Jen-Cheng Wang

Abstracts

3 Analysis of Accurate Direct-Estimation of the Maximum Power Point and Thermal Characteristics of High Concentration Photovoltaic Modules

Authors: Chen-Kang Huang, Joe-Air Jiang, Jen-Cheng Wang, Yan-Wen Wang, Min-Sheng Liao, Chu-Yang Chou, Hsuan-Hsiang Hsu, Cheng-Ying Chou, Kun-Chang Kuo

Abstract:

Performance-related parameters of high concentration photovoltaic (HCPV) modules (e.g. current and voltage) are required when estimating the maximum power point using numerical and approximation methods. The maximum power point on the characteristic curve for a photovoltaic module varies when temperature or solar radiation is different. It is also difficult to estimate the output performance and maximum power point (MPP) due to the special characteristics of HCPV modules. Based on the p-n junction semiconductor theory, a brand new and simple method is presented in this study to directly evaluate the MPP of HCPV modules. The MPP of HCPV modules can be determined from an irradiated I-V characteristic curve, because there is a non-linear relationship between the temperature of a solar cell and solar radiation. Numerical simulations and field tests are conducted to examine the characteristics of HCPV modules during maximum output power tracking. The performance of the presented method is evaluated by examining the dependence of temperature and irradiation intensity on the MPP characteristics of HCPV modules. These results show that the presented method allows HCPV modules to achieve their maximum power and perform power tracking under various operation conditions. A 0.1% error is found between the estimated and the real maximum power point.

Keywords: Energy Performance, maximum power point, high concentrated photovoltaic, p-n junction semiconductor

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2 Power Grid Line Ampacity Forecasting Based on a Long-Short-Term Memory Neural Network

Authors: Joe-Air Jiang, Xiang-Yao Zheng, Jen-Cheng Wang

Abstract:

Improving the line ampacity while using existing power grids is an important issue that electricity dispatchers are now facing. Using the information provided by the dynamic thermal rating (DTR) of transmission lines, an overhead power grid can operate safely. However, dispatchers usually lack real-time DTR information. Thus, this study proposes a long-short-term memory (LSTM)-based method, which is one of the neural network models. The LSTM-based method predicts the DTR of lines using the weather data provided by Central Weather Bureau (CWB) of Taiwan. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed LSTM-based prediction method. A case study that targets the 345 kV power grid of TaiPower in Taiwan is utilized to examine the performance of the proposed method. The simulation results show that the proposed method is useful to provide the information for the smart grid application in the future.

Keywords: Smart Grid, electricity dispatch, line ampacity prediction, dynamic thermal rating, long-short-term memory neural network

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1 Application of Particle Swarm Optimization to Thermal Sensor Placement for Smart Grid

Authors: Huan-Chieh Chiu, Hung-Shuo Wu, Chien-Hao Wang, Yu-Cheng Yang, Joe-Air Jiang, Chwan-Lu Tseng, Xiang-Yao Zheng, Jen-Cheng Wang

Abstract:

Dynamic Thermal Rating (DTR) provides crucial information by estimating the ampacity of transmission lines to improve power dispatching efficiency. To perform the DTR, it is necessary to install on-line thermal sensors to monitor conductor temperature and weather variables. A simple and intuitive strategy is to allocate a thermal sensor to every span of transmission lines, but the cost of sensors might be too high to bear. To deal with the cost issue, a thermal sensor placement problem must be solved. This research proposes and implements a hybrid algorithm which combines proper orthogonal decomposition (POD) with particle swarm optimization (PSO) methods. The proposed hybrid algorithm solves a multi-objective optimization problem that concludes the minimum number of sensors and the minimum error on conductor temperature, and the optimal sensor placement is determined simultaneously. The data of 345 kV transmission lines and the hourly weather data from the Taiwan Power Company and Central Weather Bureau (CWB), respectively, are used by the proposed method. The simulated results indicate that the number of sensors could be reduced using the optimal placement method proposed by the study and an acceptable error on conductor temperature could be achieved. This study provides power companies with a reliable reference for efficiently monitoring and managing their power grids.

Keywords: Smart Grid, Proper orthogonal decomposition, Particle Swarm Optimization, dynamic thermal rating, sensor placement

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