A coordinated control strategy using supercapacitor energy storage and series dynamic resistor for enhancement of fault ride-through of doubly fed induction generator

Power fluctuation and fault-related complication are the two major issues for doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). The occurrence of fault leads to the rotor over current, stator over current, and DC-link overvoltage as well. These uncertainties may damage the rotor circuit, converter circuit and force the disconnection of wind system from the grid. To get rid of these issues, a supercapacitor energy storage element along with a passive series dynamic resistor (SDR) is suggested in this paper. Supercapacitor energy storage system (SCESS) is located across the DC-link, which able to handle the power fluctuation and the SDR is placed in rotor circuit, which will reduce the overcurrent possibility. Simulation is carried for a DFIG-based WECS for three phase to ground fault and two phase to ground fault. During symmetrical fault as well as asymmetrical fault, various operational disorders appeared such as rotor overcurrent, stator overcurrent and DC-Link overvoltage are found to be within their permissible limits. The results reveal the effectiveness of the proposed strategy over the conventional vector control scheme and SCESS as well.     

Improving the dynamic response of frequency and power in a wind integrated power system by optimal design of compensated superconducting magnetic energy storage

The frequency deviation and power fluctuation need to be controlled in a wind-integrated power system (WIPS) for keeping the balance between system power generation and demand, which support the quality and stability of overall power system. The present paper addresses this problem while concerning the integration of intermittent wind power and load disturbance into the WIPS. With this intent, it proposes the compensated superconducting magnetic energy storage (CSMES) system with proportional integral derivative (PID) controller for improving the frequency and power deviation profile. A novel swarm intelligence-based artificial bee colony (ABC) algorithm is used for optimal design of PID-CSMES system. Robustness of the proposed ABC-based PID-CSMES control strategy is tested in WIPS under various disturbance patterns of load and wind power. To demonstrate the improved dynamic response, their simulation results are compared with particle swarm optimization-based PID-CSMES, PID with SMES, and only PID controller technique. The performance indices and transient response characteristics of frequency and power deviation are used to evaluate and compare the accuracy and efficiency of each controller. Stability of various system configurations is analyzed using eigenvalue location. Comparing the results of different controller in WIPS indicates a substantial improvement in the dynamic response of system frequency and power deviations by utilizing the proposed control strategy.     

Availability estimation of a multi-state wind farm in fuzzy environment

Wind is one of the fastest growing renewable energy resources in the electric power system. Availability of wind energy is volatile in nature due to the stochastic behavior of wind speed and non-linear variation of the wind power curve of wind turbine generator. Because of this impression and uncertainty, the availability estimation of wind power has become a challenging issue. In this paper, Markov Fuzzy Reward technique has been proposed for finding out the reliability of wind farm by assessing the availability of wind power. According to this technique, availability of the wind power has been estimated considering wind farm and demand both as a multi-state system. In addition to the availability, different reliability indices such as the number of absolute failures, mean time to deficiency, and probability of failures of a wind farm have been assessed in a time horizon, which can provide useful information for the power system planner at wind farm installing stage. A comparison of this study reveals the efficacy of the proposed Markov Fuzzy Reward approach over the conventional Markov Reward approach.     

Large-scale baseload wind power in China

This paper presents a novel strategy for developing wind power in large-scale (multi-GW) wind farms in China. It involves combining oversized wind farms, large-scale electrical storage and long-distance transmission lines to deliver 'baseload wind power' to distant electricity demand centers. Baseload wind power is typically more valuable to the electric utility than intermittent wind power, so that storage can be economically attractive even in instances where the cost per kWh is somewhat higher than without storage.
The prospective costs for this approach to developing wind power are illustrated by modifying an oversized wind farm at Huitengxile, Inner Mongolia. The site has an average power density of 580 W/m² at 50 m hub heights and is located 500 km north of Beijing. Using locally mass-produced wind turbines there are good prospects that wind power would be cost-competitive with coal power, on a lifecycle cost basis, while providing substantial net environmental benefits.
Finally, the institutional challenges related to the prospect of large-scale wind energy development are addressed. Especially important are policies aimed at developing the capacity for mass production of as much of this technology in China as is feasible. Promising instruments for speeding up the introduction of this technology include: (i) international joint ventures between foreign vendors and developers and Chinese manufacturers; and (ii) wind resource development concessions.     

A solar hybrid thermoelectric generator and distillation system

A solar still of a single basin-slope coupled with a finned condensing chamber and installed thermoelectric modules at the bottom of the water basin has been presented in this paper. A mathematical model under steady state conditions has been introduced and improved to investigate the system performance. An increase of solar radiation and ambient temperature or a decrease in wind velocity affect positively the distillation rate, the still efficiency, and the system efficiency. Integrating a condenser and finned condenser increases the distillation rate of the proposed system. The results of the simulation have been verified by comparing them with published theoretical and experimental results, and the comparison shows very good agreement.     

Economic dispatch of power system containing wind power and photovoltaic considering carbon trading and spare capacity variation

The drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.     

On the wind resource mapping of Burkina Faso

In this work, mesoscale wind resource maps, at 5-km resolution, of the country of Burkina Faso (274,200 km²) were developed using the Anemoscope and mesoscale compressible community models. Results show that the northeast region of Burkina Faso has a good wind regime at 80 m above ground level (agl), while the wind regime in other parts of the country is generally low, even at 80 m agl. In addition, the technical power potential and the potential annual energy production that can be generated from the wind in Burkina Faso are identified using analysis tools based on geographical information systems and economic constraints. Results from the technical power potential at 80 m agl show that a total of 312 MW of wind farms, generating annually a total of 741 GWh of energy, could be installed in Burkina Faso. On the other hand, a total of 4411 MW of small wind turbines (50 kW) could be installed over the territory, corresponding to an annual energy production of 7843 GWh. The Wind Atlas of Burkina Faso provides an opportunity for local stakeholders to consider wind energy for the electricity portfolio of the country.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

Fuel cell system with sodium borohydride hydrogen generator for small unmanned aerial vehicles

A 100 W proton exchange membrane fuel cell (PEMFC) system with a sodium borohydride (NaBH₄) hydrogen generator was investigated for small unmanned aerial vehicles (UAVs). The performance of a cobalt–phosphorous/nickel foam catalyst was evaluated to determine the change in catalytic activity under real operating conditions. The response time increased owing to oxidation of the metals and accumulation of sodium; however, the catalyst remained active at high reaction temperatures. A NaBH₄ hydrogen generator with the catalyst was developed for a 100 W PEMFC system. The hydrogen generation rate was stable for 3 h, and the conversion efficiency was 97.8%. Finally, a 100 W PEMFC system with the NaBH₄ hydrogen generator was investigated for small UAVs. The maximum power and energy density of the PEMFC system were 95.96 W and 185.2 Wh/kg, respectively.     

A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

The acceptability of wind farms: the impact of public participation

The planning process for wind farm projects appears to be a complicated matter in many cases. Despite the positive attitude towards wind power in general, local wind farm projects often face strong opposition. The aim of this study is to shed more light on residents’ perceptions of participation in the planning process of wind farms. This study is based on interview data (N?=?22) and survey data (N?=?291) collected from residents living near two Finnish large-scale onshore wind farms built about 1.5 years before the data were collected. The results indicate that residents’ participation in the planning process was rather passive and the vast majority of the respondents perceived that they did not have an opportunity to participate. Quite interestingly, perceived participation in the planning process resulted in a decrease in acceptability in terms of perceived well-being. Furthermore, the results indicate that the need for participation does not expire after the planning process for a wind farm has concluded. Thus, project developers should be prepared to continue communication with residents after the planning phase.     

Performance enhancement of a stand-alone induction generator-based wind energy system using neural network controller

ABSTRACT

The limitation of self-excited induction generator (SEIG) when used in the stand-alone wind energy system (WES) is poor voltage regulation at variable speed. The indirect vector control (IVC) technique is employed for both the generator-side converter (GSC) and load-side converter (LSC) to regulate the variation of SEIG speed, DC link voltage, and electromagnetic torque independently. Further performance of the proposed IVC technique has been analyzed independently with neural network controller (NNC) and fuzzy logic controller (FLC) as its components. The FLC is replaced by an NNC to improve the performance of the proposed system. IVC of SEIG-based WES has been simulated in MATLAB/SIMULINK software, and the prototype model of the proposed WES is developed to experimentally validate the performance using dSPACE DS-1104 R&D controller board.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

A case study of an isolated wind powered electricity system meeting 100% of local demand

