EV fleet charging load forecasting based on multiple decomposition with CEEMDAN and swarm decomposition.

As the transition to electric mobility is accelerating, EV fleet charging loads are expected to become increasingly significant for power systems. Hence, EV fleet load forecasting is vital to maintaining the reliability and safe operation of the power system. This paper presents a new multiple decomposition based hybrid forecasting model for EV fleet charging. The proposed approach incorporates the Swarm Decomposition (SWD) into the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method. The multiple decomposition approach offers more stable, stationary, and regular features of the original signals. Each decomposed signal is fed into artificial intelligence-based forecasting models including multi-layer perceptron (MLP), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM). Real EV fleet charging data sets from the field are used to validate the performance of the models. Various statistical metrics are used to quantify the prediction performance of the proposed model through a comparative analysis of the implemented models. It is demonstrated that the multiple decomposition approach improved the model performance with an R2 value increasing from 0.8564 to 0.9766 as compared to the models with single decomposition

Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting.

With the increasing penetration of grid-scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long-short-term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high-frequency component. A deep learning-based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two-stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving R2 values up to 9.5% higher than those obtained using standard LSTM models

Finsler Geometry for Two-Parameter Weibull Distribution Function

To construct the geometry in nonflat spaces in order to understand nature has great importance in terms of applied science. Finsler geometry allows accurate modeling and describing ability for asymmetric structures in this application area. In this paper, two-dimensional Finsler space metric function is obtained for Weibull distribution which is used in many applications in this area such as wind speed modeling. The metric definition for two-parameter Weibull probability density function which has shape (k) and scale (c) parameters in two-dimensional Finsler space is realized using a different approach by Finsler geometry. In addition, new probability and cumulative probability density functions based on Finsler geometry are proposed which can be used in many real world applications. For future studies, it is aimed at proposing more accurate models by using this novel approach than the models which have two-parameter Weibull probability density function, especially used for determination of wind energy potential of a region

Offshore wind speed short-term forecasting based on a hybrid method: swarm decomposition and meta-extreme learning machine.

As the share of global offshore wind energy in the electricity generation portfolio is rapidly increasing, the grid integration of large-scale offshore wind farms is becoming of interest. Due to the intermittency of wind, the stability of power systems is challenging. Therefore, accurate and fast offshore short-term wind speed forecasting tools play important role in maintaining reliability and safe operation of the power system. This paper proposes a novel hybrid offshore wind forecasting model based on swarm decomposition (SWD) and meta-extreme learning machine (Meta-ELM). This approach combines the advantages of SWD which has proven efficiency for non-stationary signals, with Meta-ELM which provides faster calculation with a lower computational burden. In order to enhance accuracy and stability, the signal is decomposed by implementing a swarm-prey hunting algorithm in SWD. To validate the model, a comparison against four conventional and state-of-the-art hybrid models is performed. The implemented models are tested on two real wind datasets. The results demonstrate that the proposed model outperforms the counterparts for all performance metrics considered. The proposed hybrid approach can also improve the performance of the Meta-ELM model as a well-known and robust method

Short-Term Solar Power Forecasting Based on CEEMDAN and Kernel Extreme Learning Machine

The use of renewable energy sources contributes to environmental awareness and sustainable development policy. The inexhaustible and nonpolluting nature of solar energy has attracted worldwide attention. Accurate forecasting of solar power is vital for the reliability and stability of power systems. However, the effect of the intermittency nature of solar radiation makes the development of accurate prediction models challenging. This paper presents a hybrid model based on Kernel Extreme Learning Machine (Kernel-ELM) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for short-term solar power forecasting. The decomposition technique increases the number of stable, stationary, and regular patterns of the original signals. Each decomposed signal is fed into Kernel-ELM. To validate the performance of the hybrid model, solar power data from the BSEU Renewable Energy Laboratory, measured at 5-minute intervals, are used. To validate the proposed model, its performance is compared to some state-of-the-art forecasting models with seasonal data. The results highlight the good performance of the proposed hybrid model compared to other classical algorithms according to the metrics

Data Acquisition And Processing Software For Impulse Voltage Generator

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2013Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2013Güç sistemlerinin kapasitelerinin artmasının yanısıra çalışma güvenilirliği de büyük önem taşımaktadır. Güç sistemlerinde kullanılan bir elemanda meydana gelecek arıza, sistemin büyük kısmının devre dışı kalmasına ve güvenilirliğinin azalmasına neden olabilmektedir. Arızaların önemli bir kısmı yalıtım kaynaklı arızalardır. Yalıtım bozulmaları, yüksek gerilim altında çalışan cihazlarda, çeşitli nedenlerle oluşan aşırı gerilimler nedeniyle yalıtımın zorlanmasından veya zamanla yalıtımın zayıflamasından oluşur. Sistemde meydana gelecek aşırı gerilimler şebeke kaynaklı olabileceği gibi, yıldırım vb. şebeke dışı kaynaklı da olabilmektedir. Bu nedenle yüksek gerilim elemanlarının aşırı gerilimlere karşı dayanımları sağlanmalıdır. Yüksek darbe gerilimleri, atmosferik dış aşırı gerilimlerin yol açtığı zorlanmaları deneysel olarak saptamak, yalıtkan malzemelerin yüksek gerilime dayanım şartlarını araştırmak ve üretilen ürünlerin ulusal ve uluslararası standartlara uyumluluğunu deneysel olarak belirlemek için kullanılırlar. Bu tez çalışması kapsamında da, İTÜ Fuat Külünk yüksek gerilim laboratuvarında bulunan 6 katlı 1 MV, 10 kJ luk darbe gerilimi üretecinden elde edilen yıldırım darbe gerilimi işaretinin bilgisayar ortamına aktarılarak analizinin kolaylıkla yapılabilmesi amaçlanmıştır. Yüksek darbe gerilimlerinin, standartlarda öngörülen parametrelerinin belirlenmesi ve yorumlanması gerekmektedir. Standart osiloskoplar ile darbe gerilim ölçümlerinde, genlik ve zaman parametrelerini ölçmemiz mümkündür. Fakat kullanıcı, genlik ve zaman parametrelerini belirlerken osiloskobun yakaladığı işaret üzerinde, genlik ve zaman ayar düğmelerini elle hareket ettirerek bazı kritik noktaları tespit eder ve çeşitli matematiksel işlemleri yaptıktan sonra işaretin istenen genlik ve zaman değerlerini hesaplar. Bu yöntem uzun zaman alan ve hataya açık bir yöntemdir. Tez çalışması kapsamında oluşturulan yazılım sayesinde yüksek darbe gerilimi işaretleri, 2 GS/s örnekleme frekansına sahip 200 MHz lik bir osiloskoptan .CSV formatında MATLAB GUI ortamında oluşturulan arayüze aktarılmıştır. Yazılım ile yıldırım darbe gerilim parametreleri bilgisayar ortamında otomatik olarak belirlenebilir, işaretler zaman ekseninde ve frekans ekseninde ayrı ayrı çizdirilebilir, istenildiği takdirde yazılım vasıtasıyla işaret üzerindeki gürültü bileşenleri yok edilebilir ve girilen çevirme oranına göre darbe gerilimi çizdirilebilir. Ayrıca alınan darbe gerilim parametreleri Microsoft Excel ortamına aktarılarak kayıt edilebilmekte ve raporlanabilmektedir. Yazdırma önizleme, direk yazdırma özellikleri ile raporlar alınabilir, aynı zamanda büyütme özelliği ile grafik üzerinde istenilen noktada analizler yapılabilmektedir. Çalışma kapsamında diğer darbe gerilimi ölçüm sistemlerinden farklı olarak, yıldırım darbe gerilimindeki gürültülerin nedenleri de araştırılmıştır. Yazılımın frekans ekseninde analiz yapma özelliği, hangi frekans bileşenlerinde gürültülerin meydana geldiğini bizlere gösterebilmektedir. Yine araştırılan makaleler ve bildiriler doğrultusunda elektromanyetik girişim kaynaklı gürültüler, yıldırım darbe şekli üzerinden belirlenmiştir. Savitzky-Golay yöntemi referans alınarak, işaret üzerinde gürültü bileşenleri ayrıştırılmıştır. Gürültülü veya gürültüsüz işaretin gözlemlenmesi tuşlar vasıtalasıyla kullanıcıya bırakılmıştır. Frekans analizinin getirmiş olduğu üstünlüklerden biri de; gürültünün ölçüm sisteminden veya sistem elemanlardan kaynaklandığına ilişkin yorum yapabilmesini sağlamasıdır. Yıldırım darbe gerilimleri ile yapılan deneylerde, kullanıcının okuma hatalarından uzak, kolaylıkla kullanabileceği böyle bir yazılım ile bilgisayar destekli analizlerin yapılabilmesi ve işaretin işlenebilmesi ile çalışma, hedefine ulaşmıştır. Ulaşılan bu hedef ile bundan sonraki çalışmalarda, yazılıma otomasyon sisteminin de eklenerek geliştirilebilmesine ve gürültünün çeşitine göre otomatik olarak süzme işlemini yapabilen özelliklerde bir programın oluşturulmasına temel hazırlayarak bu alanda çalışanlara yeni ufuklar açmıştır.An uninterrupted supply of electricity is of supreme importance in our daily activities. Transient overvoltages and overcurrents due to lightning and switching surges are the main causes of interruption of the continuous supply of electricity. They cause