This document discusses using data mining algorithms to predict faults in wind turbines. It presents a three-stage process: 1) Predict any fault, 2) Predict specific faults, and 3) Identify unseen faults. The authors analyzed data from 17 wind turbines over 4 months using various data mining algorithms. Random forest algorithms provided the most accurate predictions. The models were validated on previously unseen fault data from the turbines. The research aims to help improve wind turbine operations and maintenance through condition-based fault prediction.
Real Time and Wireless Smart Faults Detection Device for Wind Turbineschokrio
In new energy development, wind power has boomed. It is due to the proliferation of wind parks and their operation in supplying the national electric grid with low cost and clean resources. Hence, there is an increased need to establish a proactive maintenance for wind turbine machines based on remote control and monitoring. That is necessary with a real-time wireless connection in offshore or inaccessible locations while the wired method has many flaws. The objective of this strategy is to prolong wind turbine lifetime and to increase productivity. The hardware of a remote control and monitoring system for wind turbine parks is designed. It takes advantage of GPRS or Wi-Max wireless module to collect data measurements from different wind machine sensors through IP based multi-hop communication. Computer simulations with Proteus ISIS and OPNET software tools have been conducted to evaluate the performance of the studied system. Study findings show that the designed device is suitable for application in a wind park.
A transition from manual to Intelligent Automated power system operation -A I...IJECEIAES
This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.
IRJET- Fuzzy predictive control of Variable Speed Wind Turbines using Fuzzy T...IRJET Journal
This document presents a fuzzy logic controller for maximum power extraction from variable speed wind turbines. It proposes estimating the effective wind speed using three algorithms that solve power balance equations, since wind speed cannot be directly measured. The estimated wind speed is used to determine the optimal rotor speed reference for generator control. A fuzzy logic controller is designed using two inputs (error and change in error) and one output (control output change). Simulation results show the effectiveness of the proposed wind speed estimation and fuzzy control approaches in tracking the optimal rotor speed compared to PI control.
Energy harvesting earliest deadline first scheduling algorithm for increasing...IJECEIAES
In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.
This document provides an overview of using neural network techniques in power systems. It discusses how neural networks have been applied to areas like fault diagnosis, security assessment, load forecasting, economic dispatch, and harmonic analysis. The number of published papers in these areas has grown significantly from 1990-1996 to 2000-2005, particularly in load forecasting, fault diagnosis/location, economic dispatch, security assessment, and transient stability. The document then reviews in more detail how neural networks have been applied to load forecasting, fault diagnosis/location, and economic dispatch problems in the power industry.
This document discusses electrical energy management and load forecasting in smart grids using artificial neural networks. It presents a study applying backpropagation neural networks to short-term load forecasting for Sudan's National Electric Company. The neural network model was used to forecast load, with error calculated by comparing forecasted and actual load data. The document also discusses generation dispatch, demand forecasting techniques, and designing a neural network for one-day load forecasting. It evaluates network performance and error for different training data sizes, finding that a ten-day training dataset produced the best results with minimum error. The neural network approach was able to reliably predict the nonlinear relationship between historical data and load.
Stand alone-pv-hybrid-systems barcelona05 excellent modelMhamed Hammoudi
This document introduces a simple technique for sizing stand-alone PV hybrid systems that requires little information. The technique proceeds in practical steps to design the rated powers of key system components. An energy balance equation is developed to estimate the daily PV energy required based on parameters like solar fraction, solar-load mismatch, and efficiencies. A case study demonstrates how to implement the technique and provides helpful sizing curves. The technique can be adapted to user requirements, component characteristics, and technical specifications.
Real Time and Wireless Smart Faults Detection Device for Wind Turbineschokrio
In new energy development, wind power has boomed. It is due to the proliferation of wind parks and their operation in supplying the national electric grid with low cost and clean resources. Hence, there is an increased need to establish a proactive maintenance for wind turbine machines based on remote control and monitoring. That is necessary with a real-time wireless connection in offshore or inaccessible locations while the wired method has many flaws. The objective of this strategy is to prolong wind turbine lifetime and to increase productivity. The hardware of a remote control and monitoring system for wind turbine parks is designed. It takes advantage of GPRS or Wi-Max wireless module to collect data measurements from different wind machine sensors through IP based multi-hop communication. Computer simulations with Proteus ISIS and OPNET software tools have been conducted to evaluate the performance of the studied system. Study findings show that the designed device is suitable for application in a wind park.
A transition from manual to Intelligent Automated power system operation -A I...IJECEIAES
This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.
IRJET- Fuzzy predictive control of Variable Speed Wind Turbines using Fuzzy T...IRJET Journal
This document presents a fuzzy logic controller for maximum power extraction from variable speed wind turbines. It proposes estimating the effective wind speed using three algorithms that solve power balance equations, since wind speed cannot be directly measured. The estimated wind speed is used to determine the optimal rotor speed reference for generator control. A fuzzy logic controller is designed using two inputs (error and change in error) and one output (control output change). Simulation results show the effectiveness of the proposed wind speed estimation and fuzzy control approaches in tracking the optimal rotor speed compared to PI control.
Energy harvesting earliest deadline first scheduling algorithm for increasing...IJECEIAES
In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.
This document provides an overview of using neural network techniques in power systems. It discusses how neural networks have been applied to areas like fault diagnosis, security assessment, load forecasting, economic dispatch, and harmonic analysis. The number of published papers in these areas has grown significantly from 1990-1996 to 2000-2005, particularly in load forecasting, fault diagnosis/location, economic dispatch, security assessment, and transient stability. The document then reviews in more detail how neural networks have been applied to load forecasting, fault diagnosis/location, and economic dispatch problems in the power industry.
This document discusses electrical energy management and load forecasting in smart grids using artificial neural networks. It presents a study applying backpropagation neural networks to short-term load forecasting for Sudan's National Electric Company. The neural network model was used to forecast load, with error calculated by comparing forecasted and actual load data. The document also discusses generation dispatch, demand forecasting techniques, and designing a neural network for one-day load forecasting. It evaluates network performance and error for different training data sizes, finding that a ten-day training dataset produced the best results with minimum error. The neural network approach was able to reliably predict the nonlinear relationship between historical data and load.
Stand alone-pv-hybrid-systems barcelona05 excellent modelMhamed Hammoudi
This document introduces a simple technique for sizing stand-alone PV hybrid systems that requires little information. The technique proceeds in practical steps to design the rated powers of key system components. An energy balance equation is developed to estimate the daily PV energy required based on parameters like solar fraction, solar-load mismatch, and efficiencies. A case study demonstrates how to implement the technique and provides helpful sizing curves. The technique can be adapted to user requirements, component characteristics, and technical specifications.
Wireless data acquisition for photovoltaic power system copyYaseen Ahmed
This document presents a wireless system for monitoring the input and output parameters of a photovoltaic generation plant. The system includes sensors to measure temperature, irradiance, voltage and current from the solar arrays. A microcontroller acquires the analog sensor data and transmits it wirelessly via Wi-Fi to a user computer. The user can then view the acquired performance data graphically to analyze the plant's operation and output.
This document describes a visually-informed decision-making platform (VIDMAP) for model-based design of wind farms. It aims to quantify and illustrate the criticality of information exchanged between different models in the wind farm layout optimization process. The platform consists of three main components: (1) uncertainty quantification to quantify variability in inputs and uncertainties introduced by upstream models, (2) sensitivity analysis to analyze sensitivity of downstream models, and (3) information visualization to visualize uncertainties and inter-model sensitivities. Sensitivity analysis is performed to quantify the sensitivity of an energy production model to first-level inputs and errors in upstream models like wind distribution, shear, turbine power response, and wake models.
Experimental investigation, optimization and performance prediction of wind t...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Experimental investigation, optimization and performance prediction of wind t...eSAT Journals
Abstract Man has been doing business with Winds since a very long period of time; the wind power has been used as long as humans have put sails into the wind. For more than two millennia wind powered machines have ground grain and pumped water. This affiliation of man and wind is inexhaustible which makes Wind Energy a very significant and rapidly developing renewable energy sources in all over the world. But when it comes to electricity generation by harnessing the wind energy, indeed, it requires more technicality, since it requires a great need for correct and reliable installations of new wind turbines in a more optimized way for smooth operations and electricity productions, and for that, a precise knowledge of wind energy regime is a prerequisite for the efficient and optimized extraction of power from the wind. The main purpose of this paper is to present, in brief, wind potential and to perform an investigation on wind flow characteristics at RGPV Hill Top. In this work the recorded time series wind data for a period of five months as from july-2013 to November-2013 at the heights of 20 meters and 40 meters was fetched and analyzed for studying the observed wind climate, which was recorded by the NRG Symphonie Data logger wind mast installed at Energy Park, RGPV campus with an fixed averaging interval of 10 minutes, the analyzed data which was worked upon comprises of wind speed data in meter per second and its direction of flow in degrees. As a part of this work, the Wind Atlas Analysis and Application Program (WAsP) was employed to predict average mean-wind speeds for all directions for some desired sites. The influences of roughness of the terrain, for the area were also taken into consideration, followed by the vector map of the area. These data were analyzed using WAsP software and regional wind climate of the area was determined, leading to a wind resource map of the whole site, providing crucial details which helped in selecting the proposed turbine sites. Also, the AEP for the two already installed turbines was calculated.
PhD defence: Self-Forecasting EneRgy load Stakeholders (SFERS) for Smart GridsDejan Ilic
This document discusses enabling traditionally passive electricity consumers to actively participate in smart grids through self-forecasting of their energy loads. It proposes a system called SFERS that uses smart metering and variable energy storage to allow consumers to accurately forecast and control their energy usage, making it predictable. This would meet prerequisites for smart grid services. The system is evaluated using real usage data from an office building, showing it can improve forecasting accuracy beyond what energy retailers currently achieve.
This document summarizes a master's thesis that applied machine learning models to short-term electric load forecasting in Greece. It describes testing various machine learning algorithms like SVM, random forests, XGBoost and neural networks on electricity demand data from the Greek grid combined with meteorological data. The best performing models achieved a prediction error of 2.41%, outperforming the existing operator models. A shiny web application called ipto-ml was developed to allow for short-term load forecasting on new dates using the models.
DFIG CONVERTER CONTROLLERS USED IN WIND FARMS TO IMPROVE POWER TRANSFER CAPAB...AM Publications
To address the challenges of global warming resulting worldwide climate change leading to devastating calamities and posing basic threat to all living things on earth, modern day research is rightfully directed towards clean sources of green energy. Wind Power is the fastest growing environmental friendly emission free green source of energy. Effective long distance power transfer capability of the system is warranted due to locational disadvantages of availability of some Wind Power sites situated in remote areas away from load centre and even some best sites available offshore. This paper presents the study of doubly fed induction generators (DFIG) converter controllers integrated to flexible alternating current transmission system (FACTS) to improve power transfer capability of Wind farms over long distances.
Wind power forecasting: A Case Study in Terrain using Artificial IntelligenceIRJET Journal
This document presents a study on using artificial neural networks to forecast wind power. Real-time data on wind speed, direction, temperature, humidity and pressure was collected from a measurement station. 100 artificial neural networks with different structures were trained and tested. The best performing network was a multilayer perceptron with 6 inputs, 24 hidden layers, exponential activation and identity output activation. This network achieved a 99% success rate in estimating wind power compared to real measured data. The study demonstrates that artificial neural networks can accurately estimate wind power for short-term forecasting.
Power system operation & control( Switching & Controlling System)UthsoNandy
This document discusses a course on power system operation and control. It includes:
- An overview of principles like SCADA systems, unit commitment, and security analysis.
- A list of recommended textbooks and software like PowerWorld and Matlab.
- A description of the general structure of modern power systems including generation, transmission, distribution, and loads.
Automated Solar Tracking System for Efficient Energy Utilizationvivatechijri
This paper proposes a project that involves an automated solar tracking system which will make use
of LDR’s to track the position of sun. The output of LDR’s will be compared and analyzed to provide correct
alignment of the solar panel. Also another tracking technique is being implemented along, which uses the relation
of sun earth position at a given location. This telemetric data is given to microcontroller which will drive the
motors to align the solar panel. This is useful during cloudy weather and rainy days when it is difficult to check
the position of sun. Solar panels give output efficiency of around 15% to 20% based on the type of panel. The use
of solar tracking system increases it to a range of about 30% to 35%. This project further involves use of reflective
sheets on the sides of solar panel which will concentrate the reflected rays on the panel. Due to this the efficiency
is further increased around 40%. This project is a cost effective solution for stationary solar systems to increase
efficiency.
This paper presents a novel optimization technique using genetic algorithms to develop an optimized emergency defence plan for power systems. The technique determines the optimal combination of generator tripping, load shedding, and islanding to regain system stability following severe contingencies. It was applied to the Libyan power system using time-domain simulations to evaluate solutions. Results showed the optimized defence plan required less load shedding than the existing Libyan plan and improved system response during a 2003 blackout event.
Power system planning & operation [eceg 4410]Sifan Welisa
The document discusses power load forecasting and substation planning. It explains that accurate load forecasting is important for power system planning and operation. Several load forecasting methods are described, including those based on historical load data, economic factors, and standardized load curves. Load forecasts can be short, medium, or long-term. The document also discusses factors to consider in substation planning and design, such as location, equipment requirements, and configuration. Feasibility studies are important for assessing potential hydroelectric and substation projects.
The document discusses the design of transmission lines. It covers the need for transmission lines, the components used including conductors, earth wires, insulators and transmission towers. It then describes the design methodology which involves collecting data, calculating loads on components, selecting correction factors and designing components to withstand loads. Key factors in transmission line design are the electrical requirements, selection of transmission voltage based on line length and power loss, and choice of conductor and insulator materials.
Adjusting post processing approach for very short-term solar power forecastsMohamed Abuella
This document presents research on adjusting post-processing approaches for very short-term solar power forecasts. It begins with motivating the need for solar power forecasts, then describes the methodology used which includes data collection from 3 PV sites, data preparation involving cleaning, imputation and detrending, and forecasting models including persistence, ARIMA, NAR, ANN and ELM. The models' forecasts are combined using random forest, and the combined forecasts are adjusted using a novel approach involving fitting to target-horizon and double target-horizon forecasts and their ramp rates. The adjusted forecasts and a simple average approach are evaluated on 9 cases across locations and horizons using RMSE. The adjusting approach achieves lower RMSE than the simple average
White Paper: The Role and Importance of Weather Data in Solar Plant Performan...Inaccess
This document discusses the importance of accurate weather data for evaluating the performance of solar plants. It outlines the common meteorological sensors used - pyranometers to measure solar irradiance, reference cells, temperature sensors, and others. Correct sensor placement, installation of redundant sensors, fast data sampling, and spatial averaging of measurements across the plant are emphasized to ensure accurate weather readings. An example compares irradiance measurements from different sensors and shows how erroneous data can significantly impact calculated performance metrics like the performance ratio.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
New calculation methodology for solar thermal systems Aiguasol
The document describes a new methodology called METASOL for calculating the sizing of solar thermal systems in Spain. It was developed to address limitations of the existing F-Chart methodology. METASOL is based on over 69,000 detailed TRNSYS simulations of 7 different solar thermal system configurations across 5 locations in Spain. Monthly correlations were extracted from over 800,000 data points to generate a simple calculation method that considers factors like multiple system types, locations, loads, and temperatures not addressed by F-Chart. The new methodology is integrated into a free and easy-to-use graphical interface to standardize solar thermal system sizing in Spain.
Design and Implementation of High Resolution Data Acquisition Systemijsrd.com
Fuel cell stacks containing hundreds of individual cells are capable of generating high voltage and current values needed for transportation, commercial, residential, portable and industrial power applications. Although majority of hydrogen produced today comes from reformulated natural gas generated through a process that creates a significant amount of carbon dioxide, fuel cell is still a viable energy source for the future electrical power applications. One of the hard cases of the fuel-cell power systems is proper monitoring, instrumentation and data acquisition of system parameters such as fuel flow into the system, AC and DC voltage values, load current, humidity, power, pressure, temperature, fuel utilization, overall system efficiency, noise, etc. Fuel cell test systems must precisely monitor and control the aforementioned hundreds of measurements in real-time. It is necessary to have an instrumentation system which is able to monitor and control fuel cell operation under varying conditions and accurately get information relating to real-time performance and operational characteristics to calculate fuel cell efficiency correctly. Instrumentation and interface systems must also provide flexible data acquisition, monitoring, and control capability to precisely control fuel cell operation. Therefore, a typical fuel cell test system requires high-resolution, high-voltage input, isolation, and waveform acquisition capability. The objective of this applied research project is design and implementation of a high-resolution data acquisition and interface module for a 500 W Hydrogen fuel cell power station using LabVIEW ™ PDS v8.20 software and field point based data acquisition modules.
The document discusses the potential for wind energy in Lebanon. It outlines Lebanon's national framework and policy commitments to increase renewable energy, including a target of 12% renewable energy by 2020. One of the initiatives in Lebanon's National Energy Efficiency Action Plan is to generate electricity from wind power, with a target of 400-500 megawatts of wind energy by 2020. The first phases include requests for private sector proposals for a 50-100 megawatt wind farm and expressions of interest for a public sector 1-10 megawatt wind farm.
This paper deals with subsynchronous resonance (SSR) phenomena in a capacitive series-compensated DFIG-based wind farm. Using both modal analysis and time-domain simulation, it is shown that the DFIG wind farm is potentially unstable due to the SSR mode. In order to damp the SSR, the rotor-side converter (RSC) and grid-side converter (GSC) controllers of the DFIG are utilized. The objective is to design a simple proportional SSR damping controller (SSRDC) by properly choosing an optimum input control signal (ICS) to the SSRDC block, so that the SSR mode becomes stable without decreasing or destabilizing the other system modes. Moreover, an optimum point within the RSC and GSC controllers to insert the SSRDC is identified. Three different signals are tested as potential ICSs including rotor speed, line real power, and voltage across the series capacitor, and an optimum ICS is identified using residue-based analysis and root-locus method. Moreover, two methods are discussed in order to estimate the optimum ICS, without measuring it directly. The studied power system is a 100 MW DFIG-based wind farm connected to a series-compensated line whose parameters are taken from the IEEE first benchmark model (FBM) for computer simulation of the SSR. MATLAB/Simulink is used as a tool for modeling and designing the SSRDC, and power system computer aided design/electromagnetic transients including dc (PSCAD/EMTDC) is used to perform time-domain simulation for design process validation.
IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) multidisciplinary peer-reviewed Journal with reputable academics and experts as board member. IOSR-JESTFT is designed for the prompt publication of peer-reviewed articles in all areas of subject. The journal articles will be accessed freely online
Wireless data acquisition for photovoltaic power system copyYaseen Ahmed
This document presents a wireless system for monitoring the input and output parameters of a photovoltaic generation plant. The system includes sensors to measure temperature, irradiance, voltage and current from the solar arrays. A microcontroller acquires the analog sensor data and transmits it wirelessly via Wi-Fi to a user computer. The user can then view the acquired performance data graphically to analyze the plant's operation and output.
This document describes a visually-informed decision-making platform (VIDMAP) for model-based design of wind farms. It aims to quantify and illustrate the criticality of information exchanged between different models in the wind farm layout optimization process. The platform consists of three main components: (1) uncertainty quantification to quantify variability in inputs and uncertainties introduced by upstream models, (2) sensitivity analysis to analyze sensitivity of downstream models, and (3) information visualization to visualize uncertainties and inter-model sensitivities. Sensitivity analysis is performed to quantify the sensitivity of an energy production model to first-level inputs and errors in upstream models like wind distribution, shear, turbine power response, and wake models.
Experimental investigation, optimization and performance prediction of wind t...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Experimental investigation, optimization and performance prediction of wind t...eSAT Journals
Abstract Man has been doing business with Winds since a very long period of time; the wind power has been used as long as humans have put sails into the wind. For more than two millennia wind powered machines have ground grain and pumped water. This affiliation of man and wind is inexhaustible which makes Wind Energy a very significant and rapidly developing renewable energy sources in all over the world. But when it comes to electricity generation by harnessing the wind energy, indeed, it requires more technicality, since it requires a great need for correct and reliable installations of new wind turbines in a more optimized way for smooth operations and electricity productions, and for that, a precise knowledge of wind energy regime is a prerequisite for the efficient and optimized extraction of power from the wind. The main purpose of this paper is to present, in brief, wind potential and to perform an investigation on wind flow characteristics at RGPV Hill Top. In this work the recorded time series wind data for a period of five months as from july-2013 to November-2013 at the heights of 20 meters and 40 meters was fetched and analyzed for studying the observed wind climate, which was recorded by the NRG Symphonie Data logger wind mast installed at Energy Park, RGPV campus with an fixed averaging interval of 10 minutes, the analyzed data which was worked upon comprises of wind speed data in meter per second and its direction of flow in degrees. As a part of this work, the Wind Atlas Analysis and Application Program (WAsP) was employed to predict average mean-wind speeds for all directions for some desired sites. The influences of roughness of the terrain, for the area were also taken into consideration, followed by the vector map of the area. These data were analyzed using WAsP software and regional wind climate of the area was determined, leading to a wind resource map of the whole site, providing crucial details which helped in selecting the proposed turbine sites. Also, the AEP for the two already installed turbines was calculated.
PhD defence: Self-Forecasting EneRgy load Stakeholders (SFERS) for Smart GridsDejan Ilic
This document discusses enabling traditionally passive electricity consumers to actively participate in smart grids through self-forecasting of their energy loads. It proposes a system called SFERS that uses smart metering and variable energy storage to allow consumers to accurately forecast and control their energy usage, making it predictable. This would meet prerequisites for smart grid services. The system is evaluated using real usage data from an office building, showing it can improve forecasting accuracy beyond what energy retailers currently achieve.
This document summarizes a master's thesis that applied machine learning models to short-term electric load forecasting in Greece. It describes testing various machine learning algorithms like SVM, random forests, XGBoost and neural networks on electricity demand data from the Greek grid combined with meteorological data. The best performing models achieved a prediction error of 2.41%, outperforming the existing operator models. A shiny web application called ipto-ml was developed to allow for short-term load forecasting on new dates using the models.
DFIG CONVERTER CONTROLLERS USED IN WIND FARMS TO IMPROVE POWER TRANSFER CAPAB...AM Publications
To address the challenges of global warming resulting worldwide climate change leading to devastating calamities and posing basic threat to all living things on earth, modern day research is rightfully directed towards clean sources of green energy. Wind Power is the fastest growing environmental friendly emission free green source of energy. Effective long distance power transfer capability of the system is warranted due to locational disadvantages of availability of some Wind Power sites situated in remote areas away from load centre and even some best sites available offshore. This paper presents the study of doubly fed induction generators (DFIG) converter controllers integrated to flexible alternating current transmission system (FACTS) to improve power transfer capability of Wind farms over long distances.
Wind power forecasting: A Case Study in Terrain using Artificial IntelligenceIRJET Journal
This document presents a study on using artificial neural networks to forecast wind power. Real-time data on wind speed, direction, temperature, humidity and pressure was collected from a measurement station. 100 artificial neural networks with different structures were trained and tested. The best performing network was a multilayer perceptron with 6 inputs, 24 hidden layers, exponential activation and identity output activation. This network achieved a 99% success rate in estimating wind power compared to real measured data. The study demonstrates that artificial neural networks can accurately estimate wind power for short-term forecasting.
Power system operation & control( Switching & Controlling System)UthsoNandy
This document discusses a course on power system operation and control. It includes:
- An overview of principles like SCADA systems, unit commitment, and security analysis.
- A list of recommended textbooks and software like PowerWorld and Matlab.
- A description of the general structure of modern power systems including generation, transmission, distribution, and loads.
Automated Solar Tracking System for Efficient Energy Utilizationvivatechijri
This paper proposes a project that involves an automated solar tracking system which will make use
of LDR’s to track the position of sun. The output of LDR’s will be compared and analyzed to provide correct
alignment of the solar panel. Also another tracking technique is being implemented along, which uses the relation
of sun earth position at a given location. This telemetric data is given to microcontroller which will drive the
motors to align the solar panel. This is useful during cloudy weather and rainy days when it is difficult to check
the position of sun. Solar panels give output efficiency of around 15% to 20% based on the type of panel. The use
of solar tracking system increases it to a range of about 30% to 35%. This project further involves use of reflective
sheets on the sides of solar panel which will concentrate the reflected rays on the panel. Due to this the efficiency
is further increased around 40%. This project is a cost effective solution for stationary solar systems to increase
efficiency.
This paper presents a novel optimization technique using genetic algorithms to develop an optimized emergency defence plan for power systems. The technique determines the optimal combination of generator tripping, load shedding, and islanding to regain system stability following severe contingencies. It was applied to the Libyan power system using time-domain simulations to evaluate solutions. Results showed the optimized defence plan required less load shedding than the existing Libyan plan and improved system response during a 2003 blackout event.
