Pub Date : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431424
Pat Jaengarun, S. Tiptipakorn, T. Singhavilai
An increase in interconnection of power systems may lead to poor damping oscillation. This paper studies the oscillation damping performance of the Power System Stabilizer (PSS) and Power Oscillation Damper which are located on a photovoltaic plant (PV-POD). A computer simulation of a power system with PSS and PV-POD is conducted in Matlab/Simscape Power System. The study consists of five scenarios with different operating conditions. To compare the damping performance, the results from the Eigen analysis and time domain analysis from the computer simulation are used to analyze the damping quality. The results show that the oscillation damping obtained from the system with POD is better than that from the system with PSS. However, when the operating condition of the system is changed, the oscillation damping of the system with PSS is more consistent than that of the system with POD.
{"title":"Comparative Study of PSS and POD for A Power System With PV Plant","authors":"Pat Jaengarun, S. Tiptipakorn, T. Singhavilai","doi":"10.1109/ICPEI49860.2020.9431424","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431424","url":null,"abstract":"An increase in interconnection of power systems may lead to poor damping oscillation. This paper studies the oscillation damping performance of the Power System Stabilizer (PSS) and Power Oscillation Damper which are located on a photovoltaic plant (PV-POD). A computer simulation of a power system with PSS and PV-POD is conducted in Matlab/Simscape Power System. The study consists of five scenarios with different operating conditions. To compare the damping performance, the results from the Eigen analysis and time domain analysis from the computer simulation are used to analyze the damping quality. The results show that the oscillation damping obtained from the system with POD is better than that from the system with PSS. However, when the operating condition of the system is changed, the oscillation damping of the system with PSS is more consistent than that of the system with POD.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"84 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116372769","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431502
N. Intharasomchai, K. Chayakulkheeree
This paper proposes a calculation method to determine the frequency deviation of microgrid (MG) system in transferring mode, using the mathematical model of distribution slack bus load flow (DSLF). In the proposed method, the modified incorporating Newton-Raphson load flow incorporating generation control equations are used to find the primary frequency deviation. The IEEE radian distribution 33-bus was modified as an microgrid model and used to test the proposed method.
{"title":"Steady State Primary Frequency Estimation for Microgrid Transferring Mode Using Distributed Slack Bus Load Flow Analysis","authors":"N. Intharasomchai, K. Chayakulkheeree","doi":"10.1109/ICPEI49860.2020.9431502","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431502","url":null,"abstract":"This paper proposes a calculation method to determine the frequency deviation of microgrid (MG) system in transferring mode, using the mathematical model of distribution slack bus load flow (DSLF). In the proposed method, the modified incorporating Newton-Raphson load flow incorporating generation control equations are used to find the primary frequency deviation. The IEEE radian distribution 33-bus was modified as an microgrid model and used to test the proposed method.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452522","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431435
Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva
Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.
{"title":"Forecasting energy time-series data using a fuzzy ARTMAP neural network","authors":"Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva","doi":"10.1109/ICPEI49860.2020.9431435","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431435","url":null,"abstract":"Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128210748","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431421
Suti Rittijun, N. Boonpirom
This paper presents the power factor correction of single phase rectifier using fuzzy controller. The main objective are to apply the boost converter to improve power factor of single phase rectifier as unity power factor and to apply the fuzzy controller on Arduino microcontroller controlling this process. The structure of research consists of a 1500 W, 300V, single phase rectifier which includes the power factor correction boost converter with fuzzy controller. Being processed, the dc output voltage of converter is controlled by Fuzzy controller, and generated by multiplying output with dc bus voltage which results to current reference. PWM gate drive of IGBT is obtained by comparing of this current reference with dc bus current. The experimental results show the achievement of dc output voltage regulation control from 300 V and 400 V reference. In addition, the power factor increases as 0.86, and the total harmonic distortion of current decreases as 70 %. Finally, the advantages of this research is ability to apply to the large DC converter and the fuzzy controller parameter is simplify modified and develop to self-tuning controller in the future.
