Area Load Disturbances Research Articles

Automatic Generation Control of Two Area Thermal Power System using Single Objective PSO and DE Optimization Techniques

Now a days AGC has a great roll in controlling the mismatch between generation and load in interconnected power system, to attain AGC more optimal, tuning the controller using optimization techniques is needed. In this paper PSO and DE optimization techniques are employed for dynamic frequency control analysis. For dynamic analysis, the PID gain parameters obtained through single objective optimization using PSO and DE techniques, the controls are implemented by considering 1% of change in load disturbance in area 1 only and computed with sum of absolute value of ith area control error at time t as objective function and simulation result is obtained by interfacing Matlab (.m file) with Simulink block model under study . Comparison analysis is performed between PSO-PID, without controller and DE-PID. According to the investigations, better dynamic response performance is achieved through DE-PID method than the PSO-PID technique for the measured parameters of time response transient analysis such as maximum overshoot, rise time, maximum undershoot and settling time in AGC of two area system.

Guest Editorial

This is an IET Renewable Power Generation Special Section from the 3rd IET Renewable Power Generation Conference that was held in Naples. There was a wide range of topics among which were wind (turbines, offshore wind farms),photovoltaic generation, distribution power systems including power conversion, management of energy by means of demand side management and energy storage taking into consideration power markets. More than 120 papers were included in the conference's technical programme, and the topics covered a broad spectrum within the field of renewable power generation. A selection of papers presented at the Naples conference were considered appreciable by the Conference Session Chairs and invited to submit to the IET Renewable Power Generation journal. After extensive peer review, two significant papers were accepted for publication by the Editor-in-Chief and the Associate Editor. The accepted papers cover both generation by renewable sources and distribution of the energy treating, in particular, aspects related to the integration of renewable sources in the grid and the control and management of a distributed generation network. K. Musasa, M. N. Gitau and R. C. Bansal, in their paper “Dynamic analysis of DC-DC converter internal to an offshore wind farm”, have analysed the automatic generation control (AGC) problem and considered it in interconnected power systems with doubly-fed induction generator (DFIG)-based wind turbines. A non-linear least squares support vector machines (LS-SVM)-based AGC regulator is proposed; this is trained from an off-line data set obtained from designing and implementing the robust controller to power a system under diverse operating conditions and area load changes. An investigation of time response plots reveal that the designed LS-SVM-based AGC regulator shows promising performance for power systems with governor dead-band non-linearity when compared to that obtained with multi-layer perceptron neural networks and conventional PI-based AGC regulators for a wide range of parametric uncertainties and area load disturbances occurring in the area of the power system. In their paper, M. Cresta, F. M. Gatta, A. Geri, M. Maccioni, A. Mantineo and M. Paulucci analyse possible strategies for the control and management of an LV network with distributed generation connected at LV (consisting in photovoltaic power plants equipped with the last generation of inverters) and electric vehicles charging stations (EVCSs). Various possible scenarios have been studied: the LV network as actually operated; the network operated with the EVCS and the network operated with the EVCS and the NAS storage system. The evolution of voltage levels at the LV busbar, of power-factors at the secondary substation and of energy losses in the LV network have been calculated. By analysing the results, it was found that the installation of the EVCS itself does not present any criticality for the considered distribution network. A proposed optimal power flow detects optimal strategies for the management of reactive power flow supplied by the PV inverter and for the control of charge/discharge cycles of the NAS. From an economic point of view, the installation of the NAS appears to be unjustifiable because of both the high investment cost and the increase in operating costs (due to the additional energy losses). Lastly, the “demand response” practice, applied to “secondary substation” customers does not provide significant benefits on the network operation. However, this practice may generally constitute a convenient and effective solution for the implementation of the “load shifting” along a daily load diagram.

