Adaptive Noise Cancellation Technique Research Articles

Beamforming-based algorithms for recovering information from fetal electrocardiographic sensors

We deal with the extraction of the fetal electrocardiography (ECG) signal from the raw ECG signals of the mother by the beamforming- based algorithms. The foetal ECG sensors bring out signals containing information from the pregnant mother and the infant. Detailed and separate signals are already provided by the foetal ECG instruments; but for some specific studies related to the infant conditions, it is necessary to improve the quality of the signal with a dedicated processing. In this paper, four techniques, with some enhancements, are proposed to perform the processing; we have applied the following techniques: Least Mean Square (LMS) with adaptive noise cancellation technique, Discrete Wavelet Transform (DWT)-based technique, Empirical Wavelet Transform (EWT) technique, and Multiple Signal Classification (MUSIC). The LMS and the MUSIC pertain to beamforming approach. The techniques were used to decompose and identify the different elements constituting the source signal (mother's signal) and noise cancellation by Multivariate Empirical Mode Decomposition (MEMD) technique. The signal was adaptively decomposed by LMS, DWT and MUSIC according to optimised parameters to extract some hidden components of the source signal, such as the foetal features, QRS, heartbeat etc. The results have showed that LMS, with enhancements, is more effective in identifying and removing useless noise. The techniques were applied to the ECG signal of a 30-year-old healthy pregnant woman, which allowed to verify their applicability. The present research leads to the below main contributions among others: separation of the ECG signal of the foetus from the mother, highlighting the functional state of the foetal heart rhythm (heart rate and heartbeat,) and this can show us if the foetal ECG has malfunctions.

Development of Adaptive Spectrum Noise Cancellation Technique for Enhancing Heartbeat Rate Monitoring

Most of the Heart Rate Monitor (HRM) seems to be a portable monitoring device that can measure and displays real-time heartbeat rate in addition to storing heartbeat rate data for future studies. It is generally used to capture heart rate data during various types of physical exercise. Heart rhythm problems arise whenever the electrical impulses that coordinate the heartbeats aren't working properly. The ineffective signaling causes the heart to beat too fast (tachycardia), overly slowly (bradycardia), or sporadically. In present study, a focused approach on Adaptive Spectrum Noise Cancellation (ASNC) were used for enhancing heartbeat rate monitoring for humans. It finds that simply a photodetector and a source of light are applied to the surface of the skin to monitor the proportional changes in the blood flow. It is concluded that for accurate measurement of heart rate while moving or running, the proposed device ASNC utilizes the application of embedded accelerometer and gyroscope sensors to identify and eradicate the artifacts adaptively. In future, the proposed model may adopted for modification and commercialization of wearable heart-rate sensors based upon photoplethysmography (PPG), it would be better in terms of utility as well as adoptability.

Foetus Heartbeat Extraction

Fetal Electrocardiogram Extraction from Mother's Womb is the subject of this report, which includes a thorough examination and implementation. The Fetal ECG is made up of various thoracic and abdominal ECG signals from a pregnant lady. For estimating corrupted signals due to additive noise or interference, the Adaptive Noise Cancellation Technique is used. These algorithms are used to update the weights of an adaptive filter in the Adaptive Noise Cancellation system: The algorithms Least Mean Square, Normalized Least Mean Square, and Leaky Least Mean Square are utilized. This method can be used in both single-input, single-output (SISO), and multiple-input, single-output (MISO) systems (MISO). When compared to the LMS and LLMS algorithms, the NLMS algorithm performs better in both systems.

Enhancing Doubly Fed Induction Generator Low-Voltage Ride-through Capability Using Dynamic Voltage Restorer with Adaptive Noise Cancellation Technique

Some of the major challenges facing micro-grids (MGs) during their connection with the utility grid are maintaining power system stability and reliability. One term that is frequently discussed in literature is the low-voltage ride-through (LVRT) capability, as it is required by the utility grid to maintain its proper operation and system stability. Furthermore, due to their inherent advantages, doubly fed induction generators (DFIGs) have been widely installed on many wind farms. However, grid voltage dips and distortion have a negative impact on the operation of the DFIG. A dynamic voltage restorer (DVR) is a commonly used device that can enhance the LVRT capability of DFIG compared to shunt capacitors and static synchronous compensator (STATCOM). DVR implements a series compensation during fault conditions by injecting the proper voltage at the point of common coupling (PCC) in order to preserve stable terminal voltage. In this paper, we propose a DVR control method based on the adaptive noise cancelation (ANC) technique to compensate for both voltage variation and harmonic mitigation at DFIG terminals. Additionally, we propose an online control of the DC side voltage of the DVR using pulse width modulation (PWM) rectifier to reduce both the size of the storage element and the solid-state switches of the DVR, aiming to reduce its overall cost. A thorough analysis of the operation and response of the proposed DVR is performed using MATLAB/SIMULINK under different operating conditions of the grid. The simulation results verify the superiority and robustness of the proposed technique to enhance the LVRT capability of the DFIG during system transients and faults.

Adaptive Phase Noise Cancellation Technique for Fiber-Optic Interferometric Sensors

The optical source phase noise is one of the main factors influencing the noise performance of fiber-optic interferometric measurement systems. The phase noise is directly proportional to the optical path length difference of the interferometer and wavelength instability of the optical source. Conventional phase noise suppression techniques involve the use of highly stable optical sources and reduction of path length differences of sensing interferometers. However, such methods are not always appropriate for all interferometric configurations. Alternative phase noise suppression techniques suggest the use of an auxiliary reference interferometer for independent measurements of the phase noise. In this case, the phase noise signal can be directly subtracted from the sensor signal. But equal path length differences are required for all interferometers in the measurement system. In this paper, an adaptive phase noise cancellation technique for fiber-optic interferometric sensors is presented. The proposed technique is based on the real-time evaluation of optical path length differences for all interferometers in the measurement system. This approach doesn't require identical path length differences and takes into account their variations caused by environmental conditions. Thus, the optical source phase noise can be independently measured by the reference interferometer and subtracted from the sensor signal with the required attenuation. The results of experimental investigations of the proposed technique for two types of interferometric schemes are presented. It is shown that the proposed technique demonstrates the phase noise reduction up to 30 dB in the frequency range up to 500 Hz.

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.

A Research of Noise Estimation and Removal Techniques for Speech Signal

In this paper, authors made an attempt to implement the active noise control technique (ANC) to decrease the amplitude of noise communicating through the environment using an electro-acoustic (EA) system with the help of measurement sensors such as microphones and output actuators such as loudspeakers. In general, the noise signal is generated from ambient; therefore, it is easy to detect the noise in the vicinity of its source. The main objective of developing the ANC system is to generate an “anti-noise" that reduce the unwanted noise in a desired quiet region using an appropriate adaptive filter. The simulations were performed in the MATLAB 2015 environment and satisfactory results were obtained using the proposed technique. The problem under study is different from traditional adaptive noise cancellation techniques in two ways. Firstly, it is not possible to measure the desired response of a signal directly measured; only the signal with reduced magnitude is present. Secondly, the ANC system is required to take into consideration the secondary loudspeaker-to-microphone error (LME) path in its adaptation.

Adaptive Noise Cancellation Techniques for Impedance Cardiography Signal Analysis

Impedance Cardiography (ICG) evaluation facilitates the volume of heart stroke in the sudden cardiac arrest. It is a noninvasive method for measurement of stroke volume, cardiac output monitoring and observing the hemodynamic parameters by changes in the body blood volume. Bloodvolume changes caused due to various physiological processes is extracted in the form of the variations in the impedance of the body segment. In the real time clinical environment during the extraction the ICG signals are influenced with several artifacts.As these artifacts are not stationary in nature, we can’t predict their characteristics. Hence,we developed several hybrid adaptive filtering mechanisms to improve the ICG signals resolution. Least mean square (LMS) algorithm is the basic enhancement technique in the adaptive filtering. However, in the non-stationery situation the LMS algorithm suffers with low rate of convergence and weight drift problems. In this paper we developed some hybrid variantsof LMS algorithm those are Leaky LMS (LLMS) for ICG signal enhancement. More over to progress the convergence rate, filtering capability and to reduce the computational complexity we also developed various sign versions of LLMS algorithms. The sign variants of LLMS algorithms are sign regressor LLMS (SRLLMS), Sign LLMS (SLLMS), and Sign Sign LLMS (SSLLMS). Severaladaptive signal enhancement units (ASEUs) are developed based on adaptive algorithms and performance is evaluated on the real ICG signal taken from MIT-BIT database. To ensure the efficiency of these algorithms, four experiments were performed to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the ASEU associated with SRLLMS performs better in the artifacts filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 9.3388 dBs, 5.7514 dBs, 8.4449 dBs and 8.7358 dBs respectively for SA, RA, MA and EA. Hence, the SRLLMS based ASEUs are more suitable in ICG signal filtering in real time health care sensing systems.

Performance analysis of wavelet basis function in de-trending and ocular artefact removal from electroencephalogram

The event related potential (ERP) brain-computer interface (BCI) system extensively uses the scalp electroencephalogram (EEG) for communication and motor control. It is a non-invasive procedure and the signal record has ERPs buried in EEG due to its low strength and it is usually contaminated with artefacts. For BCI control applications, the ocular artefacts produced by eye movement and blink which are dominant over the other physiological artefacts are undesirable. The objective of the study is to effectively remove the ocular artefact from EEG using discrete wavelet transform (DWT) combined with recursive least mean square (RLS) adaptive noise cancellation technique using the optimal basis function with Stein's unbiased risk estimate (SURE) thresholding. The proposed methodology is tested on the datasets created from the experimental setup measuring the performance metrics - mean square error (MSE), artefact to signal ratio (ASR), correlation coefficient and coherence. The results show that db4 wavelet performs better in de-trending and ocular artefact suppression by providing better signal to noise ratio and high level of coherence from 5 Hz onwards while preserving the original EEG signal.

Dependency of Output Signal to Noise Ratio on Output Power and Signal to Noise Ratio at Reference Path in the Adaptive Noise Cancellation Technique

In Adaptive noise cancelling method uses two inputs one is primary input and other is the reference input. A “primary” input the combination of main signal and noise whereas a “reference” input contains correlated noise within the primary signal. The difference of reference input from primary gives the noise free signal. It is verified that signal to noise ratio at reference path impact the signal to noise ratio at output, and by decreasing the signal to noise ratio at reference path increase in signal to noise ratio achieved. Also, by decreasing changing the output power a SIR changes immensly.

Adaptive FIR Filtering Using ABC Algorithm: a Noise Reduction Application on Mitral Valve Doppler Signal

The Doppler signal of mitral valve is a biomedical signals and it is acquired by Doppler ultrasound device from mitral valve of hearth. It contains useful information about mitral valve and it can be used to diagnose mitral valve diseases by doctors. However, biomedical signal might be contaminated with noise or disruptive interference. In literature, different approaches have been introduced to remove the noise or interference from biomedical signal, but adaptive filtering is most popular technique for noise filtering. Therefore, a novel adaptive noise cancellation techniques based FIR digital filter was proposed using artificial bee colony algorithm (ABC) and it is employed for the noise reduction on noisy mitral valve Doppler signal. So, the meaningful data about mitral valve was saved to diagnose mitral valve diseases using denoising approach. Then, this novel approach was compared with the other methods suggested in literature such as recursive least square (RLS) algorithm or discrete wavelet transform (DWT). Also, to demonstrate the efficiency, different digital filter order and noise cases were used suggested in literature.DOI: http://dx.doi.org/10.5755/j01.eie.24.5.21845

Recovering the fetal signal in transabdominal fetal pulse oximetry

Non-invasive transabdominal fetal pulse oximetry (TFO) can provide physicians with a better metric of fetal well-being than currently-used fetal monitoring methods. There are two major challenges in this light-based measurement modality. One is in detecting the weak fetal signal at the surface of the maternal abdomen, and the other is in recovering the fetal signal from the diffuse reflectance measured, which contains a mixture of information about both maternal and fetal tissue. In this paper, we describe the TFO system we developed and evaluate its ability to recover the fetal signal in a preclinical setting. In particular, we assess its capability to measure the fetal signal over several thicknesses of maternal tissue and gauge the effectiveness of different approaches in removing the maternal influence from the mixed signal. Our results show that our TFO system, built using commodity low-cost components, can measure the highly-attenuated fetal signal through maternal tissue as thick as 5 cm. Furthermore, we determine that adaptive noise cancellation techniques are the most effective for separating the fetal signal from the maternally-influenced noise. These findings strongly support our TFO system's ability to perform well on a variety of different pregnant body types.

