Active Detection Scheme Research Articles

Active detection in mitigating routing misbehavior for MANETs

Mobile ad hoc network (MANET) is vulnerable to security attacks because of the shared radio medium and lack of centralized coordination. Since most multi-hop routing protocols implicitly assume cooperative routing and are not originally designed for security attacks, MANET has been challenged by diverse denial-of-service attacks that often interfere with the protocol and interrupt on-going communication. In this paper, we propose an explore-based active detection scheme, called EBAD, to efficiently mitigate the routing misbehaviors in MANETs running with dynamic source routing. The basic idea is that a source node broadcasts a route request packet with a fictitious destination node to lure potential malicious nodes to reply a fake route reply packet. If the source node receives the fake route reply packet or an intermediate node cannot decrypt the received route reply packet, the routing misbehavior can be detected. We also propose a route expiry timer based approach to reduce the effect of route cache pollution because of the fake route reply. We present a simple analytical model of the EBAD and its numerical result in terms of detection rate. We also conduct extensive simulation experiments using the OMNeT++ for performance evaluation and comparison with the existing schemes, CBDS and 2ACK. The simulation results show that the proposed countermeasure can not only improve the detection rate and packet delivery ratio but also can reduce the energy consumption and detection latency.

An active islanding detection scheme for inverter-based DG with frequency dependent ZIP–Exponential static load model

Abstract In this paper an analytical design method is proposed for constant voltage controlled inverter based DG with frequency dependent ZIP–Exponential static load model to obtain proper inverter output control and successful islanding detection. The proposed technique is aimed to eliminate the dependency of DG operation point on anti-islanding performance which is unavoidable in case of traditional control strategy. The generalized formulae are derived for calculating the upper and lower limits of the voltage gain, and it is recommended to select the voltage gain within the designed range for effective islanding detection. The proposed approach is tested in MATLAB/Simulink environment and result shows that the proposed scheme is found to be highly effective in islanding detection considering complex network configurations with single and multiple DG systems. The proposed control scheme is able to detect islanding within 10 ms (half cycle) from the event inception, showing the fastness of the developed technique. It is also observed that the percentage of voltage and current THD of the proposed scheme is within permissible limits, indicate that the power quality of the inverter is not degraded by the proposed control strategy, and thus overcomes the drawback of existing active anti-islanding techniques.

Adaptive active fault-tolerant control of generic hypersonic flight vehicles

A theoretical framework of active fault-tolerant control is exploited for a hypersonic flight vehicle longitudinal dynamical system in cruise phase subject to actuator loss-of-effectiveness fault. First, an active fault detection scheme is proposed by designing a nonlinear fault detection observer for the faulty hypersonic flight vehicle model. A finite-time convergence fault-tolerant controller is proposed on the basis of combined back-stepping and sliding mode technology. Moreover, the adaptive law is developed to estimate the bound information of actuator efficiency factor. The fault-free controller is accomplished which guarantees the globally asymptotic output tracking and boundedness of the closed-loop system. Finally, numerical simulations in Simulation and Verification Platform are presented to illustrate that the proposed fault-tolerant control strategy is effective in providing satisfactory tracking performance and rejecting actuator faults in a climbing maneuver case.

Radial Basis Neural Network Based Islanding Detection in Distributed Generation

This article presents a Radial Basis Neural Network (RBNN) based islanding detection technique. Islanding detection and prevention is a mandatory requirement for grid-connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The proposed algorithm is compared with the widely used rate of change of frequency relays (ROCOF) and found working effectively in the situations where ROCOF fails. This approach utilizes rate of change of frequency at the target distributed generation location and fed to the radial basis neural network for intelligent islanding detection. Hence a better reliability is provided. This approach utilizes the artificial neural network (ANN) as a machine learning technology for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loading, switching operation, and network conditions. Simulation studies showed that the RBNN-based algorithm detects islanding situation accurate than other islanding detection algorithms. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the mains power disconnection can be better distinguished

