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Power Electronics Converters Applications And Design

This paper presents a low power boost converter for thermoelectric energy harvesting that demonstrates an efficiency that is 15% higher than the state-of-the-art for voltage conversion ratios above 20. This is achieved by utilizing a technique allowing synchronous rectification in the discontinuous conduction mode. A low-power method for input voltage monitoring is presented. The low input voltage requirements allow operation from a thermoelectric generator powered by body heat. The converter, fabricated in a 0.13 μm CMOS process, operates from input voltages ranging from 20 mV to 250 mV while supplying a regulated 1 V output. The converter consumes 1.6 (1.1) μW of quiescent power, delivers up to 25 (175) μW of output power, and is 46 (75)% efficient for a 20 mV and 100 mV input, respectively.

A 20 mV Input Boost Converter With Efficient Digital Control for Thermoelectric Energy Harvesting

Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules

Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification

Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters

An improved optimization technique for estimation of solar photovoltaic parameters

Solar photovoltaic (PV) systems are attracting a huge focus in the current energy scenario. Various maximum power point tracking (MPPT) methods are used in solar PV systems in order to achieve maximum power. In this article, a clear analysis of conventional MPPT techniques such as variable step size perturb and observe (VSS-P&O), modified incremental conductance (MIC), fractional open circuit voltage (FOCV) has been carried out. In addition, the soft computing MPPT techniques such as fixed step size radial basis functional algorithm (FSS-RBFA), variable step size radial basis functional algorithm (VSS-RBFA), adaptive fuzzy logic controller (AFLC), particle swarm optimization (PSO), and cuckoo search (CS) MPPT techniques are presented along with their comparative analysis. The comparative analysis is done under static and dynamic irradiation conditions by considering algorithm complexity, tracking speed, oscillations at MPP, and sensing parameters. The single-diode model PV panel and double-diode model PV panel are also compared in terms of fill factor (FF) and maximum power extraction. Clear insight is presented supporting the suitability of MPPT techniques for different types of converter configurations.

https://www.mdpi.com/1996-1073/13/2/371/pdf

Different Conventional and Soft Computing MPPT Techniques for Solar PV Systems with High Step-Up Boost Converters: A Comprehensive Analysis

In order to increase the efficiency of the solar photovoltaic (PV) system, accurate electrical modeling of the system under different environmental conditions is necessary. The double-diode electrical model of solar PV is known to be more accurate than its single diode model counterpart since it takes into account the effect of recombination. However, because of its nonlinear characteristics, the parameters of the double-diode model (DDM) have to be identified using optimization algorithms. In this paper, the wind-driven optimization (WDO) algorithm is proposed as a potential new method for identifying the parameters of a 12-parameter DDM of the solar PV. The accuracy and flexibility of the proposed method are verified using three different sets of data: first, experimental data at the controlled environmental condition; second, data sheet values of different solar PV modules; and third, real-time experimental data at the uncontrolled environmental condition. Additionally, the performance of the WDO is compared with other well-known existing optimization techniques. The obtained results show that the WDO algorithm can provide optimized values with reduced mean absolute error in power and reduced root mean square error for different types of solar PV modules at different environmental conditions. We show that the WDO can be confidently recommended as a reliable optimization algorithm for parameter estimation of solar PV model.

/pdf/wind-driven-optimization-technique-for-estimation-of-solar-33ud0v6bui.pdf

Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

An algorithm to solve unit commitment problem (UCP) with operational, power flow and environmental constraints under contingencies has been developed to plan an economic and secure generation schedule. The unit commitment (UC) solution for the environmental constrained problem has been formulated as a multi-objective problem by considering both Economic load dispatch (ELD) and Economic emission dispatch (EED) simultaneously. The combined economic emission dispatch (CEED) bi-objective problem is converted to single objective function by adding a modified price penalty factor. The UCP solutions without operational and power flow constraints are not practical due to secure operation of the power system network. This proposed algorithm introduces an efficient UC approach that obtains the minimum operating cost satisfying both unit and network constraints when contingencies are included. Repeated OPF for the satisfactory unit combinations for every line removal under the given study period has been carried out to obtain UC solutions with both operational, power flow and environmental constraints. This proposed algorithm has been tested on IEEE 14, 30, 57, 118 buses and practical Indian utility systems. The solutions obtained are quite encouraging and useful in the economic emission environment. The algorithm and simulation are carried through Matlab environment.

/pdf/security-constrained-unit-commitment-problem-with-1p1csomfjl.pdf

Security Constrained Unit Commitment Problem with Operational, Power Flow and Environmental Constraints

Solar Photovoltaic (PV) systems are playing a major role in the present electrical energy systems. The solar PV gives nonlinear I–V and P–V characteristics. As a result, it is difficult to extract the maximum power of the solar PV. Under Partial Shading Conditions (PSCs), the solar PV characteristics consist of multiple local Maximum Power Points (MPPs) and one global MPP. The classical Maximum Power Point Tracking (MPPT) techniques cannot track the global MPP under PSCs. Accordingly, this work aims to study the performance of five soft computing MPPT techniques. The studied five soft computing MPPT techniques are Modified Variable Step Size-Radial Basis Functional Network (MVSS-RBFN), Modified Hill-Climb with Fuzzy Logic Controller (MHC-FLC), Artificial Neuro-Fuzzy Inference System (ANFIS), Perturb and Observe with Practical Swarm Optimization (P&O-PSO), and Adaptive Cuckoo Search (ACS). The comparative performance analysis of five soft computing techniques has been carried out against the Variable Step Size-Incremental Resistance (VSS-INR), and Variable Step Size-Feedback Controller (VSS-FC)-based MPPT techniques. The performance analysis of seven MPPT techniques has been done by considering the parameters are steady-state settling time, MPP tracking speed, algorithm complexity, PV array dependency, handling of partial shading, and efficiency.

C. Rani

Papers

An improved optimization technique for estimation of solar photovoltaic parameters

Different Conventional and Soft Computing MPPT Techniques for Solar PV Systems with High Step-Up Boost Converters: A Comprehensive Analysis

Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

Security Constrained Unit Commitment Problem with Operational, Power Flow and Environmental Constraints

Performance Analysis of MPPT Techniques for Dynamic Irradiation Condition of Solar PV