Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065933
Wilson C. Sant’ana, C. Salomon, G. Lambert-Torres, E. Bonaldi, Carlos Eduardo Teixeira, Mateus Mendes Campos, B. R. Gama, L. E. Borges-da-Silva, R. B. B. Carvalho
This work presents an application of RST polynomial controllers to a Voltage Source Inverter, in order to achieve zero steady-state error while tracking a sinusoidal reference. The theory of RST controllers is presented, as well as a detailed step-by-step numeric example on how to obtain its gains based on the desired system response. Simulation results on a multilevel converter have shown the performances of the controller on both steady-state and dynamic behaviour under a reference step change and load step change. It is shown that the controller accurately compensates the phase-delay introduced by the inverter's output filter.
{"title":"Achieving Zero Steady State Error on Voltage Source Inverters with Sinusoidal References using a RST Polynomial Controller","authors":"Wilson C. Sant’ana, C. Salomon, G. Lambert-Torres, E. Bonaldi, Carlos Eduardo Teixeira, Mateus Mendes Campos, B. R. Gama, L. E. Borges-da-Silva, R. B. B. Carvalho","doi":"10.1109/ISAP48318.2019.9065933","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065933","url":null,"abstract":"This work presents an application of RST polynomial controllers to a Voltage Source Inverter, in order to achieve zero steady-state error while tracking a sinusoidal reference. The theory of RST controllers is presented, as well as a detailed step-by-step numeric example on how to obtain its gains based on the desired system response. Simulation results on a multilevel converter have shown the performances of the controller on both steady-state and dynamic behaviour under a reference step change and load step change. It is shown that the controller accurately compensates the phase-delay introduced by the inverter's output filter.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065939
A. Enriquez, S. Lima, O. Saavedra
Power transformer immersed in oil is a valuable asset in the operation of the electrical system, therefore, it is of interest to the operating companies to keep the power transformers in perfect operating conditions. Early diagnosis of a fault condition in the power transformer is a fairly addressed research topic, however, inappropriate use and the limited number of data do not allow formulating a robust methodology for a real implementation in the electrical system. This document presents an optimal selection of input variables in diagnosis of power transformer failures by DGA, the sample of inputs is generated from the gas contents (hydrogen, methane, acetylene, ethane and ethylene) and the selection of optimal inputs (VE-BPSO) is extracted with Binary Particle Swarm Optimization (BPSO) in the nearest neighbor classification (Conventional K-NN Classifier). In the validation process for 63 independent data in both Conventional K-NN Classifier and Artificial Neural Network (ANN) the performances for VE-BPSO are superior to the conventional approach (IEC 60599 standard inputs). Therefore, the input variables with the best characterization (clustering) in diagnosis of faults in TP is VE-BPSO, which is the main contribution of this paper.
{"title":"Optimal Selection of Input Variables by BPSO for Diagnosis of Incipient Failures in Power Transformers (by DGA)","authors":"A. Enriquez, S. Lima, O. Saavedra","doi":"10.1109/ISAP48318.2019.9065939","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065939","url":null,"abstract":"Power transformer immersed in oil is a valuable asset in the operation of the electrical system, therefore, it is of interest to the operating companies to keep the power transformers in perfect operating conditions. Early diagnosis of a fault condition in the power transformer is a fairly addressed research topic, however, inappropriate use and the limited number of data do not allow formulating a robust methodology for a real implementation in the electrical system. This document presents an optimal selection of input variables in diagnosis of power transformer failures by DGA, the sample of inputs is generated from the gas contents (hydrogen, methane, acetylene, ethane and ethylene) and the selection of optimal inputs (VE-BPSO) is extracted with Binary Particle Swarm Optimization (BPSO) in the nearest neighbor classification (Conventional K-NN Classifier). In the validation process for 63 independent data in both Conventional K-NN Classifier and Artificial Neural Network (ANN) the performances for VE-BPSO are superior to the conventional approach (IEC 60599 standard inputs). Therefore, the input variables with the best characterization (clustering) in diagnosis of faults in TP is VE-BPSO, which is the main contribution of this paper.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065958
Vladimiro Miranda, Luís Teixeira, J. Pereira
This paper presents a method to identify the status (open or closed) of breakers in network branches, in the absence of status signal or electric measurements on the branch including the breaker. Indirect power measurements from the SCADA are combined to form a 2D image array, which is fed into a Convolutional Neural Network. The image construction is based on ranking measurements with the Cauchy-Schwarz divergence between two signal distributions (for breaker open and closed). The success rate obtained with this technique is close to 100% in the IEEE testbed adopted.