Power system planning & operation [eceg 4410]Sifan Welisa
The document discusses power load forecasting and substation planning. It explains that accurate load forecasting is important for power system planning and operation. Several load forecasting methods are described, including those based on historical load data, economic factors, and standardized load curves. Load forecasts can be short, medium, or long-term. The document also discusses factors to consider in substation planning and design, such as location, equipment requirements, and configuration. Feasibility studies are important for assessing potential hydroelectric and substation projects.
The document discusses the design of transmission lines. It covers the need for transmission lines, the components used including conductors, earth wires, insulators and transmission towers. It then describes the design methodology which involves collecting data, calculating loads on components, selecting correction factors and designing components to withstand loads. Key factors in transmission line design are the electrical requirements, selection of transmission voltage based on line length and power loss, and choice of conductor and insulator materials.
Adjusting post processing approach for very short-term solar power forecastsMohamed Abuella
This document presents research on adjusting post-processing approaches for very short-term solar power forecasts. It begins with motivating the need for solar power forecasts, then describes the methodology used which includes data collection from 3 PV sites, data preparation involving cleaning, imputation and detrending, and forecasting models including persistence, ARIMA, NAR, ANN and ELM. The models' forecasts are combined using random forest, and the combined forecasts are adjusted using a novel approach involving fitting to target-horizon and double target-horizon forecasts and their ramp rates. The adjusted forecasts and a simple average approach are evaluated on 9 cases across locations and horizons using RMSE. The adjusting approach achieves lower RMSE than the simple average
White Paper: The Role and Importance of Weather Data in Solar Plant Performan...Inaccess
This document discusses the importance of accurate weather data for evaluating the performance of solar plants. It outlines the common meteorological sensors used - pyranometers to measure solar irradiance, reference cells, temperature sensors, and others. Correct sensor placement, installation of redundant sensors, fast data sampling, and spatial averaging of measurements across the plant are emphasized to ensure accurate weather readings. An example compares irradiance measurements from different sensors and shows how erroneous data can significantly impact calculated performance metrics like the performance ratio.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
New calculation methodology for solar thermal systems Aiguasol
The document describes a new methodology called METASOL for calculating the sizing of solar thermal systems in Spain. It was developed to address limitations of the existing F-Chart methodology. METASOL is based on over 69,000 detailed TRNSYS simulations of 7 different solar thermal system configurations across 5 locations in Spain. Monthly correlations were extracted from over 800,000 data points to generate a simple calculation method that considers factors like multiple system types, locations, loads, and temperatures not addressed by F-Chart. The new methodology is integrated into a free and easy-to-use graphical interface to standardize solar thermal system sizing in Spain.
Design and Implementation of High Resolution Data Acquisition Systemijsrd.com
Fuel cell stacks containing hundreds of individual cells are capable of generating high voltage and current values needed for transportation, commercial, residential, portable and industrial power applications. Although majority of hydrogen produced today comes from reformulated natural gas generated through a process that creates a significant amount of carbon dioxide, fuel cell is still a viable energy source for the future electrical power applications. One of the hard cases of the fuel-cell power systems is proper monitoring, instrumentation and data acquisition of system parameters such as fuel flow into the system, AC and DC voltage values, load current, humidity, power, pressure, temperature, fuel utilization, overall system efficiency, noise, etc. Fuel cell test systems must precisely monitor and control the aforementioned hundreds of measurements in real-time. It is necessary to have an instrumentation system which is able to monitor and control fuel cell operation under varying conditions and accurately get information relating to real-time performance and operational characteristics to calculate fuel cell efficiency correctly. Instrumentation and interface systems must also provide flexible data acquisition, monitoring, and control capability to precisely control fuel cell operation. Therefore, a typical fuel cell test system requires high-resolution, high-voltage input, isolation, and waveform acquisition capability. The objective of this applied research project is design and implementation of a high-resolution data acquisition and interface module for a 500 W Hydrogen fuel cell power station using LabVIEW ™ PDS v8.20 software and field point based data acquisition modules.
The document discusses the potential for wind energy in Lebanon. It outlines Lebanon's national framework and policy commitments to increase renewable energy, including a target of 12% renewable energy by 2020. One of the initiatives in Lebanon's National Energy Efficiency Action Plan is to generate electricity from wind power, with a target of 400-500 megawatts of wind energy by 2020. The first phases include requests for private sector proposals for a 50-100 megawatt wind farm and expressions of interest for a public sector 1-10 megawatt wind farm.
This paper deals with subsynchronous resonance (SSR) phenomena in a capacitive series-compensated DFIG-based wind farm. Using both modal analysis and time-domain simulation, it is shown that the DFIG wind farm is potentially unstable due to the SSR mode. In order to damp the SSR, the rotor-side converter (RSC) and grid-side converter (GSC) controllers of the DFIG are utilized. The objective is to design a simple proportional SSR damping controller (SSRDC) by properly choosing an optimum input control signal (ICS) to the SSRDC block, so that the SSR mode becomes stable without decreasing or destabilizing the other system modes. Moreover, an optimum point within the RSC and GSC controllers to insert the SSRDC is identified. Three different signals are tested as potential ICSs including rotor speed, line real power, and voltage across the series capacitor, and an optimum ICS is identified using residue-based analysis and root-locus method. Moreover, two methods are discussed in order to estimate the optimum ICS, without measuring it directly. The studied power system is a 100 MW DFIG-based wind farm connected to a series-compensated line whose parameters are taken from the IEEE first benchmark model (FBM) for computer simulation of the SSR. MATLAB/Simulink is used as a tool for modeling and designing the SSRDC, and power system computer aided design/electromagnetic transients including dc (PSCAD/EMTDC) is used to perform time-domain simulation for design process validation.
IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) multidisciplinary peer-reviewed Journal with reputable academics and experts as board member. IOSR-JESTFT is designed for the prompt publication of peer-reviewed articles in all areas of subject. The journal articles will be accessed freely online
Chapter - 8.4 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
This document discusses mining sequence patterns in biological data. It begins with an overview of DNA structure and the central dogma of biology by which DNA is transcribed into RNA and translated into protein. It then describes several lab tools that can be used to determine biological data, such as DNA sequencers, mass spectrometry, and microarrays. The document concludes by noting that biological data mining can provide insights into biological processes and gain knowledge from abundant biological data sources.
Chapter - 4 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
This chapter discusses techniques for efficient computation of data cubes and data generalization from multidimensional databases. It covers topics such as:
- Methods for computing data cubes including top-down, bottom-up, and hybrid approaches.
- Data structure techniques for cube computation including multi-way array aggregation, partitioning for bottom-up computation, and H-cubing.
- Optimization techniques including iceberg cube computation, aggregation sharing, and star reduction to prune unnecessary computations.
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes Chapter 6 of the book "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction. It defines classification and prediction and discusses key issues in classification such as data preparation, evaluating methods, and decision tree induction. Decision tree induction creates a tree model by recursively splitting the training data on attributes and their values to produce leaf nodes containing target class labels. The chapter also covers other classification techniques including Bayesian classification, rule-based classification, support vector machines, and ensemble methods. It describes the process of model construction from training data and then using the model to classify new unlabeled data.
The document discusses a hybrid wind/solar energy system that allows the two intermittent energy sources to supply power separately or together through a new rectifier configuration. It notes the disadvantages of relying solely on solar or wind energy alone due to unpredictability, but explains how combining the two sources into a hybrid system and using maximum power point tracking algorithms can significantly improve the reliability and efficiency of power transfer.
This document discusses decision trees and their use in predictive modeling. It provides an example of using a decision tree to predict credit ratings. The decision tree splits the data into nodes based on variables like checking accounts, savings accounts, and duration. Each node shows the percentage of good and bad credit ratings, with deeper nodes having higher percentages. Decision trees allow targeting subsets of a population that have higher response rates to improve outcomes.
The document provides an overview of topics to be covered in a data analysis course, including cluster analysis and decision trees. The course will cover descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering methods like k-means, and decision tree techniques like CHAID. Clustering involves grouping similar objects together to identify homogeneous clusters that are heterogeneous from each other. Applications of clustering include market segmentation, credit risk analysis, and operations. The document gives an example of clustering students based on their exam scores.
The document provides an overview of cluster analysis techniques. It discusses the need for segmentation to group large populations into meaningful subsets. Common clustering algorithms like k-means are introduced, which assign data points to clusters based on similarity. The document also covers calculating distances between observations, defining the distance between clusters, and interpreting the results of clustering analysis. Real-world applications of segmentation and clustering are mentioned such as market research, credit risk analysis, and operations management.
