A novel control strategy for the load converter supplying the unbalanced AC load in a DC isolated distribution system is presented. The control algorithm results in balanced and sinusoidal load voltages under unbalanc...A novel control strategy for the load converter supplying the unbalanced AC load in a DC isolated distribution system is presented. The control algorithm results in balanced and sinusoidal load voltages under unbalanced AC loading. The unbalanced load is characterized in the d-q-0 rotating coordinate based on symmetrical sequence components. Also, the mathematical model of the load converter in both a-b-c and d-q-0 coordinates is derived by using the average large signal model. Then, two control strategies for the load converter are presented. The first one uses the conventional d-q-0 controller to ensure the voltage and current regulation. The second one is a newly proposed control strategy based on the decomposition of the voltage and current into in-stantaneous positive, negative, and zero sequences. These three sequences are controlled independently in their own reference frames as DC signals. The performance of the load converter using these two control strategies is compared. Simulation results show the validity and capability of the newly proposed control strategy.展开更多
In order to solve the problems of low overload power in MEMS cantilever beams and low sensitivity in traditional MEMS fixed beams,a novel MEMS microwave power detection chip based on the dual-guided fixed beam is desi...In order to solve the problems of low overload power in MEMS cantilever beams and low sensitivity in traditional MEMS fixed beams,a novel MEMS microwave power detection chip based on the dual-guided fixed beam is designed.A gap between guiding beams and measuring electrodes is designed to accelerate the release of the sacrificial layer,effectively enhanc-ing chip performance.A load sensing model for the MEMS fixed beam microwave power detection chip is proposed,and the mechanical characteristics are analyzed based on the uniform load applied.The overload power and sensitivity are investi-gated using the load sensing model,and experimental results are compared with theoretical results.The detection chip exhibits excellent microwave characteristic in the 9-11 GHz frequency band,with a return loss less than-10 dB.At a signal fre-quency of 10 GHz,the theoretical sensitivity is 13.8 fF/W,closely matching the measured value of 14.3 fF/W,with a relative error of only 3.5%.These results demonstrate that the proposed load sensing model provides significant theoretical support for the design and performance optimization of MEMS microwave power detection chips.展开更多
There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detec...There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation.A data allocation strategy based on capacity and workload is introduced to achieve local load balance,and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster.Moreover,data integrity is protected by using session reassemble and session partitioning.The simulation results show that the new model enjoys favorable advantages such as good load balance,higher detection rate and detection efficiency.展开更多
The calibration curves obtained using strain gages are used to predict surface crack length on plate specimen subjected to 4-point bending fatigue loading. The results shows that the proposed procedure is of high prec...The calibration curves obtained using strain gages are used to predict surface crack length on plate specimen subjected to 4-point bending fatigue loading. The results shows that the proposed procedure is of high precision with the maximum error percentage being less than 6%, and it can be easily used to estimate or monitor the surface crack length under fatigue loading both in laboratory and in engineering. It is also quite meanful for nondamage detecting.展开更多
Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical a...Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.展开更多
Technical and economical impacts of distributed resources have encouraged big industry managers and distribution systems’ owners to utilize small type of electric generations. One important preventive issue to develo...Technical and economical impacts of distributed resources have encouraged big industry managers and distribution systems’ owners to utilize small type of electric generations. One important preventive issue to develop these units is islanding situation. Expert diagnosis system is needed to distinguish network cut off from normal occurrences. It should detect islanding in time to disconnect the unit and prevent any additional failures in equipment. An important part of synchronous generator is automatic load-frequency controller (ALFC). This controller is designed properly to respond to load variations and to fix frequency at constant value when working alone as an islanding system and to control output power when operating in parallel with the main. In this paper, a new approach based on monitoring ALFC re-sponse with regard to input signal to governor is introduced. Numbers of initial crossing value are introduced as an index for islanding detection. Simulation results show that input signal to governor has different characteristics in common disturbances.展开更多