Multisignal histogram‐based islanding detection using neuro‐fuzzy algorithm

Islanding is an important concern for grid‐connected distributed resources due to personnel and equipment safety issues. Several techniques based on passive and active detection schemes have been proposed previously. Although passive schemes have a large nondetection zone (NDZ), concerns have been raised about active methods because of their degrading effect on power quality. Reliably detecting this condition is regarded by many as an ongoing challenge because existing methods are not entirely satisfactory. This article proposes a new integrated histogram analysis method using a neuro‐fuzzy approach for islanding detection in grid‐connected wind turbines. The main objective of the proposed approach is to reduce the NDZ to as close as possible to zero and to maintain the output power quality unchanged. In addition, this technique can also overcome the problem of setting detection thresholds which is inherent in existing techniques. The method proposed in this study has a small NDZ and is capable of detecting islanding accurately within the minimum standard time. Moreover, for those regions which require better visualization, the proposed approach can serve as an efficient aid for better detecting grid‐power disconnection. © 2014 Wiley Periodicals, Inc. Complexity 21: 195–205, 2015

An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation

This article proposes a new integrated diagnostic system for islanding detection by means of a neuro‐fuzzy approach. Islanding detection and prevention is a mandatory requirement for grid‐connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. Although passive schemes have a large non‐detection zone (NDZ), concern has been raised on active method due to its degrading power‐quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro‐fuzzy inference system) for islanding detection. This approach utilizes rate of change of frequency (ROCOF) at the target DG location and used as the input sets for a neuro‐fuzzy inference system for intelligent islanding detection. This approach utilizes the ANFIS as a machine learning technology and fuzzy clustering for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loading, switching operation, and network conditions. The proposed algorithm is compared with the widely used ROCOF relays and found working effectively in the situations where ROCOF fails. Simulation studies showed that the ANFIS‐based algorithm detects islanding situation accurate than other islanding detection algorithms. © 2014 Wiley Periodicals, Inc. Complexity 21: 10–20, 2015

Assessment of an adaptive neuro fuzzy inference system for islanding detection in distributed generation

This paper proposes a new integrated diagnostic system for islanding detection by means of an adaptive neuro-fuzzy inference system ANFIS. Islanding detection and prevention are mandatory requirements for grid connected distributed generation DG systems. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non detection zone NDZ, the concern has been raised on active method due to their degrading power quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques. This approach utilizes different parameters such as rate of change of frequency and rate of change of power and uses them as the input sets for training a neuro-fuzzy inference system for intelligent islanding detection. To validate the feasibility of this approach the method has been validated through several conditions and different loading, switching operation and network conditions. Simulation studies show that the ANFIS-based algorithm detects islanding situation more accurately than other algorithms and found to work effectively in the situations where other methods fail. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the main power disconnection can be better distinguished.

Adaptive neuro-fuzzy inference system (ANFIS) islanding detection based on wind turbine simulator

This paper presents a passive islanding detection method based on means of a neuro-fuzzy approach for wind turbines. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large Non detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The proposed method is based on voltage measurements and processing of the hybrid intelligent system called the Adaptive neuro-fuzzy inference system (ANFIS) for islanding detection. This new method based on passive methods will help to reduce the NDZ without any perturbation that deteriorates the output power quality opposite active methods. This method detects the islanding conditions with the analysis of these signals. The studies reported in this paper are based on an experimental system (wind turbine simulator). The results showed that, the ANFIS-based algorithm detects islanding situation accurate than other islanding detection algorithms. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the mains power disconnection can be better distinguished. Key words: Distributed generation, islanding detection, non detection zone, adaptive neuro fuzzy inference system, fuzzy subtractive clustering.

New active islanding detection scheme for constant power and constant current controlled inverter‐based distributed generation

This study presents an active islanding detection scheme based on voltage positive feedback control strategy in synchronous d–q frame with a variable voltage gain. The proposed method employs the injection of a variable voltage gain at the DG end in order to monitor the responses of the proposed islanding detection technique. In this study, a complex frequency-dependent ZIP-exponential load model is included to assess the impact of load's active power frequency dependence on the performance of the proposed islanding detection scheme. The upper and lower limits of the voltage gain are designed for constant power and constant current controlled inverter with frequency-dependent ZIP-exponential load. The complete scheme is developed and tested in MATLAB/simulink environment and the results obtained after extensive testing indicate that the proposed scheme can be reliably detect islanding events in distributed generation.