{"title":"Estimating Breaker Status with Electrical State Images and Convolutional Neural Networks","authors":"Vladimiro Miranda, Luís Teixeira, J. Pereira","doi":"10.1109/ISAP48318.2019.9065958","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065958","url":null,"abstract":"This paper presents a method to identify the status (open or closed) of breakers in network branches, in the absence of status signal or electric measurements on the branch including the breaker. Indirect power measurements from the SCADA are combined to form a 2D image array, which is fed into a Convolutional Neural Network. The image construction is based on ranking measurements with the Cauchy-Schwarz divergence between two signal distributions (for breaker open and closed). The success rate obtained with this technique is close to 100% in the IEEE testbed adopted.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122824426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065954
Melaku Matewos, N. Senroy
Grid connected DFIG system may suffer stability issues of subsynchronous resonance (SSR) and high hrequency resonance (HFR) when connected with series or shunt compensated weak network. The negative effective resistance of the system and the inappropriate phase difference margin between DFIG system and weak network at the magnitude frequency intersection point causes resonance instability in the system. This study discussed the detail analysis of SSR as well as HFR based on complete impedance model of grid connected DFIG system. The SSR/HFR analysis has been done based on 7.5 KW (small scale) and 2 MW (large scale) grid connected DFIG system. The impedance interaction at phase difference of ≥ 180° between DFIG system and weak network is a direct cause of SSR/HFR instability in the system. For SSR/HFR analysis, the size of the single DFIG system is more important than size of aggregated DFIG system. As the capacity of single DFIG system increases, the system will be more prone to SSR/HFR instability and the analysis of aggregated DFIG system can be estimated using single DFIG system. During the analysis, influencing factors such as L/LCL filter, transformer configuration, power rating, wind speed, compensation level, and PI controller parameters have shown significant impact on SSR where as L/LCL filter, transformer configuration and compensation level on HFR. Wind speed and PI controller parameters have no significant impact on HFR. Virtual impedance based HFR mitigating strategy has been implemented in grid/rotor/stator part of the DFIG system to eliminate HFR instability from the system. The mitigating strategy is more effective when it is incorporated in the grid part than stator/rotor part of the DFIG system. The effectiveness of the proposed technique in the stator/rotor can be further improved by including resonant controller in the virual impedance. SSR mitigating strategy will not be discussed in this paper.
{"title":"Resonance Investigation of Grid Connected DFIG System","authors":"Melaku Matewos, N. Senroy","doi":"10.1109/ISAP48318.2019.9065954","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065954","url":null,"abstract":"Grid connected DFIG system may suffer stability issues of subsynchronous resonance (SSR) and high hrequency resonance (HFR) when connected with series or shunt compensated weak network. The negative effective resistance of the system and the inappropriate phase difference margin between DFIG system and weak network at the magnitude frequency intersection point causes resonance instability in the system. This study discussed the detail analysis of SSR as well as HFR based on complete impedance model of grid connected DFIG system. The SSR/HFR analysis has been done based on 7.5 KW (small scale) and 2 MW (large scale) grid connected DFIG system. The impedance interaction at phase difference of ≥ 180° between DFIG system and weak network is a direct cause of SSR/HFR instability in the system. For SSR/HFR analysis, the size of the single DFIG system is more important than size of aggregated DFIG system. As the capacity of single DFIG system increases, the system will be more prone to SSR/HFR instability and the analysis of aggregated DFIG system can be estimated using single DFIG system. During the analysis, influencing factors such as L/LCL filter, transformer configuration, power rating, wind speed, compensation level, and PI controller parameters have shown significant impact on SSR where as L/LCL filter, transformer configuration and compensation level on HFR. Wind speed and PI controller parameters have no significant impact on HFR. Virtual impedance based HFR mitigating strategy has been implemented in grid/rotor/stator part of the DFIG system to eliminate HFR instability from the system. The mitigating strategy is more effective when it is incorporated in the grid part than stator/rotor part of the DFIG system. The effectiveness of the proposed technique in the stator/rotor can be further improved by including resonant controller in the virual impedance. SSR mitigating strategy will not be discussed in this paper.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124373778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065943
H. C. Ancelmo, B. M. Mulinari, Fabiana Pottker, A. Lazzaretti, T. Bazzo, E. Oroski, D. Renaux, C. Lima, R. Linhares, Adriano Gamba
The selection of the most appropriate detection, feature extraction and classification method is a fundamental step for the Non-Intrusive Load Monitoring (NILM) problem. In order to compare methods, a properly identified and annotated dataset is required. In this sense, several datasets have been proposed in the literature, real and simulated, with different features, loads and acquisition scenarios. In general, a common characteristic of these datasets is the absence of multiple simultaneous loads with a balance between the loads that are switched, precise indication of load events, and inclusion of noise and harmonic content. Such limitations may comprise a proper comparison between load disaggregation methods, hindering subsequent tasks, such as embedding the solution in electronic systems. With the aim of including all these requirements, this work presents a new simulated dataset using MATLAB-Simulink models, validated with real data, that controls the instant that each load is switched, allowing to precisely extract features during the transient of each load. Additionally, by varying the parameters of the simulation such as harmonic content and noise, it is possible to evaluate the performance of state-of-the-art methods (Voltage-Current Trajectories, Discrete Fourier and Wavelet Transforms) for load classification. In general, Voltage-Current Trajectory is the most affected method in low signal-to-noise ratio condition.
{"title":"A New Simulated Database for Classification Comparison in Power Signature Analysis","authors":"H. C. Ancelmo, B. M. Mulinari, Fabiana Pottker, A. Lazzaretti, T. Bazzo, E. Oroski, D. Renaux, C. Lima, R. Linhares, Adriano Gamba","doi":"10.1109/ISAP48318.2019.9065943","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065943","url":null,"abstract":"The selection of the most appropriate detection, feature extraction and classification method is a fundamental step for the Non-Intrusive Load Monitoring (NILM) problem. In order to compare methods, a properly identified and annotated dataset is required. In this sense, several datasets have been proposed in the literature, real and simulated, with different features, loads and acquisition scenarios. In general, a common characteristic of these datasets is the absence of multiple simultaneous loads with a balance between the loads that are switched, precise indication of load events, and inclusion of noise and harmonic content. Such limitations may comprise a proper comparison between load disaggregation methods, hindering subsequent tasks, such as embedding the solution in electronic systems. With the aim of including all these requirements, this work presents a new simulated dataset using MATLAB-Simulink models, validated with real data, that controls the instant that each load is switched, allowing to precisely extract features during the transient of each load. Additionally, by varying the parameters of the simulation such as harmonic content and noise, it is possible to evaluate the performance of state-of-the-art methods (Voltage-Current Trajectories, Discrete Fourier and Wavelet Transforms) for load classification. In general, Voltage-Current Trajectory is the most affected method in low signal-to-noise ratio condition.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127250074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065992
A. Poulose, R. P
Maintaining the stability of a power system using appropriate controller is considered as one of the most challenging task of a power system engineer. This paper proposes the application of Super-Twisting algorithm based controller, for the load frequency control (LFC) of two area interconnected power system with nonlinearities and disturbances. Even though the presence of nonlinearities such as governor dead band (GDB) and generation rate constraint (GRC), the proposed controller regulates the frequency error, tie-line power error and area control error (ACE) to zero much faster than the popular Integral controller even in the presence of disturbance. For the purpose of analysis and comparing, Matlab/Simulink software tool is used.
{"title":"Super-Twisting Algorithm based Load Frequency Control of a Two Area Interconnected Power System","authors":"A. Poulose, R. P","doi":"10.1109/ISAP48318.2019.9065992","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065992","url":null,"abstract":"Maintaining the stability of a power system using appropriate controller is considered as one of the most challenging task of a power system engineer. This paper proposes the application of Super-Twisting algorithm based controller, for the load frequency control (LFC) of two area interconnected power system with nonlinearities and disturbances. Even though the presence of nonlinearities such as governor dead band (GDB) and generation rate constraint (GRC), the proposed controller regulates the frequency error, tie-line power error and area control error (ACE) to zero much faster than the popular Integral controller even in the presence of disturbance. For the purpose of analysis and comparing, Matlab/Simulink software tool is used.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125733245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065987
S. Murali, R. Shankar
This article highlights the development of a load frequency control (LFC) scheme for a deregulated realistic interconnected power system using inertia emulation controlled (INEC) HVDC tie-line. The realistic secenario of the power system has been developed by inclusion of nonlinearities like rating limitation of generation systems, dead band of governer system and dynamics of boiler. A Proportional-Integral-Derivative controller with derivative filter (PIDN) whose gains are optimized by a successful implementation of Volleyball Premier League (VPL) optimization technique is deployed for secondary level control of LFC scheme. Furthermore, an accurately modelled HVDC tie-line in which the parameters like the capacity of the line, voltage rating and loading conditions are included is utilised along with INEC strategy for this work. The LFC scheme with support of INEC based HVDC tie-line has been justified by comparative analysis of system with and without proposed scheme. The robustness check has been done for the proposed LFC scheme under the case of power system area extension. The overall work has been developed using MATLAB/Simulink Toolbox ®.
{"title":"Load Frequency Control Scheme using Inertia Emulation Controlled HVDC Tie-Line","authors":"S. Murali, R. Shankar","doi":"10.1109/ISAP48318.2019.9065987","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065987","url":null,"abstract":"This article highlights the development of a load frequency control (LFC) scheme for a deregulated realistic interconnected power system using inertia emulation controlled (INEC) HVDC tie-line. The realistic secenario of the power system has been developed by inclusion of nonlinearities like rating limitation of generation systems, dead band of governer system and dynamics of boiler. A Proportional-Integral-Derivative controller with derivative filter (PIDN) whose gains are optimized by a successful implementation of Volleyball Premier League (VPL) optimization technique is deployed for secondary level control of LFC scheme. Furthermore, an accurately modelled HVDC tie-line in which the parameters like the capacity of the line, voltage rating and loading conditions are included is utilised along with INEC strategy for this work. The LFC scheme with support of INEC based HVDC tie-line has been justified by comparative analysis of system with and without proposed scheme. The robustness check has been done for the proposed LFC scheme under the case of power system area extension. The overall work has been developed using MATLAB/Simulink Toolbox ®.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129484591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065986
Kodai Yamada, H. Mori
This paper proposes an efficient method for electricity price forecasting. It is important to understand the behavior of electricity price in advance so that the profit is maximized while the risk is minimized through electric power trading in power markets. The behavior is related to uncertainties as well as high nonlinearity so that more sophisticated methods are required to forecast electricity prices. In this paper, a preconditioned Deep Neural Network (DNN) is proposed to evaluate better predicted values. As the preconditioned technique, k-means is employed to classify electricity prices into some clusters and DNN that consists of Autoencoder and MLP Multi-layer Perceptron (MLP) of Artificial Neural Network (ANN) is constructed at each cluster. Also, the data increase method with the Gaussian random numbers is presented to improve the precondition technique. The effectiveness of the proposed method is demonstrated for real data of ISO New England, USA.
