Induction motors have been widely used across industry,particularly with smaller loads and fixed speed services.Existing works focus on fault detection of induction motors without considering the shutdown time and pro...Induction motors have been widely used across industry,particularly with smaller loads and fixed speed services.Existing works focus on fault detection of induction motors without considering the shutdown time and production in industry.Therefore,this work aims to monitor the health conditions of the induction motor continuously through electrical signature analysis(ESA).The proposed technique is capable of predicting different kinds of faults,i.e.,rotor faults,stator phase imbalances,and supply cable faults at early stages.Moreover,ESA in real time is implemented.Thereafter,these current spectra were analyzed in frequency domain and compared with healthy current spectra.Performance evaluation is implemented by observing these spectra under different faulty conditions.A comparative study is made and analyzed through MATLAB simulations.展开更多
Early and accurate detection of Heart Disease(HD)is critical for improving patient outcomes,as HD remains a leading cause of mortality worldwide.Timely and precise prediction can aid in preventive interventions,reduci...Early and accurate detection of Heart Disease(HD)is critical for improving patient outcomes,as HD remains a leading cause of mortality worldwide.Timely and precise prediction can aid in preventive interventions,reducing fatal risks associated with misdiagnosis.Machine learning(ML)models have gained significant attention in healthcare for their ability to assist professionals in diagnosing diseases with high accuracy.This study utilizes 918 instances from publicly available UCI and Kaggle datasets to develop and compare the performance of various ML models,including Adaptive Boosting(AB),Naïve Bayes(NB),Extreme Gradient Boosting(XGB),Bagging,and Logistic Regression(LR).Before model training,data preprocessing techniques such as handling missing values,outlier detection using Isolation Forest,and feature scaling were applied to improve model performance.The evaluation was conducted using performance metrics,including accuracy,precision,recall,and F1-score.Among the tested models,XGB demonstrated the highest predictive performance,achieving an accuracy of 94.34%and an F1-score of 95.19%,surpassing other models and previous studies in HD prediction.LR closely followed with an accuracy of 93.08%and an F1-score of 93.99%,indicating competitive performance.In contrast,NB exhibited the lowest performance,with an accuracy of 88.05%and an F1-score of 89.02%,highlighting its limitations in handling complex patterns within the dataset.Although ML models show superior performance as compared to previous studies,some limitations exist,including the use of publicly available datasets,which may not fully capture real-world clinical variations,and the lack of feature selection techniques,which could impact model interpretability and robustness.Despite these limitations,the findings highlight the potential of ML-based frameworks for accurate and efficient HD detection,demonstrating their value as decision-support tools in clinical settings.展开更多
Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity m...Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the production company. The deregulation of electricity markets has made the industry become more competitive and distributed. Thus, a higher demand on reliability and quality of services will be required by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was constructed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy condition is investigated in this paper.展开更多
To fill the continuous needs for faster processing elements with less power consumption causes large pressure on the complementary metal oxide semiconductor(CMOS)technology developers.The scaling scenario is not an op...To fill the continuous needs for faster processing elements with less power consumption causes large pressure on the complementary metal oxide semiconductor(CMOS)technology developers.The scaling scenario is not an option nowadays and other technologies need to be investigated.The quantum-dot cellular automata(QCA)technology is one of the important emerging nanotechnologies that have attracted much researchers’attention in recent years.This technology has many interesting features,such as high speed,low power consumption,and small size.These features make it an appropriate alternative to the CMOS technique.This paper suggests three novel structures of XNOR gates in the QCA technology.The presented structures do not follow the conventional approaches to the logic gates design but depend on the inherent capabilities of the new technology.The proposed structures are used as the main building blocks for a single-bit comparator.The resulted circuits are simulated for the verification purpose and then compared with existing counterparts in the literature.The comparison results are encouraging to append the proposed structures to the library of QCA gates.展开更多
In this paper, an efficient computational algorithm is proposed to solve the nonlinear optimal control problem. In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into...In this paper, an efficient computational algorithm is proposed to solve the nonlinear optimal control problem. In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. The aim of applying this model is to take into account the differences between the real plant and the model used during the calculation procedure. In doing so, an expanded optimal control problem is introduced such that system optimization and parameter estimation are mutually interactive. Accordingly, the optimality conditions are derived after the Hamiltonian function is defined. Specifically, the modified model-based optimal control problem is resulted. Here, the conjugate gradient approach is used to solve the modified model-based optimal control problem, where the optimal solution of the model used is calculated repeatedly, in turn, to update the adjusted parameters on each iteration step. When the convergence is achieved, the iterative solution approaches to the correct solution of the original optimal control problem, in spite of model-reality differences. For illustration, an economic growth problem is solved by using the algorithm proposed. The results obtained demonstrate the efficiency of the algorithm proposed. In conclusion, the applicability of the algorithm proposed is highly recommended.展开更多
We present a class of axially symmetric and stationary spaces foliated by a congruence of surfaces of revolution. The class of solutions considered is that of Carter’s family [A] of spaces and we try to find a soluti...We present a class of axially symmetric and stationary spaces foliated by a congruence of surfaces of revolution. The class of solutions considered is that of Carter’s family [A] of spaces and we try to find a solution to Einstein’s equations in the presence of a perfect fluid with heat flux. This approach is an attempt to find an interior solution that could be matched to a corresponding exterior solution across a surface of zero hydrostatic pressure. The presence of a congruence of surfaces of revolution, described as the quotient space of the commoving observers, can be useful to the determination of the surface of zero pressure. Finally we present two formal solutions representing ellipsoids of revolutions.展开更多
Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually.Prior studies have mos...Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually.Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances.Low-power consumer appliances have comparable power consumption patterns,which can complicate the detection task and can be mistaken as noise.This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures.A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power appliances.Fractal dimension,Hurst exponent,multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various‘turn-on’appliance signatures for classification.Four classifiers,i.e.,deep neural network,support vector machine,decision trees,and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted features.The simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers,achieving an accuracy of up to 96%in classifying low-power appliances.展开更多
Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact o...Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration.Since reaching a high CACC market penetration level is not occurring in the near future,this study presents a progressive deployment approach that demonstrates to have a great potential of reducing trafc congestions at low CACC penetration levels.Using a previously developed microscopic trafc simulation model of a freeway with an on-ramp—created to induce perturbations and trigger stop-and-go trafc,the CACC system s efect on the trafc performance is studied.The results show signifcance and indicate the potential of CACC systems to improve trafc characteristics which can be used to reduce trafc congestion.The study shows that the impact of CACC is positive and not only limited to a high market penetration.By giving CACC vehicles priority access to high-occupancy vehicle(HOV)lanes,the highway capacity could be signifcantly improved with a CACC penetration as low as 20%.展开更多
In this paper, an expanded optimal control policy is proposed to study the coupled tanks system, where the random disturbance is added into the system. Since the dynamics of the coupled tanks system can be formulated ...In this paper, an expanded optimal control policy is proposed to study the coupled tanks system, where the random disturbance is added into the system. Since the dynamics of the coupled tanks system can be formulated as a nonlinear system, determination of the optimal water level in the tanks is useful for the operation decision. On this point of view, the coupled tanks system dynamics is usually linearized to give the steady state operating height. In our approach, a model-based optimal control problem, which is adding with adjusted parameters, is considered to obtain the true operating height of the real coupled tanks system. During the computation procedure, the differences between the real plant and the model used are measured repeatedly, where the optimal solution of the model used is updated. On this basis, system optimization and parameter estimation are integrated. At the end of the iteration, the iterative solution approximates to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. In conclusion, the efficiency of the approach proposed is shown.展开更多
In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal con...In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimal control problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended.展开更多
This paper provides a comprehensive analysis of local and concurrent commutation failure(CF)of multi-infeed high-voltage direct current(HVDC)system considering multi-infeed interaction factor(MIIF).The literature indi...This paper provides a comprehensive analysis of local and concurrent commutation failure(CF)of multi-infeed high-voltage direct current(HVDC)system considering multi-infeed interaction factor(MIIF).The literature indicates that the local CF is not influenced by MIIF,whereas this paper concludes that both the local CF and concurrent CF are influenced by MIIF.The ability of remote converter to work under reduced reactive power enables its feature to support local converter via inter-connection link.The MIIF measures the strength of electrical connectivity between converters.Higher MIIF gives a clearer path to remote converter to support local converter,but at the same time,it provides an easy path to local converter to disturb remote converter under local fault.The presence of nearby converter increases the local commutation failure immunity index(CFII)while reducing concurrent CFII.Higher MIIF causes reactive power support to flow from remote converter to local converter,which reduces the chances of CF.A mathematical approximation to calculate the increase in local CFII for multi-infeed HVDC configurations is also proposed.A power flow approach is used to model the relation between MIIF and reactive power support from remote end.The local and concurrent CFIIs are found to be inverse to each other over MIIF;therefore,it is recommended that there is an optimal value of MIIF for all converters in close electric proximity to maintain CFII at a certain level.The numerical results of established model are compared with PSCAD/EMTDC simulations.The simulation results show the details of the influence of MIIF on local CF and concurrent CF of multi-infeed HVDC,which validates the analysis presented.展开更多
