Sudden Load Changes Research Articles

OPTIMAL CONTROL DESIGN FOR FREQUENCY REGULATION IN ELECTRIC POWER SYSTEM WITH LOW INERTIA

Electricity is a very important element in this era because almost all aspects of modern life depend on electricity. Therefore, electricity plays a very important role in improving people's quality of life and maintaining an efficient and productive life. An efficient and reliable electrical system is essential to ensure adequate electricity availability and maintain system reliability. Therefore, planning, designing and operating electrical systems must be carried out carefully to ensure stability, reliability and efficiency. However, a decrease in frequency in the electrical system sometimes occurs when there is a sudden change in load. This can affect system stability. Therefore, Load Frequency Control (LFC) and Linear Quadratic Regulator (LQR) analysis is needed to maintain the stability of the electrical system frequency. The combination of these two techniques, namely LFC with LQR modeling, provides a better solution for maintaining frequency stability and optimizing electrical system performance. LFC analysis regulates power generation settings automatically to compensate for fluctuations in load demand and maintain a stable frequency, while LQR is a control technique used to minimize system errors and optimize system performance. Therefore, LFC with LQR results in system performance increasing very significantly with a faster response, undershoot that can be reduced to 0.001 and a better settling time of 300s in area-1 and 450s in area-2 and rise time reaching 270s in area-1 and 405s in area-2 as well as the use of LQR can maintain the system frequency at its nominal limit and the presence of New Renewable Energy (EBT) has an effect in the form of a greater undershoot level than without EBT.

Backstepping Control Design in Conjunction with an EKF-based Sensorless Field-Oriented Control of an IPMSM

The collector and brushless electronic commutation machines have gained widespread utilization in industrial applications. Among these machines, internal permanent magnet synchronous motors (IPMSMs) have become increasingly popular due to their numerous advantages, including high torque/current and torque/inertia ratios, robust construction, high efficiency, and reliability. However, the incorporation of position sensors in IPMSMs has introduced challenges concerning their application, performance, mass production, and cost. Consequently, the implementation of sensorless control techniques has become essential in drive systems and various applications. This paper proposes a backstepping control approach for achieving speed sensorless control of IPMSMs, employing an extended Kalman filter (EKF). The first step involves developing a comprehensive nonlinear dynamical model of the IPMSM in the direct and quadrature (d-q) rotor frame and obtaining its corresponding state-space representation. Subsequently, backstepping controllers for rotor speed and current tracking are designed to ensure precise tracking and anti-disturbance performance. These controllers are integrated into the field-oriented control (FOC) scheme. The Lyapunov stability theorem is employed to guarantee the asymptotic stability condition of the backstepping controller. To address the challenge of estimating immeasurable mechanical parameters of the IPMSM and accurately tracking the system states, an EKF is designed. This filter enables the estimation of mechanical parameters and achieves high steady-state precision within a finite time. The effectiveness of the proposed methodology is demonstrated through simulations conducted under various dynamic operating conditions, including sudden torque load changes, command speed changes, and parameter variations.

Empirical Investigation of a Single-phase Input Switched AC-DC Boost Converter with Improved Power Quality

The Boost converter topology is most widely used for power factor correction (PFC) converters. The conventional passive Boost PFC converter inherently suffers from the lack of controllability and non-ideal characteristics of the diode-bridge rectifier. Replacing diodes with active switch(es) offers viable solutions but increases the circuit complexity. Addressing this trade-off, a novel design of a single–phase input switched AC to DC Boost converter has been presented in this article. The active switch of the Boost DC converter is shifted to the input side of the full-bridge diode rectifier which provides more control on the input current without increasing much of the circuit complexity. Two Boost inductors and two output split capacitors are used for providing higher step-up operation compared to the conventional. The converter has been simulated both in open-loop and closed-loop conditions using the PSIM software. The simulation results show that the proposed converter provides good dynamic response and can maintain a constant stable output voltage during sudden load change. Furthermore, to evaluate the experimental performance of the suggested converter, a prototype converter has been developed. A scaled-down experiment has been performed which shows that the converter can achieve up to 85.2 % efficiency and an input power factor of 0.9.

Integrated CFD and MBD methods for dynamic performance analysis of a high-speed train transitioning through varied windbreak corridor designs

With increasing train speeds, the wind environment along routes has become complex, variable, and extreme. Constructing windbreak facilities is an effective strategy to enhance train operational safety in windy zones. Various windbreak designs exhibit notable differences in improving train dynamic performance, and economic considerations are also crucial. Utilizing the non-constant compressible Reynolds time-averaged equation URANS (Unsteady Reynold-averaged Navier–Stokes) alongside the shear-stress transport (SST) k-ω turbulence model, this study simulates the non-constant aerodynamic characteristics of a 350 km/h high-speed train traversing through different forms of windbreak corridors in a 30 m/s wind zone. The train's dynamic response was captured using a combined CFD–MBD (Computational Fluid Dynamics and Multi-Body Dynamics) offline time-domain simulation, the accuracy of which was verified experimentally. Results indicate that vortices of various positions and shapes form in the flow field along the windbreak corridor depending on the size of the openings. Using the no-windbreak corridor (Case 1) and the fully enclosed corridor (Case 4) as control groups, it was observed that smaller openings lead to more stable airflow, enhancing peak damping and fluctuation effects, albeit with varying stages of aerodynamic load fluctuation. The 1/3-opening windbreak corridor (Case 3) effectively mitigated the sudden aerodynamic load changes at the wind zone transition of the 2/3-opening windbreak corridor (Case 2), with the primary fluctuation area in Case 3 being the wind section. Dynamic analyses revealed that Case 2 exhibited insufficient lateral aerodynamic performance, raising derailment concerns. In contrast, Case 3 ensured travel safety and comfort effectively, while Case 4 offered excessive protective capacity. This study's findings serve as a valuable reference for designing windbreak corridors and ensuring the safe operation of trains in windy regions.

Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control.

The cerebellum plays a central role in motor control and learning. Its neuronal network architecture, firing characteristics of component neurons, and learning rules at their synapses have been well understood in terms of anatomy and physiology. A realistic artificial cerebellum with mimetic network architecture and synaptic plasticity mechanisms may allow us to analyze cerebellar information processing in the real world by applying it to adaptive control of actual machines. Several artificial cerebellums have previously been constructed, but they require high-performance hardware to run in real-time for real-world machine control. Presently, we implemented an artificial cerebellum with the size of 104 spiking neuron models on a field-programmable gate array (FPGA) which is compact, lightweight, portable, and low-power-consumption. In the implementation three novel techniques are employed: (1) 16-bit fixed-point operation and randomized rounding, (2) fully connected spike information transmission, and (3) alternative memory that uses pseudo-random number generators. We demonstrate that the FPGA artificial cerebellum runs in real-time, and its component neuron models behave as those in the corresponding artificial cerebellum configured on a personal computer in Python. We applied the FPGA artificial cerebellum to the adaptive control of a machine in the real world and demonstrated that the artificial cerebellum is capable of adaptively reducing control error after sudden load changes. This is the first implementation and demonstration of a spiking artificial cerebellum on an FPGA applicable to real-world adaptive control. The FPGA artificial cerebellum may provide neuroscientific insights into cerebellar information processing in adaptive motor control and may be applied to various neuro-devices to augment and extend human motor control capabilities.

Finite‐time sliding mode fault‐tolerant control of PEM fuel cell air supply system

AbstractProton exchange membrane (PEM) fuel cell air supply system is primarily responsible for delivering air to the fuel cell at a specific stoichiometric ratio for chemical reaction. The stoichiometric ratio will be impacted by the sudden change in load and system failure. Under the aforementioned circumstances, precise and timely air supply regulation is required to avoid irreparable PEM damage. In order to enhance the robustness and performance of the fuel cell, a cascaded finite time sliding mode fault‐tolerant control method is proposed for the air supply system of PEMFC. The control objective is to adjust the oxygen excess ratio at the cathode flow field, which can avoid the occurrence of oxygen starvation in the PEM fuel cell when the load current changes rapidly or when fault occurs. A cascaded controller structure is introduced, where, in the inner loop, an adaptive sliding mode controller is designed to reconstruct fault signal and track the optimal output trajectory and, in the outer loop, a finite‐time observer is employed to obtain the expected output trajectory. The proposed control scheme is capable of fault‐tolerant tracking of the optimal oxygen excess ratio within finite time. The effectiveness of proposed fault‐tolerant control algorithm is verified through hardware‐in‐the‐loop (HIL) studies.

Flutter analysis of a rigid-flexible coupled composite space structure with momentum wheels under thermal load

The communication and observation spacecraft generally consists of the spacecraft body, the composite connecting beams, the working payload and the CMG/momentum wheel for integrated attitude-vibration control. When such complex space structure operates in near-Earth orbit, the sudden change in thermal load may cause a solar flux shock to the structure, which is likely to excite the flutter motion of the structure. The flutter mechanism of a rigid-flexible coupled composite space structure configured with the momentum wheel is investigated. In this study, the heat conduction equation of the composite beam appendage is derived. Then, the dynamic equations of the system influenced by the momentum wheel and composite material parameters are obtained based on the Kane's equations. Using the Routh–Hurwitz stability criterion, the stability boundaries of the flexible appendage are obtained for different composite material parameters, different operating conditions of the momentum wheel, and different attitude angular velocities. Finally, the flutter behavior is analyzed based on the dynamic response of the flexible structure and the flutter stability boundaries are verified.

Design of fuzzy logic controlled zero voltage switching based interleaved high gain converter

The conversion of low level DC voltage to high level DC voltage at lower duty ratios with less ripple current at source side is a challenging task in the distributed generation systems. Active switched inductor (ASL) converter is a high gain converter and has a large amount of input ripple current due to the parallel charging of the inductors during on time of the switches and series discharge of the inductors during off time of the switches. This large input ripple current in the converter when used in distributed generation (DG) affects the life time of low voltage batteries and fuel cells, the efficiency of photovoltaic source, increases the size and losses in the filtering stages and decreases the power density of the converters. In this paper, soft switching of interleaved active switched inductor (IASL) through Zