Novel Global-MPPT Control Strategy Considering the Variation in the Photovoltaic Module Output Power and Loads for Solar Power Systems

This research proposed a novel global maximum power point tracking (global-MPPT) algorithm. The proposed algorithm eliminates the perturbation and observation (P&O) technique disturbance problem that the power point will be stuck at the local peak power point under a partial shading condition (PSC). The proposed global-MPPT algorithm detects the photovoltaic module (PV-M) environment irradiance level by the relationship between the output power and voltage of the PV-M. In the proposed algorithm, the important parameter w is determined by the PV-M output power and irradiance level, which is also the compensation parameter that corresponds to the relationship of temperature. The proposed global-MPPT algorithm is aimed to predict the best duty cycle of the global-MPPT based on the irradiance level, parameter w, PV-M output voltage, and load, and then achieve the maximum power point (MPP) quickly and accurately. The measurement results under UIC and PSC verify that the proposed global-MPPT technique performs better than the particle swarm optimization (PSO) and P&O techniques. This research contributes to the proposed method that can find the global-MPP in time based on the irradiance level, temperature, and load

Experimental Research and Control Strategy of Pumped Storage Units Dispatching in the Taiwan Power System Considering Transmission Line Limits

Taiwan’s power system is isolated and not supported by other interconnected systems. Consequently, the system frequency immediately reflects changes in the system loads. Pumped storage units are crucial for controlling power frequency. These units provide main or auxiliary capacities, reducing the allocation of frequency-regulating reserve (FRR) and further reducing generation costs in system operations. Taiwan’s Longmen Nuclear Power Plant is set to be converted for commercial operations, which will significantly alter the spinning reserves in the power system. Thus, this study proposes a safe and economic pumped storage unit dispatch strategy. This strategy is used to determine the optimal FRR capacity and 1-min recovery frequency in a generator failure occurrence at the Longmen Power Plant. In addition, this study considered transmission capacity constraints and conducted power flow analysis of the power systems in Northern, Central, and Southern Taiwan. The results indicated that, in the event of a failure at Longmen Power Plant, the proposed strategy can not only recover the system frequency to an acceptable range to prevent underfrequency load-shedding, but can also mitigate transmission line overloading

Design and Implementation of Real-Time Intelligent Control and Structure Based on Multi-Agent Systems in Microgrids

By consulting various worldwide definitions of microgrids and distributed energy, this study presents a microgrid-structured multi-agent system and uses Matlab/Simulink to construct a circuit with microgrid features, which enables the changes in each electrical source and load in the microgrid to be monitored and controlled. This multi-agent system adheres to the Java Agent Development Framework (JADE) platform specifications of the Foundation for Intelligent Physical Agents (FIPA), facilitating communication, information transfers, and the receipt of real-time information regarding the microgrid and each component in the microgrid. Furthermore, the real-time state in the microgrid can be correspondingly controlled, achieving the most efficient real-time monitoring and control for electrical sources and load management in the microgrid

Implementation of the Low-Voltage Ride-Through Curve after Considering Offshore Wind Farms Integrated into the Isolated Taiwan Power System

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

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

Abstract PV fault diagnosis remains difficult due to the non‐linear characteristic of PV output, which makes PV output to be likely disturbed by the ambient environment. This study proposes a novel convolutional extension neural network (CENN) algorithm, which is a jointed architecture based on convolutional neural network (CNN) and extension neural network (ENN), takes advantage of CNN and ENN. The CENN is combined with the symmetrized dot pattern (SDP) analysis method to diagnose the common eight PV array faults. The SDP is used to transform the measured PV signals into the point coordinate feature image; then, the CENN is trained to identify the different PV faults. Experimental results show an obvious improvement in short detection times and high accuracy compared with traditional CNN and the histogram of oriented gradient (HOG) extraction method with support vector machine (SVM), K‐nearest neighbours (KNN), and back propagation neural network (BPNN) classifiers, with 95.3%, 94%, 93.5%, and 93.3% accuracy, respectively. Using the proposed CENN, the accuracy can be raised to 97.3%. Additionally, the signals measured by various sensors are collected using programmable logic controller (PLC). The human–machine interface (HMI) and the proposed algorithm are developed using LabVIEW for graphical design. Finally, the information is transmitted to a tablet PC for performing real‐time remote monitoring

Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status

Surge arresters primarily restrain lightning and switch surges in the power system to avoid damaging power equipment. When a surge arrester fails, it leads to huge damage to the power equipment. Therefore, this study proposed the application of a convolutional neural network (CNN) combined with a symmetrized dot pattern (SDP) to detect the state of the surge arrester. First, four typical fault types were constructed for the 18 kV surge arrester, including its normal state, aging of the internal valve, internal humidity, and salt damage to the insulation. Then, the partial discharge signal was measured and extracted using a high-speed data acquisition (DAQ) card, while a snowflake map was established by SDP for the features of each fault type. Finally, CNN was used to detect the status of the surge arrester. This study also used a histogram of oriented gradient (HOG) with support vendor machine (SVM), backpropagation neural network (BPNN), and k-nearest neighbors (KNN) for image feature extraction and identification. The result shows that the proposed method had the highest accuracy at 97.9%, followed by 95% for HOG + SVM, 94.6% for HOG + BPNN, and 91.2% for HOG + KNN. Therefore, the proposed method can effectively detect the fault status of surge arresters

Research on Fault Diagnosis of Wind Turbine Gearbox with Snowflake Graph and Deep Learning Algorithm

Wind power generation is one of the important development projects for renewable energy worldwide. As wind turbines operate in harsh environments, failure of the wind turbines often occurs, thus leading to lower power generation efficiency and high maintenance cost. Earlier detection of the fault type can reduce the maintenance cost. This study proposed a hybrid recognition algorithm based on the symmetrized dot pattern (SDP) and convolutional neural network (CNN) for wind turbine gearbox fault diagnoses. In addition to a fault-free type, four fault types were discussed in this paper, including gear rustiness, broken tooth, wear, and aging. A vibration sensor was used for measurement. The original vibration signals of the gearbox were captured by a NI-9234 high-speed data acquisition card, filtered by a fast Fourier transform, and imported into the SDP to create the snowflake image features. Afterward, CNN diagnosed the gearbox fault type. There were 1500 test data in this study. A total of 200 data items for each fault type were used as training samples, and 100 data of each type were used as test samples. The test result shows that the training accuracy was 98.8%. The proposed method can diagnose the fault condition of the gearbox effectively and identify the fault type of the gearbox accurately. This is favorable for the quick maintenance of wind turbines

Power Quality Fault Identification Method Based on SDP and Convolutional Neural Network

The global power demand is increasing year by year. Power quality fault recognition plays an important role in determining the fault type when a fault occurs, so as to maintain power quality and supply stability. Therefore, this paper proposed using the symmetrized dot pattern (SDP) algorithm plus the convolutional neural network (CNN) to study power quality fault recognition. Six common power quality fault models were taken as the subjects of discussion, including normal voltage, voltage sag, voltage swell, voltage interruptions, voltage flickers, and voltage harmonics. First, the voltage variation data of five cycles were extracted from a 60 Hz power supply and introduced into the SDP. Afterwards, the data were converted into graph data, which could be used for fault recognition. Lastly, the power quality fault type was identified by CNN. In this study, 600 random fault data (100 data per fault) were imported into the algorithm, and the recognition rate reached as high as 92.9%. Additionally, SDP could reduce the mass original data. After the subtle changes in the output signals were captured, they could be observed by images. The power quality fault state could thus be accurately recognized by CNN