Majlesi Journal of Energy Management
Volume:6 Issue: 4, Dec 2017

The key objective of modern power generation, being dynamic and multifarious in nature, is to maintain power exchanges and system frequency to their contractual values to meet growing energy needs. This can be achieved by Load-Frequency Regulation using adaptive controllers. The present study illustrates an innovative approach for adaptive tuning of Support Vector Machine (SVM), a supervised machine learning algorithm, which can be used as a controller for Load Frequency Control (LFC) problem of electric grid to regulate the frequency and tie-line power flows in an interconnected power system. Primarily, an optimized Proportional Integral Derivative (PID) controller is designed for a two interconnected non-reheat thermal areas in which Monte Carlo parameter estimation method is used for sampling the values of uncertain parameters randomly. The input-output data set of optimized-PID controller is used to design a PID based Support Vector Machine (SVMPID) controller. The simulation studies are conducted to find the deviations in frequency and tie-line power exchanges resulting from a step load perturbation in each area. The comparative results are presented with conventional controller, optimized-PID controller and SVMPID controller. The efficacy of the trained SVMPID controller is tested on a three area interconnected thermal-thermal-hydropower system by considering generation rate constraint (GRC), dead band (DB) and boiler dynamics (BD) to represent real-time situation. The results show the performance of the proposed SVMPID controller and its capability to ensure zero steady state error by sustaining minimum over/undershoot and settling time in the system dynamic responses under multi-operating conditions.

In this article, we present a comparative study between neural space vector modulation (NSVM) and fuzzy pulse width modulation (FPWM) strategy in neuro-sliding mode controller (NSMC) of active and stator reactive power control of a doubly fed induction generator (DFIG). The obtained results showed that the proposed NSMC with FPWM technique have rotor current with low harmonic distortion and low powers ripples than NSVM technique.

Energy consumption due to street lights needs proper monitoring and control to reduce wastage of power. Conventional street light systems suffer from certain drawbacks as they are manually controlled and are powered through the electrical broad power station. This may lead to more power consumption if not monitored properly. The automatic street lightning system that we have implemented provides automatic control and fault detection of street lights and to monitor the typical situations occurring in the walkway. The ON/OFF condition of street lamp is controlled using LDR sensor and brightness of the lamp is controlled using IR sensor. If there is any malfunctioning in the street lamp the relay senses and generates a signal to the authority using IOT. The system also provides security alerts using Digital cam. Hardware implementation has been done and the results are also provided .

Wind energy (WG) generation industry has taken more concentration of fabrics. The WG promising renewable source of electrical energy generation for the future. This article applied neural network (NN) technique on the sliding mode command (SMC) of a 1.5 MW doubly-fed induction generator (DFIG) wind turbine (WT). In order to command the energy following between the stator of the DFIG and the grid, a proposed command design uses NN method is applied for implementing a neural command low to remove completely the chattering phenomenon on a traditional SMC strategy. The use of this technique provides very satisfactory performance for the DFIG command. The DFIG is tested in association with a WT. The simulation schemes were developed in Matlab/Simulink environment and the simulation results are presented and discussed for the whole system.

This paper investigates a novel technique for Model Order Reduction (MOR) in Multi Input Multi Output (MIMO) systems. The problem of finding a Reduced Order Model (ROM) has been investigated by solving an H_∞ optimization problem as an equal convex optimization procedure. The reduced order model approximation derives out by simply solving a series of Linear Matrix Inequalities (LMIs). A comparative study have been made to illustrate the performance and efficiency of the proposed method. The important characteristics of the step response of both main system and its approximation model also have been considered in both time and frequency domain.

In this paper, a new dynamic voltage restorer (DVR) based on a Trinary Hybrid Multilevel Inverter (THMI) is proposed, which is capable of compensating for voltage sag, swell and flickers for sensitive loads. A Trinary Hybrid nine-level inverter is composed of a smaller number of IGBTs and circuitry compared to similar structures. The base structure of this inverter is based on the connection of the H bridges and consists of two inverters of a single-phase bridge with a different DC voltage, each of which has a voltage of HB three times the previous HB. The inverter is also able to produce a number of higher output voltage levels and less harmonic distortion than cascade topologies, floating capacitors and diodes. This feature enables the structure to be used to compensate for the power quality of power distribution networks. Nearest Level Control (NLC) in the inverter is used to create the desired waveform. The In-Phase control method is selected to control the proposed DVR and use the synchronous reference frame (SRF) method to detect the network voltage fluctuations. To verify and validate the proposed DVR performance, simulations are carried out in the MATLAB / SIMULINK software environment, and the results indicate the optimal performance and desirability of the proposed DVR to compensate for the voltage sag, swell and flicker power distribution grids