Pub Date : 2022-10-09DOI: 10.1109/IAS54023.2022.9939672
Akintonde Abbas, Raheem Ariwoola, B. Chowdhury, S. Kamalasadan, Yashen Lin
Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.
{"title":"Evaluation of Equivalent Battery Model Representations for Thermostatically Controlled Loads in Commercial Buildings","authors":"Akintonde Abbas, Raheem Ariwoola, B. Chowdhury, S. Kamalasadan, Yashen Lin","doi":"10.1109/IAS54023.2022.9939672","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939672","url":null,"abstract":"Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128117278","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9939799
P. Gadde, S. Brahma
Microgrids formed from existing distribution networks involve unequal distribution of loads and feeder impedances, causing inherent unbalance, which increases the neutral current and causes associated voltage unbalance problems. Therefore, while being fed predominantly by inverter-based resources (IBRs), such a microgrid needs a three-phase grid forming inverter (GFI) to supply unbalanced currents for stable operation as an island. This requirement can lead to over-sizing the inverter and stress its passive components, effectively reducing its lifetime. This paper proposes an algorithm that can be used in the Microgrid Energy Management System (MEMS) to actively reduce the unbalance using existing single-phase inverters with storage instead of additional equipment. The IBR models and control methodology are discussed. Working of the proposed controller is demonstrated in real-time co-simulation of power network, controller and communication network on a section of the IEEE 123-node distribution feeder.
{"title":"Unbalance Compensation Using single-phase Inverters in Inverter-Dominated Microgrids","authors":"P. Gadde, S. Brahma","doi":"10.1109/IAS54023.2022.9939799","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939799","url":null,"abstract":"Microgrids formed from existing distribution networks involve unequal distribution of loads and feeder impedances, causing inherent unbalance, which increases the neutral current and causes associated voltage unbalance problems. Therefore, while being fed predominantly by inverter-based resources (IBRs), such a microgrid needs a three-phase grid forming inverter (GFI) to supply unbalanced currents for stable operation as an island. This requirement can lead to over-sizing the inverter and stress its passive components, effectively reducing its lifetime. This paper proposes an algorithm that can be used in the Microgrid Energy Management System (MEMS) to actively reduce the unbalance using existing single-phase inverters with storage instead of additional equipment. The IBR models and control methodology are discussed. Working of the proposed controller is demonstrated in real-time co-simulation of power network, controller and communication network on a section of the IEEE 123-node distribution feeder.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121618960","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9939921
Liangcai Xu, Jianxing Liu, S. Zhuo, Y. Huangfu, F. Gao
In this paper, a cascaded extended state observer-based sliding mode control (SMC) is proposed for the typical microgrid (MG) interfaced dc-dc boost converter. For a given MG system, the power fluctuation is unpredictable, which may cause an unstable dc-bus voltage. In that situation, all the dc-bus connected electronic loads cannot work normally. In order to maintain a stable dc-bus voltage, additional power sources with a suitable power converter and the advanced controller are necessary. Especially, the controller for the power converter plays a crucial role in reducing the power fluctuation and regulating the dc-bus voltage. In this paper, to improve the ability of the converters to reject external power disturbance, a cascaded extended state observer is designed to estimate the lumped disturbance more accurately. Besides, the continuous SMC method is adopted to provide a feedback control loop. The corresponding simulation results could highly validate the effectiveness of the proposed control method.
{"title":"Cascaded Extended State Observer-Based Sliding Mode Control for DC-DC Converter with Time-Varying Power Fluctuation","authors":"Liangcai Xu, Jianxing Liu, S. Zhuo, Y. Huangfu, F. Gao","doi":"10.1109/IAS54023.2022.9939921","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939921","url":null,"abstract":"In this paper, a cascaded extended state observer-based sliding mode control (SMC) is proposed for the typical microgrid (MG) interfaced dc-dc boost converter. For a given MG system, the power fluctuation is unpredictable, which may cause an unstable dc-bus voltage. In that situation, all the dc-bus connected electronic loads cannot work normally. In order to maintain a stable dc-bus voltage, additional power sources with a suitable power converter and the advanced controller are necessary. Especially, the controller for the power converter plays a crucial role in reducing the power fluctuation and regulating the dc-bus voltage. In this paper, to improve the ability of the converters to reject external power disturbance, a cascaded extended state observer is designed to estimate the lumped disturbance more accurately. Besides, the continuous SMC method is adopted to provide a feedback control loop. The corresponding simulation results could highly validate the effectiveness of the proposed control method.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"46 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120913645","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9940061
Md. Shamsul Arifin, M. Uddin, Wilson Q. Wang
This paper presents an adaptive neuro-fuzzy interface system (ANFIS) based direct torque and flux control (DTFC) scheme for grid connected doubly fed induction generator (DFIG) based wind energy conversion system (WECS). The proposed ANFIS based DTFC compares the actual developed torque and stator flux with their respective references and generate required PWM logic signals for the Rotor Side Converter (RSC) that enhance the dynamic performance of the DFIG based WECS. The ANFIS is utilized in this work due to its capability of handling nonlinear system accurately, fast convergence and incorporating the advantages of both the neural network as well as the fuzzy system. A hybrid training algorithm is developed to adapt the membership functions of the ANFIS structure to handle the WECS nonlinearities and wind speed uncertainties. The training data for the ANFIS is obtained from the conventional PI controller based DFIG system running at different operating conditions. The stability analysis of the proposed ANFIS based WECS is performed by approximating the system to a standard second order system which confirms the stability of the proposed WECS. The proposed scheme is simulated using MATLAB-Simulink software. The performance of the proposed ANFIS based adaptive DTFC scheme for DFIG-WECS is found superior to both the traditional fuzzy logic and PI controllers in terms of robust control over electromechanical torque and stator current at various wind speed conditions. The real-time implementation of the proposed control scheme for a laboratory prototype DFIG-WECS is currently underway.
{"title":"Neuro-Fuzzy Adaptive Direct Torque and Flux Control of a Grid Connected DFIG-WECS with Improved Dynamic Performance","authors":"Md. Shamsul Arifin, M. Uddin, Wilson Q. Wang","doi":"10.1109/IAS54023.2022.9940061","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9940061","url":null,"abstract":"This paper presents an adaptive neuro-fuzzy interface system (ANFIS) based direct torque and flux control (DTFC) scheme for grid connected doubly fed induction generator (DFIG) based wind energy conversion system (WECS). The proposed ANFIS based DTFC compares the actual developed torque and stator flux with their respective references and generate required PWM logic signals for the Rotor Side Converter (RSC) that enhance the dynamic performance of the DFIG based WECS. The ANFIS is utilized in this work due to its capability of handling nonlinear system accurately, fast convergence and incorporating the advantages of both the neural network as well as the fuzzy system. A hybrid training algorithm is developed to adapt the membership functions of the ANFIS structure to handle the WECS nonlinearities and wind speed uncertainties. The training data for the ANFIS is obtained from the conventional PI controller based DFIG system running at different operating conditions. The stability analysis of the proposed ANFIS based WECS is performed by approximating the system to a standard second order system which confirms the stability of the proposed WECS. The proposed scheme is simulated using MATLAB-Simulink software. The performance of the proposed ANFIS based adaptive DTFC scheme for DFIG-WECS is found superior to both the traditional fuzzy logic and PI controllers in terms of robust control over electromechanical torque and stator current at various wind speed conditions. The real-time implementation of the proposed control scheme for a laboratory prototype DFIG-WECS is currently underway.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129685516","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9939992
R. Ilka, Jiangbiao He, W. Yin, J. Contreras, Carlos G. Cavazos
Power transformers are the essential components in almost every electric power network. Uninterrupted operation of power transformers plays a critical role in guaranteeing the reliability and safety of the power grid. In this paper, aiming at predicting the reliability of large power transformers, multi-physics modeling and simulations are carried out based on three-dimensional (3D) finite element analysis (FEA) and finite volume method (FVM). Specifically, FEA electromagnetic modeling and simulation is performed in Ansys Maxwell to extract the transformer winding losses. Afterwards, thermal model is established in Ansys Fluent to obtain the temperature distribution, and more importantly to identify the transformer winding hot-spot temperature (HST). Accordingly, aging acceleration factor is determined by the winding HST. A sensitivity analysis is also conducted to determine the effects of oil properties on the temperature distribution and HST.
{"title":"Multi-Physics Modeling and Simulation of Oil-Immersed Power Transformers Based on 3D Finite Element Analysis and Finite Volume Method","authors":"R. Ilka, Jiangbiao He, W. Yin, J. Contreras, Carlos G. Cavazos","doi":"10.1109/IAS54023.2022.9939992","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939992","url":null,"abstract":"Power transformers are the essential components in almost every electric power network. Uninterrupted operation of power transformers plays a critical role in guaranteeing the reliability and safety of the power grid. In this paper, aiming at predicting the reliability of large power transformers, multi-physics modeling and simulations are carried out based on three-dimensional (3D) finite element analysis (FEA) and finite volume method (FVM). Specifically, FEA electromagnetic modeling and simulation is performed in Ansys Maxwell to extract the transformer winding losses. Afterwards, thermal model is established in Ansys Fluent to obtain the temperature distribution, and more importantly to identify the transformer winding hot-spot temperature (HST). Accordingly, aging acceleration factor is determined by the winding HST. A sensitivity analysis is also conducted to determine the effects of oil properties on the temperature distribution and HST.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129873806","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9939775
Latifa Bachouch, N. Sewraj, P. Dupuis, L. Canale, G. Zissis
This paper presents a DC-DC LED driver for a 200 W luminaire dedicated to greenhouses lighting applications. Plants are more sensitive to particular radiations for their growth. Hence, we adopted only five LED types that favor photosynthesis process. In order to better control the luminous flux of plants, we used an inverted-Buck converter in each LED string type through four parallel devices of 50 W. A pulse width modulation (PWM) is carried out using the proportional-integral (PI) in order to control the LED's current. The present driver is designed and simulated using PSIM environment. Simulations results prove that the PWM current control guarantees an average current 280 mA with a low current ripple of the order of 10 mA maximizing efficiency energy.
{"title":"Pulse Width Modulation current control for LED lighting horticulture systems","authors":"Latifa Bachouch, N. Sewraj, P. Dupuis, L. Canale, G. Zissis","doi":"10.1109/IAS54023.2022.9939775","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939775","url":null,"abstract":"This paper presents a DC-DC LED driver for a 200 W luminaire dedicated to greenhouses lighting applications. Plants are more sensitive to particular radiations for their growth. Hence, we adopted only five LED types that favor photosynthesis process. In order to better control the luminous flux of plants, we used an inverted-Buck converter in each LED string type through four parallel devices of 50 W. A pulse width modulation (PWM) is carried out using the proportional-integral (PI) in order to control the LED's current. The present driver is designed and simulated using PSIM environment. Simulations results prove that the PWM current control guarantees an average current 280 mA with a low current ripple of the order of 10 mA maximizing efficiency energy.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134102038","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9939898
Yuyang Liu, Yuqing Fei, Zhongzheng Zhou, Zizhong Zhou, Weilin Li
Both DC-DC converter and DC solid-state circuit breaker are very important to the reliability of DC microgrid. However, they will affect each other when cascading, resulting in the failure of circuit breaker, or magnifying voltage ripple of the input. In this paper, a proposed topology of DC converter with integrated solid-stat breaker is introduced to solve the problems above. It combines the inductor of Buck-Boost converter with mutual inductor to realize the function of fault isolation. Reuse of inductor not only ensures the performance of power conversion, but also reduces the volume of the system and retains low-pass filter characteristic. At first, the working principle of the proposed topology in steady-state and fault transient states is analyzed by using the state-space averaging modeling. Then the simulation model is built on Simulink, and verifies the theoretical derivation. Finally, the experimental prototype proves the proposed topology performance.
{"title":"A Buck-Boost Converter with Integrated Solid-State Circuit Breaker","authors":"Yuyang Liu, Yuqing Fei, Zhongzheng Zhou, Zizhong Zhou, Weilin Li","doi":"10.1109/IAS54023.2022.9939898","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939898","url":null,"abstract":"Both DC-DC converter and DC solid-state circuit breaker are very important to the reliability of DC microgrid. However, they will affect each other when cascading, resulting in the failure of circuit breaker, or magnifying voltage ripple of the input. In this paper, a proposed topology of DC converter with integrated solid-stat breaker is introduced to solve the problems above. It combines the inductor of Buck-Boost converter with mutual inductor to realize the function of fault isolation. Reuse of inductor not only ensures the performance of power conversion, but also reduces the volume of the system and retains low-pass filter characteristic. At first, the working principle of the proposed topology in steady-state and fault transient states is analyzed by using the state-space averaging modeling. Then the simulation model is built on Simulink, and verifies the theoretical derivation. Finally, the experimental prototype proves the proposed topology performance.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133595587","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9939864
Michael Abdelmalak, Mukesh Gautam, Jitendra Thapa, E. Hotchkiss, M. Benidris
The extensive integration of communication, computation, and control technologies into cyber-physical power systems (CPPSs) has increased the vulnerabilities of CPPSs to cyberattacks. This calls for developing solutions that assess and reduce the impacts of cyber-induced failures on CPPSs. This paper proposes a defensive islanding strategy to isolate impacted parts of the CPPS and form self-sufficient islanded grids with an objective of minimum load curtailment. The defensive islanding aims to split a power system into smaller grids to improve its resilience against a potential extreme event. A clustering approach that leverages the hierarchical spectral clustering method is utilized for the optimal defensive islanding. The proposed approach captures the fragility behavior and loading conditions of power system components due to cyber-induced failures. A graphical-based coupling framework is used to map the impacts of cyber failures into operation of power system components. The proposed method is demonstrated on a modified 33-node distribution feeder system integrated with distributed energy resources. The amount of load curtailment and radiality constraints have been used to evaluate the performance of the proposed clustering strategies. The results show the capability of the proposed algorithm to create islands considering the cyber-induced failures for enhanced resilience.
{"title":"Defensive Islanding to Enhance the Resilience of Distribution Systems against Cyber-induced Failures","authors":"Michael Abdelmalak, Mukesh Gautam, Jitendra Thapa, E. Hotchkiss, M. Benidris","doi":"10.1109/IAS54023.2022.9939864","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939864","url":null,"abstract":"The extensive integration of communication, computation, and control technologies into cyber-physical power systems (CPPSs) has increased the vulnerabilities of CPPSs to cyberattacks. This calls for developing solutions that assess and reduce the impacts of cyber-induced failures on CPPSs. This paper proposes a defensive islanding strategy to isolate impacted parts of the CPPS and form self-sufficient islanded grids with an objective of minimum load curtailment. The defensive islanding aims to split a power system into smaller grids to improve its resilience against a potential extreme event. A clustering approach that leverages the hierarchical spectral clustering method is utilized for the optimal defensive islanding. The proposed approach captures the fragility behavior and loading conditions of power system components due to cyber-induced failures. A graphical-based coupling framework is used to map the impacts of cyber failures into operation of power system components. The proposed method is demonstrated on a modified 33-node distribution feeder system integrated with distributed energy resources. The amount of load curtailment and radiality constraints have been used to evaluate the performance of the proposed clustering strategies. The results show the capability of the proposed algorithm to create islands considering the cyber-induced failures for enhanced resilience.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133752078","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9940017
Ziyu Qu, X. Ge, Fei Wang
In deregulated electricity markets, reliable electricity price forecasting (EPF) is the basis for developing bidding strategies, operating dispatch controls, and hedging volatility risks. However, electricity prices are highly volatile, non-stationary and multi-seasonal, making it challenging to estimate future trends, so the accuracy of most existing forecasting models falls short of the practical requirements. To this end, a hybrid model combining feature extraction, pattern recognition, neural network models and machine learning is proposed for day-ahead EPF. The model is divided into two main steps: first, feature extraction is performed with Lasso. And then, k-means is used to cluster all historical daily electricity price curves into different patterns, and the SVM model is proposed to recognize the price patterns. Second, a novel improved wavelet neural network (IWNN) model supported by extreme learning machine (ELM) initialization is proposed to build classification prediction models for different daily patterns, which effectively solves the problem of slow or even non-convergence of the traditional WNN. Case studies based on PJM market data show that the proposed approach outperforms other approaches, especially when the volatility of electricity prices is high.
{"title":"A Two-stage Forecasting Approach for Day-ahead Electricity Price Based on Improved Wavelet Neural Network with ELM Initialization","authors":"Ziyu Qu, X. Ge, Fei Wang","doi":"10.1109/IAS54023.2022.9940017","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9940017","url":null,"abstract":"In deregulated electricity markets, reliable electricity price forecasting (EPF) is the basis for developing bidding strategies, operating dispatch controls, and hedging volatility risks. However, electricity prices are highly volatile, non-stationary and multi-seasonal, making it challenging to estimate future trends, so the accuracy of most existing forecasting models falls short of the practical requirements. To this end, a hybrid model combining feature extraction, pattern recognition, neural network models and machine learning is proposed for day-ahead EPF. The model is divided into two main steps: first, feature extraction is performed with Lasso. And then, k-means is used to cluster all historical daily electricity price curves into different patterns, and the SVM model is proposed to recognize the price patterns. Second, a novel improved wavelet neural network (IWNN) model supported by extreme learning machine (ELM) initialization is proposed to build classification prediction models for different daily patterns, which effectively solves the problem of slow or even non-convergence of the traditional WNN. Case studies based on PJM market data show that the proposed approach outperforms other approaches, especially when the volatility of electricity prices is high.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130081150","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 : 2022-10-09DOI: 10.1109/IAS54023.2022.9939792
M. Hossain, M. E. Haque, M. Arif, Saumajit Saha, A. Oo
This paper presents an improved variable forgetting factor recursive least square (IVFF-RLS) and extended Kalman filter (EKF) based technique for accurate modeling and real-time state of charge (SoC) estimation of Li-ion batteries. In the proposed approach, the IVFF-RLS is used for an accurate estimation of varying battery parameters under abnormal change of operating states such as an abrupt shifting of the battery from charging to discharging state, data loss, etc. The IVFF-RLS is augmented with the extended Kalman filter (EKF) for real-time and improved SoC estimation of Li-ion batteries. Extensive validation studies are performed in the Matlab environment and then experimental studies have been carried out in the LabVIEW platform to validate the proposed IVFF-RLS-EKF technique. The outcomes of the experimental studies validate the higher accuracy and robustness of the proposed approach under a broad spectrum of operating temperature and system disturbances such as abrupt shifting from charging to discharging state and vice versa. The efficacy of the proposed approach has been compared against the coulomb counting technique (CCT) and traditional VFF-RLS-EKF approaches through experimental studies. The results show that the proposed IVFF-RLS-EKF technique outperforms the existing techniques ensuring highly accurate battery model parameters and SoC.
{"title":"Modeling and SoC Estimation of Li-ion Batteries with an Improved Variable Forgetting Factor RLS Method Augmented with Extended Kalman Filter","authors":"M. Hossain, M. E. Haque, M. Arif, Saumajit Saha, A. Oo","doi":"10.1109/IAS54023.2022.9939792","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939792","url":null,"abstract":"This paper presents an improved variable forgetting factor recursive least square (IVFF-RLS) and extended Kalman filter (EKF) based technique for accurate modeling and real-time state of charge (SoC) estimation of Li-ion batteries. In the proposed approach, the IVFF-RLS is used for an accurate estimation of varying battery parameters under abnormal change of operating states such as an abrupt shifting of the battery from charging to discharging state, data loss, etc. The IVFF-RLS is augmented with the extended Kalman filter (EKF) for real-time and improved SoC estimation of Li-ion batteries. Extensive validation studies are performed in the Matlab environment and then experimental studies have been carried out in the LabVIEW platform to validate the proposed IVFF-RLS-EKF technique. The outcomes of the experimental studies validate the higher accuracy and robustness of the proposed approach under a broad spectrum of operating temperature and system disturbances such as abrupt shifting from charging to discharging state and vice versa. The efficacy of the proposed approach has been compared against the coulomb counting technique (CCT) and traditional VFF-RLS-EKF approaches through experimental studies. The results show that the proposed IVFF-RLS-EKF technique outperforms the existing techniques ensuring highly accurate battery model parameters and SoC.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116684871","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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