Area Power System Research Articles

Application of WOA Tuned Type-2 FLC for LFC of Two Area Power System With RFB and Solar Park Considering TCPS in Interline

In this article, interval type-2 fuzzy logic controller (IT2FLC) has been proposed as secondary load frequency controller (LFC) for hybrid two area power system instead of type-1 fuzzy logic controller (T1FLC) and PID based controllers. The investigated power system integrates both conventional and renewable power resources like large scale solar parks. These large scale solar parks in addition to demand load changes increases load frequency control problems due to their continuous and severe output power variation. In order to reduce the effect of solar irradiance variation on the power system, another type-2 fuzzy logic controller has been proposed to control the output for the solar park during cloudy days instead of maximum power point trackers. As one of the best energy storage systems utilized in modern power systems, Reduction oxidation flow battery (RFB) has been integrated in the investigated power system to act as fast active power source which absorbs and discharges power during disturbances caused by generation or demand changes. Power flow controller like thyristor controlled phase shifter (TCPS) has been proposed in this paper to control tie-line power shared between generating areas during system disturbances. In order to enhance the dynamic performance for the proposed controller, meta-heuristic nature inspired optimization algorithm like whale optimization algorithm (WOA) has been proposed for proposed controller gains off line tuning. The superiority of the proposed IT2FLC controller against T1FLC controller has been investigated by simulating their performance during severe demand load changes and solar irradiance variations, while WOA enhanced the dynamic performance of the proposed controller compared to other optimization algorithms like particle swarm optimization (PSO) and grey wolf optimization (GWO).

COMPONENT BASED AGGREGATE LOAD MODELLING OF MODERN LOADS

Modelling of the different power system components is very essential for the adequate operation and planning of the network. Load modelling is one of the areas in power system engineering that is often overlooked but has a considerable impact on power system dynamics. Load modelling still needs further investigation to develop an adequate and precise load model. Consumer's demand depends on their behaviour, seasonal factor, time factor and other exogenous factors. It is a challenging task to maintain the power quality, stability and adequacy of the system with a changing load behaviour. This paper focuses on the mathematical representation of residential and commercial load characteristics of a distribution feeder from bottom-up approach. A composite static load modelling approach is presented in the paper, which is classified into three major sections i.e. load inventory survey, laboratory experiment and parameter estimation. Consumer's load behaviour and appliances have been determined from the survey. Similarly, voltage-power correlation of load from laboratory experiments assist to classify the consumer's load and parameter estimation is based on the load composition of a certain time duration. Load composition here incorporates the duty cycle and weekly factor along with other related consumer data.

A Hybrid GWO-PSO technique for the solution of Reactive Power Planning Problem

Over the years the optimization in various areas of power system has immensely attracted the attention of power engineers and researchers. RPP problem is one of such areas. This is done by the placement of reactive power sources in the weak buses and thereafter minimizing the operating cost of the system which is directly dependent on the system transmission loss. The work proposed in this article utilizes FVSI method to detect the weak bus. GWO-PSO is proposed in the current work for providing optimal solution to RPP problem. To test the efficacy of the proposed technique, comparative analysis is then performed among the variants of PSO and hybrid GWO-PSO. The optimal solution rendered by the proposed method is compared with other heuristic algorithms. The proposed method of GWO-PSO generates a reduction of 4.25% in operating cost for IEEE 30 bus and 5.99% for New England 39 bus system. The comparison thus yields that the GWO-PSO hybrid method is superior in generating optimality, diversity and is efficient to generate solution strategies for RPP even in a practical power network.

Algorithm for Visualizing Substation Areas in Electric Power Systems

Algorithm for Visualizing Substation Areas in Electric Power Systems

Preface

Welcome to the EPES21--the International Conference on Frontiers of Electrical Power & Energy Systems 2021 which is organized by Beijing Institute of Technology (Zhuhai) and supported by Zhejiang University on December 18-19, 2021, in Zhuhai, China. An ideal place to gather new findings and ideas, discuss across the industrial community. EPES 2021 highlights the latest advances, innovations, and applications in the field of electrical and electronic engineering, energy and Power system and automation and interdisciplinary engineering applications.This proceeding comprises selected papers of EPES 2021, 67 papers from 165 contributions. The topics discussed revolve around the both theoretical and practical knowledge and understanding primarily in the areas of electrical engineering, electronic engineering, energy and power system, automation, transdisciplinary engineering and technology. The contributions were selected by means of a rigorous international peer-review process, present a wealth of exciting ideas that will open novel research directions and foster multidisciplinary collaboration among different specialists.We seek this conference as a to equip our participants to work closely with industry, and to promote innovation and entrepreneurship in our research and education and to promote the engineering profession and to provide leadership within engineering and scientific activities both nationally and internationally.List of General Chairs, General Co-Chair, Publication Editors, Prgram Chair, Advise Chair, Local Chairs, International Scientific Committee Members are available in this pdf.

Enhancement the Frequency Stability and Protection of Interconnected Microgrid Systems Using Advanced Hybrid Fractional Order Controller

Substituting conventional energy sources with new renewable sources is crucial issue nowadays in energy generation systems to face climate changes and increased load demands. Due to the increased penetration levels of renewable sources in power systems, the benefits of the high-inertia of conventional sources are being insufficient. The resulting low-inertia power systems introduce several stability, reliability, and coordination problems for power system operation and control. Therefore, this paper tackles the coordination assessment and enhancement between digital frequency relays using a new fractional order load frequency controller equipped with superconducting magnetic energy storage (SMES) virtual inertia system. The improved coordination method is established using optimized fractional order controller based on slime mould optimization algorithm (SMA). The proposed SMA-based design method benefits the adaptive weights of SMA algorithm. The proposed design is generalized to be applied on single area and multi-area interconnected power systems as well. Compared to existing literature, this paper presents an advanced fractional order controller with coordinated operation with existing protection relays. The obtained results show the coordination shortcomings of renewable energy based microgrids with traditional control systems. However, improved design and coordination are obtained using the proposed SMA-optimized fractional order controllers. The superiority and feasibility of the proposed analysis and methods are verified on different case studies using single and multiple interconnected areas.

Decentralized Detection and Mitigation of Multiple False Data Injection Attacks in Multiarea Power Systems

This article presents an observer-based decentralized detection and mitigation (ODDM) scheme for the interconnected power system subject to multiarea multichannel false data injection attacks (FDIAs). In the ODDM scheme, the FDIAs on the measurement channel and control channel of power system are modeled as unknown input and unknown output, respectively, which are described by extended states of the system. A state and attack observer (SAO) is developed using local measurements for each control area of power system, so as to simultaneously estimate the system states and attack signals in the presence of model uncertainties. During the occurrence of malicious attacks, the attack estimations provided by observer can automatically detect the FDIA on each control area. Besides, the load frequency control system is reconfigured by attack compensation based on the attack estimations of observer, thus achieving the mitigation of multiarea multichannel FDIAs. Case studies are undertaken on a three-area interconnected power system model under the conditions of single attack, multiple attack, and parameter variations to validate the performance of the proposed ODDM scheme.

Optimized Load Frequency Control for Single Area Power System Using Linear Quadratic Gaussian Technique and Coefficient Diagram Method

Optimized Load Frequency Control for Single Area Power System Using Linear Quadratic Gaussian Technique and Coefficient Diagram Method

Frequency Enhancement in Multi Area Power System by Electric Vehicle Support

Frequency Enhancement in Multi Area Power System by Electric Vehicle Support

Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications

As demands of renewable energy and more energy storages in power systems promote, the digital twin technology for multi-energy system draws attention from the researchers in power system and energy area. The load balancing algorithm of the digital twin server cluster significantly impacts the computing performance of the computing nodes. Optimal complete matching of a weighted bipartite graph is proposed to allocate computing task blocks to each computing node evenly. In the proposed algorithm, the computation times of simulation computing task blocks are estimated and the computation time of one single step in the task blocks are used as dynamic load indicators of the computing nodes. The indices are fed back to the scheduling server in real time. With the total steps of the simulation computing task blocks and the computation time of each single step for the task blocks, a fully weighted bipartite graph of computing nodes and simulation computing task blocks is constructed, greedy expansion of the Hungarian algorithm is used to optimize the complete matching of this weighted bipartite graph. Based on the optimization results, the task blocks are allocated to the computing nodes correspondingly in real time. Simulation-driven model parameter correction function, the representative simulation computing applications of digital twin for multi-energy system was used to test the proposed algorithm and the results show a significant improvement of the load balancing effect at the computing nodes.

