Pub Date : 2015-11-01DOI: 10.1109/NUICONE.2015.7449625
S. Pujara, C. Kotwal
This paper describes the design and simulation of Unscheduled Interchange (UI) based optimized integral controller of Automatic Generation Control (AGC) in the restructured power system. The traditional AGC loop is replaced with UI based price signal linking to the frequency at the prevailing time. UI rate for corresponding system frequency signal is received from load dispatch center and it is compared with the marginal cost of individual generators to generate the error signal which decides the gain of integral controller for correction in frequency to its nominal value. The simulation is carried out for different cases of generation-demand transactions for two area restructured electricity market to demonstrate the effectiveness of UI based control in system frequency regulations. A non derivative classical Particle Swarm Optimization (PSO) technique is used to obtain the optimal value of gain of integral controllers to achieve the optimum response of the system under study.
{"title":"Optimized unscheduled interchange based secondary control for two area deregulated electricity market","authors":"S. Pujara, C. Kotwal","doi":"10.1109/NUICONE.2015.7449625","DOIUrl":"https://doi.org/10.1109/NUICONE.2015.7449625","url":null,"abstract":"This paper describes the design and simulation of Unscheduled Interchange (UI) based optimized integral controller of Automatic Generation Control (AGC) in the restructured power system. The traditional AGC loop is replaced with UI based price signal linking to the frequency at the prevailing time. UI rate for corresponding system frequency signal is received from load dispatch center and it is compared with the marginal cost of individual generators to generate the error signal which decides the gain of integral controller for correction in frequency to its nominal value. The simulation is carried out for different cases of generation-demand transactions for two area restructured electricity market to demonstrate the effectiveness of UI based control in system frequency regulations. A non derivative classical Particle Swarm Optimization (PSO) technique is used to obtain the optimal value of gain of integral controllers to achieve the optimum response of the system under study.","PeriodicalId":131332,"journal":{"name":"2015 5th Nirma University International Conference on Engineering (NUiCONE)","volume":"22 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125781972","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 : 2015-11-01DOI: 10.1109/NUICONE.2015.7449592
P. Joshi, C. Sheth
In this work the AC Load is fed from solar radiation energy by means of Three-level Diode-Clamped Inverter. The entire system is implemented with three phase supply system. For making the system more efficient, Maximum Power Point Tracking (MPPT) device is used. The Perturb & Observe technique is adopted to obtain voltage at maximum power. In this paper, Space Vector Pulse Width Modulation (SVPWM) technique is used to generate gate pulses to make inverter switches ON and OFF periodically. IGBTs are used as a switching device. With the help of latest new vectors, the new gate pulses are generated and the system is interfaced with Grid. Without a filter, the %THD of line voltage is 32.24% and with the incorporation of filter in the system the %THD of line voltage drastically reduces to 4.91%. The modelling of the entire system is performed through MATLAB/Simulink in this work The paper also presents the comparison of the results of line voltages for the proposed system with Sinusoidal Pulse Width Modulation (SPWM) and SVPWM techniques.
{"title":"Modelling of grid tied 3-level diode clamped inverter using space vector PWM for PV system","authors":"P. Joshi, C. Sheth","doi":"10.1109/NUICONE.2015.7449592","DOIUrl":"https://doi.org/10.1109/NUICONE.2015.7449592","url":null,"abstract":"In this work the AC Load is fed from solar radiation energy by means of Three-level Diode-Clamped Inverter. The entire system is implemented with three phase supply system. For making the system more efficient, Maximum Power Point Tracking (MPPT) device is used. The Perturb & Observe technique is adopted to obtain voltage at maximum power. In this paper, Space Vector Pulse Width Modulation (SVPWM) technique is used to generate gate pulses to make inverter switches ON and OFF periodically. IGBTs are used as a switching device. With the help of latest new vectors, the new gate pulses are generated and the system is interfaced with Grid. Without a filter, the %THD of line voltage is 32.24% and with the incorporation of filter in the system the %THD of line voltage drastically reduces to 4.91%. The modelling of the entire system is performed through MATLAB/Simulink in this work The paper also presents the comparison of the results of line voltages for the proposed system with Sinusoidal Pulse Width Modulation (SPWM) and SVPWM techniques.","PeriodicalId":131332,"journal":{"name":"2015 5th Nirma University International Conference on Engineering (NUiCONE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128490138","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 : 2015-11-01DOI: 10.1109/NUICONE.2015.7449613
Mukesh Kumar Pareek, P. Thakkar
Optimizing a stock market portfolio requires decision making at two distinct stages, first is to select the stocks and second is to assign distribution of investment amount among these selected stocks. Given the historical data of stocks, the role of optimization models is to select stocks and assign portfolio proportion to the selected stocks. Selection and weight assignment to stocks are co-occurring activities. Investors prime motive is to maximize the return and minimize the risk of portfolio. Stock market is uncertain and volatile and therefore, Artificial Intelligence, Machine Learning and Soft Computing techniques are viable candidates which can help in optimization and making decisions using such data. This paper surveys the research carried out in the domain of stock market portfolio optimization. Paper compares research efforts in the domain on the basis of techniques used, risk models and stock markets considered. It is observed from the surveyed papers that Artificial Intelligence, Machine Learning and Soft Computing techniques are widely accepted for studying and evaluating stock market behavior and optimizing portfolios.
{"title":"Surveying stock market portfolio optimization techniques","authors":"Mukesh Kumar Pareek, P. Thakkar","doi":"10.1109/NUICONE.2015.7449613","DOIUrl":"https://doi.org/10.1109/NUICONE.2015.7449613","url":null,"abstract":"Optimizing a stock market portfolio requires decision making at two distinct stages, first is to select the stocks and second is to assign distribution of investment amount among these selected stocks. Given the historical data of stocks, the role of optimization models is to select stocks and assign portfolio proportion to the selected stocks. Selection and weight assignment to stocks are co-occurring activities. Investors prime motive is to maximize the return and minimize the risk of portfolio. Stock market is uncertain and volatile and therefore, Artificial Intelligence, Machine Learning and Soft Computing techniques are viable candidates which can help in optimization and making decisions using such data. This paper surveys the research carried out in the domain of stock market portfolio optimization. Paper compares research efforts in the domain on the basis of techniques used, risk models and stock markets considered. It is observed from the surveyed papers that Artificial Intelligence, Machine Learning and Soft Computing techniques are widely accepted for studying and evaluating stock market behavior and optimizing portfolios.","PeriodicalId":131332,"journal":{"name":"2015 5th Nirma University International Conference on Engineering (NUiCONE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121800535","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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