Comparative Performance Analysis of PID Controller with Filter for Automatic Generation Control with Moth-Flame Optimization Algorithm

Renewable Energy Resources (RES) can supply a clean and a smart solution to satisfy the rapidly growing load demand. The Photovoltaic (PV) and wind turbines are considered as resources of Distributed Generation (DG). The integration of PV - DG units into distribution systems has a significant effect on reducing the system active power losses. In this paper, a new technique based on the Moth Flame Optimization (MFO) algorithm, is used to optimally allocate the PV -DG units in Radial Distribution Systems (RDSs) and determine the optimal size of each unit. The objective of the technique is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed optimization algorithm is tested using three different IEEE standard RDSs of various sizes, which are 33, 69 and 118-bus systems. In order to demonstrate the applicability and the performance of the MFO algorithm, numerical simulations are obtained when applying the MFO algorithm, and compared with different existing optimization techniques. This comparison shows the validity of the proposed MFO algorithm.

A recently proposed swarm inspired optimization algorithm based on the navigation approach of Moths in space entitle as Moth-Flame Optimization (MFO) algorithm, is used for solve equality and inequality constrained optimization, real challenging layout problems. The navigating strategy of moths in universe entitles transverse orientation, a well active mechanism for travel so far distance in the straight direction. In fact, artificial lights trick moths, so they follow a deadly spiral path. MFO algorithm gives the competitive results with both continuous and discrete control variables. Real Challenging Constrained Optimization is a way of optimising an objective function in presence of constraints on some control variables. MFO have an ability to solve both constraints that may be either hard constrained or soft constrained. A statical representation of MFO algorithm expresses the best objective function value with reference to accuracy and standard deviation over recently proposed and most popular optimization algorithms. Fourteen constrained benchmark function of real engineering problems have been calculated and gained solutions were compared with the solution obtained by various recognized algorithms. The results obtained through MFO algorithm represent better solutions in the field of engineering design problems among many recently developed algorithms.

<p class="IEEEAbtract">The main objective of Automatic Generation Control (AGC) is to keep the frequency within specified limits through primary and secondary control. In this study, a model of two area thermal non-reheat power system with integration of Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion (WEC) into both areas is presented. A Proportional Integral Derivative (PID) controller and a Fuzzy Logic Controller (FLC) have been applied and compared. The Proposed controllers are used to improve the dynamic response as well as to reduce or eliminate the steady-state error in Area Control Error (ACE). FLC has been offered better and faster performance over the PID controller. The results obtained prove the impact of DFIG-based WEC on AGC and confirm the participation of the DFIG in the frequency system.</p>

The Moth flame optimization algorithm (MFO) is a nature-inspired optimization algorithm which imitates moths darts into the fire. Similarly, to other meta-heuristic algorithm, MFO still has the disadvantage of getting into the local best and the convergence rate cannot be satisfying. In this paper a Multi-operator Moth Flame Optimization algorithm (MOMFO) is proposed to improve the MFO algorithm, which combines Elite Search strategy, Adaptive control strategy and Chaos search strategy to enhance MFO.A two-stage strategy is used to balance the global search and local search ability, which makes the algorithm have better search performance and accuracy. The proposed MOMFO is benchmarked on eight well-known test functions and real-world problem, and the results show that the algorithm is effective in solving complex nonlinearity problems.

An analysis on automatic generation control (AGC) performance of an interconnected two-area hydro–hydro-power system model is presented in this paper subjected to dynamic control of damped oscillations in the presence of thyristor-controlled series compensation (TCSC) and the superconducting magnetic energy storage (SMES) unit. In real time, the load profile characteristics are un-deterministic in nature. Therefore, the current article studies a diverse prospective of area load profiles [such as step load perturbation (SLP), random SLP and sinusoidal load perturbation] in view of AGC performance analysis. The present work is to improve the dynamic responses and to pursue their significances in damping oscillation after the addition of a fast-acting TCSC (as a damping controller) in area-1, whereas SMES unit is installed in area-2 (to provide large values of energy instantaneously). In the present prospect, a new control strategy based on Taylor theorem is implemented to modify the TCSC controller as well as the two-stage phase-compensating blocks are cascaded to both the TCSC and the SMES to improve the phase lag of the system. In this paper, a nature-inspired optimization paradigm [called moth-flame optimization (MFO) algorithm] is utilized to design the controller gains. Additionally, the robustness of the designed controller is investigated in the event of loaded condition and model parameter uncertainties through sensitivity analysis. Analytically, eigenvalues, performance indices values and transient details are presented in support of the designed MFO–TCSC–SMES controller. The obtained simulation results are compared to genetic algorithm (GA)-based designed GA–TCSC–SMES controller to show the optimizing performance of the MFO algorithm in the controller design. The simulation results showed that after the addition of a TCSC–SMES unit in the studied power system model, in addition to eliminating damped oscillations, the settling times of frequency and tie-line power flow are considerably reduced.

Moth-Flame Optimization (MFO) algorithm is a widely used nature-inspired optimization algorithm. However, for some complex optimization problems, such as high dimensional and multimodal problems, the MFO may fall into the local optimal solution. Hence, in this paper an ameliorated Moth-Flame Optimization (AMFO) algorithm is presented to improve the solution quality and global optimization capability. The key features of the proposed algorithm are the Gaussian mutation produce flames and the modified position updating mechanism of moths, which can improve the ability of MFO to jump out of local optimum solutions. In addition, opposition-based learning is adopted to initialize the population. The AMFO algorithm is compared with 9 state-of-the-art algorithms (such as Lévy Moth-Flame Optimization (LMFO), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Heterogeneous Comprehensive Learning Particle Swarm Optimization (HCLPSO)) on 23 classical benchmark functions. The comparative results show that the AMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Furthermore, the AMFO is adopted to optimize the parameters of fast learning network (FLN) to build the prediction model of silicon content in liquid iron for blast furnace, and simulation experiment results from field data show that the root mean square error of the AMFO-FLN model is 0.0542, hit ratio is 91 and the relative error is relatively stable, the main fluctuation is between -0.1 and 0.1; compared with other ten silicon content in liquid iron models, the AMFO-FLN model has better predictive performance.

ABSTRACTIn this paper, Invasive weed optimization (IWO) algorithm is proposed for testing automatic generation control (AGC) of multi-area power systems. Initially, a two-area multi-source power system interconnected by a DC link is considered and IWO is employed to optimize the proportional integral derivative (PID) controller for AGC employing a modified objective function. The superiority of the proposed approach is shown by comparing the results with recently published optimal algorithms for the similar system under study. The comparison is made using various performance criteria such as integral of time multiplied absolute error (ITAE), minimum damping ratio (MDR) of dominant eigenvalues, settling times and maximum overshoots. Furthermore, sensitivity analysis is performed to show the robustness of the proposed approach by varying the system parameters, operating load conditions from their nominal values as well as size and locations of disturbance. The proposed approach is also extended to three area system considering both thermal and hydro units with generation rate constraints (GRC) and the superiority of the proposed approach is shown by comparing the results of Adaptive Neuro Fuzzy Inference System (ANFIS), hybrid Bacteria Foraging Optimization Algorithm with Particle Swarm Optimization (hBFOA–PSO) approaches for the identical interconnected power system.

Fractional order phase lead-lag controller (FOPLC) is a generalization of classical phase lead-lag controller (CPLC) using fractional calculus. Nowadays, fractional order controllers (FOC) have found wide applications in various aspects of power system control and operation. In this paper, FOPLC are used in structure of flexible ac transmission system (FACTS) based frequency stabilizer to enhance the dynamic performance of automatic generation control (AGC) system. So, Thyristor controlled series capacitor (TCSC) based damping controller with FOPLC structure is proposed in coordination with secondary integral load frequency controller (LFC). The controller is optimized via an improved particle swarm optimization (IPSO) algorithm. The dynamic performance of the proposed controller is compared with CPLC using eigenvalue analysis and time domain simulations. An interconnected multi-source realistic power system with TCSC in series with the tie-line is investigated considering the nonlinearity effects of generation rate constraint (GRC) and governor dead band (GDB). Simulation results reveal that FOPLC based TCSC damping controller achieves superior dynamic performance under various load perturbation patterns in terms of decreased performance index, settling time and amplitude of oscillations. Furthermore, the robustness of the proposed controller has been tested for large uncertainties in system parameter and loading condition.

In this research we study the optimization problem of path planning in an oceanic ecosystem with a Static current in the presences of various obstacles with the autonomy of Autonomous underwater vehicles (AUV). We introduce an improved Moth flame optimization algorithm called refraction principle and opposite-based-learning Moth flame optimization (ROBL-MFO) in a Static current environment. This algorithm addresses the differences between the traditional Moth flame optimization (MFO) algorithm. First, it adjusts the moth position update formula we deal with the term of the historically optimal flame average, and improves the convergence velocity of the algorithm. Secondly, it uses a random inverse learning strategy to narrow down the algorithm search. Finally, the principle of refraction provides the algorithm with the ability to jump from the local optima and help the algorithm avoid premature convergence. The numerical results show that the ROBL-MFO improves the classical MFO, in terms of optimization criteria.KeywordsAutonomous underwater vehiclesPath planningStatic currentRefraction principle and opposite-based learning - Moth flame optimization

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

This article examined the flow shop scheduling problem by considering the travel time between machines. The objective function of this problem was to provide a makespan. The Moth Flame Optimization (MFO) algorithm was proposed to solve the flow shop problem. The MFO experiment was carried out with a combination of iteration parameters and the population of the MFO algorithm to solve the flow shop scheduling problem. The computational results showed that MFO could produce a better solution than the actual scheduling method. Furthermore, the MFO Proposal Algorithm was able to reduce the makespan by up to 3%.

In recent years, wind power which is an intermittent and random energy source affects the frequency stability of automatic generation control (AGC) system with the large-scale wind power integration. So, it is necessary to improve the conventional frequency control method and model. Firstly, a dynamic wind farm model is set up based on wind turbines clustering for the accuracy of data in this paper. Secondly, it is analyzed that the difference between multi-area interconnection AGC model and area control error (ACE) with wind farm and without wind farm. Thirdly, an intelligent control strategy of cascade control with outer loop using fuzzy PI algorithm and inner loop using internal model controller is designed to reduce the impacting of wind power changes on AGC system frequency and improve the performance of AGC system. Finally, frequency control effects of conventional AGC system and improved AGC system are contrasted by simulation study of four-area system. It is proved that the improved AGC system has stronger ability to resist fluctuation of wind power and better frequency control performance.

Ensuring frequency stability and controlling active power are fundamental tasks in enhancing the security and reliability of power system operations. This paper investigates the influence of discrete secondary frequency controllers on the dynamic response of power systems, considering both conventional automatic generation control (AGC) and Market-based AGC (AGC<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf>), also referred to as Real-Time Energy Market (RTEM). The study explores how integrating market-based dispatch mechanisms with Artificial Neural Networks (ANN)-assisted AGC systems enhances frequency regulation under dynamic operating conditions. In order to maintain stable operation, the frequency change caused by load variations must be brought as close to zero as possible. MATLAB-Simulink is utilized to simulate the ANN-based controller's performance under both step load and random load changes in the system. The results of the simulation have been compared and studied. The response characteristics of the ANN controller integrated with the conventional controller are compared with those of the integral and PID controllers to evaluate its performance in improving system dynamics.

Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.

The aim of this research is to develop PID (proportional integral derivative) controllers for a variable load two-area intelligent power system (multi-area intelligent power system) in the context of healthcare data analysis. PID controllers are used in the present healthcare business to improve the healthcare industry. It is still debatable how the settings should be changed to create a PID controller that is dependable, adaptable, and effective. In this research, a better PID gain is demonstrated using the particle swarm optimization (PSO) algorithm. The automatic generation control (AGC) for a two-area intelligent power system in healthcare industry has been built using a PID controller and a PSO-based method. To improve frequency response and settling time of healthcare industry equipment in accident and emergency cases (A and E), a PSO-based PID controller generates and transmits the two control signals to the two regions for the algorithm convergence in emergency cases as per MATLAB/Simulink result.