Frequency regulation of wind energy integrated power system using a novel optimized type II fuzzy tilted integral derivative controller

Abstract The prime objective of the proposed research work is to study the frequency response of a wind plant integrated two area power system under sudden load disturbances when considered under deregulated market environment. The thermal power system has been modelled with suitable generation constraint and governor dead bands. Erratic behaviour of wind power makes the power system very sensitive to frequency deviations and proper frequency control is needed for stability. A new tilted integral derivative controller (TID) with type II fuzzy controller is considered as secondary controller for minimizing frequency fluctuations. The gains of the controller are set at an optimal value with the help of newly designed hybrid Dragonfly algorithm–Whale optimization algorithm for proper control action. System dynamic performance with and without renewable penetration is studied and robustness of the proposed controller is established under various market conditions and varying renewable power integration.

A Hybrid GWO-PSO Technique for the Solution of Reactive Power Planning Problem

Over the years the optimization in various areas of power system has immensely attracted the attention of power engineers and researchers. RPP problem is one of such areas. This is done by the placement of reactive power sources in the weak buses and thereafter minimizing the operating cost of the system which is directly dependent on the system transmission loss. The work proposed in this article utilizes FVSI method to detect the weak bus. GWO-PSO is proposed in the current work for providing optimal solution to RPP problem. To test the efficacy of the proposed technique, comparative analysis is then performed among the variants of PSO and hybrid GWO-PSO. The optimal solution rendered by the proposed method is compared with other heuristic algorithms. The proposed method of GWO-PSO generates a reduction of 4.25% in operating cost for IEEE 30 bus and 5.99% for New England 39 bus system. The comparison thus yields that the GWO-PSO hybrid method is superior in generating optimality, diversity and is efficient to generate solution strategies for RPP even in a practical power network.

Optimization Technique Inspired Load Frequency Controller for Multi Area Power System with Renewable Energy Source

Modern day scenario has an increasing power demand due to the growing development which indeed increases the load on the generation which might cause turbulence in the system and may bounce out of stability. The governor itself can’t handle such frequent load changes and adjust the generation amount to keep the frequency between the margins. This paper proposes an approach towards such predicament to incorporate an optimization method in order to ensure stability of the system despite the drastic changes in demand. Load frequency control is a control method for maintaining the frequency of the system during the change in demand. Use of controllers has proven to be effective in controlling the frequency deviations in the power systems and the response of the controller is further improved using optimization technique for better stability. The PID controller tuned by Particle Swarm Optimization is employed in multi-area system which reduces the time response by a considerable amount and the deviation settles much quicker despite the rapid load changes. The proposed controller is executed further for renewable energy sources connected to the individual areas and demonstration proves that the optimized controller is efficient enough in handling the frequency deviations when wind and solar with sunlight penetration is incorporated.

Power Generation Monitoring of a hybrid Power System with I-GWO designed trapezoidal type-II fuzzy Controller

ABSTRACT This article has implemented a robust type-II fuzzy PID controller for automatic generation control (AGC) of a diverse energy source-based multi-area power system under various operating conditions. The AGC aims to make stability over frequency and tie-line power of the power system. The proposed controller is accessed with Type-II Fuzzy systems with trapezoidal-based membership functions. In controller design concern, a novel soft computing algorithm called an improved-grey wolf optimizer (I-GWO) is approached for this study. The performance of the proposed trapezoidal membership function-based type-II fuzzy controller (TM-T2FC) has been extensively compared with the existing triangular membership-function based fuzzy-II controller (TrM-T2FC), type-I fuzzy controller (T1FC) and PID controller for AGC of the multi-area power system. Furthermore, the viability and effectiveness of the anticipated I-GWO algorithm have been separately assessed through suitable comparative studies. The robustness of the proposed TM-T2FC controller is investigated through different sensitive analyses and further assessment has been carried out by extending the proposed controller to a five area power system. It is observed from extensive studies and performance assessment that the proposed TM-T2FC controller can be a potential candidate for the AGC of the multi-area power system.

Combined fuzzy PID regulator for frequency regulation of smart grid and conventional power systems

In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing a CFPID controller using the proposed SCiWOA technique for a smart grid system having inexhaustible sources like sun oriented, wind, photovoltaic and capacity gadgets like a battery, flywheel just as module electric vehicles. The first advantages of the SCiWOA tuned CFPID controller over hybrid-particle-swarm-optimization and pattern-search (hPSO-PS) adjusted fuzzy proportional-integral (FPI) controller, hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) adjusted proportional-integral (PI) controller, genetic algorithm (GA) tuned proportional and integral (PI) controller, BFOA adjusted PI controller, jaya algoritm (JA) tuned PID with derivative filter (PIDN) controller and teaching learning based optimization (TLBO) tuned proportional-integral-derivative (PID) controller are demonstrated for the two-area non-reheat thermal power system. The second advantages of the SCiWOA tuned CFPID controller over artificial-bee-colony (ABC) tuned PID controller, SOSA tuned PID controller and Firefly algorithm (FA) tuned PID controller are demonstrated for two-area reheat thermal power system. It is seen that SCiWOA based CFPID controller is more effective in controlling the recurrence comparative with PID regulator.