{"title":"Power Factor Correction of Single Phase Rectifier using Fuzzy Controller","authors":"Suti Rittijun, N. Boonpirom","doi":"10.1109/ICPEI49860.2020.9431421","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431421","url":null,"abstract":"This paper presents the power factor correction of single phase rectifier using fuzzy controller. The main objective are to apply the boost converter to improve power factor of single phase rectifier as unity power factor and to apply the fuzzy controller on Arduino microcontroller controlling this process. The structure of research consists of a 1500 W, 300V, single phase rectifier which includes the power factor correction boost converter with fuzzy controller. Being processed, the dc output voltage of converter is controlled by Fuzzy controller, and generated by multiplying output with dc bus voltage which results to current reference. PWM gate drive of IGBT is obtained by comparing of this current reference with dc bus current. The experimental results show the achievement of dc output voltage regulation control from 300 V and 400 V reference. In addition, the power factor increases as 0.86, and the total harmonic distortion of current decreases as 70 %. Finally, the advantages of this research is ability to apply to the large DC converter and the fuzzy controller parameter is simplify modified and develop to self-tuning controller in the future.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128317018","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431556
Choktawee Nonprivun, B. Plangklang
This paper presents a study and analysis of flux linkage performance on 12/8 pole doubly salient permanent magnet machine in square envelope conventional. Analyzed model was using a finite element method. The investigated model was constructed by changing the size of the structure as the main parameters of the speed 500 rpm, PM coercivity 910 kA/m, PM remanence 1.2 T, copper loss 30 W, turns per coil 45, and stator side length 100 mm. The study and analysis of flux linkage, induced voltage, and torque are also included in this paper.
{"title":"Study and Analysis of Flux Linkage on 12/8 pole Doubly Salient Permanent Magnet Machine in Square Envelope","authors":"Choktawee Nonprivun, B. Plangklang","doi":"10.1109/ICPEI49860.2020.9431556","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431556","url":null,"abstract":"This paper presents a study and analysis of flux linkage performance on 12/8 pole doubly salient permanent magnet machine in square envelope conventional. Analyzed model was using a finite element method. The investigated model was constructed by changing the size of the structure as the main parameters of the speed 500 rpm, PM coercivity 910 kA/m, PM remanence 1.2 T, copper loss 30 W, turns per coil 45, and stator side length 100 mm. The study and analysis of flux linkage, induced voltage, and torque are also included in this paper.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131748110","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431392
P. Thounthong, B. Nahid-Mobarakeh, S. Pierfederici, P. Mungporn, N. Bizon, P. Kumam
In this paper, the output DC bus voltage stabilization and the current sharing of a multi-phase parallel interleaved FC boost converter is presented. The proposed robust controller by adding an integrator action is based on the Hamiltonian–Lyapunov function. The effectiveness and robustness of the designed controller have been successfully authenticated by experimental results obtained using a 2.5-W prototype FC converter (by four-phase parallel interleaved boost converters) and a dSPACE MicroLabBox platform. The FC main source system is based on a fuel reformer engine that converts fuel methanol and water into hydrogen gas H2 to a polymer electrolyte membrane FC stack (50–V, 2.5–kW).
{"title":"Hamiltonian Control Law Based on Lyapunov–Energy Function for Four-Phase Parallel Fuel Cell Boost Converter","authors":"P. Thounthong, B. Nahid-Mobarakeh, S. Pierfederici, P. Mungporn, N. Bizon, P. Kumam","doi":"10.1109/ICPEI49860.2020.9431392","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431392","url":null,"abstract":"In this paper, the output DC bus voltage stabilization and the current sharing of a multi-phase parallel interleaved FC boost converter is presented. The proposed robust controller by adding an integrator action is based on the Hamiltonian–Lyapunov function. The effectiveness and robustness of the designed controller have been successfully authenticated by experimental results obtained using a 2.5-W prototype FC converter (by four-phase parallel interleaved boost converters) and a dSPACE MicroLabBox platform. The FC main source system is based on a fuel reformer engine that converts fuel methanol and water into hydrogen gas H2 to a polymer electrolyte membrane FC stack (50–V, 2.5–kW).","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129124183","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431432
N. Junhuathon, Guntinan Sakunphaisal, K. Chayakulkheeree
Battery Management System (BMS) is a critical component in modern electrical technology. The exact knowledge of the state of health and capacity impact is useful for the estimation and control strategy of battery. Therefore, this paper proposed the particle swarm optimization-based Feedforward Neural Network (PSO-FNN) for Battery Aging Estimate (BAE). This PSO is used to optimize the weights and biases of the FNN. For validating the proposed method, conventional FNN was simulated with battery data sets provided by NASA Prognostics Center of Excellence (PCoE) and compared to the proposed method. The simulation results show the performance of PSO-FNN is noticeably better in relatively volatile systems.
{"title":"Li-ion Battery Aging Estimation Using Particle Swarm Optimization Based Feedforward Neural Network","authors":"N. Junhuathon, Guntinan Sakunphaisal, K. Chayakulkheeree","doi":"10.1109/ICPEI49860.2020.9431432","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431432","url":null,"abstract":"Battery Management System (BMS) is a critical component in modern electrical technology. The exact knowledge of the state of health and capacity impact is useful for the estimation and control strategy of battery. Therefore, this paper proposed the particle swarm optimization-based Feedforward Neural Network (PSO-FNN) for Battery Aging Estimate (BAE). This PSO is used to optimize the weights and biases of the FNN. For validating the proposed method, conventional FNN was simulated with battery data sets provided by NASA Prognostics Center of Excellence (PCoE) and compared to the proposed method. The simulation results show the performance of PSO-FNN is noticeably better in relatively volatile systems.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117018820","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431522
S. Sitjongsataporn, S. Prongnuch, T. Wiangtong
In this paper, we present an adaptive averaging step-size inverse square-root affine projection sign algorithm. Based on the QR-decomposition method, we derive the modified inverse autocorrelation matrix in order to reduce the complexity of inverse matrix, which a criterion is based on the proposed algorithm with the sign error. Adaptive averaging step-size mechanism is used for the fast adaptation. Convergence analysis in form of a posteriori error is presented. Simulation results show that the proposed algorithm can obtain clearly the better performance compared with the conventional affine projection algorithm.
{"title":"An Adaptive inverse Square-root Affine Projection Sign Algorithm based on QR-Decomposition","authors":"S. Sitjongsataporn, S. Prongnuch, T. Wiangtong","doi":"10.1109/ICPEI49860.2020.9431522","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431522","url":null,"abstract":"In this paper, we present an adaptive averaging step-size inverse square-root affine projection sign algorithm. Based on the QR-decomposition method, we derive the modified inverse autocorrelation matrix in order to reduce the complexity of inverse matrix, which a criterion is based on the proposed algorithm with the sign error. Adaptive averaging step-size mechanism is used for the fast adaptation. Convergence analysis in form of a posteriori error is presented. Simulation results show that the proposed algorithm can obtain clearly the better performance compared with the conventional affine projection algorithm.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"9 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116798340","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431478
T. Archevapanich, P. Chomtong, P. Akkaraekthalin
This paper proposes the electromagnetic band gap (EBG) reflector using split ring resonator with interdigital technique. The capacitive value between split ring gaps has the results of slow-wave effect in transmission line, that can reduce the size of split ring resonator unit cell from λ/2 to about λ/4. Moreover, it can control the second harmonics to resonance at the required frequency for the second band operation. The unit cell will be designed at the fundamental frequency of 1.8 GHz and the second resonance frequency of 2.4 GHZ, which are the frequency bands of LTE and WLAN systems. The unit cells are arranged in an array to act as a reflector for a dipole antenna. The simulation results at both operating frequency bands show the antenna gains of 8.17 dB and 8.29 dB at 1.8 GHz (460 MHz bandwidth) and 2.4 GHz (470 MHz bandwidth), respectively.
{"title":"A Dual Band Split Ring Electromagnetic Band Gap using Interdigital Technique and its Applications","authors":"T. Archevapanich, P. Chomtong, P. Akkaraekthalin","doi":"10.1109/ICPEI49860.2020.9431478","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431478","url":null,"abstract":"This paper proposes the electromagnetic band gap (EBG) reflector using split ring resonator with interdigital technique. The capacitive value between split ring gaps has the results of slow-wave effect in transmission line, that can reduce the size of split ring resonator unit cell from λ/2 to about λ/4. Moreover, it can control the second harmonics to resonance at the required frequency for the second band operation. The unit cell will be designed at the fundamental frequency of 1.8 GHz and the second resonance frequency of 2.4 GHZ, which are the frequency bands of LTE and WLAN systems. The unit cells are arranged in an array to act as a reflector for a dipole antenna. The simulation results at both operating frequency bands show the antenna gains of 8.17 dB and 8.29 dB at 1.8 GHz (460 MHz bandwidth) and 2.4 GHz (470 MHz bandwidth), respectively.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114456351","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 : 2020-10-14DOI: 10.1109/ICPEI49860.2020.9431408
P. Suwanapingkarl, S. Prakobkit, K. Srivallop, M. Boonthienthong
The conventional Medium voltage (MV) and Low Voltage (LV) distribution power network had the power flow in one direction, which flows from the power plant (calls central generating) downward to the consumer. However, this power flow direction is starting to change, and hence it is becoming the Bi-directional. Because the accommodating of Renewable Energy resources (REs) such as wind turbine, Photovoltaic (PV) and Electric Vehicles (EVs) etc. In addition, the uses of power electronic devices such as converter (stabilises the use of voltage), inverter (inverses between AC-DC or DC-AC) and charger (supports EVs) are also dramatically increasing, and thus it is also effected the Power Quality (PQ) on the distribution network as same as the power flow. According to the impacts of the REs and EVs, the distribution network must face the challenge. The demand of electricity such as the consumers' behavior, types of REs, charging/un-charging EVs is necessary to investigate. This investigation will be more complicated as the variations in demand profiles. These large data in the network can be called the ‘Big data' of the power network. The ‘Big data' can increase the development in the monitoring and controlling systems of the network, and hence it is allowed the network to become smart network. The distribution network must have the ability and capability to accommodate the REs and EVs. Therefore, the standards and regulations of REs and EVs connection, standards and regulations of Feed-In-Tariffs (FIT), the revision of Power Purchase Agreement (PPA) between provider and consumer, smart meters, smart On-Load Tap Changer Transformer (OLTC), smart EVs Car Park, Smart Energy Storage, smart Artificial Intelligent (AI) to manage Big Data, etc. are necessary required to study.
{"title":"Reviews: The Impacts of Electric Vehicles (EVs) and Renewable Energy Resources (REs) on The Distribution Power Network","authors":"P. Suwanapingkarl, S. Prakobkit, K. Srivallop, M. Boonthienthong","doi":"10.1109/ICPEI49860.2020.9431408","DOIUrl":"https://doi.org/10.1109/ICPEI49860.2020.9431408","url":null,"abstract":"The conventional Medium voltage (MV) and Low Voltage (LV) distribution power network had the power flow in one direction, which flows from the power plant (calls central generating) downward to the consumer. However, this power flow direction is starting to change, and hence it is becoming the Bi-directional. Because the accommodating of Renewable Energy resources (REs) such as wind turbine, Photovoltaic (PV) and Electric Vehicles (EVs) etc. In addition, the uses of power electronic devices such as converter (stabilises the use of voltage), inverter (inverses between AC-DC or DC-AC) and charger (supports EVs) are also dramatically increasing, and thus it is also effected the Power Quality (PQ) on the distribution network as same as the power flow. According to the impacts of the REs and EVs, the distribution network must face the challenge. The demand of electricity such as the consumers' behavior, types of REs, charging/un-charging EVs is necessary to investigate. This investigation will be more complicated as the variations in demand profiles. These large data in the network can be called the ‘Big data' of the power network. The ‘Big data' can increase the development in the monitoring and controlling systems of the network, and hence it is allowed the network to become smart network. The distribution network must have the ability and capability to accommodate the REs and EVs. Therefore, the standards and regulations of REs and EVs connection, standards and regulations of Feed-In-Tariffs (FIT), the revision of Power Purchase Agreement (PPA) between provider and consumer, smart meters, smart On-Load Tap Changer Transformer (OLTC), smart EVs Car Park, Smart Energy Storage, smart Artificial Intelligent (AI) to manage Big Data, etc. are necessary required to study.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120952339","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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