Real Operating System Research Articles

Comparative study of dynamic response by three types of energy storage systems for grid studies: A Microgrid Laboratory experimental-based study

Implementing Energy Storage Systems (ESS) is increasingly significant in power electrical systems. This is attributed to their ability to store surplus electricity generated by renewable energy sources such as wind and solar, contributing to the balance between generation and demand. Literature studies indicate that this practice enhances network stability and diminishes the need for expensive redesigns in infrastructure. This paper presents an exhaustive experimentation-based study of the dynamic response provided by three energy storage system technologies: supercapacitors, Lithium-Ion batteries, and Vanadium redox flow batteries, technologies with significant academic, research, and industrial interests nowadays. The objective of this research is the experimental evaluation of the performance of such technologies in real electrical system operations, focusing on determining the efficiency of charge and discharge, as well as the tracking of active and reactive power achieved through the associated grid interface. The experimental tests performed in a microgrid laboratory show these technologies’ advantages and limitations in different grid-integration applications, with the Lithium-Ion battery-based ESS demonstrating the highest efficiency and faster power response. The results achieved and reported in the article can serve as an essential input for researchers and technology developers to feed their models with more precise parameters and data that might provide results closer to the actual behaviour of the studied prototypes. The methodological framework used in this research is descriptive, experimental, and quantitative.

ICRH operations during the JET tritium and DTE2 campaigns

The JET-ILW pure tritium and deuterium–tritium (DTE2) experimental campaigns took place in 2021–2022. Tritium (T) and deuterium–tritium (D–T) operations present challenges not encountered in present day tokamaks (Horton et al 2016 Fusion Eng. Des. 109–111 925–36). This contribution focuses on ion cyclotron resonance heating (ICRH) operations in tritium and deuterium–tritium plasmas, starting with a summary of the program of improvements to the ICRH system which spanned a few years prior to these experiments. Procedures were implemented to address specific constraints from tritium and deuterium–tritium operations (tritium safety and reduced access to the RF generator area) and increase the system reliability and power availability during plasma pulses. Operation of the upgraded real time RF power control system that maximises the launched power while taking into account limitations from the system or antenna coupling is described. We also report on the result from dedicated pulses performed to assess the potential harmful impact of the 2nd harmonic tritium resonance in the plasma, close to the inner wall, when using the standard central hydrogen minority ICRH scheme. During DTE2, the ITER-like antenna was not used because water leaked from an in-vessel capacitor into the vessel on day-2 of the experimental campaign. The lessons learnt from this incident are highlighted. Finally, the ICRH plant adjustments required to safely perform ion cyclotron wall cleaning discharges are described.

Bi-Level Inverse Robust Optimization Dispatch of Wind Power and Pumped Storage Hydropower Complementary Systems

This paper presents a bi-level inverse robust economic dispatch optimization model consisting of wind turbines and pumped storage hydropower (PSH). The inner level model aims to minimize the total generation cost, while the outer level introduces the optimal inverse robust index (OIRI) for wind power output based on the ideal perturbation constraints of the objective function. The OIRI represents the maximum distance by which decision variables in the non-dominated frontier can be perturbed. Compared to traditional methods for quantifying the worst-case sensitivity region using polygons and ellipses, the OIRI can more accurately quantify parameter uncertainty. We integrate the grid multi-objective bacterial colony chemotaxis algorithm and the bisection method to solve the proposed model. The former is adopted to solve the inner level problem, while the latter is used to calculate the OIRI. The proposed approach establishes the relationship between the maximum forecast deviation and the minimum generation cost associated with each non-dominated solution in the optimal load allocation. To demonstrate its economic viability and effectiveness, we simulate the proposed approach using real power system operation data and conduct a comparative analysis.

Solar district heating system in Latvia: A case study

The ever growth of urban population has caused the increase of energy demand. This situation challenges the global public to find a way to produce energy in a secure, environmentally friendly, flexible, renewable and sustainable energy transformation system. One of the most promising technologies that can over-come most of the challenges is the district energy network. This paper focuses on best practice project in Latvia – fully renewable district heating system assisted by solar collector system with thermal storage tank and woodchip boilers. The article demonstrates solar impact to district heating system (DHS) in the framework of the current situation of DHS in Latvia by creating simulation in TRNSYS and validation with real system operation data. Real operational data show that the heat produced by solar collector system with thermal energy storage (TES) per year will cover up to 20% (10933,2 MWh) of the heating demand in under local climate conditions. The TRNSYS simulations concluded that the contribution of this type of solar system integrated into the DHS could be increased up to 50%, if the difference between consumption and heat produced by solar collectors were to be increased during the summer months, this could be achieved, for example, by increasing the solar collector field area.

Enhancing Early Systems R&D Capabilities with Systems —Theoretic Process Analysis

ABSTRACTSystems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering‐based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early‐systems R&D discussions. STPA—a ‘top‐down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early‐system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early‐system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. Leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.

Selection of the photovoltaic system power for the electric vehicle

The article presents a method of a photovoltaic system power selecting for proper electric vehicle. At the beginning, the characteristics of the electric vehicle itself and its traction batteries were made in order to determine the method of its charging. In the presented studies, measurement data from the operation of real photovoltaic systems was recorded and processed. The algorithm used includes examining the energy consumption profile of the owner's residential building. The combined demand for electricity of the electric vehicle and the building made it possible to determine the planned photovoltaic system. The authors presented three possibilities of its location. It can be installed on the roof of the building, on the ground next to the building or on the carport under which an electric vehicle can be parked. Finally, the Metalog family of probability distributions was used to analytically validate the power choice of the photovoltaic system. The authors have developed an algorithm using human and artificial intelligence that helps to properly select the power of the photovoltaic system for the vehicle.

Bayesian Models for Targeted Cyber Deception Strategies (Student Abstract)

We propose a model-driven decision support system (DSS) based on a Bayesian belief network (BBN) to support cyber deception based on a detailed model of attacker beliefs. We discuss this approach using a case study based on passively observed operating system (OS) fingerprinting data. In passive reconnaissance attackers can remain undetected while collecting information to identify systems and plan attacks. Our DSS is intended to support preventative measures to protect the network from successful reconnaissance, such as by modifying features using deception. We validate the prediction accuracy of the model in comparison with a sequential artificial neural network (ANN). We then introduce a deceptive algorithm to select a minimal set of features for OS obfuscation. We show the effectiveness of feature-modification strategies based on our methods using passively collected data to decide what features from a real operating system (OS) to modify to appear as a fake [different] OS.

Semi-Markov model for the Kaplan preventive repair system for water turbines

The article presents a semi-Markovian model of a system of preventive repairs according to age, carried out in water power plants with the use of Kaplan turbines. In the case of the analyzed technical objects, the profit per unit of time is considered as a criterion for the considered function. On the basis of the adopted assumptions, formulas describing the criterion function were presented for the developed mathematical model, and then the conditions for the existence of the maximum of this function were formulated. The proposed method makes it possible to determine the optimal time for preventive repair of the water power plant under consideration. Theoretical considerations presented in the article are presented on the basis of a calculation example developed on the basis of estimated data obtained from a real Kaplan water turbine operation system.

Decentralized Optimized Integral Sliding Mode-Based Load Frequency Control for Interconnected Multi-Area Power Systems

In this paper, a decentralized load frequency control (LFC) is proposed for the frequency regulation of multi-area power systems applying optimized integral sliding mode control (OISMC) scheme. A modified particle swarm optimization (MPSO) algorithm is utilised to optimize the control variables of the proposed controller for improving the performance of frequency response. A comprehensive model of a multi-area power system is developed where each area is considered to power from conventional sources, renewable energy resources (RERs), and electrical vehicle (EV) aggregators. Different types of interconnectors have been considered to tie different power regions. This system is subjected to random generation and loading conditions, as well as model uncertainties, which cause its frequency and area control error (ACE) to deviate from their nominal values. A decentralised OISMC is implemented for the proposed model to keep the frequency variation within the nominal range. Further, a validation study investigates the performance of the proposed control technique in MATLAB simulation environment to mimic the real power system operation. This study includes different test scenarios considering the dynamic events such as variations in load demand, amount of generated power, participation factor of EV reserve, system parameters, disturbances, and time delay. The simulated results support better frequency regulation with the suggested OISMC compared to other alternative control approaches.

In-situ sensor virtualization and calibration in building systems

• In-situ sensor virtualization method is proposed. • In-situ virtual sensor calibration is proposed for reliable virtual sensing in operation. • Application is conducted for real operational systems. • Findings on virtualization strategies are discussed for better virtual sensing. In this study, in-situ sensor virtualization is proposed to overcome physical sensing limitations, including sensor errors and management costs in long-term building operations. Virtualization is achieved by a backup virtual sensor for a target sensor when the physical sensor is faulty during operation. This requires in-situ virtual sensor modeling and a continuous calibration process for effective virtual sensing in operational building energy systems. The main research question is how the in-situ calibration for a virtualized sensor can be formulated without a physical counterpart/benchmark. The proposed virtualization with in-situ calibration was applied to central heating systems serving university campus buildings with real operational datasets obtained by a building automation system. The case study showed (1) the effectiveness of virtualization when the target physical sensor has absolute bias errors more than approximately 1.0 °C, (2) the virtualized sensor errors (the average root mean square error (RMSE) of 0.88 °C in the four days) in operation, and (3) the high-accuracy virtual sensor with an average RMSE of 0.30 °C in a month (in February) from the in-situ calibration. The in-situ calibration in Case 3 decreased the initial virtualized sensor errors by 44.0 % and 32.9 %, respectively, for bias and random errors, by appropriately defining the correction function, the input feature, and the optimal calibration reference value in the mathematical calibration formulation. Based on the findings, two strategies for virtualization and calibration are proposed with their design tools for suitable problem formulation at the design stage, better accuracy in operation, and automated virtualization in the future.

Optimizing Operation Strategy in a Simulated High-Proportion Wind Power Wind–Coal Combined Base Load Power Generation System under Multiple Scenes

In order to accommodate more intermittent renewable energy in coal-dominated power systems, conventional thermal power plants need to improve their operational flexibility to balance the energy system at all times. However, flexible operation of coal-fired power plants could reduce energy efficiency and increase CO2 and pollutant emission, so it is important to consider environmental implications and optimize the dispatch of wind and coal power units in the system. In this paper, based on the output profile of wind power, a wind power peak (T, H) simulation model based on Gaussian distribution was established. Using a high-proportion wind power wind–coal combined base load power generation system as an example, the economical and environmentally friendly unit operation based on different wind power penetration was studied by simulation, and the decision strategy was established. Wind energy curtailment boundary was determined with power generation cost, energy consumption, CO2 and pollutant emissions as decision targets, respectively. Weekly scale results indicate that incorporating energy consumption and pollutant emissions into consideration will lead to different decision-making strategies compared with only targeting minimizing wind curtailment. This paper established a decision-making model of wind–coal system operation strategy based on economy and environmental criteria. This work directly contributes to real system operation and is of great significance for future scheduling/dispatch studies of actual power systems.

