مجله علوم و فناوری های پدافند نوین
سال ششم شماره 2 (پیاپی 20، تابستان 1394)

Power industry, as an infrastructure industry, plays a critical role in economic progress and social welfare. In this context, continuous load meeting, reliable service, to keep low energy price, achieving the quality standards, environmental protection and preservation of strategic position of power systems are contingent upon the precise planning which should be considered by relative responsible. It this paper, the importance of incorporating the renewable energy resources into the expansion planning is evaluated from two perspectives: first, from the passive defense viewpoint, i.e. decentralization of power plants or small-scale production, second, from social welfare viewpoint, including environmental considerations and the released emission from generation power sector. Accordingly, first, a comprehensive model for generation expansion planning is presented then, regarding the obtained results from applying the gravitational search algorithm to the proposed model, the perspectives mentioned above are evaluated.

Interleaving is a widely used technique to overcome the burst error in systems featuring error correction. In a non-cooperative environment such as satellite signal in which receiver is not aware of the structure of transmitter, interleaver should be identified, otherwise it would be impossible to identify the residual components and completing the hearing process. In satellite systems, it is a common practice to use a combinatory coding block, including reed-solomon codes and an interleaver with convolutional codes. This research presumes that convolutional code is identified and an algorithm is suggested for recognition of interleaver. In this algorithm, based on the presence of reed-solomon code, interleaving period is identified, non-binary field degree is calculated, and a factor meeting the length of reed-solomon code would be the interleaver depth. Comparing with other available methods, in our suggested algorithm, computational complexity are reduced to and simultaneity and accuracy of identification is higher. Additionally, Based on the simulations performed, the in order to identify the depth of Interleaver and have been used in order to synchronization.

The civilian GPS signals are unencrypted, predictable and low power ones. Therefore, they are vulnerable to destroyer interfaces such as spoofing. In this paper, in order to reduce spoofing effect and achieve time series without interface, an adaptive filter with finite impulse response based on Least Mean Square (LMS) algorithm has been used for the first time. Contrary to the previous methods, proposed approach needs no extra hardware and structural change in GPS receiver. Input signal of utilized adaptive filter is pseudo-range data in navigation section. The principle of using adaptive filter to eliminate interference is obtaining an estimate of interfering signal and subtracting that from the corrupted signal. Therefore, what remains at final output is the authentic signal. In this paper, root mean square (RMS) criteria is used to validate the proposed method. Simulation results show that the proposed approach can neutralize laboratory interface effects average up to 95 percent and measurement spoofing effect in average of 81 percent.

Supplying sensitive areas by utilizing various type of energy sources with decentralized control, in order to improve reliability and reach maximum autonomous operation capability, is essential. In addition, autonomous operation in the area as an islanded microgrid is an important step to have certainty of sustainable operation during black out or failure of stiff power system. in utilizing distributed generation inside microgrid, due to share loads by decentralized control on Voltage Source Converters (VSC), droop control method is employed. one disadvantage of droop control is the change in frequency respect to the change in output active power. In this paper, by introducing novel supplementary control system for droop control, the frequency changes in areas including sensitive loads are omitted. This goal is reached by floating reference frequency of VSCs according to output active power changes. Load sharing proportional to the units’ size, insensitive to connective line in microgrid structure and capability of changing the number of power units without changing the microgrid structure, are the features of the proposed control system. Simulation results in PSCAD are a confirmation of novel control system operation.

Power supply of military sites with high reliability is an important issue since power stations and distributing networks are possible to be attacked and disturbed by the enemy. Thus, distributed generation units especially wind energy are used for the needed power supply. However, the intermittence and uncertainty of wind speed has an impressive effect on wind turbine power output and generation adequacy assessment. To obtain accurate result for generation adequacy assesment, it is very important to develop suitable model for wind speed. In this paper, wind speed is modeled with time series as well as hourly Weibull Pdf in which the former item has a lower accuracy and computational time in comparision with the latter one with inverse properties. In order to overcome the mentioned problem, this paper proposes a novel model for wind speed clustering pattern using self-organized neural network followed by Monte Carlo simulation for generation adequacy assessment which has better accuracy and computation time than those two hourly Weibull Pdf and time series method. The Roy Billinton Test System (RBTS) was used in the studie as military network and HLI adequacy assessment is done with Monte Carlo simulation.

The power networks as a critical infrastructures are strategic targets for terrorist attacks. Therefore, providing solutions to deal with these threats is necessary for security assessment. This paper presents a method based on the risk assessment by which the planners are able to evaluate the power network security regarding to terrorist attacks. In this way, first, the variables affecting the terrorist groups decision-making for attacking the power systems are determined, and then by employing the Bayesian network which is derived from the probability theory, a model is proposed to estimate the probability of the attack against the power network facilities and its possible consequences. Finally, by determining the extend of damage caused by the attack on power network, the proposed model is used to calculate the risk of the attack on power system facilities. Also, in order to validate the effectiveness of the proposed method, the risk value for a specific network is estimated and its facilities are ranked according to the level of risk.

Accurate spectrum sensing is very important in cognitive radio networks. False sensing results in either waste of spectrum or harmful interference to primary users. To improve accuracy, cooperative spectrum sensing, in which a set of secondary users cooperatively sense the presence of the primary user, has emerged. This technique, however, opens a window for malicious users, who may send false data to the fusion center. This kind of sending false data to the fusion center, which can severely disturb the network, is called spectrum sensing data falsification (SSDF) attacks. In this paper, focusing on stochastic behavior of secondary users and using Expectation-Maximization (EM) algorithm, detection and false alarm probabilities and then the true spectrum state are estimated. Numerical results show improvement in cooperative spectrum sensing operation in respect of common reputation based methods for defending against SSDF attacks. The results also show an increasing in system’s speed and a reduction in communication’s cost.