Dimchev, Vladimir
Preferred name
Dimchev, Vladimir
Official Name
Dimchev, Vladimir
Main Affiliation
Email
vladim@feit.ukim.edu.mk
13 results
Now showing 1 - 10 of 13
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Item type:Publication, EVALUATING THE UNCERTAINTY OF A VIRTUAL POWER QUALITY DISTURBANCE GENERATOR(IMEKO, 2023); Taskovski, Dimitar - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling the arrangement of turbines for onshore wind power plants under varying wind conditions(Wind Integration, 2018-10); ; ; ; Fickert, LotharWind field layout optimization concerning various parameters is a major point in planning and will influence the revenue for the whole life of the installation. Besides the obvious impact of wind distribution also other parameters like connection costs and levelized costs of energy influence the optimum layout and have to be included in a realistic optimization algorithm. In this paper the sophisticated optimization of wind field layout with of two fundamentally different heuristic algorithms is investigated. To do so, detailed real-world data from an existing wind field in Bogdanci, Macedonia is utilized by employing real wind field data we are able to calibrate model adequacy and ascertain a model that will serve as a referent guidance in the planning of future onshore wind fields. Different layouts were designed using sophisticated algorithms for handling the resulting high-dimensional, highly nonlinear optimization problem. In particular, a nondominated sorting genetic algorithm (NSGA) and a mixed discrete particle swarm optimization algorithm (MD-PSO) were applied. Both optimization algorithms established bi-objective fitness functions, in particular- minimizing the levelized cost of energy and maximizing the capacity factor. By comparing the results obtained with the existing layout, it is established that both optimization algorithms are adequate in determination of wind power plant layouts. It is proven that the implementation of sophisticated optimization methods can results in essential savings during the whole lifetime of the wind field. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault detection under operating wind turbine through yaw misalignment conditions(MAKO CIGRE, 2019-10); ; ; In the past few years, the interest in controlling the wind turbine yaw misalignment has re emerged. The reason for this interest is the desire for maximum exploitation of wind turbines and elimination of any omission in the production of electrical energy. The operation of wind generator systems in conditions of inadequate control and compliance with direct wind direction is reflected in the reduction of electricity produced, as well as in reducing the exploitation period of the wind turbine. Accurate yaw angle measurement is a fundamental for efficient operation of the non-compliance angle control system. In wind turbines, which lack such a modern metering system, the occurrence of fatigue working conditions is becoming more and more frequent, and thus the problems and defects that occur with all mechanisms for controlling and adapting nacelle face the upcoming wind. Of particular importance to maintenance an operating wind field, is the adequate and real time detection of such an error. Frequent changes on the direction of the nacelle, or a rare adaptation of the pitch angle to a particular wind turbine have been recorded during the operation mode of one wind turbine from the Bogdanci wind field. The analysis of its operating modes, as well as the proposed solution for optimal operation, in absence of a modern measuring system to control yaw misalignment of the nacelle and the wind direction, are presented in this paper. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimized Power Quality Events Classifier(IEEE, 2019-06); Velkovski, Bodan - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wind regimes representation modeling by using multivariable distributions(MAKO CIGRE, 2019-10); ; ; The electricity produced from one wind turbine is almost entirely dependent on the intensity of the upcoming wind. On the other hand, while making analysis of electricity production of a single wind field, equal attention should be paid to wind direction, since it defines the overall wind flow in the wind field. Accordingly, when making estimations of the expected annual production of energy from one wind field, the correlations of wind speed and direction should be appropriately taken into account. The usual practice of using Weibull distributions by a number of sectors is not the most suitable for optimization processes for defining the layout of one wind filed. For this purpose, the paper proposes a method that is simple and easy to implement, which is based on a multivariable distribution function of the parameters of the wind. For the analysis of the proposed model the measured wind data used in this paper are from one existing wind field. In addition, three stage functional distributions are obtained, which satisfactorily match the measurement data. In order to determine the matching degree of the adapted multivariable distributions with the measurement data, the quantitative parameter for error estimation - R2 was used. Various sizes of data sampling intervals are also examined in order to reduce the generation time of multiple distributions, which will not affect the quality of matching between generated distributions and measured data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling an optimal wind turbine layout by application of evolutional algorithms(MAKO CIGRE, 2019-10); ; ; Optimization of wind farm layout concerning various parameters is a major point in planning and will influence the revenue for the whole life of the installation. Besides the obvious impact of wind distribution also other parameters like connection costs and levelized costs of energy influence the optimum layout and have to be included in a realistic optimization algorithm. In this disertation the sophisticated optimization of wind farm layout with of two fundamentally different heuristic algorithms is investigated. To do so, detailed real-world data from an existing wind farm in Bogdanci, Macedonia is utilized by employing real wind farm data we are able to calibrate model adequacy and ascertain a model that will serve as a referent guidance in the planning of future onshore wind farms. The major unique feature of the research is the simultaneous optimization taking into account all major technical influence and cost factors, including: (i) detailed and advanced models for power modeling due to bivariate distribution of wind speed and direction; (ii) accurate estimation of levelized cost of energy (LCOE); (iii) analysis of the shortest electrical interconnections among wind turbines and (iv) correction of hub height on each wind turbine in the wind farm with taking also the wake effect into consideration. Different layouts were designed using sophisticated algorithms for handling the resulting high-dimensional, highly non-linear optimization problem. In particular, a non dominated sorting genetic algorithm (NSGA) and a mixed-discrete particle swarm optimization algorithm (MDPSO) were applied. Both optimization algorithms established bi-objective fitness functions, in particular- minimizing the levelized cost of energy and maximizing the capacity factor. By comparing the results obtained with the existing layout, it is established that both optimization algorithms are adequate in determination of wind power plant layouts. Results show also a remarkable improvement of 2.05% and 5.59% for levelized costs and capacity factor, respectively, compared to the as built wind farm layout. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Uncertainty Evaluation in Resource Assessment of Wind Energy Potential(WindEurope, 2019-06); The decision for exploitation wind for electrical energy production is based on assignment of its long-term mean speed and distribution parameters. This data is usually obtained from a measurement campaign with a short duration. Taking into account the real characteristics of the instrumentation, such as it is: accuracy, finite resolution, dynamic response, it is obvious that the measurements will provide information with appended uncertainty. Next, the wind speed obtained from a measurement campaign is supposed to be extrapolated in long-term period. The extrapolation possess its own uncertainty component as well. Additionally, uncertainty is present because of terrain's characteristics of the observed site. The overall uncertainty prescribed to a long-term wind speed can be easily transferred to the Weibull distribution scale and shape parameters, These uncertainties, on the other hand, lead to uncertainty existence in the calculated mean annual energy production. Several other factors affect the estimated energy production, such as turbine efficiency, and the characteristics provided by the manufacturer, especially the power curve. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Real-Time Implementation of Optimized Power Quality Events Classifier(Institute of Electrical and Electronics Engineers (IEEE), 2020); Velkovski, Bodan - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of near perfect reconstruction filter banks in power quality disturbances classification methods(IEEE, 2012-09) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reducing Uncertainty in Wind Energy Resource Assessment by using Multivariable Distribution Model(WindEurope, 2019-06); ; Demerdziev, KirilHaving reliable and precise wind energy resource assessment is essential for further analysis for conversion of wind energy into electricity. Previous practices that are common for representing wind data by sector-wise Weibull distribution, over time have been replaced with different multivariate and multimodal wind distribution models which are far more precise. The paper presents an upgraded model for accurate characterization and predict the annual variation of wind conditions. It is proven that the assumption of a constant air density value, can lead to notable differences between the predicted and real wind power available at a given site. Therefore, along with the main wind parameters, speed and direction, air density treated as a variable in this paper. The method, based on the Multivariate Kernel Distribution model, is an improvement of the existing methods for representing the wind regimes. Before representing the multivariable wind distribution, a piecewise Bivariate probability density function is constructed. It is important to note that when modelling wind with sector-wise Weibull, we are assuming that the wind speed satisfies the same probability distribution inside a direction sector. Following, a Bivariate probability density function using piecewise joint distribution is carried out. Namely, this distribution contains all input parameters for calculating the multivariable Kernel distribution. For comparison of these two distributions, coefficients of determination are used. From Kernel's probability distribution function, the three parameters of the wind (speed, direction and air density) are further treated as continuous variables, which facilitates and refines all further steps for optimizing the distribution of wind turbines in one wind farm. Aside of the other advantages, this model provides information on strengths of wind speeds and the energy content in them, it also enables selection of the appropriate type of wind turbine design for deployment at a given location. The measured wind data used in this paper are from one existing wind farm and one wind measuring station with a good potential for further investigation. By this approach, we can calibrate model adequacy and ascertain a model that will serve as a referent guidance in the planning of future onshore wind farms.