This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances(PQDs).A particular focus is given to voltage sags and notches,as voltage sags cause huge economi...This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances(PQDs).A particular focus is given to voltage sags and notches,as voltage sags cause huge economic losses while research on voltage notches is still very incipient.A systematic method based on scientometrics,text similarity and the analytic hierarchy process is proposed to structure the review and select the most relevant literature.A biblio-metric analysis is then performed on the bibliographic data of the literature to identify relevant statistics such as the evolution of publications over time,top publishing countries,and the distribution by relevant topics.A set of articles is subsequently selected to be critically analyzed.The critical review is structured in steps for real-time detection and classification of PQDs,namely,input data preparation,preprocessing,transformation,feature extraction,feature selec-tion,detection,classification,and characterization.Aspects associated with the type of disturbance(s)addressed in the literature are also explored throughout the review,including the perspectives of those studies aimed at multiple PQDs,or specifically focused on voltage sags or voltage notches.The real-time performance of the reviewed tools is also examined.Finally,unsolved issues are discussed,and prospects are highlighted.展开更多
The paper deals with the application of Volterra bound Interval type−2 fuzzy logic techniques in power quality assessment.This work proposes a new layout for detection,localization and classification of various types ...The paper deals with the application of Volterra bound Interval type−2 fuzzy logic techniques in power quality assessment.This work proposes a new layout for detection,localization and classification of various types of power quality events.The proposed method exploits Volterra series for the extraction of relevant features,which are used to recognize different PQ events by Interval type-2 fuzzy logic based classifier.Numerous single as well as multiple powers signal disturbances have been simulated to testify the efficiency of the proposed technique.This time–frequency analysis results in the clear visual detection,localization,and classification of the different power quality events.The simulation results signify that the proposed scheme has a higher recognition rate while classifying single and multiple power quality events unlike other methods.Finally,the proposed method is compared with SVM,feed forward neural network and type−1 Fuzzy logic system based classifier to show the efficacy of the proposed technique in classifying the Power quality events.展开更多
Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,t...Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the customers.One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the PCC.The proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic distortion.The input parameter used in the models is easily measured with basic power meters.The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing plants.Eight(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden layer.The results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.展开更多
The high penetration of distributed generation(DG)has set up a challenge for energy management and consequently for the monitoring and assessment of power quality(PQ).Besides,there are new types of disturbances owing ...The high penetration of distributed generation(DG)has set up a challenge for energy management and consequently for the monitoring and assessment of power quality(PQ).Besides,there are new types of disturbances owing to the uncontrolled connections of non-linear loads.The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems.Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions.In this context,we propose a measurement method that postulates the use of two-dimensional(2D)diagrams based on higher-order statistics(HOSs)and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign.Being suitable for both PQ and reliability applications,the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform,extracting the individual customers’pattern fingerprint,and compressing the data from both time and spatial aspects.The method allows a continuous and robust performance needed in the SG framework.Consequently,the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.展开更多
基于并联有源滤波器(shunt active power filter, SAPF)和动态电压调节器(dynamic voltage restorer, DVR),首先,搭建了包含光伏和风机的混合动力系统,用以模拟分布式电源接入配电网中产生的电能质量(power quality, PQ)扰动。其次,利...基于并联有源滤波器(shunt active power filter, SAPF)和动态电压调节器(dynamic voltage restorer, DVR),首先,搭建了包含光伏和风机的混合动力系统,用以模拟分布式电源接入配电网中产生的电能质量(power quality, PQ)扰动。其次,利用模糊逻辑、神经网络和自适应神经模糊推理系统控制算法对SAPF的动态性能进行优化,对电能质量扰动进行治理,使用人工智能技术进行管理,使光伏和风能系统均实现最大功率点跟踪(maximum power point tracking, MPPT)。最后,在搭建的仿真系统中进行验证,线性负载和非线性负载输出侧谐波畸变率分别降至0.20%和2.05%,满足配电系统对于电能质量的要求。展开更多
The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms sig...The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.展开更多
Wind energy is one of the world's fastest growing energy technologies, as wind is an intermittent renewable source, the wind source extracted by a wind turbine is therefore not constant. For this reason, the fluct...Wind energy is one of the world's fastest growing energy technologies, as wind is an intermittent renewable source, the wind source extracted by a wind turbine is therefore not constant. For this reason, the fluctuation of wind power results in fluctuated power output from wind turbine generator. From the point of view of utilities, due to the fluctuation of generator output, it’s not appropriate for the generator to be directly connected to the power grid. In order to achieve the condition that the generator output power is suitable for grid-connection, it is necessary to use a controller to manage the output produced by the wind turbine generator. This paper proposes a novel control scheme of a three-phase grid-connected wind energy conversion system to improve the power quality of WECS. The WECS model consists of a permanent magnet generator and the electronic power conditioning system is composed of full bridge rectifier, close loop boost converter, three phase inverter. Wind generation is being increasingly connected at distribution level due to increasing load demand. The inverter is controlled to perform following function 1) power converter to inject power generated from WECS to the grid, and 2) shunt APF to compensate current unbalance, load current harmonics, load reactive power demand Validation of the proposed system is verified through MATLAB/Simulink simulation.展开更多
Power generation becomes the need of developed, developing and under developed countries to meet their increasing power requirements. When affordability increases their requirement of power increases, this happens whe...Power generation becomes the need of developed, developing and under developed countries to meet their increasing power requirements. When affordability increases their requirement of power increases, this happens when increased per capita consumption. The existing power scenario states that highest power is produced using firing of coals called thermal energy. A high efficiency Switched Reluctance Generator (SRG) based high frequency switching scheme to enhance the output for grid connectivity is designed, fabricated and evaluated. This proposed method generates the output for the low wind speed. It provides output at low speed because of multi-level DC-DC converter and storage system. It is an efficient solution for low wind power generation. The real time readings and results are discussed.展开更多
文摘This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances(PQDs).A particular focus is given to voltage sags and notches,as voltage sags cause huge economic losses while research on voltage notches is still very incipient.A systematic method based on scientometrics,text similarity and the analytic hierarchy process is proposed to structure the review and select the most relevant literature.A biblio-metric analysis is then performed on the bibliographic data of the literature to identify relevant statistics such as the evolution of publications over time,top publishing countries,and the distribution by relevant topics.A set of articles is subsequently selected to be critically analyzed.The critical review is structured in steps for real-time detection and classification of PQDs,namely,input data preparation,preprocessing,transformation,feature extraction,feature selec-tion,detection,classification,and characterization.Aspects associated with the type of disturbance(s)addressed in the literature are also explored throughout the review,including the perspectives of those studies aimed at multiple PQDs,or specifically focused on voltage sags or voltage notches.The real-time performance of the reviewed tools is also examined.Finally,unsolved issues are discussed,and prospects are highlighted.
