a. zakariazadeh

Rice blast disease caused by Pyricularia oryzae is one of the most important rice diseases worldwide. Given the growing demand for non-toxic and chemical-free products, identification of plant growth-promoting microorganisms that can assist in mitigating challenges to plant growth is very important in sustainable rice cultivation. This study aimed to investigate the effect of two plant growth-promoting rhizobacteria (PGPR) isolates, Alcaligenes faecalis strain O1R4 and Bacillus idriensis strain MR2, on the induction of resistance-related enzymes such as peroxidase and catalase and on the severity of rice blast in Hashemi and Hassan Sarai cultivars.

The roots of the seedlings (cv. Hashemi and Hassan Sarai) were inoculated with a suspension of bacterial isolates (106-107 CFU/ml) for one hour and planted into pots. The plants were then sprayed in the four-leaf stage with an aqueous spore suspension (2×105 spore/ml) until run-off. The inoculated plants were transferred to the greenhouse at 28 °C and 90% relative humidity. Catalase and peroxidase activity in rice leaf extracts from different treatments were assayed one, three, five, and seven days after inoculation with blast fungus using a spectrophotometer. The activity was expressed in U µg–1 protein. The experiment was done in a completely randomized design with eight treatments and three replications for each cultivar. Also, the blast severity was assessed in the leaves of inoculated plants with six replications. The data were analyzed by SAS software (version 9.1) and Tukey's test at 0.05 and 0.01 probability levels. 

According to the analyses of variance, there was a significant influence of bacterial inoculation on the activity of peroxidase enzyme on the third, fifth, and seventh days. On the other hand, the activity of catalase enzyme increased on the first day to the seventh day at the 1% probability level. Mean comparison revealed in the treatments consisting of A. faecalis + P. oryzae, the levels of peroxidase and catalase enzymes were respectively elevated by 97% and 43% in Hashemi cultivar and by 174% and 125% in Hassan-Sarai cultivar compared to the positive control. Also, in the presence of B. idriensis + P. oryzae, the amount of peroxidase and catalase enzymes were respectively heightened in cv. Hashemi by 69% and 33% and in cv. Hassan-Sarai by 195% and 121% compared to the positive control. In the simultaneous use of two bacterial isolates and P. oryzae, the amount of catalase enzyme increased in Hashemi and Hassan-Sarai cultivars by 73% and 166% and the amount of peroxidase enzyme reached 174% and 319% in Hashemi and Hassan-Sarai cultivars, respectively compared to the positive control. Finally, the results showed that the blast severity was reduced using A. faecalis strain O1R4 and B. idriensis strain MR2 by 37% and 20% in the cultivars respectively.

In the present study, peroxidase activity in all treatments consisting of blast fungus was approximately at the same level within 24 hours post-inoculation. Then, the peroxidase level increased and maintained until the seventh day. Further, the catalase activity increased in the presence of the blast fungus and PGPRs. The level of peroxidase activity increased one to three days after inoculation. In comparison, the level of catalase increased within 24 hours after inoculation. The simultaneous use of two bacterial isolates along with pathogenic fungus was more effective than usage of each isolate separately. In general, the results of this research were consistent with previous studies that indicate the bacterial isolates can reduce the blast severity by inducing systemic resistance in rice.

Optimal placement and sizing of distributed renewable energy resources (DER) in distribution networks can remarkably influence voltage profile improvement, amending of congestions, increasing the reliability and emission reduction.  However, there is a challenge with renewable resources due to the intermittent nature of their output power. This paper presents a new viewpoint at the uncertainties associated with output powers of wind turbines and load demands by considering the correlation between them. In the proposed method, considering the simultaneous occurrence of real load demands and wind generation data, they are clustered by use of the k-means method. At first, the wind generation data are clustered in some levels, and then the associated load data of each generation level are clustered in several levels. The number of load levels in each generation level may differ from each other. By doing so the unrealistic generation-load scenarios are omitted from the process of wind turbine sizing and placement. Then, the optimum sizing and placement of distributed generation units aiming at loss reduction are carried out using the obtained generation-load scenarios. Integer-based Particle Swarm Optimization (IPSO) is used to solve the problem. The simulation result, which is carried out using MATLAB 2016 software, shows that the proposed approach causes to reduce annual energy losses more than the one in other methods. Moreover, the computational burden of the problem is decreased due to ignore some unrealistic scenarios of wind and load combinations.

The development of communications and telecommunications infrastructure, followed by the extension of a new generation of smart distribution grids, has brought real-time control of distribution systems to electrical industry professionals’ attention. Also, the increasing use of distributed generation (DG) resources and the need for participation in the system voltage control, which is possible only with central control of the distribution system, has increased the importance of the real-time operation of distribution systems. In real-time operation of a power system, what is important is that since the grid information is limited, the overall grid status such as the voltage phasor in the buses, current in branches, the values of loads, etc. are specified to the grid operators. This can occur with an active distribution system state estimation (ADSSE) method. The conventional method in the state estimation of an active distribution system is the weighted least squares (WLS) method. This paper presents a new method to modify the error modeling in the WLS method and improve the accuracy SVs estimations by including load variations (LVs) during measurement intervals, transmission time of data to the information collection center, and calculation time of the state variables (SVs), as well as by adjusting the variance in the smart meters (SM). The proposed method is tested on an IEEE 34-bus standard distribution system, and the results are compared with the conventional method. The simulation results reveal that the proposed approach is robust and reduces the estimation error, thereby improving ADSSE accuracy compared with the conventional methods.

State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and pseudo measurements data. This study presents a new approach to model errors for the distribution system state estimation purpose. In this paper, pseudo measurements are generated using a couple of real measurements data by means of the artificial neural network method. In the proposed method, the radial basis function network with the Gaussian kernel is also implemented to decompose pseudo measurements into several components. The robustness of the proposed error modeling method is assessed on IEEE 123-bus distribution test system where the problem is optimized by the imperialist competitive algorithm. The results evidence that the proposed method causes to increase in detachment accuracy of error components which results in presenting higher quality output in the distribution state estimation.</span></span></span></div>

In this paper, a distributed method for reactive power management in a distribution system has been presented. The proposed method focuses on the voltage rise where the distribution systems are equipped with a considerable number of photovoltaic units. This paper proposes the alternating direction method of multipliers (ADMMs) approach for solving the optimal voltage control problem in a distributed manner in a distribution system with high penetration of PVs. Also, the proposed method uses a clustering approach to divide the network into partitions based on the coupling degrees among different nodes. The optimal reactive power control strategy is conducted in each partition and integrated using ADMM. The proposed method is tested on a 33 bus IEEE distribution test system and a modified IEEE 123-node system. The result evidence that the proposed method has used the lower reactive power if compared to the conventional method.