Ravi Rajendra Bholane

Bio: Ravi Rajendra Bholane is an academic researcher from National Institute of Technology, Warangal. The author has contributed to research in topics: Maximum power principle & Maximum power point tracking. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Grid connected PV system using FB-PSO

Abstract: This Paper presents a MPPT (maximum power point tracking) design for a Grid connected PV (Photo-voltaic) system using FB-PSO (Forward Backward particle swarm optimization) Technique. The FB-PSO is a new improved method which results in maximum efficiency, fast tracking of maximum power point, less steady state oscillations compare to PO (Perturb and Observe) and CPSO (Conventional PSO) methods. The proposed scheme is examine under PSC (partial shading conditions) and its results are compare with other two methods. The FB-PSO algorithm is implemented in MATLAB/SIMULINK and it is observed that proposed method is best compare to PO and CPSO MPPTs.

Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibinde Kullanılan Algoritmaların Kısmi Gölgeleme Koşulları Altında Karşılaştırılması

Abstract: Fotovoltaik (FV) sistemlerde verimliligi arttirmak icin guc elektronigi donusturuculeri yardimiyla maksimum guc noktasi takibi (MGNT) islemi yapilmaktadir. Esit dagilimli isima kosullarinda MGNT yapilirken geleneksel algoritmalardan biri olan degistir ve gozle (D&G) algoritmasi oldukca verimlidir. Ancak kismi golgeleme kosullari meydana geldiginde bu algoritma global maksimum guc noktasini bulamamakta ve yerel maksimum guc noktalarina takilmaktadir. Buna karsin parcacik suru optimizasyonu (PSO) ve guguk kusu optimizasyon (GKO) algoritmasi gibi dogadan esinlenen meta sezgisel algoritmalar global maksimum noktanin bulunmasinda daha basarili olmaktadir. Bu calismada MATLAB/SIMULINK’de FV dizi, DA-DA yukselten donusturucu ve yukten olusan bir sistem gelistirilmistir. Bu sistem kullanilarak kismi golgeleme kosullari altinda D&G, PSO ve GKO algoritmalariyla MGNT islemi gerceklestirilmis ve bu algoritmalarin karsilastirmali analizi yapilmistir. Bu algoritmalar uc farkli kismi golgeleme konfigurasyonu ile takip hizi ve dogrulugu acisindan birbiriyle kiyaslanmistir. Simulasyonlar sonucunda, D&G algoritmasi yerel bir maksimum guc noktasina yakalanirken PSO ve GKO algoritmalari global maksimum guc noktasinin bulunmasinda basarili olmustur. PSO ve GKO algoritmasi birbiriyle kiyaslandiginda ise GKO algoritmasinin PSO algoritmasindan daha hizli bir sekilde global maksimum guc noktasina ulastigi gorulmustur.

Techno-economic Assessment of Hybrid Photovoltaic/Solar Thermal Power Plants: Modeling and Potential for Synergies

Abstract: In this master thesis a hybrid photovoltaic / solar thermal power plant was modeled for a high irradiance location. The selected location was Chile’s northern region. For the evaluation the software greenius was used, a program being continuously developed at DLR since 1999. This program did not possess a storage function for PV simulations. Therefore one of the aims of this thesis was firstly to analyze the accuracy of the PV model and subsequently develop and implement a battery model to evaluate the financial viability of standalone PV systems. For this reason the greenius model was compared with PVWatts and PVSyst photovoltaic models. Additionally, an electric storage element based on a charge balance model was implemented in the system. This model proved to accurately depict the performance of batteries without the need for many parameters. The second aim of this thesis was to optimize and evaluate the financial feasibility of hybrid power plants with a high capacity factor (> 90%). The designed plants were based on the published specifications of Cerro Dominador, Chile for two main reasons. On one hand, this is going to be the first hybrid PV/CSP plant to be commissioned in South America (2019). Therefore it is going to be the benchmark for hybrid solar power plants in the future. On the other hand, Chile’s climate is perfect for solar projects. Thus it was important to assess the profitability of a sample plant in this region for future projects to be developed. The calculations were performed with the latest PV benchmark costs of 2017, which have decreased by 21% compared to 2016. The results showed that hybrid solar plants are more cost-efficient for base-load electricity supply than standalone CSP plants in high-irradiance regions. Photovoltaic power plants with battery storage can be competitive starting 2032.

Comparison of Maximum Power Tracking using Artificial Intelligence based optimization controller in Photovoltaic Systems

Abstract: This paper analyzes performance of Artificial Intelligence based optimization controller for the comparative study of maximum power point tracking (MPPT) in PV Systems. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) control methods are the two such techniques used, and are simulated in MATLAB-Simulink using Trina Solar TSM-250PD05.08. The simulation results suitably depict the performance of these methods on the basis of some parameters like their rise time, settling time, time taken to reach maximum power point and their efficiency. It is found that maximum power point is tracked in PV systems with greater efficiency using PSO as compared to ANN.