One of the methods to abate congestion is to reschedule the real power of the generators. The purpose of the present work is to benefit the Independent System Operator (ISO) in reliving congestion. (1) In order to meet this objective effectively, a FPA algorithm has been proposed for relieving congestion and is simulated on a modified IEEE 30-bus system initially. (2) Congestion cost, compared with and without the application of FPA, is computed. (3) To validate its effectiveness, the obtained results are compared with recent power system optimization algorithms present in the literature. (4) Further, the work has been extended with the incorporation of a Pumped Hydro Storage Unit (PHSU). Here an economic analysis of congestion cost reduction employing FPA before and after the incorporation of PHSU is investigated applying FPA. In comparison with other evolutionary algorithms, the uniqueness of generating a new population is attained in FPA by the levy flight procedure. It is one of the latest evolved algorithms and is suited for different power system problem due to fewer clear-cut tuning parameters in contrast with other algorithms. (5) Furthermore, the effects of other network parameters, including system losses and voltage, has been computed. The result obtained is tested in terms of congestion mitigation with and without the incorporation of PHSU, in terms of novel objective improvement, and with and without applying recently evolving FPA for the above application. Thus the objective-wise and algorithmic-wise innovative concept has been presented. This proves effectiveness of the algorithm in terms of minimized cost convergence and other parameter including system losses and voltage before and after the incorporation of PHSU as compared with other recent trendsetting reported optimization techniques
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