Lyapunov Analysis of Two Imbalanced Power System Areas

The transient stability is the capability of the system to preserve synchronism while being affected by large disturbances. It is a nonlinear problem that requires a simultaneous solution for many differential equations. Therefore, a thorough analysis is needed to resolve it. In this paper, we present the transient stability for multimachine under different fault cases and to analyze using the Lyapunov function. It serves as an analytical tool to determine the necessary condition to be stable. The system is stable as long as it is contained in the region of attraction. Meanwhile, the swing equation and reduced admittance matrix are used to model the system in three conditions, pre-fault, during the fault, and post-fault. The numerical simulations are conducted to verify that the synchronism can be preserved despite under faults on the transmission lines by achieving the critical clearing time.

Risk-Constrained Minimization of Combined Event Detection and Decision Time for Online Transient Stability Assessment

This paper addresses the problem of `quickest possible' online transient stability assessment, by minimizing the decision time of combined event detection that might lead to a system split and unstable generator group prediction, from real-time wide area power system measurements. More importantly it does so by respecting predefined probabilistic error constraints for the prediction. The statistical theory of optimal detection is applied, firstly to choose the detection threshold and secondly to select a flexible assessment time, after using probabilistic neural networks to provide a temporal representation of the data. On simulated wide area measurements from the interconnected New England test system and New York power system this approach is between two and three times faster on average than strategies based on fixed assessment times, despite having comparable error rates.

Automatic Generation Control Using an Improved Artificial Electric Field in Multi-Area Power System

This paper proposes a novel Improved Artificial Electric Field Algorithm (IAEF)-based AGC in a multi-area power system to minimize the area control error and to obtain an optimal solution. Here, four area AGC systems equipped with the PID controller are examined for the study processes. Also, an optimal gain value for the PID controller is obtained by employing an IAEFA; thus minimizing the area control error (ACE). In addition to this, nine different test functions are employed to evaluate the performances of the proposed IAEF-based AGC of a multi-area power system and are compared with several optimization approaches such as AEF algorithm, BBO Algorithm, PSO Algorithm as well as DE Algorithm. Also, the performance evaluation based on three different simulation strategies such as simulation based on time domain, Random disturbances of the AGC system as well as System Dynamics for maximum frequency deviation and Percentage Improvement for a four area power system. Here, the maximum frequency deviations for four areas are −0.0132, −0.0145, −0.0122 and −0.0120, respectively. Moreover, the percentage improvements with respect to the IAEF with respect to the PID controllers are 67.32%, 57.21%, 47.26% and 55.47%, respectively. The experimental analysis reveals that the proposed approach provides very less tie-line control error and better optimal tuning of the PID controller when compared with all other approaches.

An Optimal Control of Integrated Hybrid Power System with FACTS Devices Using Student Psychology‐Based Optimization Algorithm

AbstractIn this paper, a student psychology‐based optimization algorithm (SPBOA)‐based proportional‐integral‐derivative (PID) controller is implemented for load frequency control (LFC) of an isolated hybrid power system (IHPS) and a grid‐connected HPS (GHPS) models. In the designed models, a combined‐cycle gas turbine, a diesel engine generator, a wind turbine generator and a solar photovoltaic generating unit are used as the main power generating units. A superconducting magnetic energy storage is used with IHPS model to improve the dynamic stability of the studied system. A single area power system is connected with IHPS model through a tie‐line to form a GHPS. In the GHPS, a thyristor‐controlled series controller and a unified power flow controller are associated with the tie‐line power flow control. In this work, electric vehicle is also connected as a load to both the IHPS and GHPS models. The output profile of the proposed SPBOA‐based PID controller of both models is compared with whale optimization algorithm (WOA) and quasi‐oppositional‐based WOA (QOWOA). The performance of IHPS and GHPS models are studied under the different load perturbations. The robustness analysis and frequency domain analysis in terms of Bode plot are also shown in favor of the SPBOA method.

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.