{"title":"Development of Preconditioned Deep Neural Network for Electricity Price Forecasting","authors":"Kodai Yamada, H. Mori","doi":"10.1109/ISAP48318.2019.9065986","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065986","url":null,"abstract":"This paper proposes an efficient method for electricity price forecasting. It is important to understand the behavior of electricity price in advance so that the profit is maximized while the risk is minimized through electric power trading in power markets. The behavior is related to uncertainties as well as high nonlinearity so that more sophisticated methods are required to forecast electricity prices. In this paper, a preconditioned Deep Neural Network (DNN) is proposed to evaluate better predicted values. As the preconditioned technique, k-means is employed to classify electricity prices into some clusters and DNN that consists of Autoencoder and MLP Multi-layer Perceptron (MLP) of Artificial Neural Network (ANN) is constructed at each cluster. Also, the data increase method with the Gaussian random numbers is presented to improve the precondition technique. The effectiveness of the proposed method is demonstrated for real data of ISO New England, USA.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124145239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065956
J. C. Bedoya, Chen-Ching Liu, Jing Xie
The evolution of the power grid has brought increasing deployment of advance metering infrastructure, penetration of intelligent electronic devices, and integration of physical power system components with information and communications technologies. With the fast-expanding connectivity, cyber vulnerabilities arise due to the use of internet-based communication systems. These systems are targets of cyber-intrusions which attempt to disturb the normal power system functions. Traditional intrusion detection algorithms have been developed without an explicit model of the cyber components. In this paper, an algorithm to detect false data injections in the power system is proposed considering both cyber and physical models of the power system. The algorithm is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) which collects information from state variables of the cyber-physical system to meet the performance requirements of the grid. Simulations of the proposed approach using the IEEE 13-bus test system validate the effectiveness of this artificial intelligence-based algorithm.
{"title":"Adaptive Neuro Fuzzy Inference System for Cyber-Intrusion Detection in a Smart Grid","authors":"J. C. Bedoya, Chen-Ching Liu, Jing Xie","doi":"10.1109/ISAP48318.2019.9065956","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065956","url":null,"abstract":"The evolution of the power grid has brought increasing deployment of advance metering infrastructure, penetration of intelligent electronic devices, and integration of physical power system components with information and communications technologies. With the fast-expanding connectivity, cyber vulnerabilities arise due to the use of internet-based communication systems. These systems are targets of cyber-intrusions which attempt to disturb the normal power system functions. Traditional intrusion detection algorithms have been developed without an explicit model of the cyber components. In this paper, an algorithm to detect false data injections in the power system is proposed considering both cyber and physical models of the power system. The algorithm is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) which collects information from state variables of the cyber-physical system to meet the performance requirements of the grid. Simulations of the proposed approach using the IEEE 13-bus test system validate the effectiveness of this artificial intelligence-based algorithm.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130354234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065964
Rashmi Prasad, N. Padhy
The paper highlights the revived requirement of parameter estimation of the induction machine. The data derived identification technique is introduced to provide the approximate induction machine model parameters. The active and reactive power output of the induction machine at some known condition is compared with active and reactive power output of the induction machine simulated in real-time digital simulator environment at different values of parameters at given operating conditions. Thus a real-world optimization problem is formed and is addressed by the mean-variance optimization scheme. The results are platformed on the MATLAB-RTDS environment which shares information via the TCP/IP connection. The estimated parameters will help in providing increased reliability in designing the advanced control scheme. The proposed methodology is tested in single as well as double cage rotor winding type of the induction motor and also in reduced voltage level scenario for the validation of the technique.
{"title":"Data Derived Identification Methodology for Online Estimation of Parameters of Induction Machine","authors":"Rashmi Prasad, N. Padhy","doi":"10.1109/ISAP48318.2019.9065964","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065964","url":null,"abstract":"The paper highlights the revived requirement of parameter estimation of the induction machine. The data derived identification technique is introduced to provide the approximate induction machine model parameters. The active and reactive power output of the induction machine at some known condition is compared with active and reactive power output of the induction machine simulated in real-time digital simulator environment at different values of parameters at given operating conditions. Thus a real-world optimization problem is formed and is addressed by the mean-variance optimization scheme. The results are platformed on the MATLAB-RTDS environment which shares information via the TCP/IP connection. The estimated parameters will help in providing increased reliability in designing the advanced control scheme. The proposed methodology is tested in single as well as double cage rotor winding type of the induction motor and also in reduced voltage level scenario for the validation of the technique.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134624071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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