discharges in the insulation of the transmission lines and various power apparatus thus resulting severe damage to these equipments. So, power apparatuses are generally subjected to several insulation tests to demonstrate that the equipment fulfills the specified requirements of the quality standards. High voltage laboratories are an essential requirement for making research as well as performing the acceptance tests for the equipments those will be the part of the high voltage transmission systems. This study was carried out at Istanbul Technical University Fuat Kulunk High Voltage Laboratory. All tests are performed by using 1 MV, 10 kJ, six stages impulse voltage generator. Istanbul Technical University, Fuat Kulunk High Voltage Laboratory is the biggest high voltage laboratories in Turkey where high voltage tests are performed for industrial utilities as well as academic researches are conducted. The laboratory consists of several blokes that perform various tests. Six stages, 10 kJ impulse voltage generator that is located in B block of the laboratory can perform lightning impulse voltage tests up to 1 MV. There has been a major change in waveform recording devices and methods of high-voltage impulse tests over the last four decades. Nowadays, digital waveform recorders have become dominant although, prior to 1970, virtually all impulse recordings were performed using oscilloscopes. This change has been driven by five factors: technical improvements in digitizers, economical benefits of using digitizers, elimination of subjective judgements by the operator, growth in the use of quality systems, and the use of digital signal processing to extract more information from the test records. Early digitizers had limitations that prevented them from being fully utilized in impulse measurements. However, they sparked widespread interest and several organizations developed programs to evaluate digitizers and digital techniques. Further developments have led to a large number of models, made by different manufacturers, which are as good as oscilloscopes for impulse measurements. Indeed, several of these digitizers are better than oscilloscopes for some applications. The economic benefits include: automated reading of records (usually on a desktop computer), easy transfer of the results to reports when compatible software is used, and the ability to compare several different analysis algorithms without compromising the record. The application of standardized reading algorithms for smooth impulses has eliminated subjective judgements by the operator (standardized algorithms for distorted impulses are under consideration). The standards on high-voltage tests define all parameters in terms of a smooth impulse. High voltage impulse generators are dominated by the values of resistance and capacitance used and usually produce smooth impulses that are approximately double-exponential in form when used with a capacitive test object. The main amplitude parameter is the peak voltage. Time parameters are defined in terms of the differences between the two times at which the impulse crosses particular fractions of the peak value, e.g., the front time is defined as 1.67 times the difference between when the impulse first crosses 30% of the peak value and the time when it first crosses 90% of the peak value. Lightning impulses have standard front times of about 1.2 µs. Standard peak values for tests range from 50 kV to 2 MV. The voltage divider is the most important component of the circuit, and the quality of the measured waves is closely related to its performance. However, recent reports have shown that using appropriate materials with the correct design and construction technique can lead to a performance suitable for the measurement of almost all HV impulses. Moreover, the new philosophy of using a chain of reference dividers to improve the traceability of test results ensures that divider performance is adequate for a given measurement. After dividing the high voltage signal to the measurable low voltage level, evaluation of this signal is made by high voltage measurement systems such as peakvoltmeters, digital recorders or oscilloscopes. It is not enough to use the peak voltmeters alone at impulse high voltage measurements since they can not measure the time parameters. It is possible to measure the amplitude and the time parameters of the impulse voltages with standart oscilloscopes. However, such a measurement involves