This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily ...implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.
•Several Power Flow Controllers are modeled and inserted into MT-HVDC grid.•A security-based sensitivity analysis is proposed to find optimal place for PFCs.•A security-based DC OPF is developed to ...determine optimal control reference for PFCs.•The HVDC line overload is mostly surmounted by the proposed PFC placement approach.
This research proposes an approach to select an optimal place and control variable setting for recently proposed Power Flow Controller (PFC)s including series, cascaded, and interline PFCs in Multi-Terminal High-Voltage DC (MT-HVDC) grids based on sensitivity analysis technique to enhance static security. To do so, appropriate static power injection models of the PFCs are obtained. Accordingly, sensitivity relationships between the defined power performance index and each PFCs control variable are obtained. It is for the optimal PFCs placement purpose in order to utilize maximum capacities of the transmission grid. Optimal settings for the PFC as well as the controllable voltage source converters are computed in turn by applying sequential quadratic programming solver to the developed security-based DC optimal power-flow problem which includes several corrective constraints. The study scope is single contingencies affecting HVDC lines with the objective of eliminating the consequent overloaded HVDC lines and thereby enhancing the MT-HVDC grid security by controlling the power flows within the MT-HVDC grid. Static simulations are performed considering two generic four-terminal and eighth-terminal MT-HVDC test grids in order to demonstrate the proposed methods effectiveness and authenticate its robustness. The obtained results indicate that the MT-HVDC grid can remain secure under single HVDC line contingencies in most cases by implementing the proposed method.
Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current ...(DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current–Voltage (I–V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively.
This paper proposes a communication-free control strategy at the offshore wind farm (OWF) level to enhance onshore fault ride-through (FRT) grid code compliance of the voltage source converter ...(VSC)-based multi-terminal high voltage direct current (MT-HVDC) grid. In this proposal, the emerging virtual synchronous generator (VSG) concept is employed to equip the Type 4 wind turbine generator (WTG)s with inherent grid forming ability. Accordingly, it is proposed to switch the offshore HVDC converters control mode from grid forming to grid feeding during onshore FRT period to realize direct wind power in-feed reduction as a function of the severity of MT-HVDC grid's overvoltage. The related dynamics are mainly characterized by the high-speed current control loop, so improved OWF response is achieved during onshore FRT period as conventional voltage/frequency modulation strategies are not employed. New analysis/amendments are also proposed to study and improve the transient active power reduction sharing between the WTGs in first few power cycles under wind wake effect. Finally, with the objective of a smooth transfer of HVDC converters and WTGs in several proposed operation states, a set of state machines are proposed considering whole WTG's dynamics. Comprehensive time-domain simulations are performed with averaged electromagnetic transient models to demonstrate the improved onshore FRT behavior in terms of minimizing the electrical stress at both MT-HVDC grid and OWF levels.
Line-line (LL) and line-ground (LG) faults may not be detected by common protection devices in Photovoltaic (PV) arrays as these faults are not detectable under high fault impedance and low mismatch ...level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. In this article, we propose a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the dc side of PV systems. For this purpose, the main features of current-voltage curves under different fault events and normal conditions are extracted. The faults are categorized using the hierarchical classification platform. Later, the LL and LG faults are detected and classified by machine learning methods. The proposed method aims to reduce the amount of dataset, which is required for the learning process, and also obtains a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared with other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under different conditions and severity with the accuracy of 96.66% and 91.66%, respectively.
•A comprehensive review of LL fault diagnosis methods in PV systems.•An ensemble learning model based on probabilistic strategy is developed.•The main features of the fault are extracted from I-V ...curves using a Simulink based model of PV arrays.•A feature selection algorithm is used for each learning algorithm to reduce the dataset and increase the classification accuracy.•The proposed model is very reliable and able to detect and classify all the LL faults accurately.
The fault diagnosis of photovoltaic (PV) arrays aims to increase the reliability and service life of PV systems. Line-Line (LL) faults may remain undetected under low mismatch level and high impedance due to low currents of faults, resulting in power losses and fire potential disaster. This paper proposes a novel and intelligent fault diagnosis method based on an ensemble learning model and Current-Voltage (I-V) characteristics to detect and classify LL faults at the DC side of PV systems. For this purpose, first, the key features are extracted via analyzing I-V characteristics under various LL fault events and normal operation. Second, a feature selection algorithm has been applied to select the best features for each learning algorithm in order to reduce the amount of data required for the learning process. Third, an ensemble learning model is developed that combines several learning algorithms based on the probabilistic strategy to achieve superior diagnostic performance. Here, we find an excellent agreement between simulation and experimental results that the proposed method can obtain higher accuracy in detecting and classifying the LL faults, even under low mismatch levels and high fault impedances. In addition, the comparison results demonstrate that the performance of the proposed method is better than individual machine learning algorithms, so that the proposed method precisely detects and classifies LL faults on PV systems under the different conditions with an average accuracy of 99% and 99.5%, respectively.
This paper proposes a novel algorithm of predictive torque control (PTC) for induction motor based on discrete space vector modulation (DSVM). DSVM technique increases the number of voltage vectors, ...which must be evaluated in the PTC method. This new technique is called PTC-DSVM, which presents a low sampling frequency. However, the high number of virtual vectors increases computational burden significantly. To overcome this problem, in this paper, a novel algorithm that reduces the number of admissible voltage vectors, based on the well-known direct torque control switching table, is presented. Both simulation and experimental results demonstrate that proposed PTC-DSVM has the same behavior as conventional PTC at the same switching frequency while the sampling frequency is three times lower. The low sampling frequency of the proposed algorithm makes it suitable for industries to use low-cost hardware or perform a high computational analysis.
Impedance network inverters are the best alternatives for voltage-source and current-source inverters. The boosting capability and the shoot-through solution of such converters make them excellent ...solutions for various applications. Nevertheless, hard switching forces the converter to operate in low switching frequency, which results in a bulky impedance network. Moreover, the coupling between the shoot-through duty cycle and the inverter modulation index makes a tradeoff between the voltage gain and the modulation index (output voltage harmonics). This paper presents a novel dual switching frequency modulation to mitigate the switching losses of the converter, which results in a more compact impedance network. The proposed method combines a high-frequency pulse width modulation with a low-frequency sinusoidal pulse width modulation (SPWM). Therefore, the switching losses decrease considerably with respect to the conventional simple boost modulation. In addition, the impedance network design frequency remains high, and the network size decreases obviously. The proposed approach removes the interdependence between the shoot-through duty cycle and the inverter modulation index. A comparison between the proposed method and the conventional simple boost method is performed to show its advantages over the conventional method. Finally, experimental results are presented to validate the performance of the proposed method.
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