Islanding detection for inverter-based DG coupled with using an adaptive neuro-fuzzy inference system

This paper investigates a new integrated diagnostic system for islanding detection by means of a neuro-fuzzy approach for grid-connected inverter-based distributed generation. Islanding is one important concern for grid connected distributed resources due to personnel and equipment safety. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for islanding detection. The simulations results, carried out by MATLAB/Simulink, shows that the proposed method has a small non-detection zone. Also, this method is capable of detecting islanding accurately within the minimum standard time. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the mains power disconnection can be better distinguished.

Generalised Spatial Modulation System with Multiple Active Transmit Antennas and Low Complexity Detection Scheme

A generalised spatial modulation (SM) scheme with multiple active transmit antennas, named as multiple active-spatial modulation (MA-SM), is proposed in this paper. By allowing multiple transmitting antennas in the SM system to transmit different symbols at the same time instant, MA-SM takes advantages of the low complexity of SM and high multiplexing gain of Vertical-Bell Lab Layered Space-Time (V-BLAST) system. In the MA-SM system, the transmitted symbols are mapped into a high dimensional constellation space including the spatial dimension. The general principle for designing the efficient MA-SM for arbitrary number of transmit antennas and modulation scheme is presented. Moreover, a near-optimal detection scheme with low complexity for MA-SM is also proposed and analyzed. A closed form bound for the bit error probability (BEP) of the proposed detection scheme is also derived in this paper. Numerical results with the comparison among the existing multiple-input multiple-output (MIMO) systems such as space time block code (STBC) and V-BLAST demonstrate the efficiency of MA-SM.

Development of EN50438 compliant wavelet-based islanding detection technique for three-phase static distributed generation systems

The increased penetration of photovoltaic (PV) distributed generation (DG) often connected to grid through dc-ac interface has made islanding detection an important and challenging issue for power engineers. Several methods based on passive and active detection schemes have been proposed in the literature. Although passive schemes have a large non-detection zone (NDZ), concern has been raised about active methods because of their degrading effect on power quality. Therefore each grid-connected PV systems needs to be EN50438 compliant to ensure proper interface and power quality standard. This study proposes a db4 wavelet-based passive islanding detection scheme with almost zero NDZ for dc-ac inverter interfaced grid-connected PV DGs. The key idea is to utilise spectral changes in the higher frequency components of voltage at the point of common coupling, which occur after an islanding condition. The effectiveness of the proposed method is tested in simulation of a PV DG system and validated experimentally with an actual 7.5-kW grid-connected inverter system using the dSpace DS1104 DSP. The designed detection algorithm has been computationally less burden on the DSP.

Modelling and Simulation of Microturbine as Distributed Generation and Present a New Method for Islanding Detection

Nowadays, distributed generations (DGs) have taken a special role in power systems. Distributed generations have received significant attention as a means to improve the performance of the electrical power system, provide low cost energy, and increase overall energy efficiency. A problem with such generators is the unwanted islanding phenomenon. Islanding detection an important and challenging issue to power engineers. Several methods based on passive and active detection scheme have been proposed in the literature. While passive schemes have a large non detection zone (_DZ), concern has been raised on active method due to its degrading power quality effect. In this paper, a new technique to detect islanding conditions has been proposed for micro turbine (MT) as distributed generation. The performance of the proposed method based on passive methods is much more appropriate than that of previous methods. The simulation results performed in Simulink-MATLAB, clearly show improved operation of this method.

