Operational and maintenance costs are the Achilles’ heel for any wind energy installation. The determination of appropriate mitigation actions and strategies is critical for the economic growth and the performance stability of wind plants. Parallel to the latter, a diligence evaluation of the factors that influence the project’s success is indispensable. Preventing failures is an additional characteristic that can increase the overall performance of the installation as it is widely appreciated that a problematic situation can be averted before it occurs.
Depending on the wind turbine quality and the local climate conditions, the wind turbine project and its design components’ lifetime cycle, guarantee low failure probability in 20 years of operation. Operational cost may additionally be affected by the malfunctioning component interaction and any possible lack and unavailability of spare parts. However, several statistic analysis investigations indicate that after a 10 year period of operation the maintenance costs are enlarged to a proportion of 25%.

Regular wind plant maintenance is vitally important for a reliable performance due to the complicated nature of the unscheduled failure and breakdown. Machine, system failures and overhauls, should increase the downtime of wind turbines and their associated cost synergies for maintenance and subsystems’ replacement. In addition to that, the loss of electricity sales revenue would reduce the profitability and the attractiveness of the investment due to the cohesive nature of the cost. To get the full picture a relevant mathematical equation for the Cost Of Energy (COE) is presented:

in which ICC represents Initial Capital Cost and FCR used for Fixed Charge Rate.

It can clearly be seen, that reliability influences not only the Operation and Maintenance (O&M) cost but also the Levelised Cost of Replacement (LRC). Any potential reliability optimization increase the AEP, and simultaneously decrease LCR and O&M costs, thereby reduce COE. It is also very practical to create a taxonomy of the different cost types for wind energy installations, in order to identify, quantify and declare the reliability assessment methods. These costs and their dependence can be listed as follows:

1	Initial costs: Dependent on reliability level
2	O&M costs: Dependent on O&M strategies from the developer and investor, availability and reliability
3	Failure costs: Dependent on reliability

Failure Main Criteria for Wind Energy

The criticality of wind energy systems reliability metrics, necessitates careful consideration of how to interpret potential confounding factors and appropriately design, in an optimal way, the modern power system structures and operation strategies. To establish meaningful conclusions from the analysis of failure probabilities regarding wind energy reliability, two factors are the most important: Failure Rate and Downtime. The main failure criteria are presented as follows:

1	Mean Time To Failure MTTF
2	Mean Time to Repair, or downtime MTTR
3	Mean Time Between Failures MTBF
4	Failure rate, λ where λ = 1/MTBF
5	Repair rate, μ where μ =1/MTTR
6	Availability where A = (MTBF-MTTR)/MTBF = 1-(λ/μ)

Improving the control and monitoring techniques in order to minimize the wind plant uncertainties, ensures performance reliability. These methods are associated with the maintenance costs which are considerably low for new wind turbines and high for aged machines. Indicatively, an interesting article from [H. Braam and R.P, 2009] states that machines with a low capacity of 400-600 kW have a significantly large operational cost (15%) than modern wind turbines with a higher capacity.

Lost revenue is interrelated to the maintenance of the machine components in regard to the years of operation along with the implemented strategy on their maintenance model. Outaged machine characteristics, generators, gearboxes, drive trains and blades are some of the components that are subjected to failures.

Needless to say, the majority of gearboxes are designed for 10 years efficient operation but in reality they become problematic after 6 to 8 years. Moreover, the malfunctioning of the components along with their synergistic negative influence have a direct impact on the O&M costs due to the engaged stoppage of the turbine operation during the replacement process.

Bathtub Curves

An intersting article from Garrad Hassan , states that in order to estimate the average failure rate, we are assuming the the sub-assemblies can be perfectly rapaired or made of non repairable components that are replaced at any action. This assumption can be depicted as the constant and flat part of the Bathtub curve. The Bathtub curve consists of three areas: an infant mortality period with a decreasing failure rate followed by a normal life period which is actually the usefull life of the component with a considerably low and constant failure rate and ending with a wear-out period that displays high failure rates.

Reliability Modelling

A power system's overall reliability characteristics can be determined by the development and implementation of reliability models. The complexity of these models is dependent upon various factors such as failures data availability and integrity, empirical data, operators’ interviews, history control management data or data obtained from the wind energy maintenance plant information systems. Reliability modeling is a polysynthetic process used to identify system complexities and last but not least, offers the framework to understand how and why a component or system has failed. The most critical process, of this approach is to hierarchically divide the overall system into sub-systems and components. Block diagrams and fault trees are the most common methods to achieve a realistic representation of the power system. Next, a combination of empirical distributions and statistical analysis methods provide an estimation of the failure rate and promote alternative solutions for failure prevention or innovative guidelines in the design, manufacture or operational phase.

In wind energy engineering Influence diagrams, which graphically represent the same components as a decision tree, provide a fundamental framework for assessing the reliability characteristics of the components and identify the dependencies among failure events and decisions. The following diagram shows the decisions and main interactions at a management level which will have an impact on performance and value nodes for a wind energy plant installation.

The diagram above, provides an insight into the synergistic interaction between critical thinking methods and decisions involved in the design and operation of a wind energy plant. Failure occurs at the damage size 1, and at the inspections there is assumed to be an additive measurement error that is normal distributed with mean zero and standard deviation 0.05.

In general the varia bles used in the model are continuous, but for a LIMID it is necessary to discretize all variables. The variable representing the damage size, D, is dis cretized in 30 states, where the last state is failure. The other states have exponentially increasing size, and are numbered 1 to 29.

We can conclude that value comes from understanding and manage the nature of the failure. Reliability assessment seems to be a critical path in order to develop these methods and techniques for failure prevention but the logic always proceeds along the lines of inventing. Thus, reliability assessment becomes an important foundation framework of designing the future wind energy systems and their associated components. In other words, reliability bridges pragmatism with prediction and prognosis with solutions.