Pub Date : 2022-10-09DOI: 10.1109/IAS54023.2022.9939764
I. N. Jiya, A. Salem, H. V. Khang
This paper presents a novel converter system for integrating multiple renewable energy sources for both dc and ac grids. The proposed converter system is formed by integrating a novel multiport dc converter topology with a multilevel inverter topology, aiming to achieve multiple source integration with low component count and higher efficiency on the multiport converter section and efficient dc to ac conversion on the multilevel inverter section. As compared to counterparts in literature, where each energy source requires its own dc converter and the dc to ac conversion is achieved using a two-level converter, the converter system proposed in this paper has more attractive features of buck-boost operation, better power quality characteristics and low part counts. Within the framework, an auxiliary circuit-based dc link voltage balancing technique is proposed to balance the voltage on the dc link as compared to the more complex control-based balancing scheme. Open and closed loop operations of the converter system are numerically verified using simulations and validated by a high-fidelity hardware-in-the-loop implementation platform.
{"title":"Integrated Multiport DC-DC and Multilevel Converters for Energy Sources","authors":"I. N. Jiya, A. Salem, H. V. Khang","doi":"10.1109/IAS54023.2022.9939764","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939764","url":null,"abstract":"This paper presents a novel converter system for integrating multiple renewable energy sources for both dc and ac grids. The proposed converter system is formed by integrating a novel multiport dc converter topology with a multilevel inverter topology, aiming to achieve multiple source integration with low component count and higher efficiency on the multiport converter section and efficient dc to ac conversion on the multilevel inverter section. As compared to counterparts in literature, where each energy source requires its own dc converter and the dc to ac conversion is achieved using a two-level converter, the converter system proposed in this paper has more attractive features of buck-boost operation, better power quality characteristics and low part counts. Within the framework, an auxiliary circuit-based dc link voltage balancing technique is proposed to balance the voltage on the dc link as compared to the more complex control-based balancing scheme. Open and closed loop operations of the converter system are numerically verified using simulations and validated by a high-fidelity hardware-in-the-loop implementation platform.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"53 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":"117074870","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/IAS54023.2022.9940107
Oscar Chisava, G. Ramos, David F. Celeita
Legacy differential protections are highly efficient and selective for high current and low impedance faults (LIF). In the case of high impedance faults (HIF), the differential and impedance protections lose that efficiency in detecting such events, and sometimes the failure evolves from a HIF fault to a LIF fault. This is due to the failure of the protection relay to detect these phenomena in certain power lines. This work presents a new sensitive algorithm for the detection of HIF combined with the differential line function in the time domain. The proposed solution is capable of detecting both LIF and HIF, by measuring and processing voltage and current signals. The efficiency of the novel approach relies on the classification techniques for instantaneous values.
{"title":"Time-domain Sensitive Differential Protection Approach for High Impedance Faults (HIF)","authors":"Oscar Chisava, G. Ramos, David F. Celeita","doi":"10.1109/IAS54023.2022.9940107","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9940107","url":null,"abstract":"Legacy differential protections are highly efficient and selective for high current and low impedance faults (LIF). In the case of high impedance faults (HIF), the differential and impedance protections lose that efficiency in detecting such events, and sometimes the failure evolves from a HIF fault to a LIF fault. This is due to the failure of the protection relay to detect these phenomena in certain power lines. This work presents a new sensitive algorithm for the detection of HIF combined with the differential line function in the time domain. The proposed solution is capable of detecting both LIF and HIF, by measuring and processing voltage and current signals. The efficiency of the novel approach relies on the classification techniques for instantaneous values.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"12 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":"114995794","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/IAS54023.2022.9939962
S. Mirsaeidi, Alice Giramata, D. Tzelepis, Jinghan He, D. M. Said, K. Muttaqi
Line-Commutated Converter High Voltage Direct Current (LCC-HVDC) technology plays an irreplaceable role in power transmission systems due to its thyristor's superior power handling capability and low operating power losses. Nevertheless, one of the main challenges associated with such systems is the high risk of commutation failure caused by inverter AC faults which leads to temporary cessation in transmitted power and severe stress on the converter equipment. The purpose of this study is to provide a comprehensive overview of the available strategies for commutation failure mitigation in LCC-HVDC networks, and then to investigate their mechanism, effectiveness, and limitations. In addition to describing the existing solutions presented to date, and classifying them into specific groups, a comparative analysis has been carried out in which the main merits and demerits of each category are presented. Finally, based on the analyzed technical literature, some insights and future research directions are pointed out.