Microgrids(μ-grids)are gaining increased interest around the world for supplying cheap and clean energy.In this paper,aμ-grid comprising a wind turbine generator(WTG)and diesel generator(DG)is considered.It is one o...Microgrids(μ-grids)are gaining increased interest around the world for supplying cheap and clean energy.In this paper,aμ-grid comprising a wind turbine generator(WTG)and diesel generator(DG)is considered.It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas.However,wind energy is intermittent in nature while load demand changes frequently.Therefore,aμ-grid can experience large frequency and power fluctuations.The speed governor of the DG tries to minimize the frequency and power deviations inμ-grid though its operation is slow and cannot adequately minimize system deviations.The paper proposes a novel arrangement based on a dual structured fuzzy(DSF)whose structure changes according to the switching limit with a reduced rule base.It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of theμ-grid.The proposed strategy is tested in aμ-grid and the results considering step load alteration,load alteration at different instants of time,nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design.In addition,it shows that ultra-capacitor establishment in aμ-grid has a positive impact in minimizing system deviations with DSF for the studied cases.展开更多
A novel controller is proposed to regulate the DC-link voltage of a single phase active power filter （SPAPF）. The proposed switched fractional controller （SFC） consists of a conventional PI controller, a fractiona...A novel controller is proposed to regulate the DC-link voltage of a single phase active power filter （SPAPF）. The proposed switched fractional controller （SFC） consists of a conventional PI controller, a fractional order PI （FO-PI） controller and a decision maker that switches between them. Commonly, the conventional PI controller is used in regulation loops due to its advantages in steady-state but it is limited in transient state. On the other hand, the FO-PI controller overcomes these draw- backs but it causes dramatic degradation in control performances in steady-state because of the fractional calculus theory and the approximation method used to implement this kind of controller. Thus, the purpose of this paper is to switch to the PI controller in steady-state to obtain the best power quality and to switch to the FO-PI controller when external disturbances are detected to guarantee a fast transient state. To investigate the efficiency and accuracy of the SFC considering all robustness tests, an experimental setup has been established. The results of the SFC fulfill the requirements, confirm its high performances in steady and transient states and demon- strate its feasibility and effectiveness. The experiment results have satisfied the limit specified by the IEEE harmonic standard 519.展开更多
基金Fundamental Research Funds for the Central Universities,China(No.2232019D3-51)Shanghai Sailing Program,China(No.19YF1402100)。
文摘Induction motors have been widely used across industry,particularly with smaller loads and fixed speed services.Existing works focus on fault detection of induction motors without considering the shutdown time and production in industry.Therefore,this work aims to monitor the health conditions of the induction motor continuously through electrical signature analysis(ESA).The proposed technique is capable of predicting different kinds of faults,i.e.,rotor faults,stator phase imbalances,and supply cable faults at early stages.Moreover,ESA in real time is implemented.Thereafter,these current spectra were analyzed in frequency domain and compared with healthy current spectra.Performance evaluation is implemented by observing these spectra under different faulty conditions.A comparative study is made and analyzed through MATLAB simulations.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R235),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Early and accurate detection of Heart Disease(HD)is critical for improving patient outcomes,as HD remains a leading cause of mortality worldwide.Timely and precise prediction can aid in preventive interventions,reducing fatal risks associated with misdiagnosis.Machine learning(ML)models have gained significant attention in healthcare for their ability to assist professionals in diagnosing diseases with high accuracy.This study utilizes 918 instances from publicly available UCI and Kaggle datasets to develop and compare the performance of various ML models,including Adaptive Boosting(AB),Naïve Bayes(NB),Extreme Gradient Boosting(XGB),Bagging,and Logistic Regression(LR).Before model training,data preprocessing techniques such as handling missing values,outlier detection using Isolation Forest,and feature scaling were applied to improve model performance.The evaluation was conducted using performance metrics,including accuracy,precision,recall,and F1-score.Among the tested models,XGB demonstrated the highest predictive performance,achieving an accuracy of 94.34%and an F1-score of 95.19%,surpassing other models and previous studies in HD prediction.LR closely followed with an accuracy of 93.08%and an F1-score of 93.99%,indicating competitive performance.In contrast,NB exhibited the lowest performance,with an accuracy of 88.05%and an F1-score of 89.02%,highlighting its limitations in handling complex patterns within the dataset.Although ML models show superior performance as compared to previous studies,some limitations exist,including the use of publicly available datasets,which may not fully capture real-world clinical variations,and the lack of feature selection techniques,which could impact model interpretability and robustness.Despite these limitations,the findings highlight the potential of ML-based frameworks for accurate and efficient HD detection,demonstrating their value as decision-support tools in clinical settings.
