Evaluation of Synthetic Wind Speed Time Series for Reliability Analysis of Offshore Wind Farms

A method for synthesising wind speed time series (WSTS) from limited data is required that can be used for reliability examination of wind farms and maintenance strategies for a range of wind speed scenarios. Key characteristics of the wind resource need to be captured, including energy availability and maintenance weather windows. 4 WSTS simulators were used to produce synthetic WSTS based on benchmark data from a meteorological mast data at the offshore Egmond aan Zee wind farm in the Netherlands. These synthetic WSTS were compared with test criteria to determine their suitability for reliability analysis. This included comparing the synthetic WSTS to the benchmark data in terms of the energy availability in the wind and from a typical turbine, residence time at wind speeds, number of transitions between 1m/s wind speed bins, replication of seasonal characteristics including weather windows, and underlying statistical properties. Based on the chosen criteria, the most appropriate WSTS simulator was the modified Markov process. However, no modelling technique performed best against all criteria and none capture the autocorrelation function (ACF) as closely as desired. Therefore, there is scope for a more advanced technique for wind speed modelling for reliability analysis which combines the best aspects of the models used in this work

A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation

International audienceThe purpose of this work is the analysis of the uncertainties affecting an electric transmission network with wind power generation and their impact on its reliability. A stochastic model was developed to simulate the operations and the line disconnection and reconnection events of the electric network due to overloads beyond the rated capacity. We represent and propagate the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and wind power generation variability. The model is applied to a case study of literature. Conclusions are drawn on the impact that different sources of variability have on the reliability of the network and on the seamless electric power supply. Finally, the analysis enables identifying possible system states, in terms of power request and supply, that are critical for network vulnerability and may induce a cascade of line disconnections leading to massive network blackout

Exploring the Regulation Reliability of a Pumped Storage Power Plant in a Wind–Solar Hybrid Power Generation System

In the coming decades, the proportion of wind–solar energy in power system significantly increases, resulting to uncertainties of power fluctuation in abundant wind–solar energy regions. The flexibility operation of Pumped Storage Power Plants (PSPPs) has already been widely recognized to regulate wind–solar power fluctuations; however, less is known about the regulation reliability of the PSPP affected by them. It is a challenge, since various uncertainties exist during this regulation process. Here, a mathematical model with a solar–wind–hydro hybrid power generation system is adopted to investigate the regulation reliability of PSPP. The uncertainties and limitations of model parameters are considered during this process. Five regulation indexes, i.e., rise time, settling time, peak value, peak time and overshoot of the reactive power generator terminal voltage, guide vane opening and angular velocity, are extracted to evaluate the PSSP’s regulation quality. Finally, the PSPP reliability probability affected by parametric uncertainties is presented. The obtained results show that the inertia coefficient is the most sensitivity parameters for the settling time, peak value and peak time with sensitivity index 33.7%, 72.55% and 71.59%, respectively. The corresponding total contribution rate of the top 10 sensitive parameters are 74.45%, 93.45% and 87.15%, respectively. Despite some types of uncertainties not being considered, the results of this research are important for the regulation reliability evaluation of PSPPs in suppressing power fluctuations of wind and solar generation.Peer ReviewedPostprint (published version

Monte-carlo based robust analytical method for optimal sizing and reliability of hybrid renewable energy system