{"title":"An Introspective Review on Commutation Failure Inhibition Strategies in LCC-HVDC Transmission Networks","authors":"S. Mirsaeidi, Alice Giramata, D. Tzelepis, Jinghan He, D. M. Said, K. Muttaqi","doi":"10.1109/IAS54023.2022.9939962","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939962","url":null,"abstract":"Line-Commutated Converter High Voltage Direct Current (LCC-HVDC) technology plays an irreplaceable role in power transmission systems due to its thyristor's superior power handling capability and low operating power losses. Nevertheless, one of the main challenges associated with such systems is the high risk of commutation failure caused by inverter AC faults which leads to temporary cessation in transmitted power and severe stress on the converter equipment. The purpose of this study is to provide a comprehensive overview of the available strategies for commutation failure mitigation in LCC-HVDC networks, and then to investigate their mechanism, effectiveness, and limitations. In addition to describing the existing solutions presented to date, and classifying them into specific groups, a comparative analysis has been carried out in which the main merits and demerits of each category are presented. Finally, based on the analyzed technical literature, some insights and future research directions are pointed out.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"8 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":"123030495","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/IAS54023.2022.9939826
C. Johnathon, A. Agalgaonkar, C. Planiden, J. Kennedy
Electricity markets around the world are undergoing a rapid transformation from a centralized structure involving large-scale fossil fuel-based generation to several small-scale, widespread generating technologies such as the variable renewable energy. The existing electricity markets were developed for conventional generators; integration of more VRE into grids can lead to adverse events within these market models. As a result, new market designs are being developed to reform the structure of existing electricity markets. In this paper, an evaluation framework is proposed to aid decision makers in assessing the performance of the newly developed electricity markets. The proposed evaluation framework assesses a market model based on five desirable attributes of electricity markets: Economic Efficiency, Macroeconomic Stability, Sustainability, Revenue Stability, and Viability. Furthermore, the applicability of the proposed market evaluation framework is demonstrated using various market designs.
{"title":"An Evaluation Framework to Assess the Performance of Electricity Market Models","authors":"C. Johnathon, A. Agalgaonkar, C. Planiden, J. Kennedy","doi":"10.1109/IAS54023.2022.9939826","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939826","url":null,"abstract":"Electricity markets around the world are undergoing a rapid transformation from a centralized structure involving large-scale fossil fuel-based generation to several small-scale, widespread generating technologies such as the variable renewable energy. The existing electricity markets were developed for conventional generators; integration of more VRE into grids can lead to adverse events within these market models. As a result, new market designs are being developed to reform the structure of existing electricity markets. In this paper, an evaluation framework is proposed to aid decision makers in assessing the performance of the newly developed electricity markets. The proposed evaluation framework assesses a market model based on five desirable attributes of electricity markets: Economic Efficiency, Macroeconomic Stability, Sustainability, Revenue Stability, and Viability. Furthermore, the applicability of the proposed market evaluation framework is demonstrated using various market designs.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"76 5 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":"123224695","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/IAS54023.2022.9939848
Mansi Maurya, A. Goswami
Intervals in wind energy predictions are an excellent way to quantify uncertainty. Wind power's highly variable nature makes it challenging to achieve good-quality prediction intervals (PIs). The Lower Upper Bound Estimation (LUBE) method is commonly used in interval prediction. However, the existing LUBE technique is trained either using shallow statistical models or rudimentary profound learning models that restrict its capability. As a result, the authors of this paper choose to combine the LUBE method with two hybrid models, namely CNN-LSTM (Convolutional Neural Network-Long Short Term Memory) and BiLSTM (Bidirectional LSTM). A developed interval-based optimization strategy with an improved cost function was used to highlight the advantages of these two networks. This improved cost function takes into account the location disparity between prediction intervals and constructed intervals, resulting in better control over PICP (Prediction Interval Coverage Probability) and PINRW (Prediction Interval Normalized Root Mean Squared Width), ensuring better adjustment capability. The suggested CNN-LSTM and BiLSTM algorithms were compared to the performance of other deep learning models on two different datasets that differed geographically. To reduce the data's complexity, it was treated with a noise-free procedure known as VMD (Variational Mode Decomposition). To break down the data and pick subseries, Sample entropy was used. The CNN-LSTM model beat other models in multiple experiments and provided a narrower prediction band with a high coverage probability. According to the results, hybrid models also had a longer run time and took longer to train than non-hybrid models.
{"title":"Sample Entropy based Variational Mode Decomposition with Hybrid RNN for Short Term Wind Power Interval Prediction","authors":"Mansi Maurya, A. Goswami","doi":"10.1109/IAS54023.2022.9939848","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939848","url":null,"abstract":"Intervals in wind energy predictions are an excellent way to quantify uncertainty. Wind power's highly variable nature makes it challenging to achieve good-quality prediction intervals (PIs). The Lower Upper Bound Estimation (LUBE) method is commonly used in interval prediction. However, the existing LUBE technique is trained either using shallow statistical models or rudimentary profound learning models that restrict its capability. As a result, the authors of this paper choose to combine the LUBE method with two hybrid models, namely CNN-LSTM (Convolutional Neural Network-Long Short Term Memory) and BiLSTM (Bidirectional LSTM). A developed interval-based optimization strategy with an improved cost function was used to highlight the advantages of these two networks. This improved cost function takes into account the location disparity between prediction intervals and constructed intervals, resulting in better control over PICP (Prediction Interval Coverage Probability) and PINRW (Prediction Interval Normalized Root Mean Squared Width), ensuring better adjustment capability. The suggested CNN-LSTM and BiLSTM algorithms were compared to the performance of other deep learning models on two different datasets that differed geographically. To reduce the data's complexity, it was treated with a noise-free procedure known as VMD (Variational Mode Decomposition). To break down the data and pick subseries, Sample entropy was used. The CNN-LSTM model beat other models in multiple experiments and provided a narrower prediction band with a high coverage probability. According to the results, hybrid models also had a longer run time and took longer to train than non-hybrid models.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"24 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":"129463573","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/IAS54023.2022.9939731
Hongjun Zhao, Guoqing Li, Ruifeng Chen, Z. Zhen, Fei Wang
Timely and accurate wind farm cluster power prediction is of great significance to the stability of the power system. Due to the strong randomness and uncertainty of wind, traditional prediction methods cannot meet the requirements of power prediction tasks. Moreover, many methods ignore the temporal and spatial correlation between wind farms, so it is difficult to achieve more accurate prediction. In this paper, we propose a power prediction method based on the prediction of the cluster output pattern. We use the spatio-temporal graph neural network to extract the spatio-temporal correlation between wind farms. We base the cluster power prediction problem on a graph instead of using methods such as griding to simplify the spatial correlation between power farms. Experiments show that our proposed method is superior to other methods of real wind farm cluster power dataset.
{"title":"Ultra-short-term Power Forecasting of Wind Farm Cluster Based on Spatio-temporal Graph Neural Network Pattern Prediction","authors":"Hongjun Zhao, Guoqing Li, Ruifeng Chen, Z. Zhen, Fei Wang","doi":"10.1109/IAS54023.2022.9939731","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939731","url":null,"abstract":"Timely and accurate wind farm cluster power prediction is of great significance to the stability of the power system. Due to the strong randomness and uncertainty of wind, traditional prediction methods cannot meet the requirements of power prediction tasks. Moreover, many methods ignore the temporal and spatial correlation between wind farms, so it is difficult to achieve more accurate prediction. In this paper, we propose a power prediction method based on the prediction of the cluster output pattern. We use the spatio-temporal graph neural network to extract the spatio-temporal correlation between wind farms. We base the cluster power prediction problem on a graph instead of using methods such as griding to simplify the spatial correlation between power farms. Experiments show that our proposed method is superior to other methods of real wind farm cluster power dataset.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"70 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":"127669433","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/IAS54023.2022.9939801
Guilin Sun, Shoujun Song, Jianan Jiang, Lefei Ge, Weiguo Liu
This paper describes the basic concepts and advantages of more electric aircraft (MEA). Shaft-line-embedded starter/generator is one of the key technologies to enhance the comprehensive performance of MEA. Switched reluctance machines (SRM) have no permanent magnets and are suitable for high speed which makes them perform well at high temperatures and high-speed operation. Besides, the concentrated stator windings have strong fault tolerance. So it possesses good application prospects in shaft-line-embedded integrated aero-engine starter/generator system to satisfy the high-temperature and high-speed operation. To broaden the application of SRM in high-temperature environments and achieve the efficient transmission of airborne energy, this paper aims at high-temperature applications in aero-engine which is over 350°C. Lastly, the direct instantaneous torque control method was used to reduce the torque fluctuation during the motoring process. It demonstrates that the high-temperature environment is beneficial to reduce the torque ripple of the SRM.