Subsequence Action to Eliminate Blackout after Detecting Islanding using Solid State Transfer Switch Implemented in PSCAD/EMTDC

Abstract – One operation circumstance in Dispersed Generation (DG) concepts, an island, is formed when one or more DGs and an aggregate of local loads are disconnected from the main grid and then, remain operational as an islanded entity. This condition of islanding is conventionally undesirable. Consequently, the customers will suffer the blackout. Recently, this blackout phenomenon has been eliminating by facilitating islanded operation. This paper presents a passive method in detecting islanding for DGs by measuring the negative-sequence impedance differences between islanded mode and grid-connected mode at the line frequency. Subsequently, the islanded entity is connected to a backup system by Solid State Transfer Switch (SSTS) in order to smoothly and quickly keep electricity available to customers. Keywords : Islanding detection, DG, SSTS, Blackout, Autonomous operation. 1. Introduction The currently generalized industrial standard requires disconnecting all the DG sources from the islanded entity as fast as possible. For example, under the IEEE 1547 standard, that period is within two seconds [1]. For a smooth transition from a grid-connected mode to an islanded mode of operation, fast islanding detection (ID) is essential to allow DGs to adapt their control strategy to the new operational one. The ID methods can be separated into two basic categories: communication and local one. Local detection methods can further subdivided into active and passive detection schemes [2]. In 2008, C.Wrinch developed a simple control scheme which is easy to accomplish in software as well as hardware [3]. It presents passive ID by using negative-sequence impedance measurement. Information about the step change of actual grid negative-sequence impedance could be used for detecting an islanding situation. That method helps detect an islanding in as fast as a few cycles for the specific settings described afterwards in the simulation in Section 4. Thus, it facilitates an islanding's subsequence actions which fall into two options. The first one is to connect immediately to a backup system. The second option is to regulate control topology of DG which is still in operation inside the islanded system. If subsequent to islanding, the voltage and frequency remain under the limits (which means the DGs can stiffly track islanded power demand with enough active and reactive power), then a proper control topology can maintain the autonomous operation. Nowadays, research approaches are focusing on making the control topology of DG in an islanded entity become perfect. This so-called autonomous operation needs further refinement due to its complexity. Conversely, in this research, a simpler concept is presented which coincides with the first mentioned option after islanding. Distribution systems usually work in radial operation that contains a high number of backup grid terminals. Solid State Transfer Switch (SSTS) conven-tionally transfers the preferred utility to a backup grid in the case of fault occurring at the preferred utility [4]. Instead of mechanical circuit breakers, SSTS can promote the power quality by its superior advantages. Therefore, the proposed solution ignores any blackout to consumers.

Gas flaring monitoring from space using the ATSR instrument series

Gas flaring flames are characterised by high temperatures and ATSR instruments are equipped with the appropriate spectral bands to detect them. In order to monitor gas flaring on global scale a new active flame detection scheme from satellite night-time Short Wavelength Infra Red measurements (SWIR, 1.6μm) has been developed and tested using the Along Track Scanning Radiometer (ATSR) family measurements. The new algorithm, called ALGO3, is based on the verified assumption that, at SWIR wavelengths, the background contribution to the night-time total radiation measured by ATSR is negligible, while that emitted by active flames is fully detectable. ALGO3 products are suitable for detecting gas flares, due to their peculiar high temperature/small area flames. Flaring sites have been discriminated according to time persistency criteria, i.e. location for which hot spots are found at frequencies higher than 4 times a year are assumed to be industrial settlements. Continuity and consistency between the ATSR missions has been verified, and results relative to 1991–2009 time window are reported. Validation of flaring site discrimination has been performed by visual inspection of high resolution Earth surface images. The comparison of ALGO3 retrievals with light count data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) show very good agreement.

A novel digital active homodyne demodulation scheme based on square‐wave phase modulation for interferometric fiber‐optic sensors

AbstractA novel digital active homodyne demodulation scheme to eliminate the signal fading in interferometric fiber‐optic sensors is proposed. The error signal is obtained by the square‐wave phase modulation and then the digital correlation detection technique is applied to demodulate the low‐frequency environmental phase drift and generate the offset phase, which is then fed back the integrated optical phase modulator to lock the interferometer in quadrature. The high‐frequency signal phase can be read out and recovered from the photodetector output with the interferometer in quadrature. The simulation and experimental results verify the feasibility of the proposed scheme, which obviates the feedback integrator saturation and the system resetting problem encountered in the conventional active homodyne detection schemes. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:20–23, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25640

Application of a combined active control and fault detection scheme to an active composite flexible structure

