A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Prediction of double-regulated hydraulic turbine on-cam energy characteristics by artificial neural networks approach

Određivanje energetskih kombinatorskih karakteristika dvojno regulisane hidraulične turbine se zasniva na rezultatima opsežnih i skupih eksperimentalnih ispitivanja na modelu u laboratoriji i terenskih merenja na prototipu u hidroelektranama. Eksploatacioni dijagram se dobija na osnovu prostornih interpolacija reprezentativnih mernih tačaka koje pripadaju kombinatorskim krivama formiranih za različite brzinske faktore. U radu je dat akcenat na primeni savremene metode veštačkih neuronskih mreža u određivanju kombintorskih karakteristika turbine posebno u radnim režimima koji nisu mereni. Deo postojećih podataka o energetskim parametrima Kaplan turbine koji su dobijeni eksperimentalnim putem iskorišćeni su za obučavanje tri razvijena modela veštačkih neuronskih mreža. Analizom, testiranjem i validacijom dobijenih energetskih parametara turbine međusobnim upoređivanjem sa ostalim eksperimentalnim podacima razmatrana je pouzdanost primenjene metode.The determination of the energy characteristics of a double-regulated hydro turbine is based on numerous measuring points during extensive and expensive experimental model tests in the laboratory and on site prototype tests at the hydropower plant. By the spatial interpolation of representative measured points that belong to the so-called on-cam curves for different speed factors, the hill performance diagram is obtained. The focus of the paper is the contemporary method of artificial neural network models use for the prediction of turbine characteristics, especially in not measured operation modes. A part of the existing set of experimental data for the Kaplan turbine energy parameters is used to train three developed neural network models. The reliability of applied method is considered by analysing, testing and validating the predicted turbine energy parameters in comparison with the remaining data

Prediction of double-regulated hydraulic turbine on-cam energy characteristics by artificial neural networks approach

Određivanje energetskih kombinatorskih karakteristika dvojno regulisane hidraulične turbine se zasniva na rezultatima opsežnih i skupih eksperimentalnih ispitivanja na modelu u laboratoriji i terenskih merenja na prototipu u hidroelektranama. Eksploatacioni dijagram se dobija na osnovu prostornih interpolacija reprezentativnih mernih tačaka koje pripadaju kombinatorskim krivama formiranih za različite brzinske faktore. U radu je dat akcenat na primeni savremene metode veštačkih neuronskih mreža u određivanju kombintorskih karakteristika turbine posebno u radnim režimima koji nisu mereni. Deo postojećih podataka o energetskim parametrima Kaplan turbine koji su dobijeni eksperimentalnim putem iskorišćeni su za obučavanje tri razvijena modela veštačkih neuronskih mreža. Analizom, testiranjem i validacijom dobijenih energetskih parametara turbine međusobnim upoređivanjem sa ostalim eksperimentalnim podacima razmatrana je pouzdanost primenjene metode.The determination of the energy characteristics of a double-regulated hydro turbine is based on numerous measuring points during extensive and expensive experimental model tests in the laboratory and on site prototype tests at the hydropower plant. By the spatial interpolation of representative measured points that belong to the so-called on-cam curves for different speed factors, the hill performance diagram is obtained. The focus of the paper is the contemporary method of artificial neural network models use for the prediction of turbine characteristics, especially in not measured operation modes. A part of the existing set of experimental data for the Kaplan turbine energy parameters is used to train three developed neural network models. The reliability of applied method is considered by analysing, testing and validating the predicted turbine energy parameters in comparison with the remaining data