{"title":"Characteristics Testing and Torque Control of Switched Reluctance Machine in Aero-engine Shaft-Line-Embedded Starter/Generator","authors":"Guilin Sun, Shoujun Song, Jianan Jiang, Lefei Ge, Weiguo Liu","doi":"10.1109/IAS54023.2022.9939801","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939801","url":null,"abstract":"This paper describes the basic concepts and advantages of more electric aircraft (MEA). Shaft-line-embedded starter/generator is one of the key technologies to enhance the comprehensive performance of MEA. Switched reluctance machines (SRM) have no permanent magnets and are suitable for high speed which makes them perform well at high temperatures and high-speed operation. Besides, the concentrated stator windings have strong fault tolerance. So it possesses good application prospects in shaft-line-embedded integrated aero-engine starter/generator system to satisfy the high-temperature and high-speed operation. To broaden the application of SRM in high-temperature environments and achieve the efficient transmission of airborne energy, this paper aims at high-temperature applications in aero-engine which is over 350°C. Lastly, the direct instantaneous torque control method was used to reduce the torque fluctuation during the motoring process. It demonstrates that the high-temperature environment is beneficial to reduce the torque ripple of the SRM.","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":"116542511","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/IAS54023.2022.9940043
Sruthi Mavila Pathayapurayil, Rupesh Kumari, T. Chelliah
The variable speed doubly fed induction generators (DFIGs) are the future of hydrogenating plants owing to their flexible nature and speedy response. The new digitalized power scenario needs attention on cyber-attacks and measures suggested as per IEC Standard 62433 (2019): Security for Industrial Automation and Control Systems. Different system responds differently to cyber-attacks. This work focus on the impact analysis of speed and DC link voltage sensor attacks on grid connected large hydrogenating unit, of capacity 250MW. MATLAB simulations are carried out for False Data Injection (FDI) and Denial of Service (DoS) attacks and analyze the system behavior. The simulation results demonstrate that the system is affected severely under different attack scenarios.
{"title":"Impact Analysis of Sensor Cyber-Attacks on Grid-Tied Variable Speed Hydropower Plants","authors":"Sruthi Mavila Pathayapurayil, Rupesh Kumari, T. Chelliah","doi":"10.1109/IAS54023.2022.9940043","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9940043","url":null,"abstract":"The variable speed doubly fed induction generators (DFIGs) are the future of hydrogenating plants owing to their flexible nature and speedy response. The new digitalized power scenario needs attention on cyber-attacks and measures suggested as per IEC Standard 62433 (2019): Security for Industrial Automation and Control Systems. Different system responds differently to cyber-attacks. This work focus on the impact analysis of speed and DC link voltage sensor attacks on grid connected large hydrogenating unit, of capacity 250MW. MATLAB simulations are carried out for False Data Injection (FDI) and Denial of Service (DoS) attacks and analyze the system behavior. The simulation results demonstrate that the system is affected severely under different attack scenarios.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"46 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":"123759571","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/IAS54023.2022.9939680
Mustafa Çalişkan, Özgül Salor-Durna, M. Çiydem
In this research work, a new method which determines the individual harmonic voltage contributions of the EAF plants supplied from a point of common coupling (PCC) to the PCC voltage is presented. EAFs are one of the most significant sources of the harmonics, especially the uncharacteristic ones, therefore it is important to be able to discriminate the amount of individual contributions from the feeders of a PCC supplying multiple EAFs. The proposed method uses the relationship derived between the correlation coefficient of the PCC voltage and the feeder current waveforms and the harmonic voltage contribution of each plant supplied from the PCC. The main idea is based on the fact that harmonic voltage at the PCC is a result of the additive effect of voltage drops on the source side impedance of the power system caused by the individual feeder currents. After computing the Pearson correlation coefficients at each harmonic frequency using 10-cycle synchronized feeder current and PCC voltage waveforms, harmonic voltage contribution of the related feeder is obtained using the proposed method. This procedure can be repeatedly used to obtain the contribution of each EAF plant at each frequency component. Field measurements from a PCC supplying multiple EAF plants are used to verify the results and the performance is compared with the previously proposed methods. With a specified source side impedance by the utility, the proposed approach is a fast method of obtaining the harmonic responsibilities, since no real-time impedance measurements are required and no need for the measurements of the other feeders to compute the contribution of a specific feeder in contrast to some other methods. The proposed method can be easily adapted as a real time harmonic contribution detection tool for the power quality analyzers, all of which have synchronized voltage and current waveform measurements.