Using meteorological and electricity demand data for a 4-year period, electricity demand in Shetland was modeled to provide an estimate of the demand over a 30-year period from 1 January 1981. That modeled demand was then compared to estimated wind power output over the same period using the WAsP model. The wind farm output was estimated for a range of sizes of wind farm up to the consented 370 MW Viking Wind Farm in Shetland. Some wind power was available for 94% of the time and the 370 MW wind farm would meet 100% of demand for nearly 80% of the time. The statistics of single and accumulated deficits were calculated for a range of wind farms and estimates of the amount of additional generation capacity and additional power requirements were assessed. The study suggests that with storage, wind power in Shetland could meet all electricity demand in Shetland at around £130 to £150/MWh (excluding subsidy) and with a grid connection allowing the sale of excess power, those costs could be reduced.     

Wind energy potential assessment and techno-economic performance of wind turbines in coastal sites of Buenos Aires province,Argentina

An assessment of wind energy potential was carried out in five sites (four onshore and one offshore) in South-West (SW) of Buenos Aires province (Argentina). We use high-resolution wind data (2 and 5 min) for the period 2009–2012. The power law was used to estimate the wind speed at 30, 40, and 60 m height from the anemometer position. Turbulence intensity and wind direction were analyzed. Statistical analyses were conducted using two-parameter Weibull distribution. A techno-economic analysis based on a set of commercial wind turbines was performed in those sites. The results derived from this work indicate that the SW of Buenos Aires province represents a promising area for the wind energy extraction, which would encourage the construction of wind farms for electricity generation.     

Problems and Prospects of Meeting the Basic Energy Needs of Rural Communities in Developing Countries Through the Utilization of Wind and Solar Energy Systems

The problems and prospects of solar and wind energy technologies for rural energy supply in developing countries are examined, followed by an overview of key attributes of these technologies. The application of wind and solar systems for cooking, water pumping, drying, water heating, and electric power supply are reviewed. Two detailed case studies are given, the first discussing the potential of solar and wind systems for rural water pumping in Morocco, and the second, examining the "wind farms" producing electric power in California and the potential for their use in developing countries.     

Different Shades of Green: A Case Study of Support for Wind Farms in the Rural Midwest

Benton County, in north-central Indiana, USA has successfully sited more than 500 turbines. To understand Benton County’s acceptance of wind farms, a holistic case study was conducted that included a document review, a survey of local residents and interviews with key stakeholders. Survey questionnaires were sent to 750 residents asking questions about attitudes toward the wind farms, perceived benefits and impacts from the wind farms, environmental attitudes, and demographic information. Key stakeholders were also interviewed for a deeper understanding of the historical timeline and community acceptance of the wind farm development. While there is limited opposition to the turbines, on the whole the community presents a front of acceptance. Financial, rather than environmental, benefits are the main reason for the acceptance. Although significant in other case studies, transparency and participation do not play a large role in Benton County’s acceptance. Most residents are not concerned with either visual impacts or noise from the wind turbines. More concrete benefits to the community, such as reduced energy bills for county residents, could help to extend acceptance even further within the community. Although there are concerns about the acceptance of wind farms and the impacts of those farms on local residents in both peer-reviewed literature and popular media, we found little evidence of those concerns in Benton County. Instead, we found Benton County to be a community largely accepting of wind farms.     

Design and analysis of artificial bee-colony-based MPPT algorithm for DFIG-based wind energy conversion systems

In this article, the proposed maximum power point tracking (MPPT) method is designed by taking rotor speed as an optimization problem, which is solved by artificial bee colony (ABC) algorithm to generate the maximum power output. The main advantage of this algorithm is that its optimal solution is independent of the initial positions and requirement of lesser number of control parameters, which leads to simple and robust MPPT algorithm than other algorithm. Furthermore, the hill climb search and particle swarm optimization-based MPPT algorithm are also discussed and the results obtained by these are compared to verify the effectiveness of proposed algorithm. Simulations for MPPT control along with doubly fed induction-generator-based wind energy conversion system is carried out in MATLAB/Simulink environment. Three statistical methods are used to evaluate the accuracy of each MPPT algorithm. All results are analyzed and compared under randomly selected wind as well as real wind speed configuration. Comparison of both numerical and simulation results under two different varying wind speed conditions strongly suggest that the proposed ABC-based MPPT algorithm is superior than other two MPPT algorithms.