Intelligent Protection Scheme of Electrical Energy Distribution Systems in the presence of Distributed Generation Sources using Agent-Based Distributed Controller

The ever-increasing of renewable distributed generation sources in distribution networks, as well as increasing network size, have faced agent-based protection schemes with a heavy communicational load. Accordingly, despite the fast and reliable nature of multi-agent systems, they have the possibility of improper performance, especially in centralized protection systems. This paper presents an intelligent self-healing method that has the ability to replace common multi-agent systems during fault conditions. Therefore, protection tasks are performed in a single control level, without dependence on higher communicational levels, to clear the fault. Decentralized operation of this structure is provided by using intelligent electronic devices and distributed communications. In this way, the proposed scheme is described with high-speed peer-to-peer communication capability using the IEC-61850 GOOSE protocol. Then, a penetration-free algorithm, without the help of a central controller, is provided by using GOOSE message capabilities, to prevent any electricity interruption due to insufficient protection settings. Finally, by planning different scenarios and simulating a practical distribution network via ETAP software, the accuracy of the proposed algorithm has been proven

Gender difference in motor impairments induced by chronic administration of vinblastine

Objective(s): Neurotoxicity of anticancer drugs complicates treatment of cancer patients. Vinblastine (VBL) is reported to induce motor and cognitive impairments in patients receiving chronic low-dose regimen. Materials and Methods: The effects of VBL treatment on motor, learning and memory functions of male and female Wistar rats were studied by behavioral related tests. Animals were given chronic intraperitoneal injections of VBL (0.2 mg/kg/week for 5 weeks) from postnatal day 23 to 52. Motor function was evaluated using grasping test and balancing was evaluated by the rotarod. Spatial learning and memory and anxiety-like behavior were determined using Morris water maze (MWM) task and open field test, respectively. Results: Administration of VBL caused severe damage to motor and balance function of male rats in comparison to female rats treated with VBL and rats treated with saline. Memory and locomotion were affected in both male and female rats compared with saline treated rats, while a sex difference was also observed in these parameters; male rats showed more impairment compared with female ones. Both male and female rats showed cognitive impairments in MWM task and no sex differences were observed in these functions. Conclusion: Results revealed that VBL is a potent neurotoxic agent and despite the profound effect of VBL on motor and cognitive functions, it seems that male rats are more susceptible to motor deficits induced by VBL

Monomethyl auristatin E Exhibits Potent Cytotoxic Activity against Human Cancer Cell Lines SKBR3 and HEK293

Background: Monomethyl auristatin E (MMAE) is a synthetic analog of dolastatin 10, a compound originally isolated from the marine mollusk. MMAE, as a highly potent microtubule inhibitor, exerts its potent cytotoxic effect by inhibiting microtubule assembly, tubulin-dependent GTP hydrolysis and microtubes polymerization. This molecule, by itself, lacks the tumor specificity required to elicit therapeutic benefit. Nevertheless, the extremely cytotoxic potential of MMAE could be harnessed in the form of MMAE-antibody conjugates. The aim of the present study was to evaluate the cytotoxic activity of MMAE against breast (SKBR3) and kidney (HEK293) cancer cell lines in an in vitro cell-based assay.Materials and Methods: SKBR3 and HEK293 cells were treated with different concentrations ranging from 0.002048, 0.01024, 0.0512, 0.256, 1.28, 6.4, 32, 160, 800 and 4000 nM of MMAE, and cell viability was determined after 72 hours using an MTT colorimetric assay. The effect of MMAE was regularly monitored by direct observation using an invert microscope.Results: Microscopic observation showed that there was a concentration-dependent increase in cell death. Results from the MTT assay revealed a statistically significant loss of viability (P<0.0001) at concentrations ranging from 0.01024 to 4000 nM in SKBR3 cells, and 0.0512 to 4000 nM in HEK293 cells. Our findings showed that MMAE inhibited the growth of SKBR3 and HEK293 cells in a concentration-dependent manner, with IC50 values of 3.27 ± 0.42 and 4.24 ± 0.37 nM, respectively.Conclusion: MMAE was able to significantly inhibit cell growth at nanomolar concentrations, emphasizing its great potential for the development of antibody-drug conjugates

Adaptive Load Shedding Analysis in Power Systems by Using Artificial Neural Network

The stability of frequency and voltage is one of the basic principles in the power systems. One of the latest control measures for power system frequency control and stability is load shedding. A fast and optimal adaptive load shedding method using Artificial Neural Networks (ANN) is presented in this paper. In this paper, the total power generation and the total existing load in power system were selected as the ANN inputs. This method has been tested on theNew England test system. The simulation results show the ability of this frequency control algorithm for optimal solving problem related to conventional method