<span style="font-family:Verdana;font-size:12px;">The Federal Office for Economic Affairs and Export Control (BAFA) of</span><span style="font-family:Verdana;font-size:12px;"> Ger...<span style="font-family:Verdana;font-size:12px;">The Federal Office for Economic Affairs and Export Control (BAFA) of</span><span style="font-family:Verdana;font-size:12px;"> Germany promotes digital concepts for increasing energy efficiency as part of the “Pilotprogramm Einsparz<span style="white-space:nowrap;">ä</span>hler”. Within this program, Limón GmbH is developing software solutions in cooperation with the University of Kassel to identify efficiency potentials in load profiles by means of automated anomaly detection. Therefore, in this study two strategies for anomaly detection in load profiles are evaluated. To estimate the monthly load profile, strategy 1 uses the artificial neural network LSTM (Long Short-Term Memory), with a data period of one month (1</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) or three months (3</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M), and strategy 2 uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving Average). By comparing with original load profile data, residuals or summed residuals of the sequence lengths of two, four, six and eight hours are identified as an anomaly by exceeding a predefined threshold. The thresholds are defined by the Z-Score test, </span><i><span style="font-size:12px;font-family:Verdana;">i</span></i><span style="font-size:12px;font-family:Verdana;">.</span><i><span style="font-size:12px;font-family:Verdana;">e</span></i><span style="font-size:12px;font-family:Verdana;">., residuals greater than 2, 2.5 or 3 standard deviations are considered anomalous. Furthermore, the ESD (Extreme Studentized Deviate) test is used to set thresholds by means of three significance level values of 0.05, 0.10 and 0.15, with a maximum of </span><i><span style="font-size:12px;font-family:Verdana;">k</span></i><span style="font-size:12px;font-family:Verdana;"> = 40 iterations. Five load profiles are examined, which were obtained by the cluster method </span><i><span style="font-size:12px;font-family:Verdana;">k</span></i><span style="font-size:12px;font-family:Verdana;">-Means as a representative sample from all available data sets of the Limón GmbH. The evaluation shows that for strategy 1 a maximum </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value of 0.4 (1</span></span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M) and for all examined companies an average </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value of maximum 0.24 and standard deviation of 0.09 (1</span></span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) could be achieved for the investigation on single residuals. In variant 3</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M the highest </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value could be achieved with an average </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value of 0.21 and standard deviation of 0.06 (3</span></span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) for summed residuals of the partial sequence length of four hours. The PEWMA-based strategy 2 did not show a higher anomaly detection efficacy compared to strategy 1 in any of the investigated companies.</span>展开更多
Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples...Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.展开更多
文摘A novel control strategy for the load converter supplying the unbalanced AC load in a DC isolated distribution system is presented. The control algorithm results in balanced and sinusoidal load voltages under unbalanced AC loading. The unbalanced load is characterized in the d-q-0 rotating coordinate based on symmetrical sequence components. Also, the mathematical model of the load converter in both a-b-c and d-q-0 coordinates is derived by using the average large signal model. Then, two control strategies for the load converter are presented. The first one uses the conventional d-q-0 controller to ensure the voltage and current regulation. The second one is a newly proposed control strategy based on the decomposition of the voltage and current into in-stantaneous positive, negative, and zero sequences. These three sequences are controlled independently in their own reference frames as DC signals. The performance of the load converter using these two control strategies is compared. Simulation results show the validity and capability of the newly proposed control strategy.
基金supported by the National Natural Science Foundation of China(61904089)the Province Natural Science Foundation of Jiangsu(BK20190731).
文摘In order to solve the problems of low overload power in MEMS cantilever beams and low sensitivity in traditional MEMS fixed beams,a novel MEMS microwave power detection chip based on the dual-guided fixed beam is designed.A gap between guiding beams and measuring electrodes is designed to accelerate the release of the sacrificial layer,effectively enhanc-ing chip performance.A load sensing model for the MEMS fixed beam microwave power detection chip is proposed,and the mechanical characteristics are analyzed based on the uniform load applied.The overload power and sensitivity are investi-gated using the load sensing model,and experimental results are compared with theoretical results.The detection chip exhibits excellent microwave characteristic in the 9-11 GHz frequency band,with a return loss less than-10 dB.At a signal fre-quency of 10 GHz,the theoretical sensitivity is 13.8 fF/W,closely matching the measured value of 14.3 fF/W,with a relative error of only 3.5%.These results demonstrate that the proposed load sensing model provides significant theoretical support for the design and performance optimization of MEMS microwave power detection chips.
