A Review on Equipment Protection and System Protection Relay in Power System

Power system equipment is configured and connected together with multiple voltage levels in existing electrical power system. There are varieties of electrical equipment obtainable in the power system predominantly from generation side up to the distribution side. Consequently, appropriate protections must be apt to prevent inessential disturbances that lead to voltage instability, voltage collapse and sooner a total blackout took place in the power system. The understanding of each component on the system protection is critical. This is due to any abnormal condition and failure can be analyzed and solved effectively due to the rapid changing and development on the power system network. Therefore, the enhancement of power quality can be achieved by sheltering the equipment with protection relay in power system. Moreover, the design of a systematic network is crucial for the system protection itself. Several types of protective equipment and protection techniques are taken into consideration in this paper. Hence, the existing accessible types and methods of system protection in the power system network are reviewed

Reliable Power System Operation Plan: Steady State Contingency Analysis

Steady state contingency analysis focuses at the evaluation of the risk certain contingency possibly causes to an electrical network. This analysis is used to review the outage of elements such as transmission lines, transformers and generators, and investigation of the resulting effects on line power flows and bus voltages in Sabah, Malaysia grid transmission system. This is an extremely significant duty for network operators since network stability issues become essentially critical in electricity deregulation. In this paper, the analysis is performed to ensure the system meets grid code standards during normal operations and variety of contingencies condition. Therefore, this paper intended to put forward issues and recommendations towards attaining a steady power system operation plan. Steady state contingency analysis is to calculate power flows in outage states in which one or more system components are out of service. A transmission system must satisfy security criteria in both normal and outage states. This paper presented the steady state contingency analysis for the period of year 2015. The contingency analysis are performed by using the Siemens PTI software, Power System Simulator for Engineering (PSS/E)

An Overview of Out-Of-Step Protection in Power Systems

Power system is subjected to an extensive variety of little or bigger disturbance to the system during the operation. The power system that designed as one of the main requirement is to survive from the larger type of disturbances like faults. The power swing in certain system is the variation in three phase power flow in the power system. This paper mainly discussed the power swing and distance relay and the effect of the power swing on the distance relay and demonstrate about the basic power system stability and power swing phenomena. Moreover, out of step protection and detection applications are revised as well. At the end, the paper also demonstrated the past study of out of step application of TNB 275 KV network

An overview of out-of-step protection in power systems

Power system is subjected to an extensive variety of little or bigger disturbance to the system during the operation. The power system that designed as one of the main requirement is to survive from the larger type of disturbances like faults. The power swing in certain system is the variation in three phase power flow in the power system. This paper mainly discussed the power swing and distance relay and the effect of the power swing on the distance relay and demonstrate about the basic power system stability and power swing phenomena. Moreover, out of step protection and detection applications are revised as well. At the end, the paper also demonstrated the past study of out of step application of TNB 275 KV network

Pseudo-solidification of dredged marine soils with cement - fly ash for reuse in coastal development

The dislodged and removed sediments from the seabed, termed dredged marine soils, are generally classified as a waste material requiring special disposal procedures. This is due to the potential contamination risks of transporting and disposing the dredged soils, and the fact that the material is of poor engineering quality, unsuitable for usage as a conventional good soil in construction. Also, taking into account the incurred costs and risk exposure in transferring the material to the dump site, whether on land or offshore, it is intuitive to examine the possibilities of reusing the dredged soils, especially in coastal development where the transportation route would be of shorter distance between the dredged site and the construction location. Pseudo-solidification of soils is not a novel idea though, where hydraulic binders are injected and mixed with soils to improve the inherent engineering properties for better load bearing capacity. It is commonly used on land in areas with vast and deep deposits of soft, weak soils. However, to implement the technique on the displaced then replaced dredged soil would require careful study, as the material is far more poorly than their land counterparts, and that the deployment of equipment and workforce in a coastal environment is understandably more challenging. The paper illustrates the laboratory investigation of the improved engineering performance of dredged marine soil sample with cement and fly ash blend. Some key findings include optimum dosage of cement and fly ash mix to produce up to 30 times of small strain stiffness improvement, pre-yield settlement reduction of the treated soil unaffected by prolonged curing period, and damage of the cementitious bonds formed by the rather small dosage of admixtures in the soil post-yield. In short, the test results show a promising reuse potential of the otherwise discarded dredged marine soils