The need for a more reliable power from the utility grid and ever-increasing concerns on Greenhouse Gas (GHG) emission effect has globally promoting Renewable Energy Sources (RES). RES is increasingly being adopted in complementing traditional fossil fuels in the energy power supplies. Hybrid Renewable Energy (HRE) systems incorporating wind and solar sources offers lower costs, higher reliability, reduced investment risks, fuel diversification etc. However, wind speed and solar radiation are characterized by their limitations of inherent intermittency and variability. These limitations have led to the concept of optimal sizing and reliability assessments to maintain a balance between generated power and the system loads. Nonetheless, RES reliability assessment studies are site-specific, but existing studies are inexhaustive given the capacity availability and reliability requirements of various sites as well as their performance evaluations. This thesis presents the optimal sizing and reliability assessment of a hybrid solar and wind energy systems for a selected location. Weibull statistical method and air temperature amplitude based statistical models are adopted for wind and solar energy potential assessments of the selected site. The Weibull parameters were estimated using standard deviation method for wind energy potential assessment. Moreover, the air temperature based models of Hargreaves and Samani; Allen; Samani; and Bristow-Campbell models were used for solar energy potential assessment. Simulation of the uncertainty in the wind speed and its probability distribution is performed by using Auto-Regressive Moving Average (ARMA) model to improve wind speed normal distribution. In this approach, the best normal distribution for the simulated wind speed for the reliability analysis is chosen. To improve the performance of the Photovoltaic (PV) module, a single diode six parameter model is developed. First, the P-V and I-V curves were used to generate the required constraints. These constraints were then used to obtain the solution vector of the six parameters using MATLAB and System Advisor Model (SAM). Also, the system’s capacity availability and reliability was assessed using Monte Carlo (MC) simulation. Finally, the result of the MC reliability assessment is later served as Loss of Power Supply Probability (LPSP) constraints to Artificial Bee Colony (ABC) algorithm for the system’s optimal sizing and enhanced reliability assessment. Results from the study show that both wind and solar energy potential of the selected site is high and can generate power at utility level. The ARMA simulated wind speed shows an improvement of 21.8% in standard deviation over the measured wind speed. The adoption of the negative components in the ARMA model transformation resulted in least error of 23.34% in the final wind simulation. Results obtained based on the six parameter solution vector gives improved performance of the PV module. Using the developed MC technique, capacity availability of 100% and LPSP of zero is achieved. The developed ABC algorithm resulted in system reliability improvement of 98.92% when the MC results are constraint into the ABC for the optimal sizing. Various results were validated at appropriate sections and finally, the optimal sizing results of PV/battery RES power system is found to give the best reliability. Such a system has great reliability and can be implemented in facilities requiring constant power supplies such as critical infrastructure

Holistic Physics-of-Failure Approach to Wind Turbine Power Converter Reliability

As the cost of wind energy becomes of increasing importance to the global surge of clean and green energy sources, the reliability-critical power converter is a target for vast improvements in availability through dedicated research. To this end, this thesis concentrates on providing a new holistic approach to converter reliability research to facilitate reliability increasing, cost reducing innovations unique to the wind industry. This holistic approach combines both computational and physical experimentation to provide a test bench for detailed reliability analysis of the converter power modules under the unique operating conditions of the wind turbine. The computational models include a detailed permanent magnet synchronous generator wind turbine with a power loss and thermal model representing the machine side converter power module response to varying wind turbine conditions. The supporting experimental test rig consists of an inexpensive, precise and extremely fast temperature measurement approach using a PbSe photoconductive infra-red sensor unique in the wind turbine reliability literature. This is used to measure spot temperatures on a modified power module to determine the junction temperature swings experienced during current cycling. A number of key conclusions have been made from this holistic approach. -Physics-of-failure analysis (and indeed any wind turbine power converter based reliability analysis) requires realistic wind speed data as the temporal changes in wind speed have a significant impact on the thermal loading on the devices. -The use of drive train modelling showed that the current throughput of the power converter is decoupled from the incoming wind speed due to drive train dynamics and control. Therefore, the power converter loading cannot be directly derived from the wind speed input without this modelling. -The minimum wind speed data frequency required for sufficiently accurate temperature profiles was determined, and the use of SCADA data for physics-of failure reliability studies was subsequently shown to be entirely inadequate. -The experimental emulation of the power converter validated a number of the aspects of the simulation work including the increase in temperature with wind speed and the detectability of temperature variations due to the current's fundamental frequency. Most importantly, this holistic approach provides an ideal test bench for optimising power converter designs for wind turbine, or for other industries with stochastic loading, conditions whilst maintaining or exceeding present reliability levels to reduce wind turbine's cost of energy, and therefore, society

Bulk electric system reliability evaluation incorporating wind power and demand side management

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework. Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits