# Remote Monitoring and Fault Diagnosis of Ocean Current Energy Hydraulic Transmission and Control Power Generation System

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## Abstract

**:**

## 1. Introduction

## 2. Overall Design of the Remote Monitoring and Fault Diagnosis System for Hydraulic Transmission Control Power Generation

#### 2.1. Composition of Hydraulic Transmission Control Power Generation System

#### 2.2. Remote Monitoring and Diagnosis System Scheme

#### 2.3. Software Design of Remote Monitoring and Diagnosis System for Hydraulic Transmission Control Power Generation

- (1)
- Real-time monitoring: Real-time monitoring of the operating parameters of the hydraulic transmission and control power generation control, including motor output speed, pump speed, system flow, motor inlet pressure, inverter output frequency, system temperature, charge pressure, system pressure, return pressure, and generator voltage;
- (2)
- Real-time curve display: Real-time curve display of motor output speed, pump speed, motor inlet pressure, system temperature, and inverter output frequency in the hydraulic transmission, and control power generation system;
- (3)
- Historical data storage: Data storage of operating parameters in the hydraulic transmission and control power generation system, with a storage frequency of 100 Hz, to facilitate subsequent data analysis and processing;
- (4)
- Alarm data management: The upper and lower limits of the alarm value are set for specific variables, such as the liquid level and pressure of the hydraulic transmission and control power generation system;
- (5)
- Remote control function: Remote start/stop and parameter setting function for the oil replenishment and hydraulic station systems;
- (6)
- Fault diagnosis function: Fault diagnosis of the system’s normal operation, and failure of the accumulator, relief valve, hydraulic motor, and hydraulic pump, via a combination of the hydraulic transmission and control remote monitoring system and the proposed improved fault diagnosis model;
- (7)
- Data query function: The data query system is implemented using a database, in which the type and time of each failure are stored, and all information relating to previous failures in the system can be accessed via a query.

## 3. SVM Fault Diagnosis Based on Improved Particle Swarm Algorithm Optimization

#### Improved Particle Swarm Algorithm

- (1)
- Inertial weighting factor. The inertia weight factor was 0.3–0.7; this range of values was able to better neutralize the effect of local and global searches. In addition, a nonlinear decreasing change in inertia weight factor was proposed. As a result, the inertia weight factor was in the optimal interval in the iterations, and its formula is as follows:$$\omega =\frac{3{\omega}_{\mathrm{max}}-{\omega}_{\mathrm{min}}}{2}-\left({\omega}_{\mathrm{max}}-{\omega}_{\mathrm{min}}\right)\times \left(\frac{2t}{T}+\frac{\mathrm{cos}\left(\frac{\pi t}{T}\right)}{2}\right)$$
- (2)
- Acceleration factor. The acceleration factors ${c}_{1}$ (decreasing) and ${c}_{2}$ (increasing) can improve the algorithm effect during the iterative process. We proposed nonlinear decreasing variation in acceleration factor ${c}_{1}$ from 2.5 to 0.5, and in acceleration factor ${c}_{2}$ from 0.5 to 2.5. Its equation is as follows:$${c}_{1}=3.5-\frac{4t}{T}-\mathrm{sin}\left[\frac{\left(T-2t\right)\pi}{2T}\right],{c}_{2}=-0.5+\frac{4t}{T}+\mathrm{sin}\left[\frac{\left(T-2t\right)\pi}{2T}\right]$$
- (3)
- Local optimal stagnation perturbation. The particle is prone to local optimum phenomenon when searching in space. A method to cope with particles in stagnation was proposed to prevent this phenomenon from causing meaningless iterations and to determine whether the particle is in stagnation. This phenomenon was avoided by particle local optimum perturbation, which is obtained as follows:$${{p}^{\prime}}_{ij}\left(t\right)=\{\begin{array}{l}rand\times {p}_{ij}\left(t\right),{p}_{ij}\left(t\right)={p}_{ij}\left(t-1\right)={p}_{ij}\left(t-2\right)\text{\hspace{1em}}\left(t>3\right)\\ {p}_{ij}\left(t\right)\text{\hspace{1em}}\left(\mathrm{other}\right)\end{array}$$
- (4)
- Update of particle speed and position. Based on the optimized particle’s local optimum solution, the joint update of the formula for the particle velocity can be derived, as follows:$${v}_{ij}\left(t+1\right)=\omega {\nu}_{ij}\left(t\right)+{c}_{1}{r}_{1}\left(t\right)\left({p}_{ij}^{\prime}\left(t\right)-{x}_{ij}\left(t\right)\right)+{c}_{2}{r}_{2}\left(t\right)\left({p}_{gi}\left(t\right)-{x}_{ij}\left(t\right)\right)$$

## 4. Realization of Remote Monitoring and Fault Diagnosis System for Hydraulic Transmission Control Power Generation

#### 4.1. Database Selection

#### 4.2. Operation Management Module

#### 4.3. Parameter Setting Module

#### 4.4. Run Operation Module

#### 4.5. Data Display, Storage Module

## 5. Test

## 6. Results

## 7. Discussion

- (1)
- Due to restrictions relating to the experimental conditions, although numerous types of system faults exist, only four types of single faults that do not have serious impacts on the system were selected. However, actual system failure, which is not a normal operation, is caused by multiple faults, and the case of multiple concurrent faults should be considered to improve the fault diagnosis system;
- (2)
- The control index of this subject was studied under the condition of constant motor output speed. Several dynamic parameters could be simultaneously selected as control targets, thereby allowing a more accurate fault diagnosis of the system;
- (3)
- The data used in the proposed fault diagnosis model related to the hydraulic transmission and control power generation system, as the data of the entire life cycle were unavailable. However, diagnosis results would be more accurate if the data of the entire life cycle were analyzed.

## 8. Future Work

- (1)
- Compare the performance of the SVM and kNN algorithms for fault diagnosis of ocean current energy hydraulic transmission and control power generation system;
- (2)
- Carry out a comparison of the PSO + SVM algorithm with the PSO + KNN algorithm, including comparing the accuracy and the expectation aftereffect of the two algorithms;
- (3)
- Develop a hybrid algorithm of PSO + GWO to carry out our research for the fault diagnosis of ocean current energy hydraulic transmission and control power generation system and compare with our previous algorithms.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Schematic of ocean current energy hydraulic power generation and remote monitoring system.

**Figure 2.**Hydraulic system schematic. 1—hydraulic pump, 2—relief valve, 3—check valve, 4—on/off valve, 5—hydraulic motor, 6—oil suction filter, 7—oil refill pump, 8—high-pressure filter, 9—oil fill check valve, 10—oil fill directional valve, 11—relief valve, 12—control directional valve, 13—cooler, 14—throttle valve, 15—loading pumps, and 16—loading relief valve.

**Figure 3.**Hydraulic power generation system structure. 1—pump source, 2—load simulation area, and 3—hydraulic station.

**Figure 14.**Speed sensor for test bench. (

**a**) Power generation test bench; (

**b**) pump source test bench.

Models | Running Time/h | Training Accuracy/% | Testing Accuracy/% | RMSE |
---|---|---|---|---|

PSO | 20 | 99.97 | 89.52 | 0.0192 |

PSVM | 12 | 99.96 | 99.72 | 0.0006497 |

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**MDPI and ACS Style**

Su, W.; Wei, H.; Guo, P.; Guo, R.
Remote Monitoring and Fault Diagnosis of Ocean Current Energy Hydraulic Transmission and Control Power Generation System. *Energies* **2021**, *14*, 4047.
https://doi.org/10.3390/en14134047

**AMA Style**

Su W, Wei H, Guo P, Guo R.
Remote Monitoring and Fault Diagnosis of Ocean Current Energy Hydraulic Transmission and Control Power Generation System. *Energies*. 2021; 14(13):4047.
https://doi.org/10.3390/en14134047

**Chicago/Turabian Style**

Su, Wenbin, Hongbo Wei, Penghua Guo, and Ruizhe Guo.
2021. "Remote Monitoring and Fault Diagnosis of Ocean Current Energy Hydraulic Transmission and Control Power Generation System" *Energies* 14, no. 13: 4047.
https://doi.org/10.3390/en14134047