The document discusses a proposed approach for monitoring wind turbine states using a random forest algorithm (RFA) applied to SCADA data. It involves 3 stages: 1) predicting any fault, 2) predicting specific fault types, and 3) identifying previously unseen faults. RFA provided the most accurate results among algorithms tested, with prediction accuracies of 78-98% for horizons up to 300 seconds. The model also identified various faults at turbines not in the training data, though it struggled with some gearbox-related faults due to lacking relevant sensor data.
This document provides a review of techniques for modelling wind turbine power curves. It discusses the need for accurate power curve models in applications like wind power assessment, turbine selection and performance monitoring. Various factors that influence power curves are outlined, including wind conditions, air density, turbine condition and IEC standard measurement methods. The paper then reviews different power curve modelling methods in the literature, including those using manufacturer data and measured turbine data. It identifies issues like differences between individual and grouped turbines. Overall the document aims to critically analyse existing modelling approaches and identify areas for further research to develop more accurate site-specific power curve models.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
This document summarizes a study on developing a fault detection and isolation method for sensors and actuators of a wind turbine using a modified Kalman filter. The study models the pitch system of a 2.5 MW wind turbine. It considers correlated process and measurement noises and designs residual generators to decouple disturbances from faults. Generalized likelihood ratio and cumulative variance tests are used for detection, and a bank of residual generators is used for isolation based on dual sensor redundancy. The goal is to reliably detect and isolate faults in wind turbines to improve safety and reduce downtime.
Fault Analysis and Prediction in Gas Turbine using Neuro-Fuzzy SystemIRJET Journal
This document presents a study on using a neuro-fuzzy system to detect and predict faults in a gas turbine engine. It begins with an introduction to gas turbines and some background on previous fault detection research. It then describes the methodology which includes modeling the gas turbine mathematically, designing a fault detection system using a Mu-law compressor and thermocouple, and developing a neuro-fuzzy controller. The neuro-fuzzy system is designed using four stages: fuzzification, rule evaluation, aggregation, and defuzzification. Simulation results are presented showing the neuro-fuzzy system was able to predict faults by checking signals against a threshold. The neuro-fuzzy controller outperformed a PID controller in fault detection due to its ability to train and correct
A vertical wind turbine monitoring system using commercial online digital das...IJECEIAES
The output of a green energy generator is required to be monitor continuously. The monitoring process is important because the performance of the energy gen- erator needs to be known and evaluate. However, monitoring the generator manu- ally and efficiently is troublesome. Moreover, when most of the energy generator located at uneasy to reach or at a very remote place. Added to the cost, human intervention for the monitoring process contributes to the unnecessary bill. All the highlighted limitations can be overcome using an internet cloud base system and application. Most of the existing data logging instruments use a memory card or personal computer in their operation. The stored data is accessible only at a dedicated computer alone. This work presented a complete energy generator interface with a commercial online digital dashboard. The digital dashboard, parameters of the wind turbine, such as the amount of power generates and the magnitude of instantaneous voltage can be monitored, and the recorded data can be accessed quickly, at any time and anyplace.
The Renewable energy sources, especially wind turbine generators, are considered as
important generation alternatives in electric power systems due to their non-exhausted nature and
benign environmental effects [1]. The fact that wind power penetration continues to increase has
motivated a need to develop more widely applicable methodologies for evaluating the actual benefits
of adding wind turbines to conventional generating systems. In this paper reliability evaluation of
wind power generation system is carried. Reliability evaluation of generating systems with wind
energy sources is a complex process. It requires an accurate wind speed forecasting technique for the
wind farm site. The method requires historical wind speed data collected over many years for the
wind farm location to determine the necessary parameters of the wind speed models for the
particular site [3]. The evaluation process should also accurately model the intermittent nature of
power output from the wind farm. For the data analysis excel data analysis tool is used and
probability distribution of wind speeds are calculated [10]. This study shows the system availability
for the generation of power from wind turbine generators installed at the Hanamasagar, a village
near Gajendragada of Karnataka State.
Cuckoo search algorithm based for tunning both PI and FOPID controllers for ...IJECEIAES
Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.
This summarizes a document presenting a case study on using a self-evolving neural network algorithm for fault prognosis in wind turbines. The study uses SCADA data from a real 2 MW wind turbine located in Sweden. An artificial neural network model is developed to estimate gearbox bearing temperatures based on inputs like oil temperature, nacelle temperature, turbine power, and RPM. The model is used to detect anomalies by comparing estimated vs. measured temperatures. When a major maintenance activity like gearbox replacement occurs, the model must be retrained due to changes in operating conditions. The case study demonstrates selecting an initial training dataset and later updating the model after a gearbox replacement using the proposed approach. Results show the approach allows the neural
IRJET- Wind Energy Storage Prediction using Machine LearningIRJET Journal
The document discusses using machine learning to predict hourly wind power generation at 7 wind farms based on historical wind speed and direction data. It aims to help utilities better incorporate variable wind power generation into grid operations. It first introduces the challenges of integrating intermittent wind power and the need for accurate forecasts. It then describes the dataset and various data cleaning/preprocessing steps. Finally, it proposes using machine learning algorithms like SVR and kNN regression to generate hourly forecasts based on historical wind farm output and meteorological data from nearby turbines. The models will be evaluated based on their root mean square error.
This document discusses applying an adaptive neuro-fuzzy inference system (ANFIS) neural network for predictive maintenance in a thermal power plant. It begins by providing background on predictive maintenance and previous applications of neural networks. It then describes the key subsystems of a thermal power plant and important factors that affect failures. Next, it introduces ANFIS neural networks and the process used to apply ANFIS to failure prediction for each subsystem. Results show ANFIS structures developed for the lubrication, hydraulic, fuel, cooling, and electric systems and their ability to accurately predict failures based on environmental conditions.
Quantification of operating reserves with high penetration of wind power cons...IJECEIAES
The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.
Risk assessment of power system transient instability incorporating renewabl...IJECEIAES
This document presents a risk assessment method for power system transient stability that incorporates renewable energy sources. The method uses Gaussian process regression and feature selection algorithms to build a predictive model for online transient stability assessment. Offline data is collected from simulations at different operating conditions and contingencies. Feature selection algorithms identify the most important features related to critical fault clearing time as the stability index. The predictive model based on the selected features can then assess transient stability online by predicting critical fault clearing times based on new operating conditions. The method was tested on a 66-bus power system model with wind and solar power integrated at various buses.
Insights into the Efficiencies of On-Shore Wind Turbines: A Data-Centric Anal...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/insights-into-the-efficiencies-of-on-shore-wind-turbines-a-data-centric-analysis/
Literature on renewable energy alternative of wind turbines does not include a multidimensional benchmarking studythat can help investment decisions as well as design processes. This paper presents a data-centric analysis of commercial on-shore wind turbines and provides actionable insights through analytical benchmarking through Data Envelopment Analysis (DEA), visual data analysis, and statistical hypothesis testing. The paper also introduces a novel visualization approach for the understanding and the interpretation of reference sets, the set of efficient wind turbines that should be taken as benchmark by inefficient ones.
The purpose of this work is to present the advantages of the power control (active and reactive) of a wind energy system in order to improve the quality of the energy produced to the grid by presenting two control strategies applied to the conversion system of wind energy equipped with an asynchronous generator with dual power supply. Both techniques are studied and developed and consist of a field control (FOC) and a sliding mode control. They find their strongest justifications for the problem of using a nonlinear control law that is robust to the uncertainties of the model. The goal is to apply these two commands to independently control the active and reactive powers generated by the decoupled asynchronous machine by flow orientation. Thus, a study of these commands will be detailed and validated in the Matlab / Simulink environment with the simultaneous use of the "Pitch Control" and "Maximum Power Point Tracking (MPPT)" techniques. The results of numerical simulations obtained show the increasing interest of the two controls in the electrical systems. They also attest that the quality of the active and reactive powers and voltages of the wind system is considerably improved.
IRJET- Feature Ranking for Energy Disaggregation IRJET Journal
This document discusses feature ranking for energy disaggregation. It begins with an introduction to smart energy meters and interest in monitoring loads at an appliance level. It then reviews literature on using deep neural networks for energy disaggregation, which have achieved better results than hidden Markov models. The document outlines the stages of non-intrusive load monitoring including data acquisition, feature extraction, and inference/learning. It considers features used in energy auditing devices and ranks their significance based on adoption, aiding consumers in selecting key features for analyzing appliance-level electricity bills.
2018 4th International Conference on Green Technology and Sust.docxlorainedeserre
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i ...
2018 4th International Conference on Green Technology and Sust.docxRAJU852744
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i.
IRJET- Design and Analysis of Adaptive Neuro-Fuzzy Controller based Pitch...IRJET Journal
This document describes a study that proposes using an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for pitch angle control of a wind turbine connected to a permanent magnet synchronous generator (PMSG) system. A cascaded ANFIS-PI controller is introduced to control the wind turbine speed. The ANFIS controller integrates the advantages of artificial neural networks and fuzzy logic controllers to better handle the non-linear dynamics of the system compared to a conventional PI controller. Simulations show that the ANFIS controller more efficiently controls the reactive power requirements and rotor speed of the PMSG-based system during grid disturbances compared to a PI controller alone.
Battery Remaining Useful life Forecast Applied in Unmanned Aerial Vehicle.IJERA Editor
The batteries has been widely used as an energy storage system for unmanned aerial vehicles (UAVs). To avoid faults in these systems (UAVs), which are powered by batteries, there are several approaches to forecast the Remaining Useful Life (RUL) of the batteries. In this work, it's proposed the use of a model based on Extended Kalman Filter (EKF) to estimate the RUL in Lithium-Ion batteries. The database used was available at NASA's repository in 2015.
The Genesis of BriansClub.cm Famous Dark WEb PlatformSabaaSudozai
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
Anny Serafina Love - Letter of Recommendation by Kellen Harkins, MS.AnnySerafinaLove
This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
Profiles of Iconic Fashion Personalities.pdfTTop Threads
The fashion industry is dynamic and ever-changing, continuously sculpted by trailblazing visionaries who challenge norms and redefine beauty. This document delves into the profiles of some of the most iconic fashion personalities whose impact has left a lasting impression on the industry. From timeless designers to modern-day influencers, each individual has uniquely woven their thread into the rich fabric of fashion history, contributing to its ongoing evolution.
𝐔𝐧𝐯𝐞𝐢𝐥 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐍𝐄𝐖𝐍𝐓𝐈𝐃𝐄’𝐬 𝐋𝐚𝐭𝐞𝐬𝐭 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠𝐬
Explore the details in our newly released product manual, which showcases NEWNTIDE's advanced heat pump technologies. Delve into our energy-efficient and eco-friendly solutions tailored for diverse global markets.
The Most Inspiring Entrepreneurs to Follow in 2024.pdfthesiliconleaders
In a world where the potential of youth innovation remains vastly untouched, there emerges a guiding light in the form of Norm Goldstein, the Founder and CEO of EduNetwork Partners. His dedication to this cause has earned him recognition as a Congressional Leadership Award recipient.
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
HOW TO START UP A COMPANY A STEP-BY-STEP GUIDE.pdf46adnanshahzad
How to Start Up a Company: A Step-by-Step Guide Starting a company is an exciting adventure that combines creativity, strategy, and hard work. It can seem overwhelming at first, but with the right guidance, anyone can transform a great idea into a successful business. Let's dive into how to start up a company, from the initial spark of an idea to securing funding and launching your startup.
Introduction
Have you ever dreamed of turning your innovative idea into a thriving business? Starting a company involves numerous steps and decisions, but don't worry—we're here to help. Whether you're exploring how to start a startup company or wondering how to start up a small business, this guide will walk you through the process, step by step.
NIMA2024 | De toegevoegde waarde van DEI en ESG in campagnes | Nathalie Lam |...BBPMedia1
Nathalie zal delen hoe DEI en ESG een fundamentele rol kunnen spelen in je merkstrategie en je de juiste aansluiting kan creëren met je doelgroep. Door middel van voorbeelden en simpele handvatten toont ze hoe dit in jouw organisatie toegepast kan worden.
Top 10 Free Accounting and Bookkeeping Apps for Small BusinessesYourLegal Accounting
Maintaining a proper record of your money is important for any business whether it is small or large. It helps you stay one step ahead in the financial race and be aware of your earnings and any tax obligations.
However, managing finances without an entire accounting staff can be challenging for small businesses.
Accounting apps can help with that! They resemble your private money manager.
They organize all of your transactions automatically as soon as you link them to your corporate bank account. Additionally, they are compatible with your phone, allowing you to monitor your finances from anywhere. Cool, right?
Thus, we’ll be looking at several fantastic accounting apps in this blog that will help you develop your business and save time.
Industrial Tech SW: Category Renewal and CreationChristian Dahlen
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
3 Simple Steps To Buy Verified Payoneer Account In 2024SEOSMMEARTH
Buy Verified Payoneer Account: Quick and Secure Way to Receive Payments
Buy Verified Payoneer Account With 100% secure documents, [ USA, UK, CA ]. Are you looking for a reliable and safe way to receive payments online? Then you need buy verified Payoneer account ! Payoneer is a global payment platform that allows businesses and individuals to send and receive money in over 200 countries.
If You Want To More Information just Contact Now:
Skype: SEOSMMEARTH
Telegram: @seosmmearth
Gmail: seosmmearth@gmail.com
Starting a business is like embarking on an unpredictable adventure. It’s a journey filled with highs and lows, victories and defeats. But what if I told you that those setbacks and failures could be the very stepping stones that lead you to fortune? Let’s explore how resilience, adaptability, and strategic thinking can transform adversity into opportunity.
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Digital Marketing with a Focus on Sustainabilitysssourabhsharma
Digital Marketing best practices including influencer marketing, content creators, and omnichannel marketing for Sustainable Brands at the Sustainable Cosmetics Summit 2024 in New York
2. KUSIAK AND VERMA: DATA-MINING APPROACH TO MONITORING WIND TURBINES 151
TABLE I
TURBINE STATE INFORMATION
Fig. 2. Comparison of wind turbines states.
corresponds to normal functioning operations, including run-up
idling, whereas, the Fault category corresponds to an actual or
potential fault in the system. The Weather downtime category
corresponds to turbine downtime due to poor weather condi-
objective of Phase-I is to predict a fault of any kind, whereas, tions, whereas, any other downtime is considered as Mainte-
predictions in Phase-II target specific faults. In Phase-III predic- nance downtime.
tions, unseen faults from different wind turbines are identified.
Descriptions of various wind turbine states are provided next. C. Learning Strategy
For both prediction phases, the dataset was divided into two
A. Turbine State Description parts, i.e., initial dataset and blind dataset. The data-mining al-
The variability of wind speed impacts the performance of gorithms used two-thirds of the initial data for training, and the
wind turbines and is recorded as fault states. Normal operations, remaining one-third of the initial data was used for testing. The
weather-related downtime, maintenance downtime, fault mode, performance of the data-mining algorithms on the test dataset
and emergency stop are some of the many states recorded by the was used for algorithm selection. The best performing algo-
SCADA system of a wind turbine. rithm was then used to construct prediction models on the blind
States changes may vary from insignificant (e.g., when a tur- dataset (discussed in Section III). Details regarding the param-
bine is changing its state from idle to normal operations) to a po- eter selection are discussed in the next section.
tential fault. Table I lists the 17 possible states of a wind turbine. 1) Parameter Selection: A Supervisory Control and Data
State number 17 represents the fault mode of wind turbines and Acquisition (SCADA) system records more than 100 wind tur-
there can be more than 400 possible ways in which a wind tur- bine parameters that can be broadly categorized into: 1) wind
bine can be faulted. Gearbox oil over-temperature, blade angle turbine performance parameters, 2) wind turbine control param-
asymmetry, pitch thyristor fault, and yaw runaway are some of eters, and 3) wind turbine noncontrollable parameters. Parame-
the common fault modes of a wind turbine. In the research re- ters such as power, generator speed, and rotor speed are the per-
ported in this paper, the main emphasis was to predict the fault formance parameters, whereas, blade pitch angle and generator
mode of wind turbine ahead of actual occurrence. torque are controllable parameters. Wind speed is the only non-
controllable parameter. In the research reported in this paper,
B. Abstraction of Turbine States a combination of turbine performance parameters, control pa-
A typical turbine may undergo a number of different states in- rameters, and noncontrollable parameters are used to predict
cluding turbine normal operations, run-up idling, maintenance/ the wind turbine states. To minimize the data dimensionality
repair mode, fault mode, weather downtime, etc. The predic- and to remove irrelevant parameters, parameter selection algo-
tion of a turbine’s fault mode is of particular interest as it rep- rithms are used. A month of data was used for parameter selec-
resents some potential fault in the system. A turbine in state tion and algorithm learning. A stratified subset of the original
17 can be affected by as many as 400 different fault modes of data was used for parameter selection to make the process com-
varying intensity. Fig. 2 shows the histogram of 17 wind tur- putationally efficient. Fig. 3 displays the original and stratified
bines plotted over a period of four months (from 8/27/2010 to data. Distribution of the output class is preserved in stratified
12/4/2010). Based on the frequency of fault mode, turbine 12 data to avoid bias towards any specific class. Three different
was considered in the analysis. In order to reduce the compu- data-mining algorithms, wrapper with genetic search (WGS)
tational effort required by data-mining algorithms, the recorded [19], [20], wrapper with best first search (WBFS) [21], and
states of wind turbines were further categorized using domain boosting tree algorithm (BTA) [22] were selected to determine
knowledge. Table II represents the initially recorded and cate- relevant parameters for prediction of turbine states. Wrapper is a
gorized states of turbine 12. The initial 44 turbine states were supervised learning approach using different search techniques
categorized into four states: Turbine OK, Fault, Weather down- to select the relevant parameters by performing ten-fold cross
time, and Maintenance downtime. The Turbine OK category validation. Table III lists the ten best parameters from each pa-
3. 152 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 1, JANUARY 2012
TABLE II
TURBINE STATE CATEGORIES
TABLE III
SELECTED PARAMETERS USING DATA-MINING ALGORITHMS
mining algorithms for the prediction task. The evaluation of ac-
curacy is presented in a confusion matrix (see Fig. 4).