文摘Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the production company. The deregulation of electricity markets has made the industry become more competitive and distributed. Thus, a higher demand on reliability and quality of services will be required by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was constructed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy condition is investigated in this paper.
文摘To fill the continuous needs for faster processing elements with less power consumption causes large pressure on the complementary metal oxide semiconductor(CMOS)technology developers.The scaling scenario is not an option nowadays and other technologies need to be investigated.The quantum-dot cellular automata(QCA)technology is one of the important emerging nanotechnologies that have attracted much researchers’attention in recent years.This technology has many interesting features,such as high speed,low power consumption,and small size.These features make it an appropriate alternative to the CMOS technique.This paper suggests three novel structures of XNOR gates in the QCA technology.The presented structures do not follow the conventional approaches to the logic gates design but depend on the inherent capabilities of the new technology.The proposed structures are used as the main building blocks for a single-bit comparator.The resulted circuits are simulated for the verification purpose and then compared with existing counterparts in the literature.The comparison results are encouraging to append the proposed structures to the library of QCA gates.
文摘In this paper, an efficient computational algorithm is proposed to solve the nonlinear optimal control problem. In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. The aim of applying this model is to take into account the differences between the real plant and the model used during the calculation procedure. In doing so, an expanded optimal control problem is introduced such that system optimization and parameter estimation are mutually interactive. Accordingly, the optimality conditions are derived after the Hamiltonian function is defined. Specifically, the modified model-based optimal control problem is resulted. Here, the conjugate gradient approach is used to solve the modified model-based optimal control problem, where the optimal solution of the model used is calculated repeatedly, in turn, to update the adjusted parameters on each iteration step. When the convergence is achieved, the iterative solution approaches to the correct solution of the original optimal control problem, in spite of model-reality differences. For illustration, an economic growth problem is solved by using the algorithm proposed. The results obtained demonstrate the efficiency of the algorithm proposed. In conclusion, the applicability of the algorithm proposed is highly recommended.
文摘We present a class of axially symmetric and stationary spaces foliated by a congruence of surfaces of revolution. The class of solutions considered is that of Carter’s family [A] of spaces and we try to find a solution to Einstein’s equations in the presence of a perfect fluid with heat flux. This approach is an attempt to find an interior solution that could be matched to a corresponding exterior solution across a surface of zero hydrostatic pressure. The presence of a congruence of surfaces of revolution, described as the quotient space of the commoving observers, can be useful to the determination of the surface of zero pressure. Finally we present two formal solutions representing ellipsoids of revolutions.
文摘Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually.Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances.Low-power consumer appliances have comparable power consumption patterns,which can complicate the detection task and can be mistaken as noise.This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures.A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power appliances.Fractal dimension,Hurst exponent,multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various‘turn-on’appliance signatures for classification.Four classifiers,i.e.,deep neural network,support vector machine,decision trees,and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted features.The simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers,achieving an accuracy of up to 96%in classifying low-power appliances.
文摘Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration.Since reaching a high CACC market penetration level is not occurring in the near future,this study presents a progressive deployment approach that demonstrates to have a great potential of reducing trafc congestions at low CACC penetration levels.Using a previously developed microscopic trafc simulation model of a freeway with an on-ramp—created to induce perturbations and trigger stop-and-go trafc,the CACC system s efect on the trafc performance is studied.The results show signifcance and indicate the potential of CACC systems to improve trafc characteristics which can be used to reduce trafc congestion.The study shows that the impact of CACC is positive and not only limited to a high market penetration.By giving CACC vehicles priority access to high-occupancy vehicle(HOV)lanes,the highway capacity could be signifcantly improved with a CACC penetration as low as 20%.