文摘The paper deals with the application of Volterra bound Interval type−2 fuzzy logic techniques in power quality assessment.This work proposes a new layout for detection,localization and classification of various types of power quality events.The proposed method exploits Volterra series for the extraction of relevant features,which are used to recognize different PQ events by Interval type-2 fuzzy logic based classifier.Numerous single as well as multiple powers signal disturbances have been simulated to testify the efficiency of the proposed technique.This time–frequency analysis results in the clear visual detection,localization,and classification of the different power quality events.The simulation results signify that the proposed scheme has a higher recognition rate while classifying single and multiple power quality events unlike other methods.Finally,the proposed method is compared with SVM,feed forward neural network and type−1 Fuzzy logic system based classifier to show the efficacy of the proposed technique in classifying the Power quality events.
文摘Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the customers.One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the PCC.The proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic distortion.The input parameter used in the models is easily measured with basic power meters.The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing plants.Eight(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden layer.The results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.
基金This work was supported by the Spanish Ministry of Science and Innovation(Statal Agency for Research),the EU(AEI/FEDER/UE)via project PID2019-108953RB-C21 Strategies for Aggregated Generation of Photovoltaic Plants:Energy and Meteorological Operational Data(SAGPVEMOD),the precedent TEC2016-77632-C3-3-R.
文摘The high penetration of distributed generation(DG)has set up a challenge for energy management and consequently for the monitoring and assessment of power quality(PQ).Besides,there are new types of disturbances owing to the uncontrolled connections of non-linear loads.The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems.Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions.In this context,we propose a measurement method that postulates the use of two-dimensional(2D)diagrams based on higher-order statistics(HOSs)and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign.Being suitable for both PQ and reliability applications,the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform,extracting the individual customers’pattern fingerprint,and compressing the data from both time and spatial aspects.The method allows a continuous and robust performance needed in the SG framework.Consequently,the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.
文摘基于并联有源滤波器(shunt active power filter, SAPF)和动态电压调节器(dynamic voltage restorer, DVR),首先,搭建了包含光伏和风机的混合动力系统,用以模拟分布式电源接入配电网中产生的电能质量(power quality, PQ)扰动。其次,利用模糊逻辑、神经网络和自适应神经模糊推理系统控制算法对SAPF的动态性能进行优化,对电能质量扰动进行治理,使用人工智能技术进行管理,使光伏和风能系统均实现最大功率点跟踪(maximum power point tracking, MPPT)。最后,在搭建的仿真系统中进行验证,线性负载和非线性负载输出侧谐波畸变率分别降至0.20%和2.05%,满足配电系统对于电能质量的要求。
文摘The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.
文摘Wind energy is one of the world's fastest growing energy technologies, as wind is an intermittent renewable source, the wind source extracted by a wind turbine is therefore not constant. For this reason, the fluctuation of wind power results in fluctuated power output from wind turbine generator. From the point of view of utilities, due to the fluctuation of generator output, it’s not appropriate for the generator to be directly connected to the power grid. In order to achieve the condition that the generator output power is suitable for grid-connection, it is necessary to use a controller to manage the output produced by the wind turbine generator. This paper proposes a novel control scheme of a three-phase grid-connected wind energy conversion system to improve the power quality of WECS. The WECS model consists of a permanent magnet generator and the electronic power conditioning system is composed of full bridge rectifier, close loop boost converter, three phase inverter. Wind generation is being increasingly connected at distribution level due to increasing load demand. The inverter is controlled to perform following function 1) power converter to inject power generated from WECS to the grid, and 2) shunt APF to compensate current unbalance, load current harmonics, load reactive power demand Validation of the proposed system is verified through MATLAB/Simulink simulation.
文摘Power generation becomes the need of developed, developing and under developed countries to meet their increasing power requirements. When affordability increases their requirement of power increases, this happens when increased per capita consumption. The existing power scenario states that highest power is produced using firing of coals called thermal energy. A high efficiency Switched Reluctance Generator (SRG) based high frequency switching scheme to enhance the output for grid connectivity is designed, fabricated and evaluated. This proposed method generates the output for the low wind speed. It provides output at low speed because of multi-level DC-DC converter and storage system. It is an efficient solution for low wind power generation. The real time readings and results are discussed.
