Influence of laser intensity in second-harmonic detection with tunable diode laser multi-pass absorption spectroscopy

Tunable diode laser absorption spectroscopy （TDLAS） has been widely employed in atmospheric trace gases detection. The ratio of the second-harmonic signal to the intensity of laser beam incident to the multi-pass cell is proved to be proportional to the product of the path length and the gas concentration under any condition. A new calibration method based on this relation in TDLAS system for the measurement of trace gas concentration is proposed for the first time. The detection limit and the sensitivity of the system are below 110 and 31ppbv （parts-per-billion in volume）, respectively.

FAST IMPLEMENTATION OF ORTHOGONAL EMPIRICAL MODE DECOMPOSITION AND ITS APPLIGA-TION INTO HARMONIC DETECTION

Since the empirical mode decomposition （EMD） lacks strict orthogonality, the method of orthogonal empirical mode decomposition （OEMD） is innovationally proposed. The primary thought of this method is to obtain the intrinsic mode function （IMF） and the residual function by auto-adaptive band-pass filtering. OEMD is proved to preserve strict orthogonality and completeness theoretically, and the orthogonal basis function of OEMD is generated, then an algorithm to implement OEMD fast, IMF binary searching algorithm is built based on the point that the analytical band-pass filtering preserves perfect band-pass feature in the frequency domain. The application into harmonic detection shows that OEMD successfully conquers mode aliasing, avoids the occurrence of false mode, and is featured by fast computing speed. Furthermore, it can achieve harmonic detection accurately combined with the least square method.

Harmonic detection an AC excited generation system based on in-phase correlation filtering

The paper reports results of investigation on the harmonic detection technique of a complicated power supply system such as an AC excited generation system, which has a variable fundamental frequency and low order harmonics with rich sub-harmonics whose frequencies are lower than the fundamental one. The in-phase correlation filtering technique, based on the frequency shifting principle, is proposed in this paper.Theoretical analysis and experimental results validate the effectiveness of this technique for the harmonic detections of AC excited generation systems.

Power System Harmonic Detection Using Frequency-Domain Interpolation Wavelet Transform

Aiming at harmonic detection, fast Fourier transform can only detect integer harmonics precisely, short time Fourier transform can detect non-integer harmonics with low resolution, and some former wavelet based methods have no aliasing-reduction scheme which result in low measurement precision and poor robustness. A frequency-domain interpolation algorithm to detect harmonics is proposed by choosing Shannon wavelet. Shannon wavelet is an orthogonal wavelet possessing best ideal frequency domain localization ability, it can restrict wavelet abasing but bring about Gibbs oscillation phenomenon simultaneously. An interpolation algorithm is developed to overcome this problem. Simulation reveals that the proposed method can effectively cancel aliasing, spectral leakage and Gibbs phenomenon, so it provides an effective means for power system harmonic analysis.

In-Situ Domestic Load Harmonic Detection and Reduction Based on GLRM Algorithm for Optimum Filter Excerpt

Power Quality measures the reliable operation between the system and to the connected loads of same system. A poor power quality causes physical damage to the equipment and also results in lower productivity with increase in energy costs. Power disturbances range from micro seconds to hours and the prolonged disturbances in hours would damage the equipments. The power quality decreases due to growth of nonlinear loads in domestic appliances, such as home Uninterrupted Power Supplies (UPS), Induction stove, Television etc. Nowadays Domestic loads are controlled or powered with power electronic devices. The power electronic devices with Direct Current (DC) components generate high frequency signal for DC-Alternating Current (AC) conversion. The conversion introduces multiple frequencies in the AC power supply. The multiple frequencies in AC power supply are called as harmonics. The harmonics in AC supply affects the lifetime of home appliances, consumes more electric current, affects the power factor, transformer efficiency, and other electricity supply systems. Till now, to avoid harmonics, the filters are erected only in industrial loads or in the substations. In this paper a novel method to detect and control harmonics in domestic appliances is proposed. Harmonic control with various filters in the filter bank, based on detection of harmonic voltage let out from the domestic appliances for power saving. To select the appropriate filter to improve power quality, we apply a novel Genetic Algorithm based Linear Regression Method (GLRM) algorithm for optimum Filter Selection. From the results we were able to reduce the total harmonic distortion level to 3.68%.The current consumption of each household appliance is reduced considerably and finally the electricity bill is reduced to 15% and overall system efficiency improves to 85%.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.