moving the voltage and the time cursors of the oscilloscope manually on the screen. After having determined the relevant places of voltage and time parameters with cursors, the user have to make some mathematical calculations to determine the impulse signal parameters. Such a measurement is a time consuming process and also includes high user errors. Recorders are the most ideal measurement device for the impulse voltage measurements. Digital recording systems contribute to the analysis of the wave shape during the test. The noise can be found by the analysis which can also give information about the reason of disturbances to the user. The operator then can take the relevant actions to avoid those disturbances. The corruption due to interferences in the measuring system is produced by electromagnetic radiation generated by the HV impulse, that runs in the test circuit, and is picked up by the measuring system. The disturbances have, again, an oscillating nature, but its characteristics are now closely attached to the shape of the HV impulse and, so, their characteristic frequency is higher than 500 kHz. In many occasions the durations of this type of disturbances are limited and their influence on the registered waveform is only localized in a short time gap. Beyond the difference in what concerns their characteristic frequency, the two types of disturbances have very important differences between them: The first type of disturbance presents in the HV impulse that is applied to the equipment under test, whereas the second type of disturbance only presents on the recorded waveform and, so, does not have any correspondence in the applied HV impulse. Having this fact in mind, the characterization of the impulses must only take into account the disturbances due to the test circuit, but not those due to electromagnetic interferences. The international standard specifies that the oscillating phenomena with frequency above 500 kHz has to be discarded before the impulse characterization. Working in the time domain, or in the frequency domain, several methods were proposed, by different researchers, for digital processing of HV impulses recorded during the tests. However, those methods do not allow an efficient processing. The time domain methods are basically based on fitting techniques and, so, they become very heavy and very dependent on the skill of the operators. To improve the efficiency a large set of standard curves must be used in the fitting procedure and the choice of the best standard curve to be used requires the presence of a skilled operator. The frequency domain methods are based on the analysis of the classic spectrum of the impulses, which can inform about the presence of the undesirable frequencies, but do not give any information concerned with the duration of the disturbance phenomena. Without that information and assuming that the disturbance phenomena are present in all the time duration of the impulses, the processing procedure may, in many cases, have an erroneous action on undisturbed zones of the impulse. This thesis presents such a software which is written in Matlab Gui. The software yet can calculate the peak value, the timing of peak value, front time (T1), and time to half value (T2) of impulse voltage. Software can represent the impulse voltage both in time domain and in frequency domain. It consists of noise filtering option using Savitzky-Golay method. Impulse voltage magnitude can be calculated using the divider ratio. Impulse voltage parameters can be exported as an Microsoft Excel file automatically. If required, the datas and graphics can be printed either by using direct print or by using print preview buttons. Impulse voltages are generated by six stage impulse generator and mesaured by a digital oscilloscope. An output from oscilloscope s usb port is applied to the computer s usb port. Oscilloscope data is sent to Matlab Gui interface in .CSV format. Then, the software is converted to data matrix form automatically. Time domain and frequncy domain analysis can be performed by clicking the appropriate buttons. Graphics can be zoomed in or out easily. Intended time and frequency values can be zoomed thanks to zoom button. As a result, the software can analyse impulse voltage signal in time domain and frequency domain. This will anable to differntiate noise types and the user can take the necessarry precautions with respect to appropriate domain analysis.Yüksek LisansM.Sc

