DSTATCOM deploying CGBP based icosϕ neural network technique for power conditioning

AbstractPresent investigation focuses design & simulation study of a three phase three wire DSTATCOM deploying a conjugate gradient back propagation (CGBP) based icosϕ neural network technique. It is used for various tasks such as source current harmonic reduction, load balancing and power factor correction under various loading which further reduces the DC link voltage of the inverter. The proposed technique is implemented by mathematical analysis with suitable learning rate and updating weight using MATLAB/Simulink. It predicts the computation of fundamental weighting factor of active and reactive component of the load current for the generation of reference source current smoothly. It’s design capability is reflected under to prove the effectiveness of the DSTATCOM. The simulation waveforms are presented and verified using both MATLAB & real-time digital simulator (RTDS). It shows the better performance and maintains the power quality norm as per IEEE-519 by keeping THD of source current well below 5%

Controlling Techniques for STATCOM using Artificial Intelligence

The static synchronous compensator (STATCOM) is a power electronic converter designed to be shunt-connected with the grid to compensate for reactive power. Although they were originally proposed to increase the stability margin and transmission capability of electrical power systems, there are many papers where these compensators are connected to distribution networks for voltage control and power factor compensation. In these applications, they are commonly called distribution static synchronous compensator (DSTATCOM). In this paper we have focussed on STATCOM and the controlling techniques which are based on artificial intelligence

Decreasing Harmonics via Three Phase Parallel Active Power Filter Using Online Adaptive Harmonic Injection Algorithm

Three-Phase Parallel Active Power Filter (PAPF) control mechanism via a novel Adaptive Harmonic Injection (AHI) algorithm is proposed in order to filter out harmonics generated by non-linear loads and carry out reactive power compensation. The presented PAPF mechanism is composed of two stages. Before is the extraction of reference current to determine currents with harmonics. Once the reference current is determined, according to the reference current, appropriate current harmonics are injected by triggering of the inverter switches. The proper amplitude and phase values of the harmonics that will be injected are estimated online at any instant by the AHI algorithm. In this study, the sine and the cosine of the phase angle for any harmonic order is weighted by the values estimated via the AHI algorithm, thus obtaining harmonic orders at the desired amplitude and phase. Simulations are performed using various non-linear loads in order to validate the proposed method

Adaptive neurofuzzy inference system least-mean-square-based control algorithm for DSTATCOM

This paper proposes the real-time implementation of a three-phase distribution static compensator (DSTATCOM) using adaptive neurofuzzy inference system least-mean-square (ANFIS-LMS)-based control algorithm for compensation of current-related power quality problems. This algorithm is verified for various functions of DSTATCOM, such as harmonics compensation, power factor correction, load balancing, and voltage regulation. The ANFIS-LMS-based control algorithm is used for the extraction of fundamental active and reactive power components from nonsinusoidal load currents to estimate reference supply currents. Real-time validation of the proposed control algorithm is performed on a developed laboratory prototype of a shunt compensator. The real-time performance of shunt compensator with ANFIS-LMS-based control algorithm is found satisfactory under steady-state and dynamic load conditions. The performance of the proposed control algorithm is also compared with fixed-step LMS and variable-step LMS (VSLMS) to demonstrate its improved performance