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Meng-Hui Wang
Electrical Engineering, National Chin-Yi University of Technology, Taiping, Taiwan, 4110

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Journal article
Published: 11 March 2021 in IEEE Transactions on Power Delivery
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In this paper, the discrete wavelet transform (DWT) and a chaotic system were combined with a convolutional neural network (CNN) and applied to the diagnosis of insulation faults in XLPE (cross-linked polyacetylene) power cables. First, four different types of insulation faults in power cables were constructed, including the normal state of the cable, the short outer semi-conducting layer, impurities in the insulation layer, and insulation layer damage, and a high-speed capture card (NI PXI-5105) was adopted to measure the partial discharge (PD) signal, which was then filtered through discrete wavelet transform. Then, based on the Lorenz chaotic system, a dynamic error scatter diagram was established as the feature of each fault state. Finally, the dynamic error scatter diagram was processed by CNN to recognize four different types of faults in the power cable. The test results show that the method proposed in this paper can quickly recognize the fault state of power cables and has excellent performance in terms of recognition accuracy, which reaches 97.5%. Therefore, the proposed method can effectively detect the fault signal changes of power cables and identify the fault state of power cables in real time.

ACS Style

Meng-Hui Wang; Shiue-Der Lu; Rui-Min Liao. Fault Diagnosis for Power Cables Based on Convolutional Neural Network with Chaotic System and Discrete Wavelet Transform. IEEE Transactions on Power Delivery 2021, PP, 1 -1.

AMA Style

Meng-Hui Wang, Shiue-Der Lu, Rui-Min Liao. Fault Diagnosis for Power Cables Based on Convolutional Neural Network with Chaotic System and Discrete Wavelet Transform. IEEE Transactions on Power Delivery. 2021; PP (99):1-1.

Chicago/Turabian Style

Meng-Hui Wang; Shiue-Der Lu; Rui-Min Liao. 2021. "Fault Diagnosis for Power Cables Based on Convolutional Neural Network with Chaotic System and Discrete Wavelet Transform." IEEE Transactions on Power Delivery PP, no. 99: 1-1.

Journal article
Published: 30 May 2019 in Applied Sciences
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The development of renewable energy and the increase of intermittent fluctuating loads have affected the power quality of power systems, and in the long run, damage the power equipment. In order to effectively analyze the quality of power signals, this paper proposes a method of signal feature capture and fault identification, as based on the extension neural network (ENN) algorithm combined with discrete wavelet transform (DWT) and Parseval’s theorem. First, the original power quality disturbance (PQD) transient signal was subjected to DWT, and its spectrum energy was calculated for each order of wavelet coefficients through Parseval’s theorem, in order to effectively intercept the eigenvalues of the original signal. Based on the features, the extension neural algorithm was used to establish a matter-element model of power quality disturbance identification. In addition, the correlation degree between the identification data and disturbance types was calculated to accurately identify the types of power failure. To verify the accuracy of the proposed method, five common power quality disturbances were analyzed, including voltage sag, voltage swell, power interruption, voltage flicker, and power harmonics. The results were then compared with those obtained from the back-propagation network (BPN), probabilistic neural network (PNN), extension method and a learning vector quantization network (LVQ). The results showed that the proposed method has shorter computation time (0.06 s), as well as higher identification accuracy at 99.62%, which is higher than the accuracy rates of the other four types.

ACS Style

Shiue-Der Lu; Hong-Wei Sian; Meng-Hui Wang; Rui-Min Liao. Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis. Applied Sciences 2019, 9, 2228 .

AMA Style

Shiue-Der Lu, Hong-Wei Sian, Meng-Hui Wang, Rui-Min Liao. Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis. Applied Sciences. 2019; 9 (11):2228.

Chicago/Turabian Style

Shiue-Der Lu; Hong-Wei Sian; Meng-Hui Wang; Rui-Min Liao. 2019. "Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis." Applied Sciences 9, no. 11: 2228.

Journal article
Published: 01 April 2019 in Energies
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In response to the power impact effect resulting from merging large-scale offshore wind farms (OWFs) into the Taiwan Power (Taipower) Company (TPC) system in the future, this study aims to discuss the situation where the offshore wind power is merged into the power grids of the Changbin and Changlin areas, and study a Low-Voltage Ride-Through (LVRT) curve fit for the Taiwan power grid through varying fault scenarios and fault times to reduce the effect of the tripping of OWFs on the TPC system. The Power System Simulator for Engineering (PSS/E) program was used to analyze the Taipower off-peak system in 2018. The proposed LVRT curve is compared to the current LVRT curve of Taipower. The research findings show that if the offshore wind turbine (OWT) set uses the proposed LVRT curve, when a fault occurs, the wind turbines can be prevented from becoming disconnected from the power grid, and the voltage sag amplitude of the connection point during the fault and the disturbances after the fault is cleared are relatively small. In addition, according to the transient stability analysis results, the system can return to stability after fault clearance, thereby meeting the Taipower transmission system planning criteria and technical key points of renewable energy power generation system parallel connection technique.

ACS Style

Shiue-Der Lu; Meng-Hui Wang; Chung-Ying Tai. Implementation of the Low-Voltage Ride-Through Curve after Considering Offshore Wind Farms Integrated into the Isolated Taiwan Power System. Energies 2019, 12, 1258 .

AMA Style

Shiue-Der Lu, Meng-Hui Wang, Chung-Ying Tai. Implementation of the Low-Voltage Ride-Through Curve after Considering Offshore Wind Farms Integrated into the Isolated Taiwan Power System. Energies. 2019; 12 (7):1258.

Chicago/Turabian Style

Shiue-Der Lu; Meng-Hui Wang; Chung-Ying Tai. 2019. "Implementation of the Low-Voltage Ride-Through Curve after Considering Offshore Wind Farms Integrated into the Isolated Taiwan Power System." Energies 12, no. 7: 1258.

Journal article
Published: 27 January 2019 in Applied Sciences
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When large amounts of wind power and solar photovoltaic (PV) power are integrated into an independent power grid, the intermittent renewable energy destabilizes power output. Therefore, this study explored the unit commitment (UC) optimization problem; the ramp rate was applied to solve problems with 30 and 10 min of power shortage. The data of actual unit parameters were provided by the Taiwan Power Company. The advanced priority list method was used together with a combination of a generalized Lagrangian relaxation algorithm and a random feasible directions algorithm to solve a large-scale nonlinear mixed-integer programming UC problem to avoid local and infeasible solutions. The results showed that the proposed algorithm was superior to improved particle swarm optimization (IPSO) and simulated annealing (SA) in terms of the minimization of computation time and power generation cost. The proposed method and UC results can be effective information for unit dispatch by power companies to reduce the investment costs of power grids and the possibility of renewable energy being disconnected from the power system. Thus, the proposed method can increase the flexibility of unit dispatch and the proportion of renewable energy in power generation.

ACS Style

Shiue-Der Lu; Meng-Hui Wang; Ming-Tse Kuo; Ming-Chang Tsou; Rui-Min Liao. Optimal Unit Commitment by Considering High Penetration of Renewable Energy and Ramp Rate of Thermal Units-A case study in Taiwan. Applied Sciences 2019, 9, 421 .

AMA Style

Shiue-Der Lu, Meng-Hui Wang, Ming-Tse Kuo, Ming-Chang Tsou, Rui-Min Liao. Optimal Unit Commitment by Considering High Penetration of Renewable Energy and Ramp Rate of Thermal Units-A case study in Taiwan. Applied Sciences. 2019; 9 (3):421.

Chicago/Turabian Style

Shiue-Der Lu; Meng-Hui Wang; Ming-Tse Kuo; Ming-Chang Tsou; Rui-Min Liao. 2019. "Optimal Unit Commitment by Considering High Penetration of Renewable Energy and Ramp Rate of Thermal Units-A case study in Taiwan." Applied Sciences 9, no. 3: 421.

Journal article
Published: 08 March 2016 in Energies
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An Extension Taguchi Method (ETM) is proposed on the optimized allocation of equipment capacity for solar cell power generation, wind power generation, full cells, electrolyzer and hydrogen tanks. The ETM is based on the domain knowledge containing the product specifications and allocation levels provided by suppliers and design factors since most of the renewable energy equipment available in the market comes with a specific capacity. A proper orthogonal array is used to collect 18 sets of simulation responses. The extension theory is introduced to determine the correlation function, and factor effects are used to identify the optimized capacity allocation. The hours of power shortage are simulated using Matlab for all capacity allocations at the lowest establishment cost and the optimized capacity allocation of loss of load probability (LOLP). Finally, the extension theory, extension AHP theory, ETM and Analytic Hierarchy Process (AHP) are used to determine the optimized capacity allocation of the system. Results are compared for the above four optimization simulation methods and verify that the proposed ETM surpasses the others on achieving the optimized capacity allocation.

ACS Style

Meng-Hui Wang; Mei-Ling Huang; Zi-Yi Zhan; Chong-Jie Huang. Application of the Extension Taguchi Method to Optimal Capability Planning of a Stand-alone Power System. Energies 2016, 9, 174 .

AMA Style

Meng-Hui Wang, Mei-Ling Huang, Zi-Yi Zhan, Chong-Jie Huang. Application of the Extension Taguchi Method to Optimal Capability Planning of a Stand-alone Power System. Energies. 2016; 9 (3):174.

Chicago/Turabian Style

Meng-Hui Wang; Mei-Ling Huang; Zi-Yi Zhan; Chong-Jie Huang. 2016. "Application of the Extension Taguchi Method to Optimal Capability Planning of a Stand-alone Power System." Energies 9, no. 3: 174.

Journal article
Published: 11 February 2016 in Energies
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This study uses a sliding mode control (SMC) in a generator-based exercise equipment (GBEE) with nonlinear P-V characteristic curves. A P-V characteristics curve can be influenced by varying the pedaling speed of the generator. The traditional maximum power point tracking (MPPT) control method is used with perturb and observe algorithms (P&O), extremum seeking control (ESC), etc. However, these control methods are not robust enough for control. SMC is created by two pattern methods for robustness control, approaching and sliding conditions. However, SMC allows infinite high-frequency switching of the sign function. If the sign function is used to switch the converter, it will cause the converter and switch life to be cut short, and also to form high frequency noise. Therefore, this study proposes an extension theory for an intelligent control method that will effectively improve conversion efficiency and responsiveness. This study compares generator input current waveforms for fast Fourier transform (FFT) for three different control methods. Finally, using simulation validates the stability and FFT analysis with power simulation (PSIM) software. The results of upgrading overall efficiency are about a 5% increase in efficiency and a faster response speed of about 0.5 s. The amount of generator input current harmonic is greatly reduced.

ACS Style

Meng-Hui Wang; Mei-Ling Huang; Wei-Jhe Jiang. Maximum Power Point Tracking and Harmonic Reducing Control Method for Generator-Based Exercise Equipment. Energies 2016, 9, 103 .

AMA Style

Meng-Hui Wang, Mei-Ling Huang, Wei-Jhe Jiang. Maximum Power Point Tracking and Harmonic Reducing Control Method for Generator-Based Exercise Equipment. Energies. 2016; 9 (2):103.

Chicago/Turabian Style

Meng-Hui Wang; Mei-Ling Huang; Wei-Jhe Jiang. 2016. "Maximum Power Point Tracking and Harmonic Reducing Control Method for Generator-Based Exercise Equipment." Energies 9, no. 2: 103.

Journal article
Published: 05 August 2015 in Applied Sciences
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Sliding mode strategy (SMS) for maximum power point tracking (MPPT) is used in this study of a human power generation system. This approach ensures maximum power at different rotation speeds to increase efficiency and corrects for the lack of robustness in traditional methods. The intelligent extension theory is used to reduce input saturation and high frequency switching in sliding mode strategy, as well as to increase the efficiency and response speed. The experimental results show that the efficiency of the extension SMS (ESMS) is 5% higher than in traditional SMS, and the response is 0.5 s faster.

ACS Style

Meng-Hui Wang; Her-Terng Yau; Wei-Jhe Jiang. Application of Extension Sliding Mode Strategy to Maximum Power Point Tracking in Human Power Generation Systems. Applied Sciences 2015, 5, 259 -274.

AMA Style

Meng-Hui Wang, Her-Terng Yau, Wei-Jhe Jiang. Application of Extension Sliding Mode Strategy to Maximum Power Point Tracking in Human Power Generation Systems. Applied Sciences. 2015; 5 (3):259-274.

Chicago/Turabian Style

Meng-Hui Wang; Her-Terng Yau; Wei-Jhe Jiang. 2015. "Application of Extension Sliding Mode Strategy to Maximum Power Point Tracking in Human Power Generation Systems." Applied Sciences 5, no. 3: 259-274.

Journal article
Published: 08 October 2014 in Energies
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A hybrid method comprising a chaos synchronization (CS)-based detection scheme and an Extension Neural Network (ENN) classification algorithm is proposed for power quality monitoring and analysis. The new method can detect minor changes in signals of the power systems. Likewise, prominent characteristics of system signal disturbance can be extracted by this technique. In the proposed approach, the CS-based detection method is used to extract three fundamental characteristics of the power system signal and an ENN-based clustering scheme is then applied to detect the state of the signal, i.e., normal, voltage sag, voltage swell, interruption or harmonics. The validity of the proposed method is demonstrated by means of simulations given the use of three different chaotic systems, namely Lorenz, New Lorenz and Sprott. The simulation results show that the proposed method achieves a high detection accuracy irrespective of the chaotic system used or the presence of noise. The proposed method not only achieves higher detection accuracy than existing methods, but also has low computational cost, an improved robustness toward noise, and improved scalability. As a result, it provides an ideal solution for the future development of hand-held power quality analyzers and real-time detection devices.

ACS Style

Meng-Hui Wang; Her-Terng Yau. New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network. Energies 2014, 7, 6340 -6357.

AMA Style

Meng-Hui Wang, Her-Terng Yau. New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network. Energies. 2014; 7 (10):6340-6357.

Chicago/Turabian Style

Meng-Hui Wang; Her-Terng Yau. 2014. "New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network." Energies 7, no. 10: 6340-6357.

Journal article
Published: 15 September 2011 in Expert Systems with Applications
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The power quality affects the power stability of power company and customers. In order to avoid economic losses caused by the power disturbances, it is necessary to monitor power parameters. This paper aimed at power quality analyses by wavelet transform and proposed a novel algorithm called extension genetic algorithm (EGA). The paper introduced the fundamental theory of wavelet transform, current applications and the theoretical framework of EGA. Then, it described the definition of power quality problems and the characteristics of power waves. Finally, this paper compared the analysis results of EGA and other methods. As the results of simulation, this paper mentioned of methods has a very high accuracy. It can also provide an application tool on power quality and data classification for future researchers.

ACS Style

Meng-Hui Wang; Yi-Feng Tseng. A novel analytic method of power quality using extension genetic algorithm and wavelet transform. Expert Systems with Applications 2011, 38, 12491 -12496.

AMA Style

Meng-Hui Wang, Yi-Feng Tseng. A novel analytic method of power quality using extension genetic algorithm and wavelet transform. Expert Systems with Applications. 2011; 38 (10):12491-12496.

Chicago/Turabian Style

Meng-Hui Wang; Yi-Feng Tseng. 2011. "A novel analytic method of power quality using extension genetic algorithm and wavelet transform." Expert Systems with Applications 38, no. 10: 12491-12496.

Journal article
Published: 14 November 2010 in Expert Systems with Applications
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This paper presents a thermal image matter-element used to design a circuit board signal fault diagnosis system. When a circuit element presents faults the temperature distribution will skew. Therefore, extension theory is used to build several kinds of thermal image matter-element models with fault circuits. According to the matter-element and correlation function, the fault type in the testing circuit is detected by analyzing the correlation degree between the typical fault models and test circuit boards. This new method can attain fast fault determination and reduced manpower.

ACS Style

Meng-Hui Wang; Yu-Kuo Chung; Wen-Tsai Sung. Using thermal image matter-element to design a circuit board fault diagnosis system. Expert Systems with Applications 2010, 38, 6164 -6169.

AMA Style

Meng-Hui Wang, Yu-Kuo Chung, Wen-Tsai Sung. Using thermal image matter-element to design a circuit board fault diagnosis system. Expert Systems with Applications. 2010; 38 (5):6164-6169.

Chicago/Turabian Style

Meng-Hui Wang; Yu-Kuo Chung; Wen-Tsai Sung. 2010. "Using thermal image matter-element to design a circuit board fault diagnosis system." Expert Systems with Applications 38, no. 5: 6164-6169.