Comparison Between Gross Errors Detection Methods in Surveying Measurements

The least squares estimation method is commonly used to process measurements. In practice, redundant measurements are carried out to ensure quality control and to check for errors that could affect the results. Therefore, an insurance of the quality of these measurements is an important issue. Measurement errors of collected data have different levels of influence due to their number, measured accuracy and redundancy. The aim of this paper is to examine the detection of gross error capabilities in vertical control networks using three methods; Global Test, Data Snooping and Tau Test to compare the effectiveness of these three methods. With the least squares’ method, if there are gross errors in the observations, the sizes of the corresponding residuals may not always be larger than for other residuals that do not have gross errors. This makes it difficult to find (detect) it. Therefore, it is not certain that serious errors should be detected by just examining the magnitudes of the residuals alone. These methods are used in conjunction with developed programs to calculate critical values for the distributions (in real time) rather than look for these in statistical tables. The main conclusion reached is that the tau (τ) statistic is the most sensitive to the presence gross error detection; therefore, it is the one recommended to be used in gross error detection

Experimental study of using renewable energy in Yemen

During the last decade, there has been an increased interest to develop renewable energy technologies that could contribute to ever-increasing energy needs. This paper proposes a new PV array-wind-diesel generator hybrid system model to determine the optimal configuration of power systems relevant to different regions in Yemen, Sana’a and Aden. Various renewable and nonrenewable energy sources, energy storage and their applicability in terms of cost and performance are discussed. The collected data for the wind, solar radiation and diesel price were obtained from Civil Aviation & Meteorology Authority in Yemen. The Sensitivity analysis was carried out using Homer program. The results show that wind-diesel-battery model is the most suitable solution for both regions in term of the economic performance

A neuro-fuzzy approach for stator resistance estimation of induction motor = pendekatan neuro-fuzzy untuk meramal rintangan stator pada motor induksi

During the operation of induction motor, stator resistance changes incessantly with the temperature of the working machine. This situation may cause an error in rotor resistance estimation of the same magnitude and will produce an error between the actual and estimated motor torque which can leads to motor breakdown in worst cases. Therefore, this project will propose an approach to estimate the changes of induction motor stator resistance using neuro-fuzzy. Then, it will be compared with conventional method like P1 estimator to see the effectiveness. The behaviour of the induction machine will be analyzed when the stator resistance is changed. Based on the changes, a corrective procedure will be applied to ensure the stabilities of the induction motor. Generally, this project can be divided into three main parts which are design of induction motor, design of neuro-fuzzy and PT estimator, and corrective procedure for the induction machine. The Newcastle Drives Simulation Library will be used to design the induction motor model and MATLAB SIMULINK will be used to design the stator current observer. The neuro-fuzzy estimator will be designed based on Sugeno Method Fuzzy Inference System

A multipurpose problem-based learning platform for education and research under smart grid umbrella

Developing user-friendly learning platform tailored to illustrating the integrative nature of specific subjects can be very effective in enhancing the involvement of students in the classroom. The focus of this paper is twofold; first, highlighting a specific project from a teaching perspective for the design and implementation of a multipurpose experimental test facility for plug-in electric vehicle, renewable energy and energy storage management in the smart grid context. Second, proposing the application of this project as an interactive tool to assist in demonstrating the system behaviour, when teaching the new emerging subjects in power engineering courses particularly, Smart Grid. Indeed, the scope of the test facility with the developed simulation tool is specially structured and oriented to education, training and research studies, where problem-based learning can be easily implemented. The electrical test hardware in this design can be operated independently as a multipurpose experimental setup for plug-in electric vehicle. In addition, it can also be interconnected with a small size wind or photovoltaic system to facilitate a complete hybrid integrated platform for testing and model verification. A variety of illustrative examples are presented to show the features of the test facility with its operation and how the components of this facility were selected. Additionally, several test scenarios are depicted using the developed simulation tool, which cover the assessment of grid integration with its components in the event of unforeseen circumstances. A recommendation that can be drawn from this paper is to motivate academics who are actively involved in conducting research on smart grid, to make use of their hardware or software designs in the process of teaching and learning so that undergraduate students can get the opportunity of observation and experimentation. In this sense, suggestions for future work are proposed

Analysis of Grid-connected Solar PV System Operation based on Energy Router Concept

The major limitation when using renewable energy resources is their inherent variability and dependence on climatic conditions. However, routing coordination of these resources with backup power can provide a reliable and economical electricity supply under different load demand conditions. This paper proposes the concept of routing in electrical networks to compensate for the energy deficit and manage the power transfer control in grid-connected and islanded modes. The coordination management of electrical energy resources in terms of active or reactive power is adopted using a routing matrix. A fuzzy approach is applied to realize the control mechanism from the routing topology perspective. The results show that the routing scheme meets the requirements of bidirectional power flow, where the balance of active power and the compensation of reactive power can be achieved

A multipurpose problembased learning platform for education and research under smart grid umbrella

Developing user-friendly learning platform tailored to illustrating the integrative nature of specific subjects can be very effective in enhancing the involvement of students in the classroom. The focus of this paper is twofold; first, highlighting a specific project from a teaching perspective for the design and implementation of a multipurpose experimental test facility for plug-in electric vehicle, renewable energy and energy storage management in the smart grid context. Second, proposing the application of this project as an interactive tool to assist in demonstrating the system behaviour, when teaching the new emerging subjects in power engineering courses particularly, Smart Grid. Indeed, the scope of the test facility with the developed simulation tool is specially structured and oriented to education, training and research studies, where problem-based learning can be easily implemented. The electrical test hardware in this design can be operated independently as a multipurpose experimental setup for plug-in electric vehicle. In addition, it can also be interconnected with a small size wind or photovoltaic system to facilitate a complete hybrid integrated platform for testing and model verification. A variety of illustrative examples are presented to show the features of the test facility with its operation and how the components of this facility were selected. Additionally, several test scenarios are depicted using the developed simulation tool

An Effective Power Dispatch Strategy for Clustered Microgrids While Implementing Optimal Energy Management and Power Sharing Control Using Power Line Communication

The mitigation of uncertainty in the availability ofpower generation from microgrids to enable renewable resourcesto be dispatched is a daunting task for the individual operators. In-stalling energy storage systems may reduce the impact of renewableenergy intermittency. However, a peculiarity in energy manage-ment can be arisen, particularly, when different energy providersmanage these resources. Hence, an intelligent utilization approachshould be devised to maximize the benefits of using battery energystorage, since the cost of this system is the most expensive part. Thisarticle proposes an effective power dispatch strategy for clusteredmicrogrids. The developed hybrid algorithm implements optimalenergy management and power sharing control using binary data.The frequency-shift keying (FSK) technique has been adoptedfor transmitting the binary signal over the power line commu-nication (PLC). A part of the algorithm is utilized to deal withthe optimal scheduling control, whereas the other actuates thedynamic-demand-response-based photovoltaic power forecasting.The performance of the proposed approach with the formulatedbackup injection index has been validated using data collectedfrom the practical network of “Bario, Sarawak.” The presentedresults suggest that the implementation of the proposed strategy canimprove the efficiency of the overall system, causing less operatingcost and fast return. It was also found that the binary signal can betransferred with less distortion through PLC networks when usingthe FSK technique compared to other techniques

Contingency Analysis of a Power Grid with the Participation of Utility-Scale Solar PV Units: A Case Study from Sarawak, Malaysia

Integrating renewable energy resources (RER) into the power grid may jeopardize the whole power system if the penetration level or solar PV uncertainty is not thoroughly managed. The critical impact on the behaviour of power system can be observed during the line outage in a power grid connected with large-scale RER. Therefore, contingency analysis (CA) is crucial to assess such hybrid power grid. This paper proposes a framework based on CA to assess the simultaneous effect of large-scale solar photovoltaic (PV) power plants integrated with the existing power grid, particularly, in the aspects of implementing effective measuring indices. Simulation studies have been carried out on a practical power system, which was modelled by considering the probability of solar irradiance at different locations in Sarawak. The study presented in this paper can provide an insight to identify the level of insecurity for a large-scale deployment of solar PV systems in Sarawak

Adaptive Algorithm for Optimal Route Configuration in Multi-Hop Wireless Sensor Network

Wireless Sensor Networks (WSNs) are best solutions for numerous aspects of engineering applications such as monitoring, control and surveillance of electrical plant amongst others. Autonomously, sensors will communicate with each other, collaborate, share and forward information in a multi-hop fashion without any centralized controller. To gather relevant data, the route optimization mechanism is used to solve the long routing problem and provide the shortest path amongst communicating nodes. Thus, this shortest path criterion is not suitable for WSN as it may lead to power drainage of several nodes and may cause high signaling and processing costs due to the network reconstruction. This paper proposes an optimal route configuration technique based on an adaptive genetic algorithm in which the architecture of multi-hop wireless sensor network is considered as a distributed computing infrastructure. The obtained results show that the proposed algorithm provides an optimal route configuration with the best performance in terms of evaluating the covered distance, packet loss and time delay

Collaborative caching priority for processing requests in MANETs

Accessing distant sources in MANETs leads to poor performance and sometimes impossible due to regular disconnection of mobile hosts. Several approaches have been proposed to improve data accessibility and reduce delay in serving requests. These approaches adopted the collaborative caching techniques, enabling various mobile hosts to cache and share data items in their local caches. However, processing requests based on their classification have not been tackled in previous works to reduce the average delay. In this paper, we propose a collaborative caching priority approach, which serves requests based on their classifications either priority or normal. This is to ensure that priority requests are served with minimum cache discovery overhead and with less delay in fetching data items that are cached in MANETs. The experimental results show that the proposed approach improved the performance of collaborative caching and outperformed the cooperative and adaptive system (COACS), with a decrement of 30.42% in average delay and an increment of 21.26% in hit ratio