文摘There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation.A data allocation strategy based on capacity and workload is introduced to achieve local load balance,and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster.Moreover,data integrity is protected by using session reassemble and session partitioning.The simulation results show that the new model enjoys favorable advantages such as good load balance,higher detection rate and detection efficiency.
文摘The calibration curves obtained using strain gages are used to predict surface crack length on plate specimen subjected to 4-point bending fatigue loading. The results shows that the proposed procedure is of high precision with the maximum error percentage being less than 6%, and it can be easily used to estimate or monitor the surface crack length under fatigue loading both in laboratory and in engineering. It is also quite meanful for nondamage detecting.
基金supported by the National Nature Science Foundation of China under 62203376the Science and Technology Plan of Hebei Education Department under QN2021139+1 种基金the Nature Science Foundation of Hebei Province under F2021203043the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.
文摘Technical and economical impacts of distributed resources have encouraged big industry managers and distribution systems’ owners to utilize small type of electric generations. One important preventive issue to develop these units is islanding situation. Expert diagnosis system is needed to distinguish network cut off from normal occurrences. It should detect islanding in time to disconnect the unit and prevent any additional failures in equipment. An important part of synchronous generator is automatic load-frequency controller (ALFC). This controller is designed properly to respond to load variations and to fix frequency at constant value when working alone as an islanding system and to control output power when operating in parallel with the main. In this paper, a new approach based on monitoring ALFC re-sponse with regard to input signal to governor is introduced. Numbers of initial crossing value are introduced as an index for islanding detection. Simulation results show that input signal to governor has different characteristics in common disturbances.
文摘<span style="font-family:Verdana;font-size:12px;">The Federal Office for Economic Affairs and Export Control (BAFA) of</span><span style="font-family:Verdana;font-size:12px;"> Germany promotes digital concepts for increasing energy efficiency as part of the “Pilotprogramm Einsparz<span style="white-space:nowrap;">ä</span>hler”. Within this program, Limón GmbH is developing software solutions in cooperation with the University of Kassel to identify efficiency potentials in load profiles by means of automated anomaly detection. Therefore, in this study two strategies for anomaly detection in load profiles are evaluated. To estimate the monthly load profile, strategy 1 uses the artificial neural network LSTM (Long Short-Term Memory), with a data period of one month (1</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) or three months (3</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M), and strategy 2 uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving Average). By comparing with original load profile data, residuals or summed residuals of the sequence lengths of two, four, six and eight hours are identified as an anomaly by exceeding a predefined threshold. The thresholds are defined by the Z-Score test, </span><i><span style="font-size:12px;font-family:Verdana;">i</span></i><span style="font-size:12px;font-family:Verdana;">.</span><i><span style="font-size:12px;font-family:Verdana;">e</span></i><span style="font-size:12px;font-family:Verdana;">., residuals greater than 2, 2.5 or 3 standard deviations are considered anomalous. Furthermore, the ESD (Extreme Studentized Deviate) test is used to set thresholds by means of three significance level values of 0.05, 0.10 and 0.15, with a maximum of </span><i><span style="font-size:12px;font-family:Verdana;">k</span></i><span style="font-size:12px;font-family:Verdana;"> = 40 iterations. Five load profiles are examined, which were obtained by the cluster method </span><i><span style="font-size:12px;font-family:Verdana;">k</span></i><span style="font-size:12px;font-family:Verdana;">-Means as a representative sample from all available data sets of the Limón GmbH. The evaluation shows that for strategy 1 a maximum </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value of 0.4 (1</span></span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M) and for all examined companies an average </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value of maximum 0.24 and standard deviation of 0.09 (1</span></span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) could be achieved for the investigation on single residuals. In variant 3</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M the highest </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value could be achieved with an average </span><i><span style="font-size:12px;font-family:Verdana;">F</span><sub><span style="font-size:12px;font-family:Verdana;">1</span></sub></i><span style="font-size:12px;font-family:Verdana;">-value of 0.21 and standard deviation of 0.06 (3</span></span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) for summed residuals of the partial sequence length of four hours. The PEWMA-based strategy 2 did not show a higher anomaly detection efficacy compared to strategy 1 in any of the investigated companies.</span>
文摘Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.
