Optimization of rotating equipment in offshore wind farm

The paper considered the improvement of rotating equipment in a wind farm, and how these could maximise the farm power capacity. It aimed to increase capacity of electricity generation through a renewable source in UK and contribute to 15 per cent energy- consumption target, set by EU on electricity through renewable sources by 2020. With reference to a case study in UK offshore wind farm, the paper analysed the critique of the farm, as a design basis for its optimization. It considered power production as design situation, load cases and constraints, in order to reflect characteristics and behaviour of a standard design. The scope, which considered parts that were directly involved in power generation, covered rotor blades and the impacts of gearbox and generator to power generation. The scope did not however cover support structures like tower design. The approaches of detail data analysis of the blade at typical wind load conditions, were supported by data from acceptable design standards, relevant authorities and professional bodies. The findings in proposed model design showed at least over 3 per cent improvement on the existing electricity generation. It also indicated overall effects on climate change.


Introduction
According to conservation law of energy (first law of thermodynamics), energy cannot be created nor destroyed, but can be transferred from one form to another. While the energy in a system may take various forms (like heat, kinetic etc), depending on its source, the energy can get diminished. Thus, the source of energy generation can be said to be either renewable or non-renewable. Unlike nonrenewable (fossil fuels), renewable energy source (solar, wind, geothermal etc) is not exhaustible and emits lower levels of carbon dioxide emissions.
In order to reduce the effects of emissions being caused by non-renewable source and avoid bad global warming, then there is a need to integrate the use of renewable source of energy in the national consumption. This need was echoed by European Union (EU) by setting 15 per cent (15 %) target of all energy consumption in UK to come from renewable source by 2020 (Carbon Trust, 2008). This target could cut across the energy consumption categories of transport, heat and electricity. The percentage is three-quarter of the 20 per cent (20 %) target that was set for the entire Europe by the Union in March 2007, according to the Carbon Trust. This target set for UK means that at least 29 GW (91 TWh) of offshore wind power could be needed to meet the 15 % EU renewable energy, even as the global leader for offshore wind energy (DECC, 2011), and wind power, as the primary renewable energy source (IMechE, 2013).
An offshore wind farm is a group of wind turbines located in the seabed or base of the ocean, to produce electric power. A wind turbine is device that converts the kinetic energy in fast-moving flow of wind to produce mechanical power, via wind medium to rotate a bladed rotor. The device consists of a foundation, tower, nacelle and a rotor. The tower holds up the rotor and a nacelle (box). While the nacelle contains components like main axle, gearbox, generator, transformer and the control system, the rotor is made of blades and hubs. The rotary equipment converts the kinetic energy into mechanical power, and therefore, the rotary equipment can be said to influence the mechanical power output, among other factors. DECC indicated that between 33-58 TWh offshore wind technology would make up for 234 TWh, which is equivalent to the estimated 15% target for year 2020. By projection, offshore wind power could cost-effectively deliver 20-50% of total electricity generation by 2050 (LCICG, 2012).

Methodology
As the largest and operational offshore wind farm in UK (as at the time of conceiving this as case study), it was believed that Greater Gabbard, being commissioned in August 2012, was among the latest commissioned wind farm that could still be improved. The improvements could be instrumental to meeting the 15 per cent renewable target by the year 2020. Its latest commissioning and being the largest in term of its capacity therefore, provided motivation for choosing it as case study.
Using turbines within the designed conditions is vital for design layout. The minimum acceptable turbine spacing is dependent on the nature of the terrain and the wind rose on a particular site. While a tight spacing turbines are affected by turbulence, positioning turbines spaced closer than five rotor diameters (5D) is likely to result in high wake losses, according to Renewable Energy World Magazine (REWM). Hence, minimum space required in positioning two turbines apart can be said to be sum of blade radius for turbine 1, minimum acceptable turbine spacing, and blade radius for turbine 2, assuming uniform blade diameter in a farm location. This is spacing efficiency.
i.e. r 1 + 5D + r 2 => where r 1 = r 2 = r for uniform blade diameter = D + 5D With respect to operation, loading and durability of the wind turbine, regulation standard (IEC 61400-1, 2005) indicated that turbines can be subjected to external condition. For the purpose of design analysis, this external condition can be categorised into normal and extreme conditions, based on parameters wind speed and turbulence intensities.
Normal Wind Profile Model (NWP) : A normal external condition occurs when there is a continual structural loading, i.e. when the loading (wind) occurs frequently during the normal operation of a wind turbine. Models under this condition are called normal wind profile models (NWP). From standard, in order to model for this profile model (NWP) with ultimate strength, then v z = v hub (z /z hub ) α (1) where v z =average wind speed as a function of height z above the sea from standard v hub = standard 15m/s at standard 10m/s z hub ; α = Power law exponent = 0.2 Extreme Wind Speed Model (EWP) : An extreme condition arise when there is an unusual or rare external design conditions .i.e. when having a 1-year or 50-year recurrence period. Models under this conditions are called extreme wind speed models (EWP). The design of load cases therefore consists of external conditions and other design situations. From standard,(IEC 61400- 1,2005), in order to model for this profile model (EWP) with ultimate strength, then v e50 (z) = 1.
where v e50 = Turbulent extreme wind speed with recurrence period of 50 years. v e1 = Steady extreme wind model with a recurrence period of 1 year.

Mathematical Model
The usable produced power is only some fraction of the available power in the wind, according to BETZ LIMIT. Hence, the need to calculate the power available in the wind. The algorithms to model output power are by calculating the power in the wind, extract it and then average output over a year.

