Anang Tjahjono

Digital overcurrent relay with conventional curve modeling using Levenberg-Marquardt backpropagation

Overcurrent relays (OCRs) play an important role in the protection component that requires high reliability to maintain high security for power systems. Modeling of the OCRcurve using methods like the direct data storage and curve fitting gave only approximate models. Therefore, in this paper proposes modeling of OCRs using Levenberg-Marquardt backpropagation (LMBP). An implementation of OCR in the digital OCR used ARM microcontroller STM32F407VGT6 is to improve performance of the relay significantly. LMBP is developed using different numbers of neurons. The current and opening time of the circuit breaker are used as input and output in the LMBP training. LMBP developed in the OCR curve model using sample data from protection coordination is implemented as real time in Hess Indonesia Corporation. The weights obtained by the LMBP are used to run the LMBP program in the digital OCR. The well known digital OCR product is used for comparison. The results show that this proposed method is accurate and encouraging with percentage error is 0.24% and very promising to be applied in the digital OCR.

Overcurrent relay curve modeling and its application in the real industrial power systems using adaptive neuro fuzzy inference system

Create an accurate model with over-current relays (OCRs) play an important role in the coordination of power system protection. Modeling of the OCR using methods like the direct data storage and software models gave only approximate models. Moreover, modeling based on mathematical models is not appropriate to deal with ill-defined and uncertain systems. Therefore, in this paper proposes modeling of OCRs using adaptive neuro fuzzy inference system (ANFIS). ANFIS is developed using different numbers and types of membership functions (MFs). Each MF is implemented using training and checking data. The load current and time of opening of the circuit breaker are used as input and output in the ANFIS training. ANFIS, which is developed in the OCR curve model using sample data from protection coordination, is implemented in Hess Indonesia Corporation. Different types of MFs are to obtain the optimal design of OCR curves. The result of ANFIS in the OCR curve modeling is accurate and encouraging; thus, the ANFIS model can be used in digital relays and applied successfully in the real systems. In all cases, ANFIS models using 30 Gbell-type MFs yields a very minimum average percentage error of 0.028419 %.

Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System

This paper proposes an intelligent control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) for maximum power point tracking (MPPT) of PV module. The method is verified under several irradiance and temperature conditions. DC - DC boost converter is connected between the PV module and the load. Duty cycle of DC - DC boost converter is controlled by ANFIS in order to obtain the MPPT. The ANFIS directly takes operating power and voltage level as input. The proposed system is developed under Simulink-Matlab and the system of PV is simulated in PSIM to verify the effectiveness of method. The results show the proposed method can obtain the highest output power than Fuzzy Logic (FL) and Perturbation and Observation (P&O) method i.e., 30.893 and 42.973 for irradiance is 750W/m2 and 1000W/m2, respectively.

Overcurrent relay curve modeling using adaptive neuro fuzzy inference system

In this paper, modeling of overcurrent relay (OCR) curves using adaptive neuro fuzzy inference system (ANFIS) are proposed. The accurate models of OCR curve with inverse time relay characteristics have an important role for protection coordination of power system. Models of OCR curve are appropriate with IEC standard. This model implements of microcontroller AT mega 128 as digital relay and personal computer as facility to design of OCR curve. ANFIS is developed to OCR curve modeling with different types of membership function and each membership function is trained for 10 iterations. Input for training to OCR curve using the load current and current setting or IL/IS. Time to opening the circuit breaker or TCB is used as output for training of OCR curve. ANFIS is developed using visual basic. The simulation results are compared with different types of membership function to obtain the optimal design of OCR curve. Moreover, the testing results are compared with OCR curve modeling to check validation and accuracy of the proposed model.

Maximum power point tracking of photovoltaic system using adaptive modified firefly algorithm

A photovoltaic (PV) module is an important source in distributed generation due to low maintenance cost, low operational cost and eco-friendly. Tracking the maximum power point (MPP) of a PV module has been a hot issue to increase energy production. Maximum power point tracking (MPPT) methods based on nature inspired algorithm such as firefly algorithm (FA) has been proposed to track the MPP. However, the problem the FA method is required long time to reach convergence. Therefore, this paper proposes an adaptive modified firefly algorithm (AMFA) to tracking faster the MPP for convergence. The proposed method is implemented on a buck converter. To evaluate the algorithm, the proposed method is compared with FA and modified FA (MFA). The proposed method is verified by PSIM simulator. The results show that the proposed method can accurately track the MPP and improve the performance of FA in tracking speed for convergence.

Maximum power point tracking of photovoltaic module for battery charging based on modified firefly algorithm

Photovoltaic (PV) Module is becoming important source as renewable energy due to low maintenance requirements and environmental friendliness. PV module has low efficiency. Hence, The Maximization of PV module become an essential concern. Maximum power point tracking (MPPT) methods required to obtain maximum power point (MPP) of PV module. In this paper, proposes modified firefly algorithm to track the MPP. This method is proposed due to firefly algorithm requires the long time for convergence and often oscillate around MPP. Combination KY and buck converter connected between PV module and battery. MPPT used to improve energy transfer efficiency in battery charging. In the simulation result, MFA is compared with FA to evaluate the performance of MFA. The result demonstrates that MFA can increase tracking speed to achieve the MPP.

Optimized PI constant for current controller of grid connected inverter with LCL filter using Genetic Algorithm

This paper presents optimized PI constant for current controller of grid connected inverter with LCL filter. The controller consists of PI controller applied in Synchronous References Frame to control inverter current. To analyze the controller, the system should be modeled. Unfortunately the system is too complex. Therefore, to simplify the system, it is converted into a balanced single-phase model. Furthermore, the complexity of system increased the difficulties of PI constants tuning. To tune the PI constants, controller loop is formulated as an objective function with constraints of transient responses and steady state error. GA (Genetic Algorithm) will optimize simultaneously to obtain optimal values for the constants of PI controller. The GA algorithm is simulated under MATLAB. Then, the optimized PI constants are verified in circuit of PSIM Simulator. Simulation results confirm the method could determine PI constants for the best response.

The modeling of directional overcurrent relay in loop system using cascade forward neural network

The problems arising in loop electrical network system is a relay setting that follows changes in the system such as power source operation, regular maintenance and damage to powers source. To obtain an adaptive relay which is capable of following the changes in the network system, this paper is proposes the modeling of the coordination of the power system network with the cascade forward neural network (CFNN) by simulating three power sources, fifteen protection relays, six buses, and three loads. CFNN applied in the directional overcurrent relay (DOCR) curve model using sample data from protection coordination in loop electrical network system. On the modeling process by comparing some number of neurons and learning rate to get the best accuracy and time speed with four combination input and two outputs. The results of modeling relay using CFNN method showed mean square error of 3,24e-06 with a current contribution of 95% and mean square error of 2,10e-03 with a current contribution of 105% and from modeling is very accurate and can be applied to digital overcurrent relay.

Modelling non-standard over current relay characteristic curves using combined lagrange polynomial interpolation and curve fitting

The characteristic curve of over current relay (OCR) has a very important role in the electrical protection devices. Standard characteristic curve such as Normal Inverse (NI), Extremely Inverse (EI), Very Inverse (VI) and Longtime Inverse (LTI) are often changed at the time of implementation, carried out for the purposes of security protection system due to other devices in the electrical network. The curve changes cause changes of the standard model to be non-standard, in this research proposed combined Lagrange interpolation polynomial and curve fitting for modeling the characteristic curve of the non-standard. It can be shown encouraging results with an average error ranging 0.197 %. It can be used as a reference for modeling the non-standard characteristic curve, especially in the digital protection relay device.

Improvement of power quality monitoring based on modified S-transform

Detection of power quality disturbance is most essential to ensure the good Power Quality (PQ). The power disturbance signal will reduce the reliability of power system and create some disadvantage on operation process. Aging of electrical device, incorrect measurement, devices malfunction are the consequence of this condition. The characteristic of power disturbance signals are non-stationary and the way to detect this disturbance needed a sample method. The term of non-stationary in signal processing is regularly used to define a process in which the spectrum is changing with time. One kind of methods for analyzed this problem is S-transform (ST). However, Due to this method have relatively fixed Gaussian window the S-transform cannot provide satisfactory time-frequency resolution for all types of disturbance signals. Modified S-transform (MST) provides more suitable means for achieving desired time-frequency for several PQ disturbances signals. This paper proposes power quality analysis (PQA) using modified S-transform (MST) method. Both the time and the frequency domain information of each PQ component are extracted by MST. Comparison between MST simulation result and ST is applied to validate the accuracy and efficiency of modified S-transform (MST) method algorithm. The result of simulation show that this method can accurately detect and show the power signal disturbance such as voltage sag, voltage swell, interruption, flicker, oscillatory transient, notch, spike, and harmonic.