Operation and approximation based on the history of failure modes recorded by SCADA system of Amougdoul Moroccan wind farm using FMECA maintenance model

Abstract

The wind industry is trying to find tools to accurately predict and know the reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique used to determine critical subsystems, causes and consequences of wind turbines. FMECA has been widely used by manufacturers of wind turbine assemblies to analyze, evaluate and prioritize potential/known failure modes. However, its actual implementation in wind farms has some limitations. This paper aims to determine the most critical subsystems, causes and consequences of the wind turbines of the Moroccan wind farm of Amougdoul during the years 2010–2019 by applying the maintenance model (FMECA), which is an analysis of failure modes, effects and criticality based on a history of failure modes occurred by the SCADA system and proposing solutions and recommendations.

Keywords

Wind farm FMECA HAWT maintenance model Failure mode effect analysis Criticity

General introduction

Morocco, among other countries in the world, faces increasing energy needs and limited conventional energy resources. The country is heavily dependent on energy imports. In addition, it is strongly affected by the effects of climate change (CC) and presents a high vulnerability to global warming at different levels. Forests, agriculture, fisheries, water supply and tourism are among the most vulnerable ecosystems. Morocco has adopted a new green energy policy that will allow the country to benefit from its abundant wind potential estimated at 25,000 MW (Ezzaidi et al., 2017). The assessment of achievements and projects underway at the end of 2019 shows that the installed capacity amounts to 3685 MW, including 700 MW for solar, 1215 MW for wind and 1770 MW for hydropower (MÉDIAS24, 2020). The integration of renewable energy resources is becoming a reality today. Wind power, offering lower costs than other energy resources, has grown rapidly around the world and has recently gained much attention due to its great benefits in reducing GHG emissions (Ezzaidi et al., 2017). A wind turbine is a device that transforms part of the kinetic energy of the wind into mechanical energy and then into electrical energy via a generator. Knowing that the energy efficiency of wind turbines depends on the reliability of the whole system, therefore it is necessary to optimize the design of the wind turbines and minimize the costs of electricity production, which is a cost forecasting tool for investors. A study focuses on the evaluation of the performance of the Amougdoul wind farm by Ezzaidi et al. (2017). They estimated the energy production of a wind turbine which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirms that the conversion efficiency does not exceed 40%. In various regions of the world, the deployment of wind turbines for power generation has become increasingly competitive Mostafaeipour (2010). Onshore wind energy can be conceptualized as a “global innovation system” in which resources can drive technological development and diffusion: knowledge, markets, investment and legitimacy Rohe (2020). The growing contribution of wind power to the overall energy structure underscores the need to comply with production standards in a safe and environmentally friendly manner and to ensure energy security at acceptable electricity prices (Burrows and Communications, 2018). The main reasons for the shortage of wind farms were found in the first batch of studies are:

Low reliability of wind turbines (Slot et al., 2020).

Underestimating access restrictions for asset maintenance (van Bussel and Zaaijer, 2001).

The application of corrective maintenance; the initiation of maintenance activities reactively after the failure of a component or part of the wind system (Gonzalez et al., 2017).

The application of this strategy and the corresponding impact on the availability of the wind turbine have been studied in some publications (Shafiee and Sørensen, 2019). A study conducted a maintenance strategy of around 12 million euros per year for a 500 MW wind farm in order to assess the impact of production losses due to downtime. The direct cost of corrective maintenance is approximately four times the cost of preventive activities (Scheu et al., 2012). In order to keep wind turbines running to generate electricity reliably, great efforts have been made. The availability depends on the conditions of the site of the wind farm and also on the technical characteristics. Efforts to achieve usability goals are preventive maintenance activities, such as regular replacement of wearing parts, oil, or grease and responding to unpredictable conditions such as failure of the machine. Wind turbine by correctly passing the helicopters and with spare parts, tools and technicians always available. As the industry has evolved, maintenance strategies have been developed and concepts such as condition based monitoring (CBM) have been developed. CBM is a form of preventive maintenance, including changes in physical conditions, analysis and all subsequent maintenance actions. CBM strategies can avoid failures by understanding the physical principles of failures and the corresponding sequence of subsequent target maintenance activities (El-Thalji and Jantunen, 2012). The implementation of CBM requires the installation of sensors and the application of analysis tools for various WT operating signals, which increases the complexity and cost of operating the wind farm (Scheu et al., 2019).

The main objective of this article will be to apply a maintenance model for the main onshore wind systems for the Amougdoul wind farm in ESSAOUIRA during the period 2010–2019, taking into account all the main assemblies of the kinematic chain and the effects of their failure on the overall performance of the turbine. A primary objective will be to examine how the method handles a significant design and optimization change proposed for the WT. A logarithmic scale for the FMECA procedure is also proposed in this article. The document is organized as follows: section 2 presents a theoretical background on risk prioritization, FMECA and PARETO analysis, section 3 concerns the baseline and survey platform. Section 4 presents the FMEA methodology and procedure used for the wind farm using the maintenance management tools and logarithmic scales, it also shows the quantitative results of FMEA. Section 4 describes the wind turbine’s preventive maintenance plan, proposed solutions, improvements and recommendations. And by the end a conclusion.

Risk prioritization, FMECA and PARETO analysis

Several qualitative and quantitative methods can be applied to determine the priority of system or failure scenarios for further investigation in our case of SCADA-type wind turbines. FMECA is classified as a semi-quantitative reliability method. It is defined as a system process that can identify potential design and process failures before they occur, either to eliminate them or to minimize the associated risks. The concept of risk is used to prioritize the system and assess what can happen, for example, component failure, the probability of occurrence and the consequences. The results can be divided into different categories depending on the objectives and the risk assessment. In general, consequences include the impact on the cost and value of assets as key performance indicators, employee perception and safety considerations and the impact on the environment. In addition, Scheu et al. (2019) have applied the FMECA maintenance model to offshore wind turbine subsystems. They determined an overview of the overall distribution of the identified failure modes for each of the main systems with a categorical classification.