Efficient adaptive noise cancellation techniques in an IOT Enabled Telecardiology System

An increasing number of elderly­­­­ and disabled people urge the need for a health care monitoring system which has the capabilities for analyzing patient health care data to avoid preventable deaths. Medical Telemetry is becoming a key tool in assisting patients living remotely where a “Real-time Remote Critical Health Care Monitoring System” (RRCHCMS) can be utilized for the same. The RRCHCMS is capable of receiving and transmitting data from a remote location to a location that has the capability to diagnose the data and affect decision making and further providing assistance to the patient. During the cardiac analysis, several artifacts solidly affect the ST segment, humiliate the signal quality, frequency resolution, and results in large amplitude signals in ECG that simulate PQRST waveform and cover up the miniature features that are useful for clinical monitoring and diagnosis. In this paper, several leaky based adaptive filter structures for cardiac signal improvement are discussed. The Circular Leaky Least Mean Square (CLLMS) algorithm being the steepest drop strategy for dropping the mean squared error gives a better result in comparison with the Least Mean Square (LMS) algorithm. To enlarge the filtering ability some variants of LMS, Normalized Least Mean Square (NLMS), CLLMS, Variable Step Size CLLMS (VSS-CLLMS) algorithms are used in both time domain (TD) and frequency domain (FD). At last, we applied this algorithm on cardiac signals occurred due to MIT-BIH database. The performance of CLLMS algorithm is better compared to LLMS counterparts in conditions of Signal to Noise Ratio Improvement (SNRI), Excess Mean Square Error (EMSE) and Misadjustment (MSD). When compared to all other algorithms VSS-CLLMS gives superior SNRI. These values are 13.5616dB and 13.7592dB for Baseline Wander (BW) and Muscle Artifact (MA) removal.

A Fundamental-and-Harmonic Dual-Frequency Doppler Radar System for Vital Signs Detection Enabling Radar Movement Self-Cancellation

This paper proposes and presents a cost-effective fundamental-and-harmonic dual-frequency (FHDF) Doppler radar system for vital signs detection from a mobile radar platform. The proposed FHDF radar architecture concurrently transmits the fundamental signal component (FSC) and its inherent second harmonic signal component (SHSC) of the voltage-controlled oscillator toward opposite directions. The FSC is transmitted toward a target in motion, while the SHSC is transmitted toward a stationary low-cost reflector. For the receiver, a coherent dual-band dual-low-IF architecture is derived and proposed for the first time. This architecture can concurrently receive the reflected FSC that is phase modulated by both motions of the target and the radar platform, and the reflected SHSC that is phase modulated only by the motion of the radar platform, without aliasing, thus enabling the reduction of the overall radar size, power consumption, and system cost. Through the subtraction of a pertinent information contained in the received SHSC from the information embedded in the received FSC using an adaptive noise cancellation technique, the resulting signal contains the desired information that is free of motion artifacts from a moving radar platform. Experimental results show that the proposed radar system is capable of extracting human vital signs, including respiration and heartbeat, even in the presence of a large radar platform movement. By using the proposed FHDF radar system, a see-through-wall vital signs detection from a mobile radar platform can also be successfully conducted without using any additional sensors.

Robust Speech Recognition Using Adaptive Noise Cancellation

This paper introduces the adaptive noise cancellation technique for the noise reduction in Robust Automatic Speech Recognition. The adaptive noise cancellation is used as front-end stage to enhance the extracted features for speech recognition under noisy conditions. More specifically, the Constrained Stability Least Mean Square (CS-LMS) algorithm which is a member of the family of adaptive filters has been applied. The Hidden Markov Model based Tool Kit (HTK) is used for training and testing the Automatic Speech Recognizer system. The result obtained shows that the application of adoptive filtering at the front-end enhances the performance of the system in noisy conditions while the CS-LMS algorithm gives the most superior performance among the family of LMS algorithms.

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

Investigation of the Effect of Video Game Play on Cognitive Behaviour using Adaptive Noise Cancellation and Wavelet Transform