Analysis of electric vehicle charging demand forecasting model based on Monte Carlo simulation and EMD-BO-LSTM

The stochastic charging behaviors of Electric Vehicle (EV) users illustrate the negative effects of bulk charging during peak hours on the grid. To overcome this problem, the bulk EV charging demand forecasting approach is investigated using historical EV charge demand dataset and EV driver mobility statictics in this paper. In this model, a Monte Carlo Simulation (MCS) is perfomed that considers the charging behavior of EV users for the generation of EV charging times. Moreover, the EV charging times are combined with the bulk EV demand hybrid forecasting model using decomposition and deep learning time series method. In first stage, the EV demand time series dataset are divided to improve the model performance by empirical mode decomposition (EMD). Then, all decomposed signals are forecasted separately using the Bayesian optimized Long Short-Term Memory LSTM network (BO-LSTM). Finally, to evaluate the model perfomance, the power system analysis using IEEE 33 busbar test system is performed in terms of distribution network power losses, busbar voltage drops and transformer loading conditions

Optimal scheduling of aggregated electric vehicle charging with a smart coordination approach

Conventional internal combustion engine vehicles are one of the main reasons for the increase in carbon emissions. The Electric Vehicles (EVs) in the transportation sector to significantly reduce these emissions, can be expanded collectively instead of these vehicles. While EVs are still hindered from adoption due to their battery life, cost and few other challenges, the global fuel crisis around the world, sanctions and incentives in government policies are helping large-scale EVs adoption. The increase in EVs penetration adds an indefinite amount of electricity to the grid and is likely to pose a very complex operating problem for distribution grid operators. Since EV users want to leave with maximum battery energy capacity, uncoordinated charging can damage grid equipment in the distribution system. Accurate charge scheduling of EVs is essential for seamless integration of EVs into the grid. However, in this charging scheduling, it is necessary to consider the battery energy capacities of the EVs as well as the charging costs. In this paper, the optimal charging scheduling of EVs under the proposed smart coordination was performed according to the battery capacity. In this way, uncoordinated charging was prevented, which led to an increase in the peak power of the distribution system. Data for EV charging time, waiting time and battery energy-capacity were obtained by Monte Carlo Simulations (MCSs) based on statistical data. The Mixed Integer Linear programming (MILP) technique was used for charging scheduling of EVs. The results show that the proposed approach is a systematic reference, as it both reduces the charging cost of the users when charging the EVssand efficiently uses the load smoothing and load-shifting strategies in the distribution network

Optimal scheduling of on-Street EV charging stations

The uncoordinated charging of Electric Vehicles (EVs) into the grid increases the stochastic rebound peak on the grid. These charging demands can strain grid equipment at the street charging points in an area. In this study, a smart coordination approach is proposed for charging process management by considering the parking times of EVs. EV types with different characteristics are used in the smart coordination approach. This approach limits the charging powers to the minimum value between the charging point and the EV maximum power ratio. Also, the approach using quadratic programming (QP) for charge scheduling of 20 EV minimizes the cost of daily charging via the Generic Algebraic Modeling System (GAMS). The results show that EV charges occur within the maximum allowable grid limits, reducing the cost of charging. Additionally, the proposed smart coordination prevented the occurrence of daily on-grid rebound peaks at street charging points in the area

A systematic data-driven analysis of electric vehicle electricity consumption with wind power integration

Real-time charging data of Electric Vehicles (EVs) cannot be easily shared between service providers, making analysis of the energy profile is difficult of collective EVs. This paper uses a real-time dataset that analyzes real-world charging load profiles of EVs to the nearest 15 minutes for one day period. This dataset includes charging data from 21 EVs at different session times and different locations in a region. The data was systematically expanded to take advantage of the Wind Turbine (WT) generation power which is one of the Renewable Energy Sources (RES) in the charge energy consumption of collective EVs in modified bus-2 network of the Roy Billington Test System (RBTS). Instead of assuming that EVs were constantly charging at maximum power in creating a charge-load profile, collective charge-load profiles were simulated based on the actual charging at varying power. Simulation results show that EV charging peak loads can decrease with an onsite WT generation power. Thus, the load balancing was performed due to the wind energy conversion system instead of load shifting in the modeled power system.Honda R and D Co., Ltd.Power Electronics in Everything (PEiE)TMEi