Fault location with DGs in radial distribution system using radial basis function neural network

Increasing penetration of Renewable Energy in energy market will contribute to increasing number of distributed generation (DG) existence in the grid, thus leading to conflict in fault location, detection and protection coordination in distribution system. This study is focuses on single-line-to-ground (LG) fault detection in a radial distribution system. The objective of this study is to estimate fault location in a radial distribution system in the presence of DGs by using Radial Basis Function Neural Network (RBFNN), with consideration to minimize monitor placement in system. Fault location has been estimated in term of faulty bus. Two types of radial distribution network with DGs have been tested in this study; 10 bus and 34 bus network. Fault analysis has been performed using Power World simulator and data generated has been applied for RBFNN development via MATLAB. RBFNN performance was then evaluated statistically, by SSE, R2 and RMSE. The proposed RBFNN has been able to accurately predict current magnitude at unmonitored buses by only few provided monitored buses readings. With accurate predicted results by the neural network, pattern of current magnitude during fault has been observed in order to identify faulty buses. It was shown in this study that faulty bus can be identified 100% using the proposed approach

IMPROVED WALSH FUNCTIONS ALGORITHM FOR SINGLE PHASE POWER COMPONENTS MEASUREMENT

ABSTRACT This paper presents an improved Walsh function (IWF) algorithm for power components measurement in linear and nonlinear, balanced and unbalanced sinusoidal load system. It takes advantage of the Walsh Functions' simple procedure to develop an algorithm to determine the active, reactive and distortion powers. The increasing use of non-linear loads causes distortion of the power supply system leading to voltage and current waveforms to become non-stationary and non-sinusoidal. As a result measurement using the IEEE standard 1459-2000 which is based on fast Fourier transform FFT is no longer realistic in non-sinusoidal load condition due to its sensitivity to the spectral leakage phenomenon. The proposed Improved Walsh function algorithm which has the features of being simple, and having high accuracy rate for measurement of both sinusoidal and non-sinusoidal signals was tested using a model created on Matlab 2011. The results were compared with the FFT approach and Wavelet transform technique and it showed that the algorithm has the potential to effectively determine the active and reactive powers of a network under different distortion load conditions better than the FFT. The algorithm is computationally less cumbersome when compared with the Wavelet transform

Harmonic Suppression of Shunt Hybrid Filter Using LQR-PSO based

In linear quadratic regulator (LQR), two different weighting matrices play an important role in presenting the performance of this controller. Instead of using classic common approach, which is trial and error method, this study proposes a particle swarm optimization (PSO) algorithm to track the best solution of the weighting matrices. The proposed algorithm is tested on shunt hybrid active power filter (APF) to mitigate the harmonic contents in voltage and current signals in a nonlinear load system. The modeling work of this proposed system is simulated using MATLAB/Simulink software. From the simulation, the obtained results proved that using PSO in tuning the LQR controller produce smoother nonlinear voltage and current signals. In fact, the amount of current to be injected into network can be reduced up to 95%. Besides, less time is consumed during searching the optimum weighting matrices using the proposed approach

A Novel Hybrid Sine Cosine Algorithm and Pattern Search for Optimal Coordination of Power System Damping Controllers

This paper presents an effective hybrid optimization technique based on a chaotic sine cosine algorithm (CSCA) and pattern search (PS) for the coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controllers. For this purpose, the design problem is considered as an optimization problem whose decision variables are the controllers’ parameters. Due to the nonlinearities of large, interconnected power systems, methods capable of handling any nonlinearity of power networks are preferable. In this regard, a nonlinear time domain-based objective function was used. Then, the proposed hybrid chaotic sine cosine pattern search (hCSC-PS) algorithm was employed for solving this optimization problem. The proposed method employed the global search ability of SCA and the local search ability of PS. The performance of the new hCSC-PS was investigated using a set of benchmark functions, and then the results were compared with those of the standard SCA and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed hCSC-PS for the coordinated design of controllers in the power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. In order to ensure the robustness and performance of the proposed controller, the objective function is evaluated for various extreme loading conditions and system configurations. The numerical investigations show that the new approach may provide better optimal damping and outperforms previous methods. Nonlinear time-domain simulation shows the superiority of the proposed controller and its ability in providing efficient damping of electromechanical oscillations

A Novel Hybrid Sine Cosine Algorithm and Pattern Search for Optimal Coordination of Power System Damping Controllers

This paper presents an effective hybrid optimization technique based on a chaotic sine cosine algorithm (CSCA) and pattern search (PS) for the coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controllers. For this purpose, the design problem is considered as an optimization problem whose decision variables are the controllers’ parameters. Due to the nonlinearities of large, interconnected power systems, methods capable of handling any nonlinearity of power networks are preferable. In this regard, a nonlinear time domain-based objective function was used. Then, the proposed hybrid chaotic sine cosine pattern search (hCSC-PS) algorithm was employed for solving this optimization problem. The proposed method employed the global search ability of SCA and the local search ability of PS. The performance of the new hCSC-PS was investigated using a set of benchmark functions, and then the results were compared with those of the standard SCA and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed hCSC-PS for the coordinated design of controllers in the power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. In order to ensure the robustness and performance of the proposed controller, the objective function is evaluated for various extreme loading conditions and system configurations. The numerical investigations show that the new approach may provide better optimal damping and outperforms previous methods. Nonlinear time-domain simulation shows the superiority of the proposed controller and its ability in providing efficient damping of electromechanical oscillations