Key Performance Indicators for Wind Farm Operation and Maintenance

Key performance indicators (KPI) are tools for measuring the progress of a business towards its goals. Although wind energy is now a mature technology, there is a lack of well-defined best practices to asses the performance of a wind farm (WF) during the operation and maintenance (O&M) phase; processes and tools of asset management, such as KPIs, are not yet well-established. This paper presents a review of the major existing indicators used in the O&M of wind farms (WFs), as such information is not available in the literature so far. The different stakeholders involved in the O&M phase are identified and analysed together with their interests, grouped into five categories. A suggestion is made for the properties that KPIs should exhibit. For each category, major indicators that are currently in use are reviewed, discussed and verified against the properties defined. Finally, we propose a list of suitable KPIs that will allow stakeholders to have a better knowledge of an operating asset and make informed decisions. It is concluded that more detailed studies of specific KPIs and the issues of their implementation are probably needed

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy

In-operation learning of optimal wind farm operation strategy

In a wind farm, power losses due to wind turbine wake effects can be up to 30-40% under certain conditions. As the global installed wind power capacity increases, the mitigation of wake effects in wind farms is gaining more importance. Following a conventional control strategy, each individual turbine maximizes its own power production without taking into consideration its effects on the performance of downstream turbines. Therefore, this control scheme results in operation conditions that yield suboptimal power production. In order to increase the overall wind farm power production, a cooperative control strategy can be used, which coordinates the control actions among the wind turbines in the wind farm. This work further investigates the model-free Bayesian Ascent optimization algorithm using SimWindFarm and a standalone Dynamic Wake Meandering model-based simulation tool An advantage of such optimization approach is that the control strategy adapts to operational conditions in the wind farm and is not model-dependent. An approximation of the wind farm power function is constructed using GP regression to fit the control action inputs and the noisy measured power outputs, which is then maximized to determine the optimal control inputs. This estimation is updated in every iteration, allowing the control system to learn from the target system while performing the optimization. The usage of all historical data, along with a trust region constraint in the sampling of new inputs, contribute to a fast convergence rate with gradual changes of the control actions. The developed learning technique is implemented in a wind farm controller and tested in both SimWindFarm and standalone Dynamic Wake Meandering model-based simulation tools. With the conducted tests, performance of the algorithm is assessed considering the different dynamics in the wind farm, thus obtaining an accurate representation of real farm operation. The developed controller reliably improves farm efficiency, even with uncertainty present in measurements. Compared to traditional control strategies, an increase in total wind farm power production is obtained when using a cooperative control strategy. Such enhancement in wind farm performance would result in an improvement of wind farm economics and hence in further growth of wind-energy based power generation

Harbour porpoises (Phocoena phocoena) and wind farms: a case study in the Dutch North Sea

The rapid increase in development of offshore wind energy in European waters has raised concern for the possible environmental impacts of wind farms. We studied whether harbour porpoise occurrence has been affected by the presence of the Dutch offshore wind farm Egmond aan Zee. This was done by studying acoustic activity of porpoises in the wind farm and in two reference areas using stationary acoustic monitoring (with T-PODs) prior to construction (baseline: June 2003 to June 2004) and during normal operation of the wind farm (operation: April 2007 to April 2009). The results show a strong seasonal pattern, with more activity recorded during winter months. There was also an overall increase in acoustic activity from baseline to operation, in line with a general increase in porpoise abundance in Dutch waters over the last decade. The acoustic activity was significantly higher inside the wind farm than in the reference areas, indicating that the occurrence of porpoises in this area increased as well. The reasons of this apparent preference for the wind farm area are not clear. Two possible causes are discussed: an increased food availability inside the wind farm (reef effect) and/or the absence of vessels in an otherwise heavily trafficked part of the North Sea (sheltering effect

Modelling offshore wind farm operation and maintenance with view to estimating the benefits of condition monitoring

Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones.To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases.Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains.Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones.To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases.Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains

Investigation and assessment of the benefits for power systems from wind farm control

As wind turbines are increasingly situated in large arrays offshore, connected to power grids by a single long cable, it is necessary to consider the operation of the whole wind farm as a single plant rather than as a series of individual units. To achieve this, the development of advanced wind farm modelling software is required to test and evaluate new control strategies for wind farm operation. This thesis considers the use of Strathfarm, The University of Strathclyde’s in-house wind farm modelling software, presenting novel wind farm control algorithms which significantly reduce the fatigue of wind turbine towers and wind turbine blades. The thesis also further develops Strathfarm in two key areas, presenting improvements to the modelled wakes and also details the development of a novel power system model. The power system model can be used to show the efficacy of previously developed dispatch algorithms for wind farms to support power grids.As wind turbines are increasingly situated in large arrays offshore, connected to power grids by a single long cable, it is necessary to consider the operation of the whole wind farm as a single plant rather than as a series of individual units. To achieve this, the development of advanced wind farm modelling software is required to test and evaluate new control strategies for wind farm operation. This thesis considers the use of Strathfarm, The University of Strathclyde’s in-house wind farm modelling software, presenting novel wind farm control algorithms which significantly reduce the fatigue of wind turbine towers and wind turbine blades. The thesis also further develops Strathfarm in two key areas, presenting improvements to the modelled wakes and also details the development of a novel power system model. The power system model can be used to show the efficacy of previously developed dispatch algorithms for wind farms to support power grids

Underwater acoustic characteristics of the OWEZ wind farm operation (T1)

In Holland the first two offshore wind farms, the Offshore Wind Farm Egmond aan Zee (OWEZ) and “Prinses Amalia” were built in respectively 2006 and 2007. Beside the main goal of producing electric energy from wind resource the construction of the first wind farm (OWEZ) was also used to demonstrate the impact of such construction to the environment. To demonstrate the impact an extensive Monitoring and Evaluation Program (MEP) was developed to evaluate the effects on benthic organisms, fish, birds and marine mammals. One of the tasks was to measure the underwater noise characteristics before and during the construction and with turbines in operation and to estimate the effects

Risk-Aware Management of Distributed Energy Resources

High wind energy penetration critically challenges the economic dispatch of current and future power systems. Supply and demand must be balanced at every bus of the grid, while respecting transmission line ratings and accounting for the stochastic nature of renewable energy sources. Aligned to that goal, a network-constrained economic dispatch is developed in this paper. To account for the uncertainty of renewable energy forecasts, wind farm schedules are determined so that they can be delivered over the transmission network with a prescribed probability. Given that the distribution of wind power forecasts is rarely known, and/or uncertainties may yield non-convex feasible sets for the power schedules, a scenario approximation technique using Monte Carlo sampling is pursued. Upon utilizing the structure of the DC optimal power flow (OPF), a distribution-free convex problem formulation is derived whose complexity scales well with the wind forecast sample size. The efficacy of this novel approach is evaluated over the IEEE 30-bus power grid benchmark after including real operation data from seven wind farms.Comment: To appear in Proc. of 18th Intl. Conf. on DSP, Santorini Island, Greece, July 1-3, 201

Fault characteristics analysis of two HVDC technologies for wind power integration

This paper analyzes the operation and control principle of the two transmission technologies, i.e. Line-Commutated Converter-HVDC (LCC-HVDC) and Voltage Source Converter-HVDC (VSC-HVDC) for wind farm integration. It explores the fault ride-through (FRT) capability of the two HVDC under fault conditions according to the characteristics of the two kinds of converters. To ensure satisfactory ride through grid faults, the current limit controller is initiated to reduce the active power injected into the HVDC during the fault. The approach adopted may avoid the overcurrent or overvoltage in the converter and the tripping of the wind turbines, as well as facilitate the fast recovery. Comparative simulation results verified that LCC-HVDC and VSC-HVDC could prevent the fault propagation and ensure the wind farm operation continuously in the event of severe grid fault. © 2014 IEEE.published_or_final_versio

Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure

Reducing downtime through predictive or condition-based maintenance is a promising strategy to help reduce costs associated with wind farm operation and maintenance. To help effectively monitor wind turbine condition, operators now rely on multiply sources of data to make informed operational decisions which can minimise downtime, increasing availability and profitability of any given site. Two of such approaches are SCADA temperature and vibration monitoring, which are typically performed in isolation and compared over time for both fault diagnostics and reliability analysis. Presenting two separate case studies, this paper describes a methodology to bring multiple data sources together to diagnose faults by using a single-class support vector machine classifier to assess normal behaviour model error, with results showing that anomalies can be detected more consistently when compared to more standard approaches of analysing each data source in isolation