This work shows the effect on cognitive performance while playing action video game based on the beta waves variation. The Present work initially shows the Signal to Noise Ratio (SNR) improvement by using an adaptive noise cancellation technique for suppressing the noise, here is to eliminate the ECG artifact from noisy EEG signal and then the wavelet analysis of EEG signals is done to study the brain activity. EEG signal is acquired experimentally from the subject by using the 10-20 system of electrode placement. In first stage the adaptive noise cancellation technique is implemented with the help of an adaptive filter to remove the artifact and hence to improve the signal to noise ratio (SNR). The adaptive algorithm used for the filter is the LMS which is a FIR based digital filter. A synthetic signal is also simulated to give a concept of the effect of the proposed active noise cancellation method to remove the noise or artifact. Synthetic signal analysis shows the suitability of the adaptive noise cancellation technique which is a de-noising tool, and here the de-noised signal is the error signal. Then in second stage, the de-noised signal is processed and the Wavelet Transform is used to provide the information of the β-activity. EEG signals from the frontal and parietal lobes are recorded and the effect is analyzed as the frontal and parietal lobe is associated with reasoning, planning, problem solving and orientation, recognition and perception of stimuli respectively. However, the presence of artifacts like electrocardiogram(ECG) in the EEG signal is a major problem in the study of brain activity. Though it is needed to record cerebral signals, it also records the signals that are not of cerebral origin called artifacts. Artifact removal from EEG signals is essential for better analysis and diagnosis. The EEG signal acquired from a patient is generally noisy and non-stationary by nature. So an adaptive noise cancellation technique is used to remove the artifact and hence the signal to noise ratio of the acquired EEG signal is improved. The EEG signal alone, is acquired from the subject and is used as the reference signal in the ANC noise cancellation technique to get the de-noise EEG signal. Then the de-noised signal is processed and the Wavelet Transform (WT) is used to provide the information of the β-activity. Experiments reveal that the proposed ANC method of noise or artifact removal along with the Wavelet Transform can be used to provide a clear information of the β-activity during the action video game play. The increase in the Beta activity in the eleventh hour of game play than the game play of the first hour indicates increase in cognition due to training in action video game play.

Hybrid Nelder-Mead search based optimal Least Mean Square algorithms for heart and lung sound separation

Algorithms for separation of heart sounds from background lung sound noises are vital for accurate diagnosis of heart diseases. In this paper, an improved adaptive noise cancellation technique based on the Least Mean Square (LMS) algorithm is used to separate heart sounds from lung sounds. The step size parameter in the LMS algorithm is optimally chosen using a hybrid Nelder-Mead (H-NM) optimization algorithm. The NM algorithm is initialized with a good initial solution by using computationally cheap random search to compute a rough estimate of the global minimum. Initialization of the NM algorithm with a good initial guess avoided convergence to shallow local minima and improved the quality of the final solution. The effects of using two state-of-the-arts biologically inspired heuristic optimization algorithms instead of the H-NM algorithm and three variants of the standard LMS algorithm are investigated. The correlation coefficient between the ideal and filtered heart sound signal and running time-complexity of different algorithms are taken as the metric for comparison of different heart sound separation approaches. Simulation results indicate that the approach presented in this paper performs significantly better than a variety of alternate approaches on heart sound separation problems.

A Correlation Study on Motion Artifact using Photodiode and Three Axis Accelerometer Signals

Objectives:The three-axis accelerometer used to reflect the Motion Artifact (MA) implemented in the Adaptive Noise Cancellation (ANC) technique has failed to provide the required MA for accurate processing. This study investigates the validity of using a Covered Photodetector (CPD) for MA detection to unify the sources of generated desired and MA signals to avoid electronic noise differences. Methods/Statistical Analysis:The two devices were fastened together and exposed to various kinds of motions. The Amplitude Fluctuations (AFs) responding in the time domain and the Linear Correlation Coefficient (r) were observed. Findings:Confirmed the proposed approach validity by pointing a rapprochement in AFs. The calculated r was0.999 in asteady state while in motion states rangesfrom 0.998 to 0.794 according to movement changes. Application/Improvements: Implementing such concept for Photoplethysmography signal processing within ANC leads to a curate critical medical diagnosis at a lower cost.

Adaptive noise cancellation based harmonic elimination in grid integrated photovoltaic system

In this study, an adaptive noise cancellation technique is applied to eliminate harmonics in a grid integrated solar photovoltaic (PV) system under non-linear load. A neuron is used to extract a fundamental component from polluted load currents. The proposed system serves multiple functions, as improvement of power quality at point of common coupling including other connected non-linear loads, extraction of maximum power from a given PV array and feeding extracted solar power into the distribution system. The multiple functionality at a time helps in fast recovery of cost of power converters. The proposed control algorithm is based on adaptive detection of fundamental component. The proposed control algorithm presents an improved sensitivity for fast load changes. This proposed two-stage PV power generating system is designed, modelled and tested to provide the solutions of several power quality problems in three-phase four-wire distribution system.