In order to apply the FMECA maintenance model, it is necessary to distribute the system according to the functional location of the equipment and to collect information on the operation of the system: First, fully understand the operations that the system must perform during normal operation; second, divide the system into several parts and use schematics and flow charts to identify the relationships between the subassemblies; third, prepare a complete parts list for each subassembly and then identify the main operational and environmental constraints that cause failure modes of each subassembly. Tables 1 and 2 below present the root causes and effects of failure modes in wind energy systems.

Table 1.

Root causes of the failure modes in wind turbine systems (Ozturk et al., 2018).

External	Structural	Electrical	Wear
High/low wind	Installation defects	Calibration error	Aging
Environmental shocks	Manufacturing and material defect	Connection faults	Corrosion
Icing	Maintenance errors	Overload	Fatigue
Lightning strike	Mechanical overload	Installation failure	Insufficient lubrication
–	–	Software failure	Aging

Table 2.

Locations, causes and effects of the failure (Qian et al., 2019).

Failure location	Failure causes	Failure effect
Structure failures	High wind	Overspeed
Rotor blade failures	Grid failure	Overload
Mechanical brake failures	Lightning	Noise
Gearbox failures	Icing	Vibration
Generator failures	Malfunction of control system	Reduced power
Yaw systems failures	Component wear of failure	Causing follow up damage
Sensors failures	Loosening of parts	Plant stoppage
Hydraulic systems failures	Other causes	Other consequences
Electrical systems failures	Cause unknown	–
Control system failures	–	–
Hub failures	–	–

The Pareto analysis method identifies the main causes (20%) that lead to 80% downtime. Once the main causes have been identified, diagnostic techniques such as the Ishikawa diagram can be used to identify the deeper causes of the problems (Mulder, 2012; Powell and Sammut-Bonnici, 2015).

To apply Pareto analysis in practice, some basic steps have been defined that can be followed to obtain a complete analysis: a complete analysis are presented in Table 3.

Table 3.

Pareto analysis in practice (Mulder, 2012).

Context and survey platform; ESSAOUIRA Amougdoul wind farm

The wind farm is located in Morocco at the city of ESSAOUIRA, the site (Coordinates: 31.413263, −9.802698) is located on one of the westernmost points of the central Atlantic coast of Morocco (Cap Sim). This park includes 71 asynchronous wind turbines, of the GAMESA G52-850 type, with a nominal power of 850 kW (Ozturk et al., 2018). All wind turbines are connected via an internal underground electricity network to the national MV 60 KV network. The park also includes an MV (Medium Voltage)/HV (High Voltage) transformer station for the evacuation of energy using digital technology. The park was built as part of the national strategy to promote renewable energies. Main onshore wind systems are shown in Figure 2. Table 4 shows the data for Gamesa G52/850 type wind turbines. Table 5 presents the characteristics of the Amougdoul wind farm.

Figure 1.

SCADA data history for Amougdoul wind farm during 2010–2019.

Figure 2.

Main systems of onshore wind turbines (Scheu et al., 2019).

Table 4.

Gamesa G52/850 wind turbines data (The Wind Power, 2018).

General data	Masses	Rotor	Gearbox	Generator	Tower
Manufacturer: Gamesa (Spain)	Basket: 23 tonnes	Minimum rotation speed: 19.44 rpm	Gearbox: yes	Type: ASYNC	Minimum platform height: 44 m
Name of the aerogenerator: G52/850	Tower: 40–91 tonnes	Maximum rotation speed: 30.8 rpm	Number of reports: 3	Number: 1	Maximum platform height: 65 m
Non-minimum power: 850 kW	Rotor + hub: 10 tonnes	Minimum wind: 4 m/s	Ratio: 61.74	Maximum speed of rotation: 1900 rpm	Manufacturer: Gamesa
Rotor diameter: 52 m	Total: 73–124 tonnes	Nominal wind: 16 m/s	Manufacturer: Echesa, Fellar, Hansen, Lohman	Output voltage: 690 V
Old model, not available for sale		Maximum wind: 25 m/s		Manufacturer: Indar, Cantarey
Wind class: IEC Ia		Manufacturer: Gamesa
Not compatible offshore
Swept area: 2124 m²
Power density: 2.5 m²/kW
Number of blades: 3
Power limitation: Pitch
Commissioning: 2001

Table 5.

Characteristics of the ESSAOUIRA Amougdoul wind farm (RCREEE, 2012).

Power installed	Number of wind turbines	Unit power of wind turbines	Forecast annual production	Energy evacuation	Fuel economy	CO2 emission reduction
60 MW	71	850 GWh	200 GWh	2 lignes	48,000 tonnes/year	150,000 tonnes/year

The database recorded in Excel according to the SCADA system is organized as follows (Figure 1): Type of maintenance, date of failure, nature of failure, end of failure, nature of intervention, downtime, Year.

Material and method

The park has gathered several problems in terms of maintenance management. It has recently suffered serious consequences as a result of this mismanagement. Consequently, a loss of production due to the organization, unreliability of the equipment and the method of maintenance monitoring.

Our objective is to study the duration and the number of breakdowns of the wind turbine, the possible and main causes which present an obstacle to the good approach of the maintenance the problems which lead to the degradation of the production and to make proposals improvement of the maintenance function for the ESSAOUIRA wind farm in 2010–2019, the aim of which is to optimize the production and cost of maintenance and to have a good operation of the maintenance department according to the following approach:

Analyze the current state of wind power and detect availability issues.

Justify the choice of critical elements to be studied, based on the calculation of maintenance indicators.

Apply the FMECA method.

Carrying out maintenance follow-up with proposed solutions.

Application of the PARETO analysis

The PARETO analysis will be applied, focusing for the moment on finding the critical elements that cause the system to fail and that reduce the availability of the machine. To properly select the critical components, we will react to the history of wind turbine failures during 2010–2019.

From the Table 6 “PARETO analysis” we draw the PARETO diagram to determine the most critical elements of the wind turbine as shown in Figure 3.

Table 6.

PARETO analyses.

System	Number of breakdowns (2010–2019)	%	Accumulation in %
Generator	580	32	32
Gearbox	565	31	63
Network	274	15	78
Blade	238	13	91
Hydraulic unit	53	3	94
Rotation system	32	2	96
Orientation system Yaw	28	2	98
Braking system	20	1	99
PLC-Converter communication fault	12	1	99
Pitch system	7	0	100
Transformer	5	0	100
Total	1814

Figure 3.