Thermal degradation assessment study of a direct vaporization ORC based micro-CHP system under close-to-real operating conditions

• Most of the thermal degradation assessment methods are far from real conditions. • The thermal degradation temperature is affected by the assessment method. • Static approaches can provide over-restrict thermal degradation temperatures. • Surface temperatures don’t need to be limited by values taken from static methods. • Degradation studies must quantify the effective time over the degradation limit. Organic Rankine cycles have the potential to be implemented at a micro-scale and to fulfil the technological gap that is preventing the retrofit of the wall-hang residential combi-boilers by cogeneration systems. Such ability requires the use of high-temperature combustion gases to vaporize the organic fluid and needs to deal with the risk of its thermal degradation. Limits to the fluid bulk temperatures are well known and easily controllable, nevertheless, the relevance of the thermal boundary layer and the temperature of the heat-transfer surfaces over the thermal degradation must be considered and analyzed in depth because of the significant temperature differences between the combustion gases and the organic fluid. With the objective of understanding the thermal degradation temperature limits presented in the literature, an extensive and updated review of the most relevant works was presented. Due to the dispersity of the information regarding the thermal degradation methods and procedures found, a new framework was developed to classify them according to their key essential features: the thermal stress, the degradation assessment and the determination of the limit temperature. The output of that classification is presented together with the degradation temperature value to expose their dependency. The review also allows identifying that the large majority of the thermal degradation temperature values were determined using operating conditions that are far from reality. Complementing the review analysis, this paper offers a first insight into the study of the effect of the thermal boundary layer on its chemical integrity. This is achieved by coupling a detailed comprehensive characterization of the system operating conditions, which comprise the determination of the heat-transfer surface temperatures in contact with the organic fluid, with the realization of dynamic thermal stress tests that include the experimental evaluation of the organic fluid thermal degradation. Operating conditions that lead to heat-transfer surface temperatures well above the threshold found in the literature are considered in this work. The absence of thermal degradation in this situation may indicate that i) the current threshold should not be used to impose upper bounds on the evaporator heat-transfer surface temperature and ii) the time (and not only the temperature) has a significant effect on the thermal degradation of the organic fluid in real operating systems.

Experimental outdoor performance assessment and energy efficiency of 11.28 kWp grid tied PV systems with sun tracker installed in saharan climate: A case study in Ghardaia, Algeria

In recent years, the demand for grid tied solar photovoltaic systems has been rising fleetly, due to the low cost of solar panels, storage systems, and inverter equipment. The expertise of the grid related photovoltaic solar system attached sun trackers doesn't depend on the following factors, the amount of sunshine, angular travel, positioning frequency, and the region where they are installed. To comprehend how they perform in the whole world, we require assessing and comparing their performance in several parts of the country. This work details the experimental PV system setup and the analytics based on the measurements from the system in Algeria (Ghardaia City).This work exhibits detailed simulation data based on PVSYST software and compared with experimental results of an 11.28 kWp grid-connected solar system with sun tracking systems, after one year of operation at Applied Research Unit of Renewable Energy (URAER) in southern Algeria. The PV system consists of three identical 3.76 kWp solar trackers systems with the same module and inverter. This study also investigates during the real outdoor PV system operation, the yearly, monthly, and daily PV array and inverter behavior based on the performance metrics indicators, meteorological data (temperature and solar irradiation), DC and AC energy produced reference yield (YF), array yield (YA), final yield (YF), PV array and system efficiency, performance ratio (PR), PV array and system losses, and capacity factor (CF) of the whole solar photovoltaic system with sun tracker and likewise the three sub-PV systems with sun tracker following to the IEC 61,724 guidance. The electricity produced for the grid was also verified using the sunny portal application. The outcomes indicate that the binary-axis solar tracker shows a preferable performance, which collects about 20.89% further energy compared to that of the steady axis, and the one-axis tracker generates about 12.91% more energy compared to the fixed PV tracker. The energy generated (EDC) over the monitored period (2021) was 6917.50 kWh, 7810.58 kWh, and 8362.68 kWh for the steady PV system, one axis PV system, and twin axis tracking PV system, respectively. The yearly energy fed into the conventional network (EAC) over the period is 7537.4 kWh, 9962.1 kWh, and 10273.5 kWh for the stationary PV system, one axis PV system, and binary axis tracking PV system, respectively. The amount of CO2 emissions avoided over the monitored period (2021) is 4.84 tons, 5.46 tons, and 5.85 tons for the stationary PV system, one axis PV system, and twin axis tracking PV system, respectively, which can significantly contribute to cleaning the environment from pollution. The analysis shows PV module and overall system efficiency varies from (10.66%/10.26%), 10.77%/10.35%, and 10.79%/10.36% noted in July to (12.18%/11.74%, 12.34%/11.9%, and 12.41%/11.95%) noted in January for twin axis solar trackers PV systems, one axis solar trackers PV systems, and PV fixed PV systems, respectively. The analysis shows that the performance ratio (PR) varies from (72.3%, 72.4% and 71.8%) in July to (83.6%, 83.2% and 82.1%) in January and the annual average value reaches (77.95%, 77.80%, and 76.95%) for twin axis solar trackers PV systems, one axis solar trackers PV systems, and PV fixed PV systems, respectively. These performance indicators have the practical value of guiding and providing good feedback for engineers, system designers, contractors, and investors to boost the growing applications of grid-tied PV systems with a sun tracker in the same or similar location and climate conditions (Sahara environment).

Transcritical carbon dioxide heat pump cycle: Validation and comparison of one-dimensional models of ejector with independent data sets

Two typical 1-D homogeneous equilibrium models from the literature (constant and variable pressure mixing models) were used to study the ejector expansion transcritical CO 2 heat pump cycle. A sensitivity analysis was performed to study the impact of the isentropic efficiency of the compressor and of each component of the ejector on the overall system performance. Two groups of independent experimental data from the literature were used to validate the two models. It was found that the cycle performance was more sensitive to the isentropic efficiency of the compressor than that of the individual components of the ejector. With appropriate input value of the compressor efficiency, both 1-D models accurately predicted the ejector outlet pressure and vapor quality. However, for the ejector entrainment ratio and cycle COP, the deviations were more than 50 %. These deviations can be significantly minimized to 10 % by eliminating the imbalance occurring in the system operation using a data post-processing method to correct the pseudo steady state in the real operation system. By comparing the simulation results of the two models, it has been shown that there is minor difference on the prediction accuracy. The research can serve as a practical reference for the operation of two-phase-flow ejector expansion transcritical heat pump cycle, and can also provide an insight and orientation for the subsequent optimization of 1-D ejector numerical models. • Two 1-D homogeneous equilibrium models of the ejector are experimental validated. • Neglecting the pressure variation during the mixing has no significant impact. • The liquid imbalance is a predominant source of the modeling deviation. • An innovative data post-processing method is proposed. • The system performance is more sensitive to the compressor isentropic efficiency.

Stabilization of Discrete-Time Hidden Semi-Markov Jump Singularly Perturbed Systems With Partially Known Emission Probabilities

This article considers the stabilization problem of discrete-time semi-Markov jump singularly perturbed systems, in which the system operation mode is hidden but can be estimated by a detector with some emission probabilities. To model this circumstance, the hidden semi-Markov model (HSMM) with partially known emission probabilities is introduced, where the hidden state represents the real system operation mode while the emitted value represents the estimated value of the system operation mode and is available for the stabilizing controller. Based on the introduced HSMM, the stabilizing controller which only relies on the estimated value is designed to guarantee the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -error mean square stability of the closed-loop system. A numerical example is used to verify the usefulness of the obtained results.

A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction

Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e. bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validations are performed using the PRONOSTIA bearing degradation datasets.

Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting

Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real power system operations. Nowadays, the Extreme Learning Machine (ELM) is significant for forecasting as it provides an acceptable value of forecasting and consumes less computation time when compared with the state-of-the-art forecasting models. However, the result of electric energy demand forecasting from the ELM was unstable and its accuracy was increased by reducing overfitting of the ELM model. In this research, metaheuristic optimization combined with the ELM is proposed to increase accuracy and reduce the cause of overfitting of three forecasting models, composed of the Jellyfish Search Extreme Learning Machine (JS-ELM), the Harris Hawk Extreme Learning Machine (HH-ELM), and the Flower Pollination Extreme Learning Machine (FP-ELM). The actual electric energy demand datasets in Thailand were collected from 2018 to 2020 and used to test and compare the performance of the proposed and state-of-the-art forecasting models. The overall results show that the JS-ELM provides the best minimum root mean square error compared with the state-of-the-art forecasting models. Moreover, the JS-ELM consumes the appropriate processing time in this experiment.

Impact of Time-of-Use Demand Response Program on Optimal Operation of Afghanistan Real Power System

Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of appropriate DR programs for developing nations, particularly considering renewable energy sources (RESs). In this paper, as two-stage programming, the effect of the time-of-use demand response (TOU-DR) program on optimal operation of Afghanistan real power system in the presence of RESs and pumped hydropower storage (PHS) system in the day-ahead power market is analyzed. Using the concept of price elasticity, first, an economic model indicating the behaviour of customers involved in TOU-DR program is developed. A genetic algorithm (GA) coded in MATLAB software is used accordingly to schedule energy and reserve so that the total operation cost of the system is minimized. Two simulation cases are considered to verify the effectiveness of the suggested scheme. The first stage programming approach leads case 2 with TOU-DR program to 35 MW (811 MW − 776 MW), $16,235 ($528,825 − $512,590), and 64 MW reductions in the peak load, customer bill and peak to valley distance, respectively compared to case 1 without TOU-DR program. Also, the simulation results for stage 2 show that by employing the TOU-DR program, the system’s total cost can be reduced from $317,880 to $302,750, which indicates a significant reduction in thermal units’ operation cost, import power tariffs and reserve cost.

Is the information provided by free satellite sources suitable for predicting or evaluating the performance of photovoltaic systems in Peru? (In memoriam to Heinrich Berg)

At present, there are free access online platforms that offer, free of charge, meteorological data on Peru; however, there is no study that validates the goodness of its use in the country. These types of tools are of the utmost interest, both in the design phase of photovoltaic systems PV and in the subsequent process of analysis and evaluation of their operation if it is intended to implement monitoring systems with Class B or C precision defined in the IEC 61724: 2017 standard. In this work, the consistency of the data offered by any of these platforms and their usefulness will be analysed when they are contrasted with experimental data on the operation of real systems.

Load Balancing and Detection of Distributed Denial of Service Attacks Using Entropy Detection

Software Defined Network (SDN) is a modern network architecture that has a centralized controller. It is more flexible, and programmable due to the separation of the control plane from the data plane. However, Distributed Denial of Service (DDoS) attacks is one of the dangers that the SDN network is facing. It could attack and stop the controller from working, causing the whole system to be down. Moreover, DDoS attacks can target the hosts and the switches to stop the services for a long time as they could cause more damage to the network or datacenter. In this work, a proposed approach is utilized to protect datacenter networks and servers from DDoS attacks using entropy and real SDN-controller Python Network Operating system (POX) by redirect traffic to the edge of the datacenter to minimize the damage. The results of this experiment show how to detect abnormal traffics in an early stage and isolate them in a server outside the datacenter to distribute the huge amount of traffic in more than one server and avoid congestion on switches. Also, the throughput of the server was increased by about %16 during the suspected attack, this means maintaining the service until further analysis to be done on the traffic. These results are compared with the direct block mitigation method which was mostly used with the entropy detection method in previous researches. Moreover, this work is done to confirm whether the suspected traffic is an actual attack or not. Therefore, this method will decrease the false positives of detection. IndexTerms— Software-Defined Networking (SDN), Distributed Denial of Service (DDoS) attacks, OpenFlow, Python Network Operating system (POX).