文摘In this paper, an expanded optimal control policy is proposed to study the coupled tanks system, where the random disturbance is added into the system. Since the dynamics of the coupled tanks system can be formulated as a nonlinear system, determination of the optimal water level in the tanks is useful for the operation decision. On this point of view, the coupled tanks system dynamics is usually linearized to give the steady state operating height. In our approach, a model-based optimal control problem, which is adding with adjusted parameters, is considered to obtain the true operating height of the real coupled tanks system. During the computation procedure, the differences between the real plant and the model used are measured repeatedly, where the optimal solution of the model used is updated. On this basis, system optimization and parameter estimation are integrated. At the end of the iteration, the iterative solution approximates to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. In conclusion, the efficiency of the approach proposed is shown.
文摘In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimal control problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended.
基金This work was supported by science and technology project of China Southern Power Grid(No.ZBKJXM20180104).
文摘This paper provides a comprehensive analysis of local and concurrent commutation failure(CF)of multi-infeed high-voltage direct current(HVDC)system considering multi-infeed interaction factor(MIIF).The literature indicates that the local CF is not influenced by MIIF,whereas this paper concludes that both the local CF and concurrent CF are influenced by MIIF.The ability of remote converter to work under reduced reactive power enables its feature to support local converter via inter-connection link.The MIIF measures the strength of electrical connectivity between converters.Higher MIIF gives a clearer path to remote converter to support local converter,but at the same time,it provides an easy path to local converter to disturb remote converter under local fault.The presence of nearby converter increases the local commutation failure immunity index(CFII)while reducing concurrent CFII.Higher MIIF causes reactive power support to flow from remote converter to local converter,which reduces the chances of CF.A mathematical approximation to calculate the increase in local CFII for multi-infeed HVDC configurations is also proposed.A power flow approach is used to model the relation between MIIF and reactive power support from remote end.The local and concurrent CFIIs are found to be inverse to each other over MIIF;therefore,it is recommended that there is an optimal value of MIIF for all converters in close electric proximity to maintain CFII at a certain level.The numerical results of established model are compared with PSCAD/EMTDC simulations.The simulation results show the details of the influence of MIIF on local CF and concurrent CF of multi-infeed HVDC,which validates the analysis presented.
文摘Microgrids(μ-grids)are gaining increased interest around the world for supplying cheap and clean energy.In this paper,aμ-grid comprising a wind turbine generator(WTG)and diesel generator(DG)is considered.It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas.However,wind energy is intermittent in nature while load demand changes frequently.Therefore,aμ-grid can experience large frequency and power fluctuations.The speed governor of the DG tries to minimize the frequency and power deviations inμ-grid though its operation is slow and cannot adequately minimize system deviations.The paper proposes a novel arrangement based on a dual structured fuzzy(DSF)whose structure changes according to the switching limit with a reduced rule base.It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of theμ-grid.The proposed strategy is tested in aμ-grid and the results considering step load alteration,load alteration at different instants of time,nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design.In addition,it shows that ultra-capacitor establishment in aμ-grid has a positive impact in minimizing system deviations with DSF for the studied cases.
文摘A novel controller is proposed to regulate the DC-link voltage of a single phase active power filter （SPAPF）. The proposed switched fractional controller （SFC） consists of a conventional PI controller, a fractional order PI （FO-PI） controller and a decision maker that switches between them. Commonly, the conventional PI controller is used in regulation loops due to its advantages in steady-state but it is limited in transient state. On the other hand, the FO-PI controller overcomes these draw- backs but it causes dramatic degradation in control performances in steady-state because of the fractional calculus theory and the approximation method used to implement this kind of controller. Thus, the purpose of this paper is to switch to the PI controller in steady-state to obtain the best power quality and to switch to the FO-PI controller when external disturbances are detected to guarantee a fast transient state. To investigate the efficiency and accuracy of the SFC considering all robustness tests, an experimental setup has been established. The results of the SFC fulfill the requirements, confirm its high performances in steady and transient states and demon- strate its feasibility and effectiveness. The experiment results have satisfied the limit specified by the IEEE harmonic standard 519.