PARETO graph.

Interpretation of the curve:

The PARETO curve is made up of three zones

Class A: 20% of the causes responsible for 80% of the effect.

Class B: 30% of the causes responsible for 15% of the effect.

Class C: 50% of the causes responsible for 5% of the effect.

Based on the analysis of the failure history, we have noticed that the most critical items are: Generator, Gearbox. Then the next step will be the application of the FMECA method for wind turbines by identifying its critical subassemblies.

Application of the FMECA method

Rating scales

From a few pivotal works (N. Daujeard, Novembre 2009), the rating used is for a four-level scale established from the literature and validated in a working group, the stages of a FMECA have been identified, including strategies aimed at applying this knowledge. In order to make the study homogeneous, the criticality of the failures of all wind turbines will be evaluated according to the same rating scale, based on three independent criteria: the frequency or probability of occurrence (F), severity (G) and probability of non-detection (D). This made it possible to calculate a criticality score (C) according to the formula C = G × F × D. The criticality threshold made it possible to identify failures which should lead to improvement actions. To each criterion we have associated a rating scale defined according to levels based on: the history of maintenance department shutdowns and staff experience. Indeed, the rating scale is based primarily on the time of downtime as well as the number of wind turbine failures.

Frequency

Frequency of failure due to a particular cause. Table 7 present the frequency scale.

Table 7.

Frequency scale.

Frequency level	Index	Definition
Very low frequency	1	Rare failure: Less than one failure per year
Low frequency	2	Possible failure 3 months
Average frequency	3	Frequent failure 1 week
High frequency	4	Very frequent failure several failures per week

Gravity

Table 8 shows the gravity scale. This is the severity of the effects of the failure:

Productivity losses (production stoppage).

Cost of maintenance.

Safety, environment.

Table 8.

Gravity scale.

Gravity level	Index	Definition
Very low gravity	1	Doesn’t cause the system to shut down or
Low gravity	2	Response time from 10 to 30 minutes
Medium gravity	3	Response time from 30 to 45 minutes
Catastrophic gravity	4	Response time-45 minutes

Non-Detection

Table 9 presents the non-detection scale in four levels.

Table 9.

Non-detection scale.

Detection level	Index	Definition
Obvious detection	1	Easily detectable failure: detection is visible on SCADA
Possible detection	2	Detectable failure
Importable detection	3	Failure difficult to detect
Detection impossible	4	Undetectable failure

Criticality

The scale of criticality presented in Table 10. It discriminates against actions to be taken and calculates them on the basis of severity, frequency and failure of non-detection.

Table 10.

Scale of criticality.

Criticality level	Definition
1 ≤ C < 10 negligible criticality	No changes: Corrective maintenance
10 ≤ C < 18 medium-rated criticality	Improvement: Systematic preventive maintenance
18 ≤ C < 27 High criticality	Special monitoring: Conditional preventive maintenance
27 ≤ C <64 Prohibited criticality	Complete questioning of equipment

FMECA grid on the critical elements of wind turbines

The database registered in the excel, it is arranged and organized according to the systems and subsystems of the wind turbines, then presented in the form of a diagram.

Generator

2880 hours as higher downtime for stator failure, with a frequency of 170 times for 9 years as shown in Figure 4. The generator step change ranks second for downtime of 1920 hours with a frequency of 100 times for 9 years. Overload; environmental effects; misalignment; fatigue; mechanical failure; Loss of transmission control are the relatively related causes of these generator failures.

Figure 4.

Diagram of downtime and frequency of generator failures in Amougdoul for the period 2010–2019.

According to the graph showing the failures associated with the rotor (Figure 5), the K52 rotor fault is the highest for a downtime of 182.13 hours and frequency of 37 during the years 2010–2019.

Figure 5.

Diagram of downtime and frequency of rotor failures for the period 2010 to 2019.

The diagram in Figure 6 shows a decrease in downtime and also the frequency of failures associated with the stator element. K52 stator fault and magnetic stator error are the most critical subsystems in the stator element with a downtime of 542.30 and 459.05 and frequency of 77 and 29 during the years 2010–2019. Figure 7 shows the effects of stator failures for the subsystems.

Figure 6.

Diagram of downtime and frequency of stator failures for the period 2010-2019.

Figure 7.

Generator failure: (a) bearing, (b) loss of magnetic wedge and (c) contamination (Zhu and Li, 2018).

The following Tables 11 and 12 show the failure modes of the generator and gearbox, their effects and their criticality values.

Table 11.

FMECA generator grid.

System: nacelle sub-system: generator						Analysis date: 2010–2019
Element/subsystem	Function	Failure mode	Detection	Cause of defects	Failure effect	Criticality				Intervention; corrective action
Element/subsystem	Function	Failure mode	Detection	Cause of defects	Failure effect	F	G	D	C	Intervention; corrective action
Stator	Creates fixed longitudinal magnetization using coils (inductor) or permanent magnets	Default K52 stator	Visual on the SCADA system	The environment.	Stopping the wind turbine	4	4	2	32	Checking contactor, conductors, connection box, establishing tests and restarting the machine.
				Inadequate manufacturing/installation quality.
				High wind speed.
				Fatigue.						Change of contactor.
				Non-compliance with recommended maintenance practices.						Checking the coil control cables, retightening and restarting the machine..
				Lightning strikes, wind loads, extreme weather conditions.
				Incorrect installations.
				Excessive vibrations.
				Tension irregularities.
				Converter failure.
				Overspeed
		Magnetic error stator	Visual on the SCADA system	Releasing the magnetic corners of the stator.	Stopping the wind turbine	3	4	2	24	Checking the ground and reset the local machine.
		Trigger automatic stator protection	Visual on the SCADA system	Incorrect grounding.	Stopping the wind turbine	2	4	2	16	Check and change the converter, locate the failure at the collector level.
		Over-intensity phase S Stator	Visual on the SCADA system	Protective relay problem.	Stopping the wind turbine	2	4	2	16	Change of manifold.
		Over-intensity phase S Stator	Visual on the SCADA system	Mechanical overload of the driven wind turbine (increased torque resistant); Lower voltage; Blocking at start-up; Short circuit.						General converter check, carrying out tests, changing the measurement card and restarting the machine.
		Over-intensity phase R Stator	Visual on the SCADA system	Protective relay problem.	Stopping the wind turbine	2	4	2	16	Tension measurement, cable check and stator protection.
		Over-intensity phase R Stator	Visual on the SCADA system	Mechanical overload of the driven wind turbine (increased torque resistant); Lower voltage; Blocking at start-up; Short circuit.	Stopping the wind turbine	2	4	2	16	Tension measurement, cable check and stator protection.
		Magnetic stator trigger	Visual on the SCADA system	Protective relay problem.	Stopping the wind turbine	2	4	2	16	Quick change of transformer and protection relay.
				Fuse problem.						Checking the generator, changing a carbon and eliminating a bad connection.
										Coupling contactor change.
		Stator protection error	Visual on the SCADA system	Stator protection problem.	Stopping the wind turbine	1	3	2	6	Checking the protection of the stator and reset of the machine.
		Thermal stator opening	Visual on the SCADA system	Fatigue	Stopping the wind turbine	1	4	2	8	Changing the bearing.
				Bearing problem.						Changing the relay and restarting the machine.
				Relay problem and collector and encode mating.						Changing the relay and restarting the machine.
				Fan problem.						Analysis and detection of the origin of the failure and change of collector and encoder coupling.
										Switching of the contactor status drivers and change of the power cabinet fan.
		Error KM52 stator	Visual on the SCADA system	Contactor.	Stopping the wind turbine	1	3	2	6	Checking the KM52 contactor and reset of the machine.
		Leak to the stator land	Visual on the SCADA system	Ancient installation, in which wires have degraded over time. But the current leak can also come from moisture in the circuit or from a broken conductor.	Stopping the wind turbine	1	3	2	6	Checking the TCs (torque control) of the stator and general rearming of the machine.
Anemometer-generator	Measures the wind speed (or pressure)	Anemometer-generator divergence	Visual on the SCADA system	Fatigue.	Stop at the wind turbine	3	4	2	24	Change of anemometer.
				Overspeed.						Add multiplier oil and machine cleaning.
				High wind speed.						Checking and retightening a badly connected connection cable.
Generator bearing	Resist the strong electromagnetic disturbances induced by the power of the engines	Change-bearing generator.	Visual on the SCADA system	Bearing fatigue.	Stop at the wind turbine	4	4	2	32	Changing the bearing on the generator coupling side.
		Noise at the generator bearing.								Changing the bearing on the generator coupling side.
		Pt100 sensor defect of generator bearing.								Change of generator bearing on the opposite side of the coupling and damaged fan.
		Change bearing generator mating rating (LA).
		High generator bearing temperature.
Rotor	The rotor can be a magnet that during its rotation induces an electric field in the coils of the generator/alternator stator	K52 rotor defect	Visual on the SCADA system	Overestimating the life of rotor performance (design-related).	Stop at the wind turbine	3	4	2	24	Checking and changing the relay.
										Changing the stator contactor.
				Rotor wire failure.						Change of cabinet fan and restart of the machine.
										Cabinet power circuit check and rotor contactor change.
										Checking the operation of the pump.
										Adjustable rotor lock change, lighting repair.
		Change-blocking the rotor	Visual on the SCADA system	Mechanical blockage of the wind turbine.	Stop at the wind	1	2	2	4	Change Mechanical blockage of the machine.
				Power.	turbine					Changing the fixed rotor lock.
		Over-intensity phase S Rotor	Visual on the SCADA system	Power elevation revealing too much resistant torque on the tree.	Stopping the wind turbine	1	4	2	8	Replacement of two insulated gate bipolar transistors and restart of the machine.
		Rotor protection defect	Visual on the SCADA system	Wind is too violent.	Stop at the wind turbine	1	3	2	6	Changing rotor protection.
				Mechanical constraints.
				Overspeed of the rotor.
		Test defect by rotor movement	Visual on the SCADA system	Rotor movement	Stop at the wind turbine	1	3	1	3	Loading GH rotor and starting the machine.
		Rotor cable repair	Visual on the SCADA system	Repairing generator rotor cables.	Stop at the wind turbine	1	1	2	2	Repairing the sheaths of the power cables.
Generator	A dipole that can circulate an electrical current in a circuit	Generator alignment	Visual on the SCADA system	Overspeed.	Stop at the wind	2	4	2	16	Inspection and completion of generator alignment and cleaning of the leak.
				Environmental charges.	turbine
										Changing the quick-axle coupling.
										Change of the rotation motor, planetary reduction gear, brake disc protection, brake hoses, oil levelling and cleaning of the machine.
										Change of the coupling rods and reducer pressure switch.
		High generator speed	Visual on the SCADA system	Wind speed 20 m/s.	Stopping the wind turbine	2	4	1	8	Checking and local reset of the machine. (Wind speed > 20 m/s).
										General rearming of the machine.
										Checking the quick axle coupling, changing the encoder and its coupling.
										Change of smoke sensor, autonomous power supply system and precisely rotor sensors.
		Generator-rotor divergence	Visual on the SCADA system	Overspeed.	Stopping the wind turbine	2	4	2	16	Checking, local reset and restarting the machine.
				Fatigue.						Verification, speed test performance.
										Checking the encoder, inductive sensor.
										Change of the inductive sensor and its cable.
										Checking and checking the rotation sensor, the rotor, testing the functioning of the rotor counter module and changing the rotor rotation sensor, mounting the elements, testing.
		Generator noise	Visual on the SCADA system	Generator rolls.	Stop at the wind turbine	2	4	2	16	Noise inspection of generator bearings.
				Over speed.						Realization of generator speed test, auditory inspection and detection of the presence of average generator rolling noise.
		Generator change	Visual on the SCADA system	End of life use time is over.	Stop at the wind turbine	2	4	2	16	Preparing the machine for the generator change.
		High generator temperature	Visual on the SCADA system	Sensor error.	Stop at the wind turbine	3	4	1	12	Checking the PT100 sensor module, changing the earth brush and checking the generator.
				Lack of tightening of the boundaries.						Retightening of terminal blocks and temperature modules.
				Short circuit.						Change of the PT100 of the generator bearing, Fan motor support, gearbox cooling system motor.
Generator collector	Creates an electrical connection between a fixed part (stator) and a rotating part (rotor) in the case of a multi-tower rotation, where a cable transmission would be impossible	Generator collector	Visual on the SCADA system	Fatigue.	Stopping the wind turbine	1	4	2	8	Checking of commutator, brushes, terminal and rotor terminal plate.
				Blocking.						Change of generator manifold, cleaning of the machine and restarting.
										Collector insulation repair, change of contactor and assembly of hardware boxes.
Rotor or generator RPM	The main rotor speed is comparable to the speed of an aircraft. It creates the airflow on the blades that produce lift	High rotor or generator rotation speed (RPM)	Visual on the SCADA system	Overspeed.	Stopping the wind turbine	1	4	1	4	Checking and reset general of the machine.

Table 12.

Gearbox FMECA grid.

System: nacelle sub-system: gearbox						Analysis date: 2010–2019
Element/subsystem	Function	Failure mode	Detection	Cause of default	Failure effect	Criticality				Intervention; corrective action
Element/subsystem	Function	Failure mode	Detection	Cause of default	Failure effect	F	G	D	C	Intervention; corrective action
Gomes multiplier shocker	The role of the damper Gomes is to absorb vibrations between the engine and the gearbox.	Change-multiplier shock absorbers (L-R)	Visual on the SCADA system	Fatigue.	Stopping the wind turbine	4	4	2	32	Changing the elastic play of the shock absorber.
				Loading by the wind.						Changing the elastic play of the shock absorber.
				Seismic loading.						Change the two elastic discs of the right shock absorber and fix the power cables and clean the machine.
		Change-multiplier shock absorber	Visual on the SCADA system	Fatigue.	Stopping the wind turbine	1	4	2	8	Change of the left multiplier shock absorber and verification of the generator quick axis coupling.
				Loading by the wind.
				Seismic loading.						Sight for oil level multiplier and machine cleaning.
Multiplier pump	The pump, the main component of a hydraulic system, allows the flow of oil in the wind turbine and to distribute the fuel to the various injectors of the engine	Multiplier oil pump defect	Visual on the SCADA system	Fatigue.	Stopping the wind turbine	4	4	2	32	Change of multiplier pump.
				Manufacturing defect.						Checking and changing the multiplier oil pump motor.
				Aeration.
				Cavitation.
				Contamination.
				Overpressure.
				Excessive temperature.						Checking of the two multiplier pump motors and thermal reset.
				Inadequate viscosity.						Change of the main pump of the multiplier.
										Resetting the thermal relay, and checking the contactor and power cables, dismantling and checking the mechanical part of the pump, checking the operation of the fans and oil delivery.
										Checking, general rearming and restarting the machine.
Multiplier fans	An essential organ of the cooling system	Thermal multiplier fans	Visual on the SCADA system	Fatigue.	Stopping the wind turbine	3	4	1	12	Changing the ventilation motor.
				Dust.						Replacing the multiplier oil exchanger cooling engine.
										Checking of the two motors, wiring and circuit breakers.
										Repair of supports and change of the two motor-fans and repair of the tower lighting.
										Motor-fan check, gearbox oil pump.
										Dismantling of the damaged multiplier fan support, change of the support, motor-fan and cleaning of the machine.
		Multiplicative fan re-feeding defect	Visual on the SCADA system	Fatigue.	Stopping the wind turbine	1	4	2	8	Checking fan engines, contactors and thermal relays.
				The dust.
				Obstructed fans or vents.
				Dust buildup on ventilators or fans.						Checking, tightening the coil’s cable.
				Inadequate ventilation.
				Physical damage.
Multiplier oil	Lubrication of the multiplier can be done by dabbling or injecting forced oil	High temperature of multiplier oil	Visual on the SCADA system	Oil tank too small.	Stopping the wind turbine	3	4	1	12	Checking the PT100 sensor.
				Bad natural thermal exchange (poor ventilation).	Stopping the wind turbine	3	4	1	12	Checking the PT100 sensor.
				Extended operation at low speed and high pressure for a hydraulic engine.						Checking the temperature and resetting the machine.
				Undersized components.						Checking the condition of the platform fans.
				Abnormally open pressure limiter.						Changing the multiplier pump.
				A mixture of air-oil (poorly de-deered oil).						Checking the two exchanger ventilation motors and changing the multiplier oil pump.
										Main pump change, multiplier oil cooling motor inspection and mechanical pump check.
										Checking the operation of the cooling system, tightening the cables, resetting the protection of the main multiplier pump.
		Low level multiplier oil	Visual on the SCADA system	Loss by evaporation of lubricating oils.	Stopping the wind turbine	3	4	2	24	Changing the multi-exchanger oil tube and cleaning the leak.
				Waterproofing leak.						Upgrade gearbox oil.
										Filling with multiplier oil then retightening and cleaning the leak.
										Checking the oil level then filling the multiplier.
										Addition of multiplier oil, local reset and restart of the machine.
		Change-oil and multiplier filters	Visual on the SCADA system	Leak.	Stopping the wind turbine	3	4	2	24	On-line filter change of the multiplier and cleaning of the leak.
				Risk of strange particles.						Multiplier air filter change.
										Change of oil exchanger, two pumps (main and additional), filter, hoses and
										Multiplier drain.
										Change of the multiplier valve.
										Change of oil and multiplier filters, extraction of oil sample, tightening of the hoses and main pump, fixing of the rear nacelle door
										Carrying out oil change.
		Change-multiplier oil exchanger	Visual on the SCADA system	Oil loses its lubricating and cooling properties.	Stopping the wind turbine	1	3	2	6	Change of the cooling system interchange.
Multiplier	Increase the number of turns: when the propeller turns, the multiplier axis will make 10	Inspection-multiplier	Visual on the SCADA system	Inspection of the speed multiplier according to the instructions indicated in its own document.	Stopping the wind turbine	3	3	2	12	General inspection of the multiplier (shock absorbers, motor pumps, ventilation, gear ...) and oil upgrade.
										Change of filters on-line and offline.
										Video inspection of the multiplier.
		Low multiplier pressure	Visual on the SCADA system	Leak.	Stopping the wind turbine	2	4	2	16	Checking the multiplier pressure and restarting the machine.
				Fatigue.						Reification of cables, connections, analog input and mechanical valve adjustment.
										Leak source repair and multiplier filling.
										Draining the multiplier and changing the mechanical multiplier pump and its coupling.
										Calibration of the multiplier oil pressure sensor and replacement of the platform door.
										Addition of oil to the multiplier, then change of the pitch system cylinder and pushing shaft.
										Main pump hose change and multiplier oil leveling.
		Multiplier change and main tree	Visual on the SCADA system	Fatigue.	Stopping the wind turbine	3	4	2	24	Full multiplier change.
										Rehabilitation of the wind turbine after changing multiplier.
Main multiplier filter	As gears and bearings are sensitive to particles and humidity, it is necessary to add an oil filter to continuously remove water, wear particles and oxidation products.	Dirty main multiplier filter	Visual on the SCADA system	Time of use is decreased.	Stopping the wind turbine	3	4	2	24	Checking and restarting the machine.
				Infiltration of strange bodies.						Checking of pressure switch, cable, and change of main filter.
				Risk of foreign particles.						Local reset of the machine and restarting.
										Change of the two multiplier filters, carrying out tests and restarting the machine.
										Check data on screen and local reset of the machine (wind speed exceeds 21 m/s).
		Dirty multiplier auxiliary filter	Visual on the SCADA system	Time of use is decreased.	Stopping the wind turbine	1	4	2	8	Change of main and auxiliary filter, addition of multiplier oil and cleaning of the machine.
				Infiltration of strange bodies.
				Risk of foreign particles.
Multiplier bearing	Are centerpieces for the performance of wind turbines, allowing the operation of the transmission	High multiplier bearing temperature	Visual on the SCADA system	Lack of lubrication.	Stopping the wind turbine	1	3	2	6	Temperature check at the bearings.
				Too much friction.						Temperature check at the bearings.
				Sensor error.						Checking the PT100 temperature sensors and modules.
										Change of bearings.

Gearbox

The diagram presented in Figure 8 illustrates the distribution of downtime per part in the wind farm over the 9 years in real time and the frequency of gearbox failures that occurred from 2010 to 2019; the results show that gearbox failures related to change of gearbox and main shaft cause the most downtime (3360 hours) with a frequency of 20 times. We can see that the fault of the multiplier pump has the greatest number of failures (100 times), followed closely by that of the rubber change of the multiplier shock absorbers (100 times).We presented a state of the art on the different failure modes of the gearbox for the horizontal axis wind turbine (Lamhour and Tizliouine, 2019). Figure 9 shows the effects on bearing failures for the subsystems.

Figure 8.

Diagram of downtime and frequency of gearbox failures for the period 2010–2019.

Figure 9.

Bearing failure: (a) bearing in the gearbox; (b) and (c) appearance of bearing failure (Zhu and Li, 2018).

PARETO analysis

We will do the PARETO analysis to determine the most critical subsystems Table 13.

Table 13.

Critical components.

Element	Criticality	% of criticality	Cumulative % of criticality
Default K52 stator	32	5	5
Change-multiplier shock absorbers (L-R)	32	5	11
Multiplier pump defect	32	5	16
Generator bearing	32	5	22
Magnetic error stator	24	4	26
Anemometer-generator divergence	24	4	30
K52 rotor defect	24	4	34
Low level multiplier oil	24	4	38
Change-oil and multiplier filters	24	4	42
Multiplier change and main tree	24	4	46
Dirty main multiplier filter	24	4	50
Trigger automatic stator protection	16	3	53
Over-intensity phase S stator	16	3	55
Over-intensity phase R stator	16	3	58
Magnetic stator trigger	16	3	61
Generator alignment	16	3	63
Generator-rotor divergence	16	3	66
Generator noise	16	3	69
Generator change	16	3	72
Low multiplier pressure	16	3	74
High generator temperature	12	2	76
Thermal multiplier fans	12	2	78
High temperature multiplier oil	12	2	80
Inspection-multiplier	12	2	82
Thermal stator opening	8	1	84
Over-intensity phase S rotor	8	1	85
High generator speed	8	1	86
Generator collector	8	1	88
Change-multiplier shock absorber	8	1	89
Multiplicative fan re-feeding defect	8	1	90
Dirty multiplier auxiliary filter	8	1	92
Error KM52 stator	6	1	93
Leak to the stator land	6	1	94
KM52 rotor error	6	1	95
Rotor protection defect	6	1	96
Change-multiplier oil exchanger	6	1	97
High multiplier bearing temperature	6	1	98
Rotor RPM or high generator	4	1	98
Change-rotor block	4	1	99
Test defect by rotor movement	3	1	100
Rotor cable repair	2	0	100
Total	593

Interpretation

According to the FMECA analysis “Analysis of Failure Modes, their Effects and their Criticality,” we noticed that the most critical components those which have a high criticality, we considered four levels of criticality according to the next board Figure 10:

Figure 10.

PARETO diagram for criticality.

Interpretation

According to the FMECA analysis “Analysis of Failure Modes, Their Effects and Criticality,” we noticed that the most critical components from Table 14 are those with a high criticality “32” which is act from:

K52 stator fault: the stator is a key part in the generator for the production of current. A simple failure in the circuit of this component can generate a reduction or even no energy production.

Change – Gomes dampers multiplier (GD): By a permanent vibration of the wind system, climate change and temperature variation in the installation, the damper gomes of the multiplier get tired and deteriorate consequently the installation of the multiplier loses the alignment with the generator and may cause a fault in the latter.

Multiplier oil pump fault: A malfunction of the pump may lead to poor or absence of lubrication of all the multiplier elements. This may cause heating due to friction at the bearings and gears, consequently the system seizing up eminent.

Generator bearing: this is a very important element in the generator and for balance. The bearings are designed to operate in a clean environment and under ideal conditions. Bearing failures can be caused by fouling and the intrusion of foreign objects. Premature wear is often the result of inadequate or insufficient lubrication. If the bearing is running loudly and there is a rapid rise in temperature, this is an indication of insufficient lubrication or too much oil. If the diameter of the shaft is too large or the bore is too narrow, deformation of the rings occurs, resulting in spalling on the raceways. When one of the tracks of a groove bearing is poorly adjusted, the latter is subjected to indeterminate loads which are added to the normal load, causing an overload which deteriorates the assembly. Poor alignment, electrical current flow, poor lubrication, overloading, welding, oversized shafts and mesh on the bearing cages can cause bearing damage.

Table 14.

Criticality levels.

Criticality level	Organs
1 ≤ C < 10 negligible criticality	Thermal stator opening
	Over-intensity phase S Rotor
	High generator speed
	Generator collector
	Change-multiplier shock absorber
	Multiplicative fan re-feeding defect
	Error KM52 stator
	Leak to the stator land
	KM52 rotor error
	Rotor protection defect
	Change-multiplier oil exchanger
	Dirty multiplier auxiliary filter
	Rotor RPM or high generator
	Change-rotor block
	Test defect by rotor movement
	High multiplier rolling temperature
	Rotor cable repair
10 ≤ C < 18 medium-rated criticality	Trigger automatic stator protection
	Over-intensity phase S stator
	Over-intensity phase R stator
	Magnetic stator trigger
	Generator alignment
	Generator-rotor divergence
	Generator noise
	Generator change
	Low multiplier pressure
	High generator temperature
	Thermal multiplier fans
	High temperature multiplier oil
	Inspection-multiplier
18 ≤ C <27 High criticality	Magnetic error stator
	Anemometer-generator divergence
	K52 rotor defect
	Low level multiplier oil
	Change-oil and multiplier filters
	Multiplier change and main tree
	Dirty main multiplier filter
27 ≤ C < 64 Prohibited criticality	Default K52 stator
	Change-multiplier shock absorbers (L-R)
	Multiplier pump defect
	Generator bearing

Critical analysis of the current maintenance of the ESSAOUIRA wind farm

According to the current maintenance policy (GAMESA manufacturer’s maintenance manual) and the maintenance carried out by ONEE technicians, we see that the main causes of failures are as follows:

Difficulties in obtaining spare parts: These difficulties are linked to insufficient maintenance or maintenance budgets, sometimes with the lack of after-sales services. These situations lead to a shortage of stocks.

Lack of training for maintenance technicians and users of wind turbine equipment: According to our findings, there is no training plan for maintenance technicians and equipment users. For maintenance technicians, training is limited to their participation in seminars and workshops devoted to maintenance management. User training is very often limited to that carried out by the supplier of the equipment during its installation, which leads to frequent equipment breakdowns resulting from handling errors. For this we note that training is essential for a good functionality of the equipment.

Exceeding the duration and neglect of some preventive maintenance tasks: Some technicians do not respect the preventive maintenance schedule of an equipment, they are not serious about the duration of each action, this leads to an increase in the probability appearance of a failure.

Lack of maintenance monitoring. There is no monitoring to verify the maintenance carried out (no maintenance report carried out).

The cost – For a breakdown outside the contract, additional cost for ONEE – Time between response and intervention: increase in unavailability and costs.

All that remains is to set a threshold beyond which we must implement preventive actions to reduce the risk. For this we will use the method.

To reduce criticality, it is necessary to reduce the three factors which are frequency, severity and non-detection by the following methods:

a. Reduce the frequency through preventive maintenance.

b. Reduce the severity by good preparation before the intervention across ranges and preparation of resources.

c. Reduce non-detectability by planning checks with the appropriate measurement resources (oil analysis, vibration analysis, etc.)

We will then focus on the actions to be taken to reduce the reappearance of failures, in addition to a presentation of some technical solutions to remedy the failures.

Recommendations

We make recommendations from Table 15 that will help improve maintenance service. For the maintenance of the multiplier pump element, the following must be done:

Table 15.

Recommendations that will help improve maintenance service.

Gearbox		Generator
Preventive maintenance of the gear unit	Additional maintenance operations	Preventive maintenance of the generator	Additional maintenance operations
Regularly check the gear pump.	Replacement of the particle detection system every 10 years	Checking the electric cables in the generator.	Verification of bearing noise.
Clean the gearbox with compressed air and lubricate the bearings regularly.	Checking the drainage pipes.	Checking for coolant and grease leaks	Checking the automatic lubrication system.
Check the oil level in the accumulators and observe the frequency of the oil change.	Replacement if necessary.	Lubrication of bearings.	Checking the cooling system.
Oil change and cleaning of the multiplier if necessary	Replacement of drainage pipes every 10 years.
Verification of the sound level during operation of the multiplier	Extraction of an oil sample for analysis
Checking the joints, absence of leaks, etc ...	Inspection of the bolts of the coupling system between the multiplier and the main shaft every 4 years
Checking for the absence of leaks at the lubrication points
Checking debris sensors
Checking the oil level
Checking the components of the hydraulic unit and pumps
Check the rate of degradation of the tooth surface of the gears.

Proposed solutions and improvement

Based on the current critical maintenance analysis, we note that this is a progressive failure requiring a maintenance check. The failure study made it possible to develop a maintenance monitoring manual to match the reliability between the maintenance performed by the technicians and the manufacturer’s schedule, as well as some suggestions for technical solutions. The process for producing the maintenance monitoring manual was based on the previous analyzes, with the use of the failure history since 2010–2019. This part aims to implement the results of the FMECA studies. We will present technical solutions to these failures Table 16.

Table 16.

Actions to be taken to reduce the recurrence of failures.

Computer applications	Technical solutions
✓ Production of a maintenance intervention report as part of the “C -Sharp” computer system which includes a database with a “wampserver” and programming with “dot.net”	✓ Gear pump for lubrication applications requiring uniform pumping and dosing.
	✓ Proposal for monitoring that performs the analysis of the oil on site and subsequently avoids wasting time.
✓ Production of a maintenance intervention report as part of the “G.L.P.I.,” Intended for the management of computer equipment, uses a Web server written in PHP. Enables the inventory and management of all the hardware or software components of a fleet, and thus optimizes the work of technicians thanks to more consistent maintenance.	✓ Continuous monitoring of dampers to decrease vibrations threaten structural stability and significantly reduce the life of wind turbines.
	✓ Replace ball bearings with roller bearings to support more load.
✓ The main functionalities of these applications revolve around two axes: The precise inventory of all existing technical, material and software resources, the characteristics of which will be stored in a database.	✓ Develop and validate a lubricant formulated with the properties appropriate to the operating conditions of the system in this environment.
	✓ Equip software to accurately assess the stresses imposed on gears and bearings, in order to develop stronger wind turbines.
✓ Management and historization of various maintenance operations and related procedures carried out on these technical resources.	✓ Measure the acoustics due to the vibration of the bearings by a means of control using a sonometer.

Conclusion

Maintenance is the critical factor affecting the life of wind turbines. Proper repairs are also essential to the reliability and longevity of the turbine generator. Once the actions are in place, the criticality is recalculated. All these actions therefore make it possible to reduce the frequency of breakdowns while optimizing the frequency of preventive interventions. At the end of this study, we can come out with the following recommendations:

The systematic maintenance instructions must be observed, such as the replacement of defective parts at the intervals recommended by the manufacturer.

Train maintenance technicians on the equipment to facilitate the detection of anomalies.

Train maintenance service personnel at AMDEC.

Maintain a safety stock of essential spare parts.

Footnotes

Appendix

Acknowledgements

We thank Mr. LAARAJ Abderrahim of ONEE (National Office of Electricity and potable water) and responsible of the Amougdoul wind farm, for kindly providingus with the data necessary to carry out this work.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Kawtar Lamhour

References

Burrows

Communications

(2018) Wind farms cause more environmental impact than previously thought. SciTechDaily, October 17, 2018. Available at: https://scitechdaily.com/wind-farms-cause-more-environmental-impact-than-previously-thought/amp/ (consulté le 08 juin 2020).

El-Thalji

Jantunen

(2012) On the development of condition based maintenance strategy for offshore wind farm: Requirement elicitation process. Energy Procedia 24: 328–339.

Ezzaidi

Elyaqouti

Bouhouch

, et al. (2017) Evaluation of the energy performance of the Amougdoul wind farm, Morocco. International Journal of Electrical & Computer Engineering 7: 692–705.

Gonzalez

Nanos

Seyr

, et al. (2017) Key performance indicators for wind farm operation and maintenance. Energy Procedia 137: 559–570.

Lamhour

Tizliouine

(2019) Different failure modes of the horizontal axis wind turbine gearbox. In: 2019 7th international renewable and sustainable energy conference (IRSEC), Agadir, Morocco, 27–30 November 2019, pp.1–8. New York City: IEEE. DOI: 10.1109/IRSEC48032.2019.9078308

MÉDIAS24 (2020) Energies renouvelables: 3.685 MW de puissance installée à fin 2019. Medias24 - Site d’information, janv. 6. Available at: https://www.medias24.com/energies-renouvelables-3-685-mw-de-puissance-installee-a-fin-2019-6619.html (consulté le 08 juin 2020).

Mostafaeipour

(2010) Productivity and development issues of global wind turbine industry. Renew. Renewable and Sustainable Energy Reviews 14(3): 1048–1058.

Mulder

(2012) Pareto Analysis and principle. Retrieved from ToolsHero: https://www.toolshero.com/problem-solving/pareto-analysis/ (consulté le janvier 11, 2021)

Ozturk

Fthenakis

Faulstich

(2018) Failure modes, effects and criticality analysis for wind turbines considering climatic regions and comparing geared and direct drive wind turbines. Energies 11(9): 2317.

10.

Qian

Zhang

Tian

, et al. (2019) A novel wind turbine condition monitoring method based on cloud computing. Renewable Energy 135: 390–398.

11.

RCREEE (2012) Capacity building 2010 day 2 exploitation des pars eoliens (fr). 07:37:52 UTC, [En ligne]. Disponible sur: S.

12.

Rohe

(2020) The regional facet of a global innovation system: Exploring the spatiality of resource formation in the value chain for onshore wind energy. Environmental Innovation and Societal Transitions 36: 331–344.

13.

Scheu

Matha

Hofmann

, et al. (2012) Maintenance strategies for large offshore wind farms. Energy Procedia 24: 281–288.

14.

Scheu

Tremps

Smolka

, et al. (2019) A systematic failure mode effects and criticality analysis for offshore wind turbine systems towards integrated condition based maintenance strategies. Ocean Engineering 176: 118–133.

15.

Scribd L’Amdec: Nicolas DAUJEARD and Ingénierie des systèmes and Science des systèmes. Scribd. Available at: https://fr.scribd.com/presentation/227158554/Amdec-Site (consulté le 20 juill 2020).

16.

Shafiee

Sørensen

(2019) Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies. Reliability Engineering & System Safety 192: 105993.

17.

Slot

Sørensen

Sudret

, et al. (2020) Surrogate model uncertainty in wind turbine reliability assessment. Renewable Energy 151: 1150–1162.

18.

The Wind Power (2018) Gamesa G52/850-Constructeurs et turbines-Accès en ligne. https://www.thewindpower.net/turbine_fr_42_gamesa_g52-850.php (consulté le 08 juin 2020).

19.

van Bussel

Zaaijer

(2001) Reliability, Availability and Maintenance aspects of large-scale offshore wind farms, a concepts study. In Proc. of the Marine Renewable Energy Conference (MAREC), Newcastle, United Kingdom, 2001.

20.

Zhu

(2018). Reliability Analysis of Wind Turbines, Stability Control and Reliable Performance of Wind Turbines, Kenneth Eloghene Okedu, IntechOpen, DOI: 10.5772/intechopen.74859. Available from: https://www.intechopen.com/books/stability-control-and-reliable-performance-of-wind-turbines/reliability-analysis-of-wind-turbines