IoT‐based approach to condition monitoring of the wave power generation system

IET Renewable Power GenerationVolume 13, Issue 12 p. 2207-2214 Research ArticleFree Access IoT-based approach to condition monitoring of the wave power generation system Peng Qian, Peng Qian Department of Mechanical, Aerospace and Civil Engineering, Brunel University London, UB8 3PH UKSearch for more papers by this authorBo Feng, Bo Feng Ocean College, Zhejiang University, Hangzhou, 310058 People's Republic of ChinaSearch for more papers by this authorDahai Zhang, Corresponding Author Dahai Zhang zhangdahai@zju.edu.cn Ocean College, Zhejiang University, Hangzhou, 310058 People's Republic of ChinaSearch for more papers by this authorXiange Tian, Xiange Tian Department of Mechanical, Aerospace and Civil Engineering, Brunel University London, UB8 3PH UKSearch for more papers by this authorYulin Si, Yulin Si Ocean College, Zhejiang University, Hangzhou, 310058 People's Republic of ChinaSearch for more papers by this author Peng Qian, Peng Qian Department of Mechanical, Aerospace and Civil Engineering, Brunel University London, UB8 3PH UKSearch for more papers by this authorBo Feng, Bo Feng Ocean College, Zhejiang University, Hangzhou, 310058 People's Republic of ChinaSearch for more papers by this authorDahai Zhang, Corresponding Author Dahai Zhang zhangdahai@zju.edu.cn Ocean College, Zhejiang University, Hangzhou, 310058 People's Republic of ChinaSearch for more papers by this authorXiange Tian, Xiange Tian Department of Mechanical, Aerospace and Civil Engineering, Brunel University London, UB8 3PH UKSearch for more papers by this authorYulin Si, Yulin Si Ocean College, Zhejiang University, Hangzhou, 310058 People's Republic of ChinaSearch for more papers by this author First published: 03 July 2019 https://doi.org/10.1049/iet-rpg.2018.5918Citations: 5AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Accurate and reliable fault detecting plays a key role in application of grid-connected wave power generation systems. This study presents a novel IoT-based approach to condition monitoring of the wave power generation system, which has faster operating rate and lower hardware requirement. The compressed sensing (CS) method is adopted to compress the data, which aims to reduce the data uploaded to cloud platform; and then, the extreme learning machine (ELM) algorithm is used to achieve the condition monitoring of wave power generation system in cloud platform. In order to validate the effectiveness of the proposed method, the IoT-based wave power generation condition monitoring system test platform is established. The experiment results illustrate the high efficiency and reliability of proposed method. The proposed method has a potential of practical applications. 1 Introduction There has been a dramatic increase in the development of ocean renewable energy over the past decade in all over the world, especially wave power generation systems [1]. The reserve of wave energy resource has been estimated to 1013 W in the ocean. However, one-tenth wave energy resource distributes around the coastline. This portion of the wave energy is easier to be utilised due to ease of access without high cost long distance cables. From previous research results, the energy density of wave power is higher than wind and solar energy, reaching 3 kW/m2, which means that wave power has a better reliability than the other renewable energy. In 2015, Chinese government issued an Eco Wave Power plan that will establish a 100 kW wave power plant in Zhoushan of the Zhejiang Province. Compared with wind power, the wave power generation systems always operate in more hostile environment. Consequently, accurate and reliable condition monitoring of wave power generation is very essential to optimise the operation cost and enhance the power system reliability [2]. Vibration and temperature analyses have been developed for the condition monitoring of the wave power generation system [3]. Spectral analysis based on vibration signals is one of the most effective methods to detect anomalies in the gearbox and bearing. However, spectral analysis based on vibration signals requires very detailed information about the bearing structural parameters. Vibration signal analysis also needs very high performance processors that increase the system cost. Temperature trend analysis can detect fault effectively; however, this method is not suitable to be applied in early failure detection. Generally, the wind passing over the surface of the sea generates waves, while wave power is determined by many factors, including wind speed, ambient temperature, wave speed, water density, and wavelength [1]. Hence, wave power generation system is a kind of a non-linear system that is difficult to describe its accurate operation condition by using mathematic equation [2]. Due to the excellent approximation and generalisation, artificial intelligence (AI) methods are extensively applied in renewable power generation condition monitoring system. In [4], a machine-learning method based on artificial neural network (ANN) was applied in condition monitoring of the wind turbine, which adopted several input signals for model training, including wind speed and power generation. Normally, ANN method has a drawback of high computational burden, which needs high performance computer to achieve computation. Traditional workstation computers adopt serial computing method, which have a very limited parallel computing capability. It means that it needs several workstation computers for the data analysis in a large-scale wave farm, due to simultaneous operation of many wave power generation systems. It poses a challenge for application of grid-connected wave power generation systems. Internet of things (IoT) is a kind inter-networking of