Equation (1) defines the geometric mean of the
output class, whereas, is the accuracy of class , is the
total number of output classes
(1)
3) Algorithm Selection: Five data-mining algorithms: neural
network (NN), support vector machine (SVM), random forest
algorithm (RFA), boosting tree algorithm (BTA), and general
Fig. 3. Output class distribution.
chi-square automatic interaction detector (CHAID) algorithm
were initially selected for building models at time stamp. NN
rameter selection algorithm. Parameters for nacelle revolution, uses backpropagation to classify instances [23]. Twenty NN
blade (1–3) pitch angle, current Phase C, temperature hub, and models with different kernels and structures were built in this
generator/gearbox speed were finally selected to build the pre- research, and the most accurate and robust model was selected.
diction models. SVM constructs a hyperplane or set of hyperplanes in a high
2) Evaluation Metric: The evaluation of data-mining algo- dimensional space, which can be used for classification, or
rithms is based on the prediction accuracy of each output class. regression. In SVM the hyperplane with the largest distance
Considering the imbalance in an output class, geometric mean to the nearest training data points of any class (so-called func-
(gmean) of the output class is used as criteria for selecting data- tional margin), yields good accuracy [24]. RFA is an ensemble
4. KUSIAK AND VERMA: DATA-MINING APPROACH TO MONITORING WIND TURBINES 153
Fig. 5. Performance of different data-mining algorithms using as a
criterion.
Fig. 4. Confusion matrix.
TABLE IV
PHASE-I PREDICTION RESULTS
Fig. 6. Misclassification rate of RFA as a function of tree size.
learning method where multiple random trees are generated
during classification. It selects random input parameters for
each node split [25]. BTA generates multiple models and ap-
plies a weighted combination of the predictions from individual
models to derive a single prediction model [22]. CHAID is
a tree-based data-mining algorithm that performs multilevel
splits for classification [26].
The prediction accuracy for each class (Phase-I predictions)
is provided in Table IV. Essentially, all the algorithms per- Fig. 7. Distribution of output class at time stamp .
formed well while predicting Turbine OK and Fault class,
however, the output class Weather downtime and Maintenance
downtime were predicted with relatively low accuracy. The overall seven output classes. Fig. 7 displays the distribution of
geometric mean metric indicates that when all classes data at time stamp . In the figure, pitch overrun 0 is triggered
are predicted with perfect accuracy its value is 1. The algorithm when limit switch experience a nonpositive angle at least one
with the highest value of was selected to build predic- of the rotor blades. Pitch thyristor 2 fault is triggered when the
tion models at different time stamps. From the graph in Fig. 5 thyristor is not ready even though the grid conductor is switched
and Phase-I prediction results (Table IV), both the boosting tree ON. Pitch thyristor fault indicate defective axle cabinet. Axle 1
algorithm and the random forest algorithms outperformed the fault pitch controller reports axle disturbance. Pulse sensor rotor
remaining three data-mining algorithms. However, RFA was monitor defect is due to no pulses to overspeed monitor when
selected to build the prediction models, as it possesses great the generator over speeds. Table V illustrates the performance
generalization ability and it is almost insensitive to the size of of different data-mining algorithms on a time stamped dataset.
the dataset. Fig. 6 illustrates the tree complexity of the random It can be seen from Table V that most of the algorithms failed
forest algorithm as a function of the misclassification rate. The to predict minority output classes (a class with few instances)
optimal number of trees was found to be 91. and thereby resulted in a equal to 0. Only NN and RFA
The same five algorithms were considered for constructing yielded a value greater than 0 (Fig. 8). As anticipated,
Phase-II prediction models. The output class Fault from the RFA outperforms the other data-mining algorithms, providing
Phase-I prediction was replaced by actual fault type, resulting in better accuracy for each output class.
5. 154 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 1, JANUARY 2012
TABLE V
PHASE-II PREDICTION RESULTS AT TIME STAMP
TABLE VI
PREDICTION ACCURACY OF OUTPUT CLASS USING RFA (PHASE-I PREDICTION)
Fig. 9. Values of at various time stamps.
Fig. 8. Performance of different data-mining algorithms using as a cri-
terion (Phase-II prediction).
III. COMPUTATIONAL RESULTS
In this section, the random forest algorithm (RFA) was used
to build eight prediction models at various time stamps, with a
maximum prediction length of 5 min. The maximum tree size
for the random forest algorithm was set to 300. The accuracy
was found to be in the range of 81%–99% for all output classes
(Table VI).
Fig. 10. Distribution of output classes (Turbine 10).
A. Phase-II Prediction
In this phase, output class Fault was replaced with the actual
fault types, these being pitch overrun 0 , pitch thyristor 2 fault, (0.435–0.817) than Phase-II predictions (0.242–0.659), because
axle 1 fault pitch controller, and pulse sensor motor defect. of the poor accuracy of one output class, the pulse sensor rotor
Table VII displays the prediction results produced by the RFA monitor defect. In the next section, Phase-III prediction results
at different time stamps. The accuracy of each output class was are illustrated and unobserved faults are identified.
found to be in the range 68%–100%, except for output class pulse
sensor rotor monitor defect for which accuracy was low (e.g., B. Phase-III Predictions
40.67%–61.9%). Fig. 9 presents the value of both predic- While the results on the testing dataset indicated the effec-
tion phases. Phase-I predictions had overall better values tiveness of the random forest algorithm, in order to validate the
6. KUSIAK AND VERMA: DATA-MINING APPROACH TO MONITORING WIND TURBINES 155
TABLE VII
PREDICTION ACCURACY OF OUTPUT CLASS USING RFA (PHASE-II PREDICTION)
Fig. 11. Distribution of output classes (Turbine 14).
Fig. 12. Distribution of output classes (Turbine 17).
robustness of the proposed model, data from other fault-prone The numbers of faults vary across turbines, however, the tur-
turbines were analyzed with the additional objective of seeing bines were found to be operating normally with no errors most
how the model would respond to unseen states types. Due to of the time. Tables VIII–X display the accuracy of output classes
the inherent variability in wind turbines, faults in a wind turbine across turbines 10, 14, and 17, respectively. It is clear from the
vary from one to another. It is interesting to observe the models’ results that the algorithms are robust enough to identify unseen
responsiveness when some unseen faults are presented. In this faults such as yaw runaway, blade angle not plausible axis 2,
section, data from three other fault-prone wind turbines, Tur- etc. The accuracy for correctly identifying unseen fault cases
bine 10, Turbine 14, and Turbine 17 are analyzed. Month-long was found to be in the range of 60%–100%, except for faults
data, from 8/28/2010 until 9/28/2010, were used for the anal- related to gearboxes (e.g., gearbox over-temperature, gearbox
ysis. The models built for Phase-I predictions were deployed oil pressure too low) which were always identified as Turbine
for analysis of this dataset. Faults such as yaw runaway, brush OK. The reasons for this include a lack of related input param-
wear warning, blade angle implausibility, and reply generator eters (e.g., gearbox temperature, gearbox oil pressure, etc.) in
high stage were studied. Figs. 10–12 display the actual distribu- the model. The results shown in Tables VIII–X confirm that the
tion of output classes for Turbines 10, 14, and 17, respectively. proposed model can be used to predict most wind turbine faults.