Fault detection and optimization for networked control systems with uncertain time-varying delay

The observer-based robust fault detection filter design and optimization for networked control systems （NOSs） with uncer- tain time-varying delays are addressed. The NCSs with uncertain time-varying delays are modeled as parameter-uncertain systems by the matrix theory. Based on the model, an observer-based residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of the linear matrix inequality. Furthermore, a time domain opti- mization approach is proposed to improve the performance of the fault detection system. To prevent the false alarms, a new thresh- old function is established, and the solution of the optimization problem is given by using the singular value decomposition （SVD） of the matrix. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Harmonicity Spectrum

Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for perceiving harmonic information, but they are often ineffective in perceiving weak harmonic signals because they are based on energy or amplitude analysis. Based on the theory of Normal time-frequency transform (NTFT) and complex correlation coefficient, a new type of spectrum, the Harmonicity Spectrum (HS), is developed to perceive harmonic information in time series. HS is based on the degree of signal harmony rather than energy or amplitude analysis, and can therefore perceive very weak harmonic information in signals sensitively. Simulation examples show that HS can detect harmonic information that cannot be detected by Fourier spectrum or time-frequency spectrum. Acoustic data analysis shows that HS has better resolution than traditional LOFAR spectrum.

Real-Time Method for Detecting Harmonic and Reactive Currents of Single-Phase Circuits

According to the characteristics of single-phase circuits and demand of using active filter for real-time detecting harmonic and reactive currents, a detecting method based on Fryze＇s power definition is proposed. The results of theoretical analysis and simula- tion show that the proposed method is effective in realtime detecting of instantaneous harmonic and reactive currents in single-phase circuits. When only detecting the total reactive currents, this method does not need a phase-locked loop circuit, and it also can be used in some special applications to provide different compensations on the ground of different requirements of electric network. Compared with the other methods based on the theory of instantaneous reactive power, this method is simple and easy to realize.

Fault detection filter design for linear discrete time-varying systems with multiplicative noise

The problem of fault detection for linear discrete timevarying systems with multiplicative noise is dealt with.By using an observer-based robust fault detection filter（FDF） as a residual generator,the design of the FDF is formulated in the framework of H ∞ filtering for a class of stochastic time-varying systems.A sufficient condition for the existence of the FDF is derived in terms of a Riccati equation.The determination of the parameter matrices of the filter is converted into a quadratic optimization problem,and an analytical solution of the parameter matrices is obtained by solving the Riccati equation.Numerical examples are given to illustrate the effectiveness of the proposed method.

H_-/H_∞ fault detection filter design for interval time-varying delays switched systems

The problem of the robust fault detection filter design for time-varying delays switched systems is considered in the framework of mixed H-/H∞. Firstly, the weighted H∞ performance index is utilized as the robustness performance, and the H- index is used as the sensitivity performance for obtaining the robust fault detection filter. Then a novel multiple Lyapunov-Krasovskii function is proposed for deriving sufficient existence conditions of the robust fault detection filter based on the average dwell time technique. By introducing slack matrix variable, the coupling between the Lyapunov matrix and system matrix is removed, and the conservatism of results is reduced. Based on the robust fault detection filter, residual is generated and evaluated for detecting faults. In addition, the results of this paper are dependent on time delays,and represented in the form of linear matrix inequalities. Finally,the simulation example verifies the effectiveness of the proposed method.

TECHNIQUE USED INDETECTING EXTRANEOUS HARMONICS FOR VARIABLE SPEED CONSTANT FREQUENCY（VSCF）POWER SUPPLY

An extraneous harmonics detection scheme is introduced, based on the frequency shift technique. The main principle is described; practical operation scheme is il-lustrated; both related microcomputer system circuit and its software designs are alsobriefly described. Finally, the scheme is validated by the experiment results.

Scheme of optimal fault detection for linear discrete time-varying systems with delayed state

This paper deals with the problem of the optimal fault detection (FD) for linear discrete time-varying (LDTV) systems with delayed state and l(2)-norm bounded unknown input. The novelty lies in the designing of an evaluation function for the robust FD. The basic idea is to directly construct an evaluation function by using a weighted l(2)-norm of the measurement output, which achieves an optimal trade-off between the sensitivity to fault and the robustness to l(2)-norm bounded unknown input. To avoid complex computation, a feasible solution is obtained via the recursive computation by applying the orthogonal projection. It is shown that such an evaluation function provides a unified scheme for both the cases of unknown input being l(2)-norm bounded and jointly normal distribution, while a threshold may be chosen based on a priori knowledge of unknown input. A numerical example is given to demonstrate the effectiveness of the proposed method.

Active Power Filter Based on Adaptive Detecting Approach of Harmonic Currents

The ip-iq detection method based on instantaneous inactive power theory has been applied widely in active power filter because of its good real-time. But it needs large computation, and three-phase currents are processed as integrity, thus calculation accuracy can't be ensured. Based on adaptive interference canceling theory, this paper presents a new ad- aptive detection method for harmonic current, it is a continuously regulated closed-loop system, and its operating characteristics are almost independent of the parameter variations of the elements, thus it performs better than that based on traditional theory. At last this paper provides the simulation of active power filter including the detecting cir-cuit which proved the design is feasible and correct.

Gearbox Deterioration Detection under Steady State,Variable Load, and Variable Speed Conditions

Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.

Hydrogen sulphide detection using near-infrared diode laser and compact dense-pattern multipass cell

Sub-ppmv level detection of hydrogen sulphide(H2 S) using a 1.578-μm distributed feedback tunable diode laser combining with wavelength modulation spectroscopy and second harmonic detection scheme is reported. A home-developed novel compact dense-pattern multipass gas cell with an effective optical path length of 29.37 m is used to improve sensitivity and reduce sample volume. Detection parameters are optimized, including modulation frequency and amplitude. The analysis of Allan variance shows that a minimum detectable concentration 60 ppbv is obtained with a lock-in time constant of 10 ms, and a detection limit of 13 ppbv can be achieved by average in 300 s. The demonstrated H2 S sensor has a strong penitential application in natural gas process for regulating and controlling H2 S concentration.

H_∞-based fault detection for nonlinear networked systems with random packet dropout and probabilistic interval delay

The fault detection problem for the nonlinear networked control system （NCS） with packet dropout and delay is investigated. A nonlinear stochastic system model is proposed to account for the NCS with random packet dropout and network- induced non-uniformly distributed time-varying delay in both from sensor to controller （S/C） and from controller to actuator （C/A）. Based on the obtained NCS model, employing an observer-based fault detection filter as the residual generator, the addressed fault detection problem is converted into an auxiliary nonlinear H∞ control problem. Then, with the help of Lyapunov functional approach, a sufficient condition for the desired fault detection filter is constructed in terms of certain linear matrix inequalities, which depend on not only the delay interval but also the delay interval occurrence rate and successful packet communication rate. Especially, a trade-off phenomenon between the maximum allowable delay bound and successful data packet transmission rate is found, which is typically resulted from the limited bandwidth of communication networks. The effectiveness of the proposed method is demonstrated by a simulation example.

Distributed Fault Detection for Consensus in Second-Order Discrete-Time Multiagent Systems with Adversary

This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detection scheme based on the theory of unknown input observability( UIO) is proposed. By constructing a bank of UIO,the states of the malicious agents can be directly estimated. Secondly,the faulty-node-removal algorithm is provided.Simulations are also provided to demonstrate the effectiveness of the theoretical results.

SPEECH ENHANCEMENT USING HARMONICS REGENERATION BASED ON MULTIBAND EXCITATION

This paper proposes an algorithm that adopts the harmonic regeneration as post-processing to improve the performance of speech enhancement using traditional Short Time Spectral Amplitude(STSA).The proposed algorithm aims to alleviate the distortion of the high harmonics of enhanced speech via the traditional STSA,and consequently improves the speech quality.We first detect the pitch,or fundamental frequency,of the enhanced speech via the traditional STSA,and then,divide the whole spectrum into multiple sub-bands which center on each harmonic.After that,a series of specially designed windows centered on each harmonic are applied to all the sub-bands,in order to redistribute the energy in the sub-bands.The results of experiment demonstrate that the method has both theo-retical and practical basis.

A New Sensorless Technique for the Speed Detection of an Induction Motor

This paper presents a new sensorless method, the so-called harmonic impedance / admittance, for detecting speed of induction motors, which is based on the impedance measurement, harmonic analysis and digital signal processing. The method improves theperformance of conventional voltage-based and current-based techniques, because the impedance or admittance harmonics is independent of input or output of motor system due to the system-inherent nature of impedance. It has been used successfully in detecting the rotor speed of three-phase induction motors. A comparison between the proposed method and the conventionalcurrent-based method is also demonstrated.