physical devices, which is embedded with sensors, software, and hardware [5]. With the rapid development of cloud computing platform technology, the application of IoT combined with the cloud computing platform has become a trend [6, 7]. Compared with the traditional client server, cloud computing platform integrates the distributed computing, parallel computing, virtualisation, and network storage technologies, thus resulting in higher computational efficiency [8]. Consequently, IoT combined with cloud computing technology is very suitable to be applied in the condition monitoring of the wave power generation system in the large scale wave farm. Furthermore, IoT combined with cloud computing technology allows operators to obtain the information of wave power generator by using smart handheld devices at anywhere, which is significantly beneficial to energy management and arranging maintenance of the wave energy converter [9]. It is the best solution that outsources the complicated computation process to the cloud utilising its literally unlimited resources at relatively low prices. Although IoT combined with cloud computing systems have many advantages mentioned above, IoT combined with cloud computing systems also suffer a problem of network security that significantly interferes the safety of the data. Some hydrologic data and meteorological data are confidential for the operator. Otherwise, it is worth noting that the cost of IoT combined with cloud computing is generally determined by the quantity of the data uploaded, thus reducing the data upload quantity contributes to decrease the cost of the condition monitoring of the wave power generation system. In the data-driven condition monitoring system [10], more than one variables will be selected as input signals for prediction model, in order to increase the prediction accuracy of the model. In terms of the condition monitoring of the wave power generation system, different kinds of hydrologic data collected from the sea is usually selected as input signals of the model. Multipliable input variables mean that a large number of data is needed to be uploaded to the cloud platform. Clearly, it would be desirable to adopt some methods to ensure data security and reduce the number of the uploaded data under the condition of satisfying the prediction accuracy of the model. AI methods are utilised by many researchers for renewable energy forecasting, including ANN and support vector machine (SVM) [11]. ANN-based methods are suitable applied in noisy environments [12], because they are robust to signal noise. However, the longer training time of ANN model limits their application. SVM-based methods usually have more accurate training results and better generalisation performance than ANN-based methods [13, 14], but training SVM-based methods with a large number of data are not straightforward [15]. Compared with ANN and SVM methods, extreme learning machine (ELM) algorithm has a faster training speed and a better generalisation performance [16-19]. Hence, a data-driven condition monitoring method by using ELM algorithm is adopted to achieve fault detection of the hydraulic system in the wave power generation system. Then, compressed sensing (CS) method is selected to compress the first hidden layer output, which is not only able to detect the faults effectively, but also considering data upload quantity reduction and data security. To author's knowledge, the IoT-based condition monitoring method is first applied in wave power generation system. The main contributions of this paper are given as follows: A novel data-driven wave power generation system condition monitoring method based on cloud computing system is proposed. Essentially, the aim of the proposed method is to implement the complicate data-driven condition monitoring algorithm in the cloud-computing platform instead of on a client server. It will significantly reduce the cost of the condition monitoring system. ELM method is applied in the condition monitoring of the wave power generation system. CS method is applied to compress the first hidden layer output of ELM model, which not only reduces data upload quantity, but also ensures data security. In this paper, a commercial Internet of Things (IoT) platform ThingSpeak is adopted to validate the proposed method. The operational data of wave power generation system is compressed by CS method; then, data-driven condition monitoring approach is able to be applied in IoT platform. A hardware prototype of IoT-based condition monitoring system is developed. The remainder of this paper is organised as follows. The overview the wave power generation system is presented in Section 2. Section 3 describes the working principle of the extreme learning. Section 4 illustrates the data compression method combined with the data-driven condition monitoring system. A case study is then performed and the results are shown and discussed to validate the effectiveness of the proposed method in Section 5. Finally, Section 6 contains conclusions. 2 Overview the wave power generation system and condition monitoring system based on IoT The irregular wave consists of many linear superposition of various linear wave components with different wave height and period in the ocean waves. From Pierson–Moskowitz Spectrum, the mathematical description of the irregular wave spectral density function can be given as follow [20]: (1) where is the significant wave height that is calculated from the mean of the three highest waves in the time series; is the wave period; is the wave frequency. Then the wave elevation of each wave component can be obtained by using (2). (2) where is the frequency interval of two adjacent waves; is the phase of each wave component. Fig. 1 shows a horizontal floating pendulum wave power generation system developed by Zhejiang University in China [21]; the wave power generation system usually consists of a number of components and subsystems, including generator, gearbox, bearing, buoy, hydraulic system, and power electronic converter. The hydraulic system transmits power from oscillating buoy to the generator resulting in reduction of the output fluctuation, which is significant to increase the grid reliability. Fig. 1Open in figure viewerPowerPoint Prototype of the horizontal floating pendulum wave power generation system The motion of the floating pendulum can be illustrated as follows [21]: (3) where J is the rotational inertia of the floating pendulum of the wave power generation system; describes the added mass coefficient when the frequency of the floating pendulum approaches infinite; S is the restoring torque coefficient caused by the gravity and hydrostatic uplift; is the wave excitation torque that is composed of the Froude-Krylov force and diffraction force; is the control torque caused by the power-take-off; The radiation force is compose of the inertia component and the convolution component; is the impulse response function in the convolution term, which can be obtained by using (4) (4) is the frequency-dependent hydrodynamic damping. According to the Newton's second law, the force balance equation of a hydraulic cylinder is as follows: (5) where m is the equivalent mass of the piston, is the coefficient of friction loss, is the external force acting on the piston, and are pressures on both sides of the piston, is the piston area under pressure. is the motion speed of piston. The PTO force is described by the A-B pressure differential and the piston area under pressure: (6)The schematic diagram of the hydraulic transmission system is shown inFig. 2. The hydraulic components insidehydraulic bridge circuit (the blue dotted line) are the base unit of the capturedwave energy. When the array of the horizontal floating pendulum wave powergeneration system is adopted, it can be achieved by connecting the abovecorresponding base unit of captured wave energy in parallel. When the piston ofhydraulic cylinder 1 moves to the non-rod chamber, the high-pressure oil will drivehydraulic motor 6 and generator 10 through hydraulic bridge circuit, which consistsof two electro-hydraulic controlled switch valves and four check valves. On thecontrary, when the piston of hydraulic cylinder 1 moves to the rod chamber, thehigh-pressure oil will flow out of the rod chamber and back to its non-rod chamberthrough hydraulic bridge circuit. Accumulator 4 is used to stabilise the outputpower fluctuation caused by wave energy. Fig. 2Open in figure viewerPowerPoint Schematic diagram of hydraulic transmission system in thehorizontal floating pendulum wave power generationsystem 1. Hydraulic cylinder 2. Electro-hydraulic controlled switch valve 3.Check valve 4. Accumulator 5. Solenoid valve 6. Hydraulic motor 7.Hydraulic pump 8. Relief valve 9. Proportional flow valve 10. Generator11. Electromotor 12. Manual switch valve 13. Pressure meter 14. Filter15. Fuel tank The motion of the floating pendulum captures the energy from the wave in the ocean, converting to the mechanical kinetic energy. The hydraulic system is the link between the mechanical kinetic energy capture system and electrical power generation system. It converts the mechanical kinetic energy to hydraulic energy that can be easier to be absorbed by electrical power generator. The faults occurred in hydraulic system, including hydraulic cylinder block, accumulator failure, will significantly reduce the operational efficiency of the wave power generation system. Thus, it would be desirable to adopt a condition monitoring method applying in the hydraulic system of the wave power generation system. From (3), the dynamic model of the hydraulic system is also highly non-linear and uncertain in the wave power generation system, because of the uncertain hydraulic oil leakage, changing temperature of the oil, variations in control volumes and frictions. It means that the dynamic model of the hydraulic system is not able to be modelled accurately by using mathematical model method. In addition, there are some unknown external disturbances in the wave power generation system. Therefore, in order to achieve the condition monitoring of hydraulic system in the wave power generation system, a data-driven condition monitoring method is adopted to achieve fault detection of the hydraulic system by using ELM algorithm. In this paper, the system architecture of a novel wave power generationcondition monitoring system based on IoT is shown in Fig. 3. The operational data of the wave power generation systems,including hydraulic oil pressure, hydraulic oil flow, power output, voltage, andcurrent, are collected by using data acquisition system in the wave farm, and thenthe data are uploaded to the cloud. In the cloud, the data is processed in cloudcomputing platform, which performs the fault diagnosis model establishment andextracts the fault features from operational data of the wave power generationsystem. The monitoring centre and the maintenance staff can download real-timedetailed information of the each wave power generation system to obtain faultinformation, which helps to optimise scheduling of maintenance tasks. It is moreefficient for maintenance service than traditional client server-based conditionmonitoring system, due to more particular and explicit information gained.Essentially, the target of the proposed method is to implement the complicatedata-driven condition monitoring algorithm in the IoT platform instead of on aclient server. Fig. 3Open in figure viewerPowerPoint System architecture of the wave power generation conditionmonitoring system based on IoT 3 Extreme learning machine algorithm Data-driven method is an effective method in condition monitoring systems. The main advantage of data-driven method is that it does not need knowledge of the process. They are gained directly from measured data that is collected either during planned experiments or normal operation processes. Compared with mechanistic modelling techniques, it requires a thorough understanding of the system structure and operational mechanism, which leads to a complex model for condition monitoring. It is clear that a complex condition monitoring model is not suitable for online condition monitoring systems. ELM algorithm was applied in the single hidden layer feed forward neural networks (SLFNs) by Huang [22]. In real ocean environment, the wave height and wave period vary continuously on time scales in response to power output change [23]. Fig. 4 shows a schematic diagram of the data-driven condition monitoring system that is adopted in this paper. Fig. 4Open in figure viewerPowerPoint Schematic diagram of the data-driven condition monitoring system Digital twin provides a digital replica of physical system that can be used for various purposes. It simulates both the elements and the dynamics of how an IoT device operates [24]. In data-driven condition monitoring system, the prediction model is used to predict the operational behaviours of the wave power generation system [25]. Consequently, the prediction model can be considered as a digital twin of the real wave power generation system. The actual output data gained from the wave power generation system is then compared to the prediction data obtained from trained ELM for the corresponding input data, which helps to diagnosis occurrence of faults [26]. In ELM algorithm training process, a finite number of arbitrary distinct samples N that is consisted of are selected, where and are an input vector and output vector, respectively. It is assumed that the hidden layer of SLFNs has L neurons; the activation function is represented by . For this example, the number of neurons L in the hidden layer is 32; the activation function is represented by . In order to estimate the N training samples with zero error, the ELM training algorithm can be described as following equation: (7) where is the weight coefficient vector connecting the ith hidden node and all the input nodes; is output weight coefficient vector between the ith hidden node and all the output nodes; represents the bias of the ith hidden node. and are randomly selected in this paper. Equation (7) can be further illustrated as, (8) where H is the output matrix of the hidden layer, which can be described as, (9) (10) where the ith column of H is the outputs of the ith hidden neuron given input variables x1, x2,…, xn. The matrix of output weights (L × m dimensions) can then be calculated by simply finding a matrix in order to minimise the error function as below. The input weights and the hidden layer biases b are initialised at the beginning of the training process. During this procedure, the values of the input weights and the hidden layer biases b are constant. Thus, the solution is expressed as the following: (11) (12) In (11), the function aims to obtain the unique smallest norm least-squares solution of the linear system. Consequently, (11) is able to be further described as (12). The matrix is the generalised Moore–Penrose inverse of the matrix H. The data from a healthy wave power generation system (this data contains full operational conditions of wave power generation system) is selected as the training dataset to obtain the value of for the ELM model. The root mean square error (RMSE) is also used here as a measure of how well the models explain the actual output data. When the value of RMSE is below a predefined threshold (0.1 in this paper), the accuracy of the ELM model is good enough for testing. In terms of the testing data, the wave power generation system with hydraulic faulty is used to detect the fault. In this paper, the condition monitoring method based on ELM algorithm is applied on the IoT platform, which validates the feasibility of combining data-driven condition monitoring technology with IoT. 4 Data compression Security concern and cost reduction are two main reasons for carrying out data compression before the data is uploaded to the cloud computing platform. Although IoT-based condition monitoring method has many advantages that mentioned above, the data security poses a challenge for the IoT-based condition monitoring method application. Data breach leads to cyber-attacks against wave power generation system and power grid. The aggressor is able to break the communication between wave power generation and monitoring centre or tamper operational parameters of the system, which will widespread disruption in electric power system [27]. Apart from data security, reducing the quantity of data uploaded is another research objective to make IoT-based condition monitoring technology mature. In most commercial IoT service platforms, the cost of the IoT service is determined by the quantity of the data uploaded. Consequently, reducing the data upload quantity takes a significant role to decrease the cost of wave power generation condition monitoring system based on IoT. In Fig. 5, it illustrates a flowchart of the proposed method. With the input data, the hidden layer output H can be calculated by using (9). The parameters and x are difficult to be decoded from hidden layer output matrix H by analysing (9). It means that the cyber intruder cannot gain the parameters of the condition monitoring model and original data collected from the wave power generation system. This advantage is beneficial to enhance the data security ability of condition monitoring system. When the hidden layer output H is calculated, the matrix H is compressed to reduce the data upload quantity. Then, compressed the hidden layer output H is uploaded to the cloud platform for further signal processing. In the cloud, the data is reconstructed and achieve the condition monitoring model modelling by using ELM algorithm. Fig. 5Open in figure viewerPowerPoint Flowchart of the proposed IoT-based condition monitoring method CS method is a kind of emerging strategy that collects the signals sub-Nyquist rate for the sparse signals or compressible signals [28]. CS method is a new method that is able to potentially reduce the sampling and computation costs for sparse signals. Meanwhile, CS approach could simultaneously compress sparse or compressible signals by randomised dimensionality reduction. The original hidden layer output matrix H obtained from (9) is partitioned to n sets of small sub-matrix (, , , and ), which is helpful to achieve parallel computation in the cloud. Each small sub-matrix is able to be processed as a discrete unit in the cloud. In the cloud, multiple sub-matrix can be calculated for reconstruction at the same time. It is beneficial to speed up signal processing progress, due to the strong parallel computation capability of the cloud computing. The overview optimising computation speed of the hidden layer output matrix reconstruction is described in Fig. 6. Fig. 6Open in figure viewerPowerPoint Optimising computation speed of the hidden layer output matrix reconstruction In this paper, CS method is adopted to compress the first hidden layer output H. The calculation amount of the data compression process is much higher than the process of the data decompression in traditional compression method. While CS method has an opposite character, data compression process has much less calculation amount than the process of the data reconstruction. This feature makes CS method very suitable to apply in the cloud computing field, because we hope to achieve complicated calculation process in the cloud computing platform. CS method for data acquisition can be represented as (13) is a vector of the original data; is a measurement matrix, and the value of m is far less then n. Hence, the output y is a vector, so the number of processed data y is far fewer than that of the original data x, which makes CS method to achieve the signal processing work with very fewer resource requirements. In CS theory, the original data x is needed to be converted to sparse representation . Therefore, (13) can be described as follow: (14) Substituting (14) into (13) gives (15) where is the sensing matrix. is the sparse transform basis. According to CS method, it is possible to reconstruct the original data x from compressed data y. Hence, the reconstruction issue can be described as follow: (16) it is the l1 norm minimisation. In real application field, the signals are not always strictly K-sparse. If only the K highest values and their locations can be known and collected during sampling period, the accuracy of the reconstructed signal is also acceptable. In order to obtain high accuracy of reconstructed signal, the approximation of real signals as K-sparse signals can be introduced as an approximation error given by (17). (17) Hence, l1 norm minimisation associated problem in (16) can be further illustrated as (18) where ɛ is the threshold for the error e. 5 Case study In this paper, the hydraulic system is selected as monitoring object, because the main function of the hydraulic system is to transmit kinetic energy from the floating pendulum to the power generator. The health condition of the hydraulic system significantly determines the operational efficiency of the wave power generation system. As it is not easy to use the prototype of the horizontal floating pendulum wave power generation system simulating faulty operational condition, the Matlab-AMESim is adopted to simulate the hydraulic system faults happened in the horizontal floating pendulum wave power generation system. From our previous research [29], the hydraulic pressure time-series from the wave power generation system simulation plotted alongside the experimentally measured values. The simulation results agreed well with the experimental result. The simulation data of fault condition lasts 80 s, and sampling rate of the simulation data is 100 Hz. The actual wave data collected from the real ocean environment is used as input for Matlab-AMESim simulation that generates the operational data of hydraulic system. The dimensions of the wave power generation device are given in Table 1. Table 1. Key parameters of the wave power generation system Component Number Size horizontal pendulum WEC 3 buoy: 2.172 m × 2 m × 0.8 m, density 300 kg/m3 oscillating arm: 1 m hydraulic cylinder 3 piston diameter: 100 mm diameter of piston rod: 50 mm high pressure accumulator 1 volume: 32 L hydraulic motor 1 variable volume 114 ml/rev in rated condition In order to validate the proposed method shown in Fig. 3, the data collected from Matlab-AMESim simulation is processed by using cloud computing platform Thingspeak. ThingSpeak is an open source Internet of Things (IoT) application that integrates numerical computing on the cloud. The wave power generation system operational data is uploaded to the cloud via Internet, and then the data-driven condition monitoring method is applied on the cloud computing platform. Finally, the operational condition of the wave power generation system can be easily obtained by the tablet computer and laptop. The hardware prototype of IoT-based condition monitoring system and IoT platform are shown in Fig. 7. It consists of three components, including national instruments card (NI-5218), analogue-digital converter (MCP 3208), and raspberry PI. The operational data obtained from Matlab-AMESim simulation is stored in the national in