Extract Actual
Power. This is amount of usable power that generates electricity. According to Betz Limit, in theory, wind mill can possibly extract a maximum of 59.3% of power from the wind only, while in reality, it is 45% maximum. This proportion of power extraction is termed coefficient of performance or efficiency (C p ). Primarily, this energy are extracted through the physical principle of lift and drag forces. So, power extracted (P e ) from the wind is modified to: P e = P * C p = ½ *ρ*A*V 3 * C p (5) where C p = Coefficient of performance of the wind turbine. C p is the average power coefficient defined by Betz Law with a limit of 0.59 for marine turbine. Amount of power produced P p = P e * η g * η b (6) where η g & η b are efficiencies for generator and gearbox. η g = 80%; * η b = 90 to 95%.

Calculating for energy produced in a year.
From eq. (7), 3.6 MW load of unit turbine runs in a year is 3.5 * 3.6 = 12.6 GWh. Hence, 140 units will produce 1764 GWh.

Proposed Optimized Analysis
In order to model for the maximum energy output in a year, this is analyzed using three approaches. Each approach followed the algorithms of each of mathematical model (section 2.1). The approaches were achieved by calculating energy produced with proposed 100 m rotor diameter using existing case study wind speed; and according to standard IEC 61400-1, normal wind profile model (NWP) and extreme wind speed model (EWP). From section 2, spacing efficiency with proposed 100 m rotor diameter, will be 600 m in positioning two turbines apart. Assuming for same numbers of unit of 140 turbines as case study, these require 84000 m 2 = 84 km 2 . However, since available case study site area is 147 km 2 , then there remains a large space that can still be maximized. In order to achieve this, the number of turbine units were therefore increased. 100 per cent use of the site area will require 245 units of turbines. i.e. 245 units turbine * 600 m = 147000 m 2 = 147 km 2 (same as case study site area). From eq. (4), P = 0.5 * 1.225 * (3.142 * 50 2 ) * 10.05 3 P = 4.88 MW. ii). From eqs. 5 & 6, Power produced P p = (4.88 * 10 6 ) * 0.59 * 0.8 * 0.925 = 2.13 MW. Therefore total power produced by maximum units of 245 turbines, at 100 % efficient use of location area = 2.13 MW * 245 = 521.85 MW. iii). From eq. (7), one unit turbine of 2.13 MW will run in a year for 3.5 * 2.13 = 7.46 GWh. Hence 1827.7 GWh of energy will be produced by 245 units.

Extreme Wind Speed Model (EWP).
According to standard 61400-1 (section 2.0), this was considered in two cases: as turbulent and steady EWP with recurrence periods of 50 years and 1 year respectively. From table 2.1, assume v ref for turbulent EWP is 50 m/s, then from eq. 2, i). average wind speed at height of case study, 170 m v e50 (z) = 1.4 * 50 * (170/10) 0.11 = 95.59 m/s. Hence, for extreme wind speed model with recurrence in 50 years period, the wind speed shall be 95.59 m/s. This means that with this speed, the turbine must be able to withstand the extreme events possible with a return period of 50 years. For steady EWP, then eq. 3 becomes v e1 (z) = 0.8 * 95.59 m/s = 76.5 m/s. Now was considered model for analysis of 1 year reoccurrence.

Results
The tabulated results show effects of analytical model and redesign of blade to the proposed diameter, and efficient location areas, while maximising the energy productivity.

Discussions
While the wind farm capacity in first approach showed an increase in total units capacity of actual power produced and energy in a year, occurrence of frequent wind loading in the second approach resulted in the wind speed almost triple the initial case capacity. Consequently, the unit power capacity an energy produced in a year also got increased.
In the third approach, the unusual event of design with recurrence 1 year period resulted in unusual increase of wind speed, and consequently the unit power and energy produced in a year. The variations in these wind speeds according to different approaches limits the capacity of power generation, and also are adequate position of the rotor blade in the wind speed direction, altitude of the tower among others. With estimated 91 TWh of offshore wind power predicted to be needed to meet the 15 % target (DECC, 2011), this would mean an approximate numbers of 50 wind farms will be needed, should each farm consists of 245 numbers of turbines, and each generating 1827.7 GWh like that in the first approach.

Conclusions
The first approach showed 3.5 per cent increase of actual power produced by total units of turbines, and 3.6 percent of energy produced in a year. The unit power and energy produced were almost 10 per cent increase each as against the given case study data in the second approach, while there was a rare very high percentage increase resulted in the third approach. With this least percent increase of 3.5 on a wind farm, this would mean a greater addition of percentage increase on all operational wind farms in the country. The percentage increments on all operational wind farm would mean serving greater number of homes and hence beneficial to everyone, as it makes environments more friendly, due to its source. As the most significant gases in the atmosphere, the effects of carbon dioxide concentration are eliminated, hence, there is less severity of changes, both physical and ecological, which could affect national population, productivities etc. Also, as predicted by the Department of Energy and Climate Change that offshore wind technology would produce most amount of energy (table 1.1), and couple with the percentage increase obtained via the analysis, it could be said that government supports would be of great importance at realizing this prediction. This could contribute immensely to meeting set target of the need for energy consumption in the country to come from renewable source, and increase chances of meeting it earlier than predicted. The redesign optimization of existing farms could save money, time and other resources instead of construction new ones. Hence, the economy thrives, while maximizing the operational qualities and productions of the existing farms.