{"title":"Waveform Correlation Based Harmonic Voltage Contribution Determination of Iron and Steel Plants Supplied From PCC","authors":"Mustafa Çalişkan, Özgül Salor-Durna, M. Çiydem","doi":"10.1109/IAS54023.2022.9939680","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9939680","url":null,"abstract":"In this research work, a new method which determines the individual harmonic voltage contributions of the EAF plants supplied from a point of common coupling (PCC) to the PCC voltage is presented. EAFs are one of the most significant sources of the harmonics, especially the uncharacteristic ones, therefore it is important to be able to discriminate the amount of individual contributions from the feeders of a PCC supplying multiple EAFs. The proposed method uses the relationship derived between the correlation coefficient of the PCC voltage and the feeder current waveforms and the harmonic voltage contribution of each plant supplied from the PCC. The main idea is based on the fact that harmonic voltage at the PCC is a result of the additive effect of voltage drops on the source side impedance of the power system caused by the individual feeder currents. After computing the Pearson correlation coefficients at each harmonic frequency using 10-cycle synchronized feeder current and PCC voltage waveforms, harmonic voltage contribution of the related feeder is obtained using the proposed method. This procedure can be repeatedly used to obtain the contribution of each EAF plant at each frequency component. Field measurements from a PCC supplying multiple EAF plants are used to verify the results and the performance is compared with the previously proposed methods. With a specified source side impedance by the utility, the proposed approach is a fast method of obtaining the harmonic responsibilities, since no real-time impedance measurements are required and no need for the measurements of the other feeders to compute the contribution of a specific feeder in contrast to some other methods. The proposed method can be easily adapted as a real time harmonic contribution detection tool for the power quality analyzers, all of which have synchronized voltage and current waveform measurements.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"646 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":"126597181","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/IAS54023.2022.9940025
A. Stratman, Tianqi Hong, Ming Yi, Dongbo Zhao
With increasing adoption of residential PV systems, net load forecasting is gradually shifting from forecasting pure load to forecasting pure load with PV generation. This paper explicitly compares two methods of net load forecasting for systems with high behind-the-meter (BTM) PV penetration. The first method is an additive method, in which PV generation and pure load are forecasted separately and combined to produce a net load forecast. First, a disaggregation algorithm is applied to aggregate net load measurements of residential homes to separate the pure load and PV generation. Then, a long short-term memory (LSTM) model is used to forecast pure load and PV separately using the historical disaggregated pure load and PV, respectively, and weather factors. The results are combined to generate a net load forecast. The additive model is compared to a direct net load forecast from an LSTM model. Results show that over the five-month test horizon, the additive method decreases the root mean square error (RMSE), maximum absolute error, and mean absolute error (MAE) of the net load forecast by 6.13%, 3.63%, and 6.06% respectively, compared to the direct method.
{"title":"Net Load Forecasting with Disaggregated Behind-the-Meter PV Generation","authors":"A. Stratman, Tianqi Hong, Ming Yi, Dongbo Zhao","doi":"10.1109/IAS54023.2022.9940025","DOIUrl":"https://doi.org/10.1109/IAS54023.2022.9940025","url":null,"abstract":"With increasing adoption of residential PV systems, net load forecasting is gradually shifting from forecasting pure load to forecasting pure load with PV generation. This paper explicitly compares two methods of net load forecasting for systems with high behind-the-meter (BTM) PV penetration. The first method is an additive method, in which PV generation and pure load are forecasted separately and combined to produce a net load forecast. First, a disaggregation algorithm is applied to aggregate net load measurements of residential homes to separate the pure load and PV generation. Then, a long short-term memory (LSTM) model is used to forecast pure load and PV separately using the historical disaggregated pure load and PV, respectively, and weather factors. The results are combined to generate a net load forecast. The additive model is compared to a direct net load forecast from an LSTM model. Results show that over the five-month test horizon, the additive method decreases the root mean square error (RMSE), maximum absolute error, and mean absolute error (MAE) of the net load forecast by 6.13%, 3.63%, and 6.06% respectively, compared to the direct method.","PeriodicalId":193587,"journal":{"name":"2022 IEEE Industry Applications Society Annual Meeting (IAS)","volume":"1 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":"126059181","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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