Day-Ahead Coordination of Vehicle-to-Grid Operation and Wind Power in Security Constraints Unit Commitment (SCUC)

In this paper security constraints unit commitment (SCUC) in the presence of wind power resources and electrical vehicles to grid is presented. SCUC operation prepare an optimal time table for generation unit commitment in order to maximize security, minimize operation cost and satisfy the constraints of networks and units in a period of time, as one of the most important research interest in power systems. Today, the relationship between power network and energy storage systems is interested for many researchers and network operators. Using Electrical Vehicles (PEVs) and wind power for energy production is one of the newest proposed methods for replacing fossil fuels.One of the effective strategies for analyzing of the effects of Vehicle 2 Grid (V2G) and wind power in optimal operation of generation is running of SCUC for power systems that are equipped with V2G and wind power resources. In this paper, game theory method is employed for deterministic solution of day-ahead unit commitment with considering security constraints in the simultaneous presence of V2G and wind power units. This problem for two scenarios of grid-controlled mode and consumer-controlled mode in three different days with light, medium and heavy load profiles is analyzed. Simulation results show the effectiveness of the presence of V2G and wind power for decreasing of generation cost and improving operation indices of power systems

Frequency Control in Autanamous Microgrid in the Presence of DFIG based Wind Turbine

Despite their ever-increasing power injection into power grid, wind turbines play no role in frequency control. On the other hand, power network frequency is mainly adjusted by conventional power plants. DFIG-based wind turbines not only are able to produce power in various mechanical speeds, but they can also reduce speed instantaneously which, in turn, leads to mechanical energy release. Thus, they can aid conventional units in system frequency control. In this paper, the effect of wind energy conversion systems, especially variable speed DFIG-based wind turbines, in controlling and tuning of frequency is investigated when different penetration coefficients are considered in a isolated microgrid comprising of conventional thermal and non-thermal generating unit. To do this, optimal tuning of DFIG's speed controller is performed in different penetration levels using particle swarm optimization (PSO) technique. In addition, optimum penetration of wind energy conversion system is studied considering frequency change parameters in a microgrid

Power system reliability enhancement by using PowerformerTM

A high-voltage generator PowerformerTM is a new generation of the AC generators. The most significant advantages of these PowerformerTM are their direct connection to high-voltage grid, higher availability, and more reactive power margin, short term overloading capacity and removing the power transformer from the structure of the power plant. In this paper, the installation effect of these generators on the power system reliability is investigated. The amount of the effects depends on the type and location of the power plant, location of the PowerformerTM, the size of load and network topology. For this purpose, in the 6-bus IEEE RBTS system, the conventional generators are replaced by these new PowerformerTM and then, the reliability indices are evaluated. The simulation results show that the reliability indices such as the expected duration of load curtailment (EDLC) and the expected energy not served (EENS) are improved.

Determination of higher order stress terms in cracked Brazilian disc specimen under mode I loading using digital image correlation technique

The digital image correlation (DIC) technique is used to calculate the coefficients of higher order terms of the Williams’ expansion in the centrally-cracked Brazilian disc specimens of different crack lengths under pure mode I loading. The specimens are subjected to diametral-compression loading and the displacement field is obtained for the cracked Brazilian disc by a correlation between the undeformed and deformed images captured before and after loading. The rigid body motion and rotation of each specimen are detected and eliminated from the displacement field by using a code developed based on the Williams’ series solution. Then, by employing an over-deterministic system of equations, the coefficients of higher order terms of the Williams expansion are calculated. The same specimens are then simulated using finite element method. It is shown that there is good agreement between the DIC and the finite element results. Therefore, the DIC technique can be proposed as a reliable method to experimentally obtain the mode I stress intensity factor KI, the T-stress and the coefficients of higher order terms in the Williams’ series expansion

Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach

Demand forecasting produces valuable information for optimal supply chain management. The basic metals industry is the most energy-intensive industries in the electricity supply chain. There are some differences between this chain and other supply chains including the impossibility of large-scale energy storage, reservation constraints, high costs, limitations on electricity transmission lines capacity, real-time response to high-priority strategic demand, and a variety of energy rates at different hours and seasons. A coupled demand forecasting approach is presented in this paper to forecast the demand time series of the metal industries microgrid with minimum available input data (only demand time series). The proposed method consists of wavelet decomposition in the first step. The training subsets and the validation subsets are used in the training and fine-tuning of the LSTM model using the ELATLBO method. The ESC dataset used in this study for electrical demand forecasting includes 24-h daily over 40 months from 21 March 2017, to 21 June 2020. The obtained results have been compared with the results of Support Vector Machine (SVM), Decision Tree, Boosted Tree, and Random Forest forecasting models optimized using the Bayesian Optimization (BO) method. The results show that performance of the proposed method is well in demand forecasting of the metal industries