Modelling and investigation of clean power wind energy systems by using UPQC

This article presents a new clean power technique for wind energy system based on Unified Power Quality Conditioner (UPQC) with usage of various Artificial Intelligent techniques (AI). Application of AI techniques such as Artificial Neural Networks, Fuzzy Logic is growing fast in the area of power electronics equipment‐based wind energy system, for instance, UPQC. A novel control scheme is developed by using different AI techniques to mitigate the balancing issues and to control the power–quality problems in UPQC and hence to provide clean power to the grid. The proposed system is composed of shunt active power filter, and the inherent proportional integral (PI) control parameters are tuned with application of PI techniques. A detailed Matlab simulation study is carried out with an application of PI techniques under sag, swell, unbalanced, and harmonic conditions. An exhaustive simulation study is carried out to investigate the application of PI technique‐based controllers and compare its performance with the conventional PI controller with its results explored. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimization of fuzzy-based MPPT controller via metaheuristic techniques for stand-alone PV systems

Due to the alteration of power-voltage characteristics of solar module output under multiple environmental conditions such as solar irradiation and ambient temperature, these systems hardly function at maximum power point (MPP). However, maximum power point tracking (MPPT) plays a significant role in their efficiency. On the other hand, solar module characteristics are extremely nonlinear and their slope on either side of MPP is asymmetric. Thus using a nonlinear control method which has the potential of adapting the operating point of the system to MPP seems useful. This has motivated authors to present MPPT method which maximizes PV's output power by tracking MPP continuously. In the present study, a fuzzy logic controller (FLC) is presented for MPPT in photovoltaic systems. Four optimization algorithms are presented in this paper for optimizing fuzzy membership functions (MFs) and generating proper duty cycle for MPPT. The presented algorithms include: Teaching Learning Based Optimization (TLBO), Firefly Algorithm (FFA), Biogeography based optimization (BBO), and Particle Swarm Optimization (PSO), which are all described and simulated. Finally, to validate performance of the proposed optimized FLC, it is compared with other algorithms such as symmetrical fuzzy logic controller (SFLC) and conventional Perturbation and Observation (P&O). According to the simulation results, P&O algorithm shows significant oscillations, energy loss, and in some cases, it cannot obtain MPP. Simulation results also indicate that TLBO and FFA based asymmetric fuzzy MFs not only increase MPPT convergence speed but also enhance tracking accuracy in comparison with symmetric fuzzy MFs and asymmetric fuzzy MFs based on BBO and PSO.     

Comparison of MPC based advanced hybrid controllers for STATCOM in medium scale PEM fuel cell systems

In this article is about off grid medium scale Proton Exchange Membrane (PEM) Fuel Cell (FC) Renewable Energy System (FCRES). It is aimed to investigate the elimination of reactive power on the loads fed from the FCRES by using PI-Model Predictive Control (PI-MPC), Fractional PI (PI^λ)-Model Predictive Control (PI^λ−MPC) and Two-Degree of Freedom PI (2DOFPI)-Model Predictive Controller (2DOFPI-MPC). The effectiveness of these controllers were examined and compared both when controlling reactive power and using different control techniques. The reactive power was attempted to be eliminated using Static Synchronous Compensator (STATCOM). An advanced controller structure has been introduced by controlling capacitor voltages by switching 24 IGBTs in the 5-level inverter within its structure. In this way, the efficiency of the system was increased. The system was simulated in MATLAB/Simulink environment. System consisted of FC, STATCOM, loads, filter, coupling inductance, measurement units and advanced hybrid controllers. In the study, system behavior was also examined in the case of compensation in the system consisting of FCs working off grid.     

Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search

In this paper, an optimization method for the reactive power dispatch in wind farms (WF) is presented. Particle swarm optimization (PSO), combined with a feasible solution search (FSSPSO) is applied in order to optimize the reactive power dispatch, taking into consideration the reactive power requirement at point of common coupling (PCC), while active power losses are minimized in a WF. The reactive power requirement at PCC is included as a restriction problem and is dealt with feasible solution search. Finally an individual set point, particular for each wind turbine (WT), is found. The algorithm is tested in a WF with 12 WTs, taking into consideration different control options and different active power output levels.     