Energy Poverty Impact on the Economics of Indonesia Using ARDL Approach

Energy poverty is a global threat to human development path. This study is about the cointegration relationship between energy poverty and the economy of Indonesia for the period of 1995 to 2014. Autoregressive Distributed Lag (ARDL) model and vector error correction model (VECM) were used in this study to study the cointegration and causality analysis. Unit root test and stability test were adopted to increase the reliability and accuracy of the model. The analysis shows that parity purchase power (PPP) has a positive relationship with inflation (INF) in both long-run and short-run. Result shows in long-run, the increment of 1% for both energy consumption (EC) and PPP will result -1.12% and 0.032% effect respectively towards inflation in Indonesia. While for 1% increase in energy consumption is expected to give 1.5297% increment on inflation in short-run cases. Granger causality test shows only unidirectional causality between parity purchase power and inflation in both the long-run and short-run. Energy consumption only shows unidirectional causality toward inflation in the long-run. Overall mean increase of PPP or EC has a single direction influence on the inflation rate. The study can aid policy planning in eradication energy poverty

Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting

Load forecasting is critical for power system operation and market planning.With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a dif�cult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model's superior performance, the proposed method is applied to Ireland's load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model's generalizability

The Implementation of Energy Efficiency For Buildings In Algeria: A Perspective of Stakeholders

Abstract: The implementation of the building design provides the sustainability of the building and the environment. However, it offers many benefits to building owners and users to reduce maintenance costs and extend the service life, which is a feature of the building design. The intent of this research is to implement the energy efficiency for building constructions in Algerian perspective of stakeholders. There are two objectives in this study which are to investigate the challenges of building designs in achieving energy efficiency and to recommend the possible ways of investigation of building designs for energy efficiency. Besides the populations in this study are 210 architect firms but only 136 architect samples are selected according to the recommended Kerjie Morgan table for sampling. The quantitative approach is used in this study where samples distributed by set of questionnaire and analyzed by using Statistical Package for the Social Sciences 23.0 (SPSS 23.0). The result for this study is measured by average mean which is looking for the highest factor to the lowest factor. The highest factor is challenge of sustainability for energy efficiency in building design, followed by challenge of energy minimising consumption in building due to building services, third challenge of high cost of building design for energy analysis, the fourth is challenge of knowledge of energy efficiency during building design and the last factor is challenge of the high cost of energy efficiency in building. Eventually this study produced several recommendations for future research, there are stakeholders must use the natural resources in lighting and ventilation and also the electrical side in the building design. they have to aware about the high value of energy wasting, government must create a regulation for energy consumption in construction in order to control the energy use in buildings, Algerian government must support the implementation of energy efficiency in building design and provide training centre, workshop, conferences about the possibility of energy efficiency analysis to elaborate daylighting analysis, and energy analysis during the design stage. This recommendation is to improve the sustainability in energy efficiency for a better future for a better generation.   Keywords: Construction, sustainability, energy, efficiency, consumption, building desig

Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction

Lithium battery applications in a variety of engineering sectors must be safe and reliable while maintaining a high level of energy efficiency. An accurate assessment of the battery's state of health (SOH) is critical in battery management systems (BMS). In recent years, it has been proved that machine learning is effective at estimating SOH. This work proposes a novel approach of health indicator (HI) extraction based on the U-chord curvature model, based on a complete analysis of battery aging data. In contrast to previous approaches for feature extraction, our method splits the discharge process into various phases based on the curvature of the discharge curve and extracts many HIs with a high correlation to battery SOH in the discharge platform stage of the discharge curve. To demonstrate the superiority of the proposed model, several well-known machine learning algorithms are employed to estimate SOH using extracted attributes. Long short-term memory (LSTM) and artificial neural networks (ANNs) are examples of these techniques. Accuracy, reliability, and robustness of the proposed model are evaluated using three publicly available data sets. According to the data, the model appears to be capable of accurately calculating the battery's SOH, with a mean absolute error of less than 1.08% and a root mean square error of less than 1.46% for various battery types

A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction

Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model (DNM) in this paper. This model is trained using improved biogeography-based optimization (IBBO), a technique that incor�porates a domestication operation to increase the performance of classical biogeography-based optimization (BBO). To be more precise, a similar day selection (SDS) technique is presented for selecting the training set, and wavelet packet transform (WPT) is used to divide the input data into many components. IBBO is then used to train DNM weights and thresholds for each component prediction. Finally, each component’s prediction results are stacked and reassembled. The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs. The simulation results indicate that the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting (PVPF)