7. 156 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 1, JANUARY 2012
TABLE VIII with an accuracy in the range 78%–98%. The proposed model
MODEL ANALYSIS ON TURBINE 10 also identified various faults that occurred at wind turbines not
included in the training data.
A month-long data set was available for this research. Future
research will involve further analysis once additional data be-
comes available. New concepts will be researched to improve
model robustness for identifying faults not reflected in the
training data sets.
REFERENCES
[1] C. Hatch, “Improved wind turbine condition monitoring using acceler-
ation enveloping,” Orbit, pp. 58–61, 2004.
[2] A. Kusiak and W. Li, “The prediction and diagnosis of wind turbine
TABLE IX faults,” Renew. Energy, vol. 36, no. 1, pp. 16–23, 2011.
MODEL ANALYSIS ON TURBINE 14 [3] C. Walford, Wind Turbine Reliability: Understanding and Minimizing
Wind Turbine Operation and Maintenance Costs Sandia National Lab-
oratories, Rep. SAND-2006-1100, Mar. 2006, 2006.
[4] R. Hyers, J. McGowan, K. Sullivan, J. Manwell, and B. Syrett, “Con-
dition monitoring and prognosis of utility scale wind turbines,” Energy
Mater., vol. 1, no. 3, pp. 187–203, 2006.
[5] Z. Hameed, Y. Hong, Y. Cho, S. Ahn, and C. K. Song, “Condition
monitoring and fault detection of wind turbines and related algorithms:
A review,” Renew. Sustain. Energy Rev., vol. 13, no. 1, pp. 1–39, 2009.
[6] Y. Amirat, M. Benbouzid, B. Bensaker, and R. Wamkeue, “Condition
monitoring and fault diagnosis in wind energy conversion systems: A
review,” in Proc. 2007 IEEE Int. Electric Machines and Drives Conf.,
May 2007, vol. 2, pp. 1434–1439, 2007.
[7] P. Tavner, G. W. Bussel, and F. Spinato, “Machine and converter relia-
bilities in wind turbines,” in Proc. 3rd IET Int. Conf. Power Electronics
Machines and Drives, 2006, pp. 127–130.
[8] P. Caselitz and J. Giebhardt, “Rotor condition monitoring for improved
operational safety of offshore wind energy converters,” J. Solar Energy
Eng., vol. 127, no. 2, pp. 253–261, 2005.
[9] E. Becker and P. Posta, “Keeping the blades turning: Condition moni-
toring of wind turbine gears,” Refocus, vol. 7, no. 2, pp. 26–32, 2006.
[10] “Managing the wind: Reducing kilowatt-hour costs with condition
monitoring,” Refocus, vol. 6, no. 3, pp. 48–51, 2005, Editorial.
[11] A. Kusiak and A. Verma, “A data-driven approach for monitoring blade
TABLE X pitch faults in wind turbines,” IEEE Trans. Sustain. Energy, vol. 2, no.
MODEL ANALYSIS ON TURBINE 17 1, pp. 87–96, Jan. 2011.
[12] P. Caselitz, J. Giebhardt, and M. Mevenkamp, “Application of condi-
tion monitoring systems in wind energy convertors,” in Proc. EWEC,
Dublin, 1997, pp. 1–4.
[13] A. Zaher and S. McArthur, “A multi-agent fault detection system for
wind turbine defect recognition and diagnosis,” in Proc. IEEE Lau-
sanne Powertech, 2007, pp. 22–27.
[14] A. Kusiak, H. Y. Zheng, and Z. Song, “On-line monitoring of power
curves,” Renew. Energy, vol. 34, no. 6, pp. 1487–1493, 2009.
[15] A. Kusiak and A. Verma, “Prediction of status patterns of wind tur-
bines: A data-mining approach,” Trans. ASME J. Solar Energy Eng.,
vol. 133, no. 1, pp. 1–10, 2011.
[16] A. Kusiak and Z. Zhang, “Adaptive control of a wind turbine with data
mining and swarm intelligence,” IEEE Trans. Sustain. Energy, vol. 2,
no. 1, pp. 28–36, Jan. 2011.
[17] P. J. Tavner, G. J. W. Bussel, and F. Spinato, “Machine and converter
reliabilities in wind turbines,” in Proc. Inst. Elect. Eng. 2nd Int. Conf.
Power Electronics, Machine & Drives, Dublin, Apr. 2006.
IV. CONCLUSION [18] P. J. Tavner and J. Xiang, “Wind turbine reliability, how does it com-
pare with other embedded generation sources,” in Proc. Inst. Elect.
In this paper, a methodology for predicting wind turbine Eng. RTDN Conf., London, U.K., Jan. 2005.
states was presented. The proposed approach involved three [19] R. Kohavi and G. H. John, “Wrappers for feature subset selection,”
Artif. Intell., vol. 97, no. 1–2, pp. 273–324, 1997.
key steps: turbine state abstraction, algorithm learning, and [20] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Ma-
state prediction. In the first step, the initial wind turbine states chine Learning. Reading: Addison-Wesley, 1989.
[21] A. Sbihi, “A best first search exact algorithm for the multiple-choice
were separated into classes using domain knowledge. To reduce multidimensional knapsack Problem,” J. Combinatorial Optimization,
the computational effort, data-mining algorithms were trained vol. 13, pp. 337–351, 2007.
using a stratified data set. Turbine parameters such as the blade [22] T. Kudo and Y. Matsumoto, “A boosting algorithm for classification
of semi-structured text,” in Proc. EMNLP, 2004, pp. 301–308.
pitch angle, generator/gearbox speed, temperature hub, nacelle [23] A. C. McCormick and A. K. Nandi, “Classification of rotating machine
revolution, and current Phase C constitute the input to the condition using artificial neural networks,” Proc. IMechE: Part C, vol.
prediction model. Among the selected data-mining algorithms, 11, no. 6, pp. 439–450, 1997.
[24] V. Kecman, Learning and Soft Computing-Support Vector Machines,
the random forest algorithm provided the most accurate results. Neural Networks, Fuzzy Logic Systems. Cambridge, MA: MIT Press,
Prediction models with up to 300-s horizons provided results 2001.
8. KUSIAK AND VERMA: DATA-MINING APPROACH TO MONITORING WIND TURBINES 157
[25] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. Anoop Verma (S’10) received the B.S. degree in
5–32, 2001. manufacturing engineering from NIFFT, India, and
[26] G. V. Kass, “An exploratory technique for investigating large quanti- the M.S. degree in mechanical engineering from the
ties of categorical data,” Appl. Stat., vol. 29, no. 2, pp. 119–127, 1980. University of Cincinnati, Cincinnati, OH, in 2007
and 2009, respectively. At present he is working to-
ward the Ph.D. degree in the Industrial Engineering
Program, The University of Iowa, Iowa City.
Andrew Kusiak (M’90) received the B.S. and M.S. He has researched areas such as machine loading,
degrees in engineering from the Warsaw University manufacturing scheduling, corporate memory,
of Technology, Warsaw, Poland, in 1972 and 1974, rapid prototyping, and information retrieval and
respectively, and the Ph.D. degree in operations published papers in various journals, including IEEE
research from the Polish Academy of Sciences, TRANSACTION ON SUSTAINABLE ENERGY, Expert Systems with Applications,
Warsaw, in 1979. International Journal of Production Research, Engineering Applications of
He is currently a Professor in the Department of Artificial Intelligence, Journal of Intelligent Manufacturing, etc. He reviewed
Mechanical and Industrial Engineering, University papers for various journals, including the International Journal of System
of Iowa, Iowa City. He speaks frequently at inter- Science, Information Science, and Journal of Intelligent Manufacturing. His
national meetings, conducts professional seminars, current doctoral research focuses on applications of data mining and compu-
and does consultation for industrial corporations. He tational intelligence in wind industry.
has served on the editorial boards of more than 40 journals. He is the author
or coauthor of numerous books and technical papers in journals sponsored
by professional societies, such as the Association for the Advancement of
Artificial Intelligence, the American Society of Mechanical Engineers, etc. His
current research interests include applications of computational intelligence
in automation, wind and combustion energy, manufacturing, product develop-
ment, and health-care.
Prof. Kusiak is the Institute of Industrial Engineers Fellow and the Editor-in-
Chief of the Journal of Intelligent Manufacturing.