Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking
- Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmad
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmad
- Date: 2017
- Subjects: ANFIS , Artificial Intelligence (AI) , Curve fitting polynomials
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/232998 , uj:23778 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2017. Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. The 8th International Renewable Energy Congress (IREC 2017).
- Description: Abstract: In this paper, an approach of designing a fast tracking MPPT is introduced using a predicted sixth order polynomial curve fitting MPPT technique. The results are compared with the lower order polynomials curve fitting MPPT and also compared with the Artificial Neuro-Fuzzy Inference System (ANFIS) results. The polynomials were generated from an offline solar data. This work was done to validate the effect of using a higher order polynomials under various weather conditions using modified CUK DC-DC converter. Findings suggest that using the 6th order polynomial curve fitting and the ANFIS techniques could track the highest maximum power point than the lower order curve techniques.
- Full Text:
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmad
- Date: 2017
- Subjects: ANFIS , Artificial Intelligence (AI) , Curve fitting polynomials
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/232998 , uj:23778 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2017. Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. The 8th International Renewable Energy Congress (IREC 2017).
- Description: Abstract: In this paper, an approach of designing a fast tracking MPPT is introduced using a predicted sixth order polynomial curve fitting MPPT technique. The results are compared with the lower order polynomials curve fitting MPPT and also compared with the Artificial Neuro-Fuzzy Inference System (ANFIS) results. The polynomials were generated from an offline solar data. This work was done to validate the effect of using a higher order polynomials under various weather conditions using modified CUK DC-DC converter. Findings suggest that using the 6th order polynomial curve fitting and the ANFIS techniques could track the highest maximum power point than the lower order curve techniques.
- Full Text:
Optimization of PV systems using ANN-PSO configuration technique under different weather conditions
- Farayola, Adedayo M., Sun, Yanxia, Ali, Ahmed
- Authors: Farayola, Adedayo M. , Sun, Yanxia , Ali, Ahmed
- Date: 2018
- Subjects: MPPT , GMPPT , P&O
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290080 , uj:31485 , Citation: Farayola, A.M., Sun, Y. & Ali, A. 2018. Optimization of PV systems using ANN-PSO configuration technique under different weather conditions.
- Description: Abstract: Conventional MPPT techniques like Perturb&observe perform ineffective under partial shading condition due to its inability to effectively track the global maximum power point (GMPP). Particle swarm optimization (PSO) technique is a recent meta-heuristic MPPT technique commonly used to extract maximum power from PV systems but takes time to iteratively locate the GMPP. This paper presents a novel use of hybrid ANNPSO technique implemented using series-connected distributive MPPT configuration approach. The results of ANN-PSO distributive MPPT, PSO, and Perturb&observe (P&O) technique were compared with theoretical power values under different weather conditions. This work was done to determine the most efficient MPPT method that can be considered for MPPT task in PV systems under uniform irradiance and partial shading conditions. Obtained results show that ANN-PSO DMPPT configuration exhibited the best performance.
- Full Text:
- Authors: Farayola, Adedayo M. , Sun, Yanxia , Ali, Ahmed
- Date: 2018
- Subjects: MPPT , GMPPT , P&O
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290080 , uj:31485 , Citation: Farayola, A.M., Sun, Y. & Ali, A. 2018. Optimization of PV systems using ANN-PSO configuration technique under different weather conditions.
- Description: Abstract: Conventional MPPT techniques like Perturb&observe perform ineffective under partial shading condition due to its inability to effectively track the global maximum power point (GMPP). Particle swarm optimization (PSO) technique is a recent meta-heuristic MPPT technique commonly used to extract maximum power from PV systems but takes time to iteratively locate the GMPP. This paper presents a novel use of hybrid ANNPSO technique implemented using series-connected distributive MPPT configuration approach. The results of ANN-PSO distributive MPPT, PSO, and Perturb&observe (P&O) technique were compared with theoretical power values under different weather conditions. This work was done to determine the most efficient MPPT method that can be considered for MPPT task in PV systems under uniform irradiance and partial shading conditions. Obtained results show that ANN-PSO DMPPT configuration exhibited the best performance.
- Full Text:
Use of MPPT techniques to reduce the energy pay-back time in PV systems
- Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmed
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmed
- Date: 2018
- Subjects: Artificial Intelligence (AI) , ANFIS , ANN
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274581 , uj:29302 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2018. Use of MPPT techniques to reduce the energy pay-back time in PV systems.
- Description: Abstract: Photovoltaic (PV) energy is a free-energy that is used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy pay-back time. This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened EPBT. To confirm the effectiveness of the ANFIS algorithm, its result was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT.
- Full Text:
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmed
- Date: 2018
- Subjects: Artificial Intelligence (AI) , ANFIS , ANN
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274581 , uj:29302 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2018. Use of MPPT techniques to reduce the energy pay-back time in PV systems.
- Description: Abstract: Photovoltaic (PV) energy is a free-energy that is used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy pay-back time. This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened EPBT. To confirm the effectiveness of the ANFIS algorithm, its result was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT.
- Full Text:
- «
- ‹
- 1
- ›
- »