Power quality enhancement of grid-connected fuel cell using evolutionary computing techniques

Fuel Cell (FC), as a type of new renewable energy sources grid-connected at Point of Common Coupling (PCC), is introduced in this study. This article presents the power quality improvement of the FC integrated to the power network through a chopper and an inverter using the conventional PI controller. Two PI controllers, tuned by three recent different evolutionary computing techniques namely Harmony Search (HS), Modified Flower Pollination Algorithm (MFPA) and Electromagnetic Field Optimization (EFO) methods are considered. The two PI controllers are used for driving the inverter connected the on-grid FC in order to govern the PCC voltage between the FC and the power network. These two controllers are exploited to drive the power and the current regulators at different voltage sag and swell conditions. The three optimization methods are compared to the Particle Swarm Optimization (PSO) with regards to voltage profile, power quality and execution time.Simulation results, using Matlab/Simulink^?, show the significance of the three optimization techniques in regulating the voltage at PCC with reduced harmonics during the system voltage sag and swell conditions when compared to the PSO. Through the numerical analysis, the superiority of MFPA method among the different optimization metaheuristic techniques is highlighted particularly for enhanced dynamic voltage response purposes.     

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.     

储能系统双向Buck-Boost变换器控制策略研究

为应对可再生能源出力波动引起储能系统功率流动方向的频繁变化,提出一种基于自抗扰控制和模型预测控制（ADRC+MPC）的储能系统双向Buck-Boost变换器控制策略。其中模型预测控制方法应用于电流内环,无需进行参数整定的同时,也提高了系统的响应速度;采用自抗扰控制策略的电压外环,通过在高频段降阶简化控制对象,达到降低自抗扰控制器复杂度的目的。仿真和样机实验显示当电感电流与输出电压参考值突变时,系统可分别在0.2与30 ms内迅速调整到给定值;当负载与电源电压突变时,系统能在20 ms内恢复稳定。实验结果证明该文提出的控制方法优于PI+MPC策略,具有响应速度快、超调量和波动幅度小的特点。     

微生物燃料电池运行影响因素的研究

研究自行设计的微生物燃料电池在常温常压下,以厌氧污泥为接种源,以葡萄糖为底物原料,以不同溶液作为电子受体的条件下测试其稳定运行的影响因素与工艺条件。实验结果表明:该微生物燃料电池可稳定运行约30d,并在注入新的底物后,电压又快速回升至稳定电压。以铁氰化钾溶液作为电子受体,输出电压可达0.75V,输出功率为2100mW/m~2;以高锰酸钾溶液作为电子受体,输出电压为1.023V,输出功率为2638mW/m~2。     

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.     

阴极电子受体对微生物燃料电池性能的影响

以双室型微生物燃料电池为试验装置,比较铁氰化钾、重铬酸钾、高锰酸钾作为阴极电子受体时微生物燃料电池的电压和功率输出。结果表明,高锰酸钾与重铬酸钾混合电子受体对微生物燃料电池性能的提高没有显著效果,不如两者的单独表现;高锰酸钾对应的最高输出电压可达1 160 mV,但很不稳定,会很快下降到600 mV左右,在实际应用中有一定障碍;在酸性条件(pH=3.0)下,重铬酸钾的开路电压为1 081.2 mV,最大输出功率密度为35.1 W/m3,电池内阻为170.27Ω,而且表现稳定,是理想的阴极电子受体。     

A practical algorithm for distribution state estimation including renewable energy sources

Renewable energy is energy that is in continuous supply over time. These kinds of energy sources are divided into five principal renewable sources of energy: the sun, the wind, flowing water, biomass and heat from within the earth. According to some studies carried out by the research institutes, about 25% of the new generation will be generated by Renewable Energy Sources (RESs) in the near future. Therefore, it is necessary to study the impact of RESs on the power systems, especially on the distribution networks. This paper presents a practical Distribution State Estimation (DSE) including RESs and some practical consideration. The proposed algorithm is based on the combination of Nelder–Mead simplex search and Particle Swarm Optimization (PSO) algorithms, called PSO-NM. The proposed algorithm can estimate load and RES output values by Weighted Least-Square (WLS) approach. Some practical considerations are var compensators, Voltage Regulators (VRs), Under Load Tap Changer (ULTC) transformer modeling, which usually have nonlinear and discrete characteristics, and unbalanced three-phase power flow equations. The comparison results with other evolutionary optimization algorithms such as original PSO, Honey Bee Mating Optimization (HBMO), Neural Networks (NNs), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) for a test system demonstrate that PSO-NM is extremely effective and efficient for the DSE problems.     

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

In recent years, many different techniques are applied in order to draw maximum power from photovoltaic (PV) modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point (MPP) and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.     

Modeling global solar radiation using Particle Swarm Optimization (PSO)

The quantity of solar radiation received by the earth’s surface is very important to numerous renewable energy applications. However, direct measurement of solar data is not widely available, especially in developing countries. This paper uses Particle Swarm Optimization (PSO) to train an artificial neural network (PSO–ANN) using data from available measurement stations to estimate monthly mean daily Global Solar Radiation (GSR) at locations where no measurement stations are available. The inputs to the networks are: month of the year, latitude, longitude, altitude, and sunshine duration, and the output is the monthly mean daily GSR at the specified location. Using training data from 31 stations and testing data from 10 locations, the PSO–ANN outperforms a neural network trained using the standard backpropagation (BP) algorithm (BP–ANN) with an average Mean Absolute Percentage Error (MAPE) of 8.85% for the PSO–ANN and 12.61% for the BP–ANN. The performance is improved significantly, when we use the leave-one-out method, where data from 40 locations is used for training and data from the 41st station is used for assessing the performance. In this case the average of MAPE on data from the 10 testing stations is about 7%. We used the same method to assess the performance of the PSO–ANN on testing data from each of the 41 stations with an overall average MAPE of about 10.3%. Comparison with BP–ANN and an empirical model showed the superiority of the PSO–ANN.     

ANN-Based Soft Starting of Voltage-Controlled-Fed IM Drive System

Soft starters are used as induction motor controllers in compressors, blowers, fans, pumps, mixers, crushers and grinders, and many other applications. Soft starters use ac voltage controllers to start the induction motor and to adjust its speed. This paper presents a novel artifical neural network (ANN)-based ac voltage controller which generates the appropriate thyristors' firing angle for any given operating torque and speed of the motor and the load. An ANN model was designed for that purpose. The results obtained are very satisfactory and promising. The advantage of such a controller are its simplicity, stability, and high accuracy compared to conventional mathematical calculation of the firing angle which is a very complex and time consuming task especially in online control applications.     

Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic

Saving consumable energy and maintaining the thermal comfort level are two main topics in the heating, ventilating and air conditioning (HVAC) control field. The reliability of the controller is important as well. This paper proposes a least enthalpy estimator (LEE) that combines the definition of thermal comfort level and the theory of enthalpy into a load predicting way to provide timely suitable settings for a fan coil unit (FCU) fuzzy controller used in HVAC. According to the settings, including temperature and relative humidity, the fuzzy controller can make decisions and adjust the output of the FCU system. From actual experiments, the LEE-based FCU fuzzy controller can achieve the requirements of the FCU control system such as thermal comfort, energy efficiency and reliability.     

电网故障条件下风储联合系统无功自适应控制

针对电网三相对称故障条件下风电场电压不稳定的问题,文章提出了一种基于神经元的风储联合系统无功功率自适应控制策略,该策略以风储联合系统公共耦合点(Point of Common Coupling,PCC)的电压和电流为控制器的输入,采用Hebb学习算法作为自适应律,以获得准确的无功补偿。通过动态调整控制器的参数,使储能系统协调风电达到自适应输出无功功率的效果,提高系统在电网故障下的电压稳定性和风电故障穿越能力。最后,利用Matlab/Simulink仿真验证了该控制策略的有效性和正确性,与常规PI控制策略相比,文章所提出的控制策略可使风储系统迅速提供无功功率,PCC点的电压得到明显上升。     

Mediator-less microbial fuel cell employing Shewanella Oneidensis

This study investigated efficient energy harvesting of a mediator-less microbial fuel cell (MFC) using Shewanella Oneidensis bacteria. Synthetic wastewater made from M9 minimal growth medium and carbon source was used to generate electricity. This experiment exhibits how concentration of bacteria in the anodic chamber, different types of carbon sources, and the substance concentration affect electricity generation. The results showed that the mediator-less MFC using Shewanella Oneidensis can produce voltage close to the maximum attainable MFC voltage. The efficient MFC can be used to treat wastewater while reducing energy needs and producing an alternative form of energy.     

基于粒子群优化的锅炉汽包水位模糊PID控制

结合粒子群优化算法、模糊控制和PID参数自整定方法,设计一种新型自适应模糊PID控制器。采用粒子群优化算法离线优化PID控制器参数,再利用模糊控制器在线对PID参数进行整定。Simulink-~件模型仿真对比表明：控制方法具有调节精度高、     

Controller design for an induction generator driven by a variable-speed wind turbine

This paper presents the modeling, controller design and a steady-state analysis algorithm for a wind-driven induction generator system. An output feedback linear quadratic controller is designed for the static synchronous compensator (STATCOM) and the variable blade pitch in a wind energy conversion system (WECS) in order to reach the voltage and mechanical power control under both grid-connection and islanding conditions. A two-reference-frame model is proposed to decouple the STATCOM real and reactive power control loops for the output feedback controller. To ensure zero steady-state voltage errors for the output feedback controller, the integrals of load bus voltage deviation and dc-capacitor voltage deviation are employed as the additional state variables. Pole-placement technique is used to determine a proper weighting matrix for the linear quadratic controller such that satisfactory damping characteristics can be achieved for the closed-loop system. Effects of various system disturbances on the dynamic performance have been simulated, and the results reveal that the proposed controller is effective in regulating the load voltage and stabilizing the generator rotating speed for the WECS either connected with or disconnected from the power grid. In addition, proper steady-state operating points for an isolated induction generator can be determined by the proposed steady-state analysis algorithm. Constant output frequency control using the derived steady-state characteristics of the isolated induction generator is then demonstrated in this paper.     

Recent insights into microalgae-assisted microbial fuel cells for generating sustainable bioelectricity

Power generation from the renewable biomass sources using microbial fuel cell (MFC) has attracted significant attention in recent years, while chemical energy stored in microalgae biomass has efficiently been used for the sustainable production of biofuels and other valuable bioproducts since the decades. The usage of these photosynthetic organisms in MFC can enhance the efficiency of MFC and provide a cost-effective and renewable approach for the bio-generation of electricity. Microalgae are commonly incorporated either with anode or cathode compartment of MFC to generate electron or oxygen, respectively. Despite microalgae-assisted MFC (MA-MFC) would be more sustainable than using MFC alone, further developments in such systems are still required for improving its efficiency and achieving a real-world application on a large scale. In this context, understanding in bio-electrochemical mechanism of MA-MFC, including electrons shuttle and oxygen generation, is very important. Moreover, many factors can limit the efficiency and performances of MA-MFCs that are needed to optimize in further research efforts. This review presents a comprehensive insight into MA-MFC, including the recent developments and potential challenges in this promising bio-electricity generating system. Specifically, it focuses a critical discussion on the configurations of MA-MFC, key operating parameters affecting performances of MA-MFC, challenges and prospective research works for improving the overall energy output of MA-MFC.     

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.     

Neuro-fuzzy control for autonomous wind–diesel systems using biomass

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of converting tons of wood chips per day into a gaseous fuel that is fed into a diesel engine. The controller inputs are the engine speed error and its derivative for the governor part of the controller, and the voltage error and its derivative for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented. It is shown that by tuning the fuzzy logic controllers, optimal time domain performance of the autonomous wind–diesel system can be achieved in a wide range of operating conditions compared to fixed-parameter fuzzy logic controllers and PID controllers.     

Modelling and simulation of two-chamber microbial fuel cell

Microbial fuel cells (MFCs) offer great promise for simultaneous treatment of wastewater and energy recovery. While past research has been based extensively on experimental studies, modelling and simulation remains scarce. A typical MFC shares many similarities with chemical fuel cells such as direct ascorbic acid fuel cells and direct methanol fuel cells. Therefore, an attempt is made to develop a MFC model similar to that for chemical fuel cells. By integrating biochemical reactions, Butler–Volmer expressions and mass/charge balances, a MFC model based on a two-chamber configuration is developed that simulates both steady and dynamic behaviour of a MFC, including voltage, power density, fuel concentration, and the influence of various parameters on power generation. Results show that the cathodic reaction is the most significant limiting factor of MFC performance. Periodic changes in the flow rate of fuel result in a boost of power output; this offers further insight into MFC behaviour. In addition to a MFC fuelled by acetate, the present method is also successfully extended to using artificial wastewater (solution of glucose and glutamic acid) as fuel. Since the proposed modelling method is easy to implement, it can serve as a framework for modelling other types of MFC and thereby will facilitate the development and scale-up of more efficient MFCs.     

老龄垃圾渗滤液体积分数对微生物燃料电池性能的影响

研究考察不同体积分数的老龄垃圾渗滤液对微生物燃料电池(MFC)性能的影响.结果表明:在体积分数为40％时,MFC的产电效能最佳,输出电压最高可达370 mV,功率密度为939 mW/m3,且化学需氧量(COD)去除率可达43.3％;无机氮的去除与产电周期有较大关系,当体积分数为100％时,氨氮去除率可达84.1％,表明...