In this paper, the problem of increasing reliability of active control procedure is considered. Indeed, a design method of rejection perturbation in presence of potentially faults, on a flexible structure with integrated piezo-ceramics, is presented. The piezo-ceramics are used as actuators and sensors. A single unit based solution, which handles both control action and fault diagnosis is proposed. The algorithm uses H ∞ optimization techniques. A full order model of the structure is first obtained via both finite-element (FE) approach and identification procedure. This model is then reduced in order to be used in our robust approach. By a suitable choice of weightings functions, the provided method is able to reject disturbance robustly and to estimate occurred faults. The case of sensors and actuators faults is discussed. The choice of weightings for diagnosis and control systems is also tackled. Finally, the effectiveness of this integrated method is confirmed by both simulation and experimental results.

Effect of polymer surface activity on cavitation nuclei stability against dissolution.

The persistence of acoustic cavitation in a pulsed wave ultrasound regime depends upon the ability of cavitation nuclei, i.e., bubbles, to survive the off time between pulses. Due to the dependence of bubble dissolution on surface tension, surface-active agents may affect the stability of bubbles against dissolution. In this study, measurements of bubble dissolution rates in solutions of the surface-active polymer poly(propyl acrylic acid) (PPAA) were conducted to test this premise. The surface activity of PPAA varies with solution pH and concentration of dissolved polymer molecules. The surface tension of PPAA solutions (55-72 dynes/cm) that associated with the polymer surface activity was measured using the Wilhelmy plate technique. Samples of these polymer solutions then were exposed to 1.1 MHz high intensity focused ultrasound, and the dissolution of bubbles created by inertial cavitation was monitored using an active cavitation detection scheme. Analysis of the pulse echo data demonstrated that bubble dissolution time was inversely proportional to the surface tension of the solution. Finally, comparison of the experimental results with dissolution times computed from the Epstein-Plesset equation suggests that the radii of residual bubbles from inertial cavitation increase as the surface tension decreases.

Cavitational mechanisms in ultrasound-accelerated thrombolysis at 1 MHz

Inertial cavitation is hypothesized to be a mechanism by which ultrasound (US) accelerates the dissolution of human blood clots when the clot is exposed to a thrombolytic agent such as tissue plasminogen activator (t-PA). To test this hypothesis, radiolabeled fibrin clots were exposed or sham-exposed in vitro to 1 MHz c.w. US in a rotating sample holder immersed in a water-filled tank at 37°C. Percent clot dissolution after 60 min of US exposure was assessed by removing the samples, centrifuging, and measuring the radioactivity of the supernatant fluid relative to the pelletized material. To suppress acoustic cavitation, the exposure tank was contained within a hyperbaric chamber capable of pneumatic pressurization to 10 atmospheres (gauge). Various combinations of static pressure (0, 2, 5, and 7.5 atm gauge), US (0 or 4 W/cm 2 SATA), and t-PA (0 or 10 μg/mL) were employed, showing statistically significant reductions in thrombolytic activity as static pressure increased. To gain further insight, an active cavitation detection scheme was employed in which 1-μs duration tonebursts of 20-MHz US (< 1 kPa peak negative pressure, 1 Hz PRF) were used to interrogate clots subjected to US and static pressure. Results of this cavitation detection scheme showed that scattering from within the clot and broadband acoustic emissions that were both present during insonification were significantly reduced with application of static pressure. However, only about half of the acceleration of thrombolysis due to US could be removed by static pressure, suggesting the possibility of other mechanisms in addition to inertial cavitation.

Direct evidence of cavitation in vivo from diagnostic ultrasound

Recent increases in the pressure output of diagnostic ultrasound scanners have led to an interest in establishing thresholds for bioeffects in many organs including the lungs of mammals. Damage may be mediated by inertial cavitation, yet there have been no such direct observations in vivo. To explore the hypothesis of cavitation-based bioeffects from diagnostic ultrasound, research has been performed on the thresholds of damage in rat lungs exposed to 4.0-MHz pulsed Doppler and color Doppler ultrasound. A 30-MHz active cavitation detection scheme complementing these studies provides the first direct evidence of cavitation in vivo from diagnostic ultrasound pulses.