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This paper introduces a novel methodology for determining wind turbines' optimal site matching. Our framework's key component is establishing four new probabilistic models to model wind turbines' capacity factors. First, we used the sum square of errors (SSE) and the determination coefficient (R2) to assess the accuracy of the proposed models. The developed models were then generalized to estimate wind farm capacity factors. After that, the proposed methodology's validity is tested by applying it to a known wind data site. We modeled the available wind data using Weibull distribution. Five different methods were used to determine Weibull parameters. The equivalent energy method combined with the invasive weed optimization algorithm yields the optimal values for these parameters. Finally, under the support of the presented models, the novel methodology is applied to a pool of 24 commercial turbines to choose the optimal ones. A comparison is made between the obtained capacity factor curves using the suggested models and other previous models for the selected wind turbines. When the presented models are utilized, the capacity factor of commercial wind turbines with non-ideal electrical power curves is more precisely predicted.

As technology advanced, electrical interruption or disturbance still becomes a significant problem in power systems. A fault is one example of electrical disturbance or power failure in a power system. In order to recover the system, the fault must be detected, classify and locate to eliminate as fast as possible. Four types of fault occur in the transmission line. Those four types are Line-to-Ground Fault (L-G), Line-to-Line Fault (L-L), Double Line-to-Ground Fault (L-L-G), and Three Line Fault (L-L-L). These Days, fault has been one of the significant problems in the transmission line system. Fault can lead to power losses in transmission lines as well as power failure. Electrical service in the transmission line system needs to be recovered immediately after fault appears to avoid more energy losses. Thus, it is crucial to create a system that will detect and eliminate fault faster, more accurately, and effectively. Typically, transmission line fault classification required complex signal processing, required expert knowledge, and complex mathematical modeling to process the output signal. This paper proposed a deep learning technique to classify ten types of fault through simulation. The objective of this study is to propose automated signal processing and features extraction. This technique can model a system that generates the automated signal processing and extract features learning with a deep learning framework and classify all the ten fault types in transmission lines accurately and effectively.

Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called Long Short-Term Memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.

This paper presents the design of a Three Stages Maximum Power Point Tracking (MPPT) charge controller for improving the charging/discharging control of the battery. In this research, Buck Converter is used to regulate the voltage from the Photovoltaic (PV) module to the required voltage. This research is limited to Valve Regulated Lead Acid (VRLA) battery for 12V system voltage. The charge control algorithm envisages controlling the charging and discharging action in all the three stages of battery charging, bulk, absorption, and float. The idea is to control the battery charging and discharging status until meeting the battery set-point. The set-point is limited to High Voltage Disconnect (HVD), Low Voltage Disconnect (LVD) and Load Voltage Reconnect (LVR) to protect the battery from over-charging and deep-discharging. The results obtained demonstrate the good performance of the charge controller. With the application of the MPPT algorithm in the bulk stage, the time taken to get the battery to fully charged state becomes faster The regulation power from the converter to the inverter has performed well and the switching relay is managed to be controlled.

Robustness of a battery management system (BMS) is a crucial issue especially in critical application such as medical or military. Failure of BMS will lead to more serious safety issues such as overheating, overcharging, over discharging, cell unbalance or even fire and explosion. BMS consists of plenty sensitive electronic components and connected directly to battery cell terminal. Consequently, BMS exposed to high voltage potential across the BMS terminal if a faulty cell occurs in a pack of Li-ion battery. Thus, many protection techniques have been proposed since last three decades to protect the BMS from fault such as open cell voltage fault, faulty cell, internal short circuit etc. This paper presents a review of a BMS focuses on the protection technique proposed by previous researcher. The comparison has been carried out based on circuit topology and fault detection technique

Solar Energy is one of the key solutions to future electrical power generation. Photovoltaic Plants (PV) are fast growing to satisfy electrical power demand. Different maximum power point tracking techniques (MPPT) are used to maximize PV systems generated power. In this paper, on grid PV system model in MATLAB SIMULINK is tested under sudden irradiance and cell temperature variations. Incremental Conductance MPPT is used to maximize generated power from the PV system with the help of new adaptive controller to withstand these heavy disturbances. The new adaptive controller is tuned for optimal operation using two different optimization techniques (Invasive weed and Harmony search).Optimization results for the two techniques are compared. .A robustness test is made to check system stability to withstand different random irradiance and cell temperature patterns without failure to track the maximum power point.Finally, a brief comparison is made with a previous literature and the new adaptive controller gives better results.

This paper presents a theoretical analysis and practical implementation of the natural and regular (symmetric, asymmetric) sampled three-phase PWM waveform, which was digitally implemented on a microprocessor, and the essential considerations involved in the feasibility of using a microprocessor software-based calculation to generate PWM. All the necessary microprocessor hardware/software techniques and algorithms required to generate three-phase PWM are developed in detail, and demonstrated using practical results from an experimental microprocessor board. The techniques developed and presented in this paper are readily applicable to other currently available microprocessors, with minor modifications to reflect the special features of the particular microprocessor used. Particular emphasis has been placed on the basic concepts and principles, rather than concentrating on the details of a particular microprocessor.