Adapted flower pollination algorithm for a standalone solar photovoltaic system

This Extraction of the maximum electrical power from a solar photovoltaic (PV) system under numerous weather conditions is required to reduce its payback time period, per unit energy price, and to compensate for the high initial price of the solar PV system. This could only be achieved by continuously operating the solar PV system at its maximum power point (MPP) under several weather conditions. Unlike under uniform weather conditions (UWC), identification of the real MPP (Global MPP) under partial shading condition (PSC) in a reasonable time is a challenging task due to the formation of multiple local MPP in the power-voltage (P-V) characteristic curve of a solar PV array. The nature-inspired MPP tracking algorithms have been proved suitable for global MPP tracking (MPPT) under PSC. In this research paper, a renowned nature-inspired flower pollination algorithm (FPA) is deeply reviewed, modified, and integrated with the random walk filter to improve its performance in terms of tracking speed, and efficiency. A comparison of the proposed ‘Adaptive Flower Pollination Algorithm (AFPA)’ and conventional FPA algorithm has been made under zero, weak, and strong PSCs for a 4S solar PV array. The proposed algorithm has produced remarkable results in tracking speed, and efficiency, for the global MPP (GMPP) tracking under different PSCs. The simulation is performed in MATLAB/Simulink software

Modified flower pollination algorithm for an off-grid solar photovoltaic system

This Operating the solar photovoltaic (PV) system at its maximum power point (MPP) under numerous environmental conditions to extract the maximum power is a challenging task. The challenge is to track the MPP, especially under partial shading conditions (PSC), where the formation of multiple MPP occurs in the characteristic curve of a PV array. Nevertheless, achieving this would benefit us with optimal power production, reducing the payback time and initial cost of the PV system. To perform this duty, an electronic circuit ruled by an algorithm is employed. The MPP tracking (MPPT) algorithms can be categorized into conventional and nature inspired. The conventional algorithms can successfully track the MPP under uniform weather conditions (UWC), and unable to identify the global MPP (GMPP) under PSC. However, the nature inspired algorithms possess the ability to perform efficiently under all weather conditions. Considering this strength of nature inspired algorithms, one of the top performing algorithms named as Flower pollination algorithm (FPA) is selected based on its brilliant searching strategy in adjacent and distant locations. In this paper, some structural modifications have been proposed in the FPA to further improve its searching capability and get more quick, accurate and efficient results for the MPPT of solar PV system. Results have proven the superiority of the proposed Modified FPA (MFPA) over the FPA in terms of efficiency, accuracy, tracking speed, energy conservation, economic saving, and payback time. Simulation is performed in MATLAB/Simulink

Frequency limited impulse response gramians based model reduction

In order to simplify the analysis of complex electronic systems, they needsto be modeled accurately. Model reduction is further required to streamline the procedural and computational complexities. Further the instability caused by the model reduction techniques worstly effects the accuracy of a system. Therefore, we have proposed some improvements in the frequency limited impulse response Gramians based model order reduction techniques for discrete time systems. The propsed techniques assures the stability of the model after it get reduced. The proposed techniques provided better results than the stability preserving techniques

Estimation of airship states and model uncertainties using nonlinear estimators

This Airships are lighter than air vehicles and due to their growing number of applications, they are becoming attractive for the research community. Most of the applications require an airship autonomous flight controller which needs an accurate model and state information. Usually, airship states are affected by noise and states information can be lost in the case of sensor's faults, while airship model is affected by model inaccuracies and model uncertainties. This paper presents the application of nonlinear and Bayesian estimators for estimating the states and model uncertainties of neutrally buoyant airship. It is considered that minimum sensor measurements are available, and data is corrupted with process and measurement noise. A novel lumped model uncertainty estimation approach is formulated where airship model is augmented with six extra state variables capturing the model uncertainty of the airship. The designed estimator estimates the airship model uncertainty along with its states. Nonlinear estimators, Extended Kalman Filter and Unscented Kalman Filter are designed for estimating airship attitude, linear velocities, angular velocities and model uncertainties. While Particle filter is designed for the estimation of airship attitude, linear velocities and angular velocities. Simulations have been performed using nonlinear 6-DOF simulation model of experimental airship for assessing the estimator performances. 1− uncertainty bound and error analysis have been performed for the validation. A comparative study of the estimator's performances is also carried out

Optimized hill climbing algorithm for an islanded solar photovoltaic system

Conventional energy generation technologies face unreliability due to the depletion of fossil fuels, soaring energy prices, greenhouse gas emissions, and continuously increasing energy demand. As a result, researchers are searching for reliable, cheap, and environmentally friendly renewable energy technologies. Solar photovoltaic (PV) technology, which directly converts sunlight into electricity, is the most attractive sustainable energy source due to the sun's ubiquitous presence. However, the non-linear behaviour of solar PV demands maximum power point tracking (MPPT) to ensure optimal power production. Although Hill Climbing (HC) is a simple, cheap, and efficient MPPT algorithm, it has a drawback of steady-state oscillations around MPP under uniform weather conditions. To overcome this weakness, we propose some modifications in the tracking structure of the HC algorithm. The proposed optimized HC (OHC) algorithm achieves zero steady-state oscillations without compromising the strength of the conventional HC algorithm. We applied both algorithms to an off-grid PV system under constant and changing weather conditions, and the results demonstrate the superiority of the proposed OHC algorithm over the conventional HC algorithm

A Novel Ten Check Maximum Power Point Tracking Algorithm for a Standalone Solar Photovoltaic System

Optimal energy extraction under partial shading conditions from a photovoltaic (PV) array is particularly challenging. Conventional techniques fail to achieve the global maximum power point (GMPP) under such conditions, while soft computing techniques have provided better results. The main contribution of this paper is to devise an algorithm to track the GMPP accurately and efficiently. For this purpose, a ten check (TC) algorithm was proposed. The effectiveness of this algorithm was tested with different shading patterns. Results were compared with the top conventional algorithm perturb and observe (P&O) and the best soft computing technique flower pollination algorithm (FPA). It was found that the proposed algorithm outperformed them. Analysis demonstrated that the devised algorithm achieved the GMPP efficiently and accurately as compared to the P&O and the FPA algorithms. Simulations were performed in MATLAB/Simulink

Performance Optimization of a Ten Check MPPT Algorithm for an Off-Grid Solar Photovoltaic System

In order to operate a solar photovoltaic (PV) system at its maximum power point (MPP) under numerous weather conditions, it is necessary to achieve uninterrupted optimal power production and to minimize energy losses, energy generation cost, and payback time. Under partial shading conditions (PSC), the formation of multiple peaks in the power voltage characteristic curve of a PV cell puzzles conventional MPP tracking (MPPT) algorithms trying to identify the global MPP (GMPP). Meanwhile, soft-computing MPPT algorithms can identify the GMPP even under PSC. Drawbacks such as structural complexity, computational complexity, huge memory requirements, and difficult implementation all affect the viability of soft-computing algorithms. However, those drawbacks have been successfully overcome with a novel ten check algorithm (TCA). To improve the performance of the TCA in terms of MPPT speed and efficiency, a novel concept of data arrangement is introduced in this paper. The proposed structure is referred to as Optimized TCA (OTCA). A comparison of the proposed OTCA and classic TCA algorithms was conducted for standard benchmarks. The results proved the superiority of the OTCA algorithm compared to both TCA and flower pollination (FPA) algorithms. The major advantage of OTCA in MPPT stems from its speed as compared to TCA and FPA, with almost 86% and 90% improvement, respectively

Ordering Technique for the Maximum Power Point Tracking of an Islanded Solar Photovoltaic System

The world’s attention has turned towards renewable energy due to escalating energy demands, declining fossil fuel reservoirs, greenhouse gas emissions, and the unreliability of conventional energy systems. The sun is the only renewable energy source that is available every day for a specific period of time. Solar photovoltaic (PV) technology is known for its direct conversion of sunlight into electricity using the photoelectric effect. However, due to the non-linear electrical characteristics, the power output of solar PV cells is bound to a lower value and can not produce the power of which it is capable. To extract the maximum possible power, the PV cell needs to be operated at its maximum power point (MPP) uninterruptedly under numerous weather conditions. Therefore, an electronic circuit driven by a set of rules known as an algorithm is utilized. To date, the flower pollination algorithm (FPA) is one of the most renowned maximum power point tracking (MPPT) algorithms due to its effective tracking ability at the local and global positions. After an in-depth analysis of the design, strengths, weaknesses, and opportunities of the FPA algorithm, we have proposed an additional filtration and distribution process named “Random walk” along with the ordering of solutions, to improve its efficiency and tracking time. The proposed structure named “Ordered FPA” has outperformed the renowned FPA algorithm under various weather conditions at all the standard benchmarks. Simulations are performed in MATLAB/Simulink