Solution to economic dispatch problem with valve-point loading effect by using catfish PSO algorithm

This paper proposes application of a catfish particle swarm optimization (PSO) algorithm to economic dispatch (ED) problems. The ED problems considered in this paper include valve-point loading effect, power balance constraints, and generator limits. The conventional PSO and catfish PSO algorithms are applied to three different test systems and the solutions obtained are compared with each other and with those reported in literature. The comparison of solutions shows that catfish PSO outperforms the conventional PSO and other methods in terms of solution quality though there is a slight increase in computational time.     

Optimization of a solid oxide fuel cell and micro gas turbine hybrid system

For a solid oxide fuel cell (SOFC) and micro gas turbine (MGT) hybrid system, optimal control of load changes requires optimal dynamic scheduling of set points for the system's controllers. Thus, this paper proposes an improved iterative particle swarm optimization (PSO) algorithm to optimize the operating parameters under various loads. This method combines the iteration method and the PSO algorithm together, which can execute the discrete PSO iteratively until the control profile would converge to an optimal one. In MATLAB environment, the simulation results show that the SOFC/MGT hybrid model with the optimized parameters can effectively track the output power with high efficiency. Hence, the improved iterative PSO algorithm can be helpful for system analysis, optimization design, and real‐time control of the SOFC/MGT hybrid system. Copyright © 2011 John Wiley & Sons, Ltd.     

光伏逆变器无功调节能力分析与控制策略研究

通过建立光伏逆变器接入配电网稳态分析模型,以接入点运行电压、最大运行电流和SPWM调制控制条件为约束,分析了不同工况下逆变器的无功调节能力。构建接入配电网运行时面向电网电压调整的无功功率控制策略,该策略以控制接入点电压为目标,逆变器通过补偿系统需求的无功对电压进行支撑。构建分布式光伏接入配电网应对配电网负荷变化和光伏注入功率变化引起的电压无功调整仿真实验,验证了该策略的有效性。     

Power quality events classification and recognition using a novel support vector algorithm

This paper presents a method of power quality classification using support vector machines (SVMs). In SVM training, the kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these kernel types, kernel parameters and features should be used for the SVM training. In this paper to get optimal features for the classifier two stage of feature selection has been used. In first stage mutual information feature selection (MIFS) and in the second stage correlation feature selection (CFS) techniques are used for feature extraction from signals to build distinguished patterns for classifiers. MIFS can reduce the dimensionality of inputs, speed up the training of the network and get better performance and with CFS can get optimal features. In order to create training and testing vectors, different disturbance classes were simulated using parametric equations i.e., pure sinusoid, sag, swell, harmonic, outage, sag and harmonics and swell and harmonics. Finally, the investigation results of this novel approach are shown. The test results show that the classifier has an excellent performance on training speed, reliability and accuracy.     

Output voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controller

Microbial fuel cell (MFC) has become a very important biotechnological tool to produce clean energy in recent years. It is very important to adjust the output voltage and power density in order to obtain the desired energy quickly and smoothly at the output of the MFC. In this study, an optimization-based neuro-fuzzy inference controller is proposed for improving voltage tracking performance of the MFC. A double-chambers MFC model including biochemical reactions, Butler-Volmer expressions and mass/charge balances was studied and Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) algorithms are used to adjust the parameters of the neuro-fuzzy controller. The results show that PSO and IGWO based controllers have efficient performances to follow the reference voltage pattern quickly and robustly against external load changes, distributions and parameter uncertainties. Moreover, it was observed that IGWO was a more stable and robust controller than PSO according to rise time, overshoot and peak time.     

Multi-objective optimization of solid oxide fuel cell/gas turbine combined heat and power system: A comparison between particle swarm and genetic algorithms

Many studies have attempted to optimize integrated Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT), although different and somehow conflicting results are reported employing various algorithms. In this study, Multi-Objective Optimization (MOO) is employed to approach the optimal design of SOFC-GT considering all prevailing factors. The emphasis is placed on the evaluation of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) performance as two effective approaches for solving the multi-objective and non-linear optimization problems. Multi- objective optimization is carried out on two vital objectives; the electrical efficiency and the overall output power of the system. The considerable achievements are the set of optimal points that aim to identify the system optimal performance which provides a practical basis for the decision-makers to choose the appropriate target functions. For the studied conditions, the two algorithms nearly exhibit similar performance, while the PSO is faster and more efficient in terms of computational effort. The PSO appears to achieve its ultimate parameter values in fewer generations compared to the GA algorithm under the examined circumstances. It is found that the maximum power of 410 kW is accomplished employing the GA optimization method with an efficiency of 64%, while PSO method yields the maximum power of 419.19 kW at the efficiency of 58.9%. The results stress that PSO offers more satisfactory convergence and fidelity of the solution for the SOFC-GT MOO problems.     

Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions

This paper presents experimental evaluations for variation in the efficiency of energy extracted from a photovoltaic (PV) module (under non-linear loading) incorporated with an incremental conductance(IC) maximum power point tracking (MPPT) algorithm. The focus is on the evaluation of the PV panel under non-linear loading conditions using the experimental installation of a 100W_p photovoltaic array connected to a DC–DC converter and a KVA inverter feeding a non-linear load. Under the conditions of non-linear loading, both the simulation and experiment show that the MPPT technique fails to attain maximum power point due to the presence of ripples in the current leading eventually to a reduction in efficiency. In this paper, panel current is taken as a function of load impedance in the MPPT algorithm to eradicate power variation, as load impedance varies with supply voltage under non-linear conditions. The system is simulated for different non-linear loads using MATLAB-Simulink. A TMDSSOLAREXPKIT was used for MPPT control. In case 2, the inverter is connected to a single phase grid. When a voltage swell occurs in the grid, PV power drops. This power loss is reduced using the proposed MPPT method. The results of simulations and experimental measurements and cost efficiency calculations are presented.     

Modeling and simulation of three phase multilevel inverter for grid connected photovoltaic systems

This paper presents a control for a three phase five-level neutral clamped inverter (NPC) for grid connected PV system. The maximum power point tracking (MPPT) is capable of extracting maximum power from the PV array connected to each DC link voltage level. The MPPT algorithm is solved by fuzzy logic controller. The fuzzy MPPT is integrated with the inverter so that a DC–DC converter is not needed and the output shows accurate and fast response. A digital PI current control algorithm is used to remain the current injected into the grid sinusoidal and to achieve high dynamic performance with low total harmonic distortion (THD). The validity of the system is verified through MATLAB/Simulink and the results are compared with three phase three-level grid connected NPC inverter in terms of THD.     

Optimization of a fuzzy logic controller for PV grid inverter control using S-function based PSO

This paper presents implementation of particle swarm optimization (PSO) algorithm as a C-Mex S-function. The algorithm is used to optimize a 9-rule fuzzy logic controller (FLC) for maximum power point tracking (MPPT) in a grid-connected photovoltaic (PV) inverter. The FLC generates DC bus voltage reference for MPPT. A digital PI current control scheme in rotating dq-reference frame is used to regulate the DC bus voltage and reactive power. The proposed technique simplifies optimal controller design and ensures fast simulation speeds due to seamless integration with the simulation platform. Validity of the proposed method was verified using co-simulation in PSIM and MATLAB/Simulink. Simulation results show that the optimized FLC gives a better performance compared to fixed-step MPPT.     

Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source

As a result of today’s rapid socioeconomic growth and environmental concerns, higher service reliability, better power quality, increased energy efficiency and energy independency, exploring alternative energy resources, especially the renewable ones, has become the fields of interest for many modern societies. In this regard, MG (Micro-Grid) which is comprised of various alternative energy sources can serve as a basic tool to reach the desired objectives while distributing electricity more effectively, economically and securely. In this paper an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it’s needed. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. To improve the optimization process, a hybrid PSO algorithm based on a CLS (Chaotic Local Search) mechanism and a FSA (Fuzzy Self Adaptive) structure is utilized. The proposed algorithm is tested on a typical MG and its superior performance is compared to those from other evolutionary algorithms such as GA (Genetic Algorithm) and PSO (Particle Swarm Optimization).     

SMC-DPC based active and reactive power control of grid-tied three phase inverter for PV systems

In this paper, sliding mode control (SMC) – direct power controller (DPC) based active and reactive power controller for three-phase grid-tied photovoltaic (PV) system is proposed. The proposed system consists of two main controllers: the DC/DC boost converter to track the possible maximum power from the PV panels and the grid-tied three-phase inverter. The Perturb and Observe (P&O) algorithm is used to transfer the maximum power from the PV panels. Control of the active and reactive powers is performed using the SMC-DPC strategy without any rotating coordinate transformations or phase angle tracking of the grid voltage. In addition, extra current control cycles are not used to simplify the system design and to increase transient performance. The fixed switching frequency is obtained by using space vector modulation (SVM). The proposed system provides very good results both in transient and steady states with the simple algorithms of P&O and SMC-DPC methods. Moreover, the results are evaluated by comparing the SMC-DPC method developed for MPPT and the traditional PI control method. The proposed controller method is achieved with TMS320F28335 DSP processor and the system is experimentally tested for 12 kW PV generation systems.     

Performance of PI controller for control of active and reactive power in DFIG operating in a grid-connected variable speed wind energy conversion system

Due to several factors, wind energy becomes an essential type of electricity generation. The share of this type of energy in the network is becoming increasingly important. The objective of this work is to present the modeling and control strategy of a grid connected wind power generation scheme using a doubly fed induction generator (DFIG) driven by the rotor. This paper is to present the complete modeling and simulation of a wind turbine driven DFIG in the second mode of operating (the wind turbine pitch control is deactivated). It will introduce the vector control, which makes it possible to control independently the active and reactive power exchanged between the stator of the generator and the grid, based on vector control concept (with stator flux or voltage orientation) with classical PI controllers. Various simulation tests are conducted to observe the system behavior and evaluate the performance of the control for some optimization criteria (energy efficiency and the robustness of the control). It is also interesting to play on the quality of electric power by controlling the reactive power exchanged with the grid, which will facilitate making a local correction of power factor.     

Improving voltage harmonic compensation of a single phase inverted-based PEM fuel cell for stand-alone applications

Fuel cell (FC) is an efficient energy conversion technology that is growing rapidly and will have a significant role to play in a number of energy end-use sectors, from small-scale applications to large-scale power plants. In this paper, two new methods for voltage harmonic compensation of a stand-alone single phase inverted-based FC are presented and evaluated. The stand-alone power system under study consists of: 1) Proton-Exchange-Membrane (PEM) FC with unidirectional DC/DC converter, which converts the DC voltage delivered by the FC to the DC bus voltage; 2) single-phase pulse width modulated (PWM) inverter; 3) transformer; 4) passive filter; and 5) linear and non-linear loads. The dynamic model of this system and the non-sinusoidal output-based controls applied to the PWM inverter for voltage regulation and harmonic compensation are detailed in this paper, and evaluated by simulation under different linear and non-linear loads. Simulation results show the effectiveness of the two purposed methods for voltage harmonic compensation to acceptable levels defined in grid codes.     

Modelling of a 5-kW wind energy conversion system with induction generator and comparison with experimental results

A 5-kW wind energy conversion system (WECS) having induction generator is designed and implemented. The induction machine is connected to the power system through PWM inverter and PWM rectifier. Two digital PI controllers are used, one of them is for regulating dc link voltage and the other is for speed control of induction machine. The whole system is governed by a single fixed point digital signal processing unit (DSP). A detailed simulation program is prepared by using Matlab facilities in order to predict the performance of the controllers before implementation.     

Metamodel based design optimization approach in promoting the performance of proton exchange membrane fuel cells

This research presents a systematization and effectiveness approach in promoting the performance of the power density of a Proton Exchange Membrane Fuel Cell (PEMFC) by Metamodel-Based Design Optimization (MBDO). The proposed methodology of MBDO combines the design of experiment (DoE), metamodeling choice and global optimization. The fractional factorial experimental design method can screen important factors and the interaction effects in DoE, and obtain optimal design of the robust performance parameters by Taguchi method. Metamodeling then adopts the ability to establish a non-linear model of a complex PEMFC system configuration of an artificial neural network (ANN) based on the back-propagation network (BPN). Finally, on the many parameters (factors) of optimization, a genetic algorithm (GA) with a high capability for global optimization is used to search the best combination of the parameters to meet the requirement of the quality characteristics. Experimental results confirmed by the test equipment demonstrate that the MBDO approach is effective and systematic in promoting PEMFC performance of power density.     

Improvement of estimation of surge arrester parameters by using Modified Particle Swarm Optimization

Metal Oxide Surge Arrester (MOSA) accurate modeling and its parameter identification are very important aspects for arrester allocation, system reliability determination and insulation coordination studies. In this paper, Modified Particle Swarm Optimization (MPSO) algorithm is used to estimate the parameters of surge arrester models. The convergence to the local optima is often a drawback of the Particle Swarm Optimization (PSO). To overcome this demerit and improve the global search capability, Ant Colony Optimization (ACO) algorithm is combined with PSO algorithm in the proposed algorithm. The suggested algorithm selects optimum parameters for the arrester model by minimizing the error among simulated peak residual voltage values given by the manufacturer. The proposed algorithm is applied to a 120 kV MOSA. The validity and the accuracy of estimated parameters are assessed by comparing the predicted residual voltage with experimental results.