A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%–98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm.

https://www.mdpi.com/2076-3417/10/2/700/pdf

Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions

Due to the power quality problems and the use of some nonlinear loads such as soft starters, rectifiers, and frequency converters, the circuit current will contain complicated harmonic components, which may affect the identification accuracy of arc fault. Aiming at the interferences of power supply harmonics and nonlinear load noise, a kind of recognition method based on kernel principal component analysis (KPCA) and firefly algorithm optimized support vector machine (FA-SVM) was proposed. KPCA was used to separate the harmonics and load noise interferences in the voltage and current signals. Kurtosis and skewness of the fifth and sixth principal components were used as arc fault features. The FA-SVM was designed to recognize arc fault. The arc fault experiments were carried out under complicated harmonic conditions. The effectiveness of the presented method was verified by experiments.

Recognition Method of AC Series Arc Fault Characteristics Under Complicated Harmonic Conditions

One of the essential challenges in brain–computer interface is to classify motor imagery (MI) signals. In this paper, an ensemble SVM-based voting system is proposed. In each line of this system, the EEG signal is transformed into different representations based on discrete cosine transform, Fourier transform, common spatial pattern, and empirical mode decomposition, and then these representations are combined in a triple-frame matrix. These frames are fed into a pre-trained deep convolutional neural network as a feature extractor. For each line, an SVM is employed to classify the extracted feature vectors. Finally, a decision is made based on voting between these SVMs. Performance of the proposed method is examined on the BCI Competition III dataset Iva to separate right hand and foot movement imagery. The simple proposed method achieves the average accuracy of 96.34% for all of the subjects, and 99.70% for the best situation that is an improvement in MI classification. In addition, it can be seen that right side of the brain is more effective than the other side in EEG-based MI classification.

/pdf/convolutional-neural-network-based-features-for-motor-26wkyvben2.pdf

Convolutional neural network based features for motor imagery EEG signals classification in brain–computer interface system

The performance of vehicle detection system is often affected by both internal and external noise. In this paper, a novel vehicle detection scheme called vehicle detector based on EMD-HT and multichannel GLRT (V-EHMG) is proposed, which composed of signal denoising part based on empirical mode decomposition (EMD) and signal detection part which takes advantage of the spatial–temporal relationship acquired by multiple sensors. In the first part, we propose a new denoising algorithm null-hypothesis interval threshold EMD denoising (EMD-HT) to check the similarity between the probability density function of the decomposed intrinsic mode functions (IMFs) from input signal and the generalized Gaussian distribution to determine relevant IMFs for signal reconstruction. Combined the interval threshold method, the denoised signals are acquired. In the second part, we use multichannel generalized likelihood ratio test (GLRT) detector to judge the presence of other vehicles with the denoised signals as the input. Simulation and physical experimental results show that with V-EHMG, the signal-to-noise ratio of the signal is doubled and the detection range is improved more than 40% compared with the traditional methods.

Research on Multiple Sensors Vehicle Detection With EMD-Based Denoising

Motorcycle accidents caused by sudden deceleration or exceed maximum safe speed of vehicles are still common. There is no tool that can provide alerts when the speed has exceeded the safe speed limit for vehicles allowed. Hence, the study aims to design and manufacture implementation of ATmega16 as an indicator of the maximum speed. Output data is obtained in the form of increasing speed. Maximum speed indicator system consists of atmega 16 as the main processing unit, sensor optocoupler, 4x4 keypad, buzzer and LCD LM162. It includes the output of the transducer which is processed by a microcontroller keypad which is then forwarded to the buzzer and LCD LMB162. Testing is conducted by direct tests on the vehicle running, and the obtained results show the maximum speed indicator device made to work properly and in accordance with the specifications of the predetermined design, so that it can give a warning when the safe speed is being and has been exceeded

Atmega 16 implementation as indicators of maximum speed

The solar home system (SHS) often experience a shading condition that caused by the objects near the PVs installment. The shading affect to the harvesting of power from generation side of the system. The system will experience lower output power because the characteristic of PVs have chance, such as the P-V curve. P-V curve changed by the rapid changing of irradiation level and temperature. In this curve, there is a point that produce highest rate of power harvesting, its called Maximum Power Point (MPP). The MPP Tracking is technique to track the MPP, so the system will run in optimal mode. In the other side, the partial shading to PV surface will cause more than one MPP, even though only one MPP that produce highest rate of output power. It is called global MPP, the other is local MPP. This article shows the genetic algorithm (GA) which is implemented in MPPT technique as a meta-heuristic search. The GA implemented in a DC/DC buck converter with the STM32 F4 microcontroller to run the algorithm and control the converter as real-time. The GA performance is compared to the PO’s by their system efficiency, power output, and tracking time. The experimental treatment done by a single tracking cycle and a single solar daily cycle in the roof of Herman Yohanes Building, Universitas Gadjah Mada. The test conducted for single solar daily cycle, shows that GA implementation on MPPT has 91% efficiency (in average) and PO implementation on MPPT has 89% efficiency (in average). In terms of output power, GA has an average output power + 10% higher than PO. While in terms of time tracking, GA has a mean time of + 61% longer than the PO

The Implementation of Genetic Algorithm to MPPT Technique in a DC/DC Buck Converter under Partial Shading Condition

This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with a n HTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gas es measured a re; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.

https://beei.org/index.php/EEI/article/download/3030/2211

Real-time monitoring system for weather and air pollutant measurement with HTML-based UI application

Measurement of fluid level in steam valves should require precise values. The instrument designed in this article is used to find out how precise the limit switch is used as an indicator control valve at certain points. The measurement technique will be implemented on a control valve on the geothermal power plant. This device is using the Arduino Due board as a data processor and modified the data signal of the vernier caliper to measure the valve opening. The TFT LCD layer is used to display the measurement and calibration results. The test results of this device indicate that this device has an accuracy level of more than 99%.

https://www.beei.org/index.php/EEI/article/download/3031/2167

Automatic limit switch calibrator for control valve at steam turbine of a geothermal power plant

Electroencephalograph (EEG) is brain activity measurement technique that it is widely used by placing electrode on the scalp. The EEG recording signals are vulnerably contaminated by noise. Thus, it can lead to misperception in analyzing EEG signals. One of the noise that has major influence on the recorded EEG signals is ocular artifacts (OAs). In this paper, a new method to remove OAs automatically is proposed by combining between Complete Ensemble Empirical Mode Decomposition (CEEMD) and Independent Component Analysis (ICA). CEEMD-ICA-OD removes OAs based on the concentration of noise that is calculated by calculating entropy for every CEEMD decomposition. Every selected decomposition of CEEMD will be used as input to the ICA method. Then, ICA will decompose selected CEEMD decomposition into independent components (ICs). Then, Chauvenet Criterion (CC) as Outlier Detection (OD) method is used to detect OAs pattern. Every detected OAs pattern will be eliminated by zeroing process on OAs pattern so that the EEG signal information remains secure. The proposed method were compared with Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complete EEMD (CEEMD) with zeroing ICA combination for every comparison method. The performance measure of the proposed method and the benchmark method is calculated by using Root Mean Square Error (RMSE) and Signal to Noise Ratio (SNR) to show the performance quality in eliminating OAs. The experiment results show that the proposed method is successfully removing OAs better than the other methods with RMSE value and SNR value are 0.2491 and −0.3449, respectively.

Removing ocular artefacts in EEG signals by using combination of complete EEMD (CEEMD) — Independent Component Analysis (ICA) based outlier data

Ineffectiveness in sending remote measurement data causes data transmission to be slow and not real-time. That is because the communication media still use conventional methods. In this paper, IoT is used to overcome the problem of the ineffectiveness of sending data from remote measurements, and using IoT remote measurements can increase the practicality and reliability of measurements. The proposed method is successfully sending several analog data from NodeMCU to computer wirelessly via WiFi and it can manage the communication and not only process the data effectively but also real-time by a visual-based application.

Brahmantya Aji Pramudita

Papers

The Implementation of Genetic Algorithm to MPPT Technique in a DC/DC Buck Converter under Partial Shading Condition

Real-time monitoring system for weather and air pollutant measurement with HTML-based UI application

Automatic limit switch calibrator for control valve at steam turbine of a geothermal power plant

Removing ocular artefacts in EEG signals by using combination of complete EEMD (CEEMD) — Independent Component Analysis (ICA) based outlier data

Wi-fi Communication Methods for Internet of Things-based Sensor Telemetry with a Visual Basic-based User Interface