Optimized PI+ load–frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units

Abstract This paper investigates a renewable energy resource’s application to the Load–Frequency Control of interconnected power system. The Proportional-Integral (PI) controllers are replaced with Proportional-Integral Plus (PI+) controllers in a two area interconnected thermal power system without/with the fast acting energy storage devices and are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network (BWNN) approaches. The energy storing devices Hydrogen generative Aqua Electroliser (HAE) with Fuel cell and Redox Flow Battery (RFB) are incorporated to the two area interconnected thermal power system to efficiently damp out the electromechanical oscillations in the power system because of their inherent efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE and RFB designed with BWNN controller reveals that the PI+ controller designed using BWNN approach is found to be superior than that of output response obtained using PI+ controller. Moreover the BWNN based PI+ controller exhibits a better transient and steady state response for the interconnected power system with Hydrogen generative Aqua Electroliser (AE) unit than that of the system with Redox Flow Battery (RFB) unit.

Beta Wavelet Neural Network Based Load-Frequency Controller for an Interconnected Reheat Power system with Hydrogen Electrolyser

This paper investigates a renewable energy resources application to the Load-Frequency Control of interconnected power system. The Proportional plus Integral(PI) controller gains of the two area interconnected thermal power system with the fast acting energy storage devices are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network(BWNN) approaches. The energy storing device Hydrogen generative Aqua Electrolizer (HAE) with fuel cell can efficiently damp out the electromechanical oscillations in the power system because of their efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% and 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE designed with BWNN Controller are found to be superior than that of output response obtained using PI Controller.

Integrated emergency frequency control method for interconnected AC∕DC power systems using centre of inertia signals

An integrated design method for emergency frequency control of interconnected AC/DC power systems with centre of inertia (COI) signals is proposed in this study. It aims to provide emergency power support from reserve-rich area(s) to the disturbed control area(s) through the paralleled AC and DC tie-lines under extreme conditions, when the normal automatic generation control (AGC) is suspended. With the assumption of area COI frequency and the employment of wide-area measurement system (WAMS), the centralised control strategy drives the area COI frequencies to track the overall system COI frequency by virtue of coordination control of emergency generation and high voltage direct current (HVDC) power such that the long-term frequency stability can be promisingly improved. The adaptive backstepping sliding-mode approach holds desired robustness to time-varying generator parameter, modelling error and area load disturbances. Simulations have been conducted on a 3-area-interconnected AC/DC system in computer and the results demonstrate the feasibility and effectiveness of the proposed design method.

Application of ANN technique based on μ-synthesis to load frequency control of interconnected power system

A nonlinear artificial neural networks (ANN) controller based on μ-synthesis for solution the load frequency control (LFC) problem is proposed in this paper. Power systems such as other industrial plants subject to some uncertainties and disturbances due to multivariable operating conditions and load changes. In order to take large modeling errors and minimize the effects of area load disturbances, the idea of μ-synthesis theory is being used for training ANN based LFC controller. This newly developed design strategy combines advantage of the ANN and μ-synthesis control techniques to achieve the desired level of robust performance for all admissible uncertainties and leads to a flexible controller with relatively simple structure, which can be useful in the real world complex power system. A two-area power system is considered as a test system to demonstrate the effectiveness of the proposed method in comparison with the conventional PI and μ-based robust controllers under various operating conditions and load changes. The simulation results show that the proposed ANN based controller achieves good robust performance even in the presence of generation rate constraints (GRC) and is superior to the other controllers.

Robust decentralized neural networks based LFC in a deregulated power system

In this paper, a decentralized radial basis function neural network (RBFNN) based controller for load frequency control (LFC) in a deregulated power system is presented using the generalized model for LFC scheme according to the possible contracts. To achieve decentralization, the connections between each control area with the rest of system and effects of possible contracted scenarios are treated as a set of input disturbance signals. The idea of mixed H 2/H ∞ control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the RBFNN based controller is to take large modeling uncertainties into account, cover physical constraints on control action and minimize the effects of area load disturbances. This newly developed design strategy combines the advantage of the neural networks and mixed H 2/H ∞ control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the proposed method is demonstrated on a three-area restructured power system. The results of the proposed controllers are compared with the mixed H 2/H ∞ controllers for three scenarios of the possible contracts under large load demands and disturbances. The resulting controller is shown to minimize the effects of area load disturbances and maintain robust performance in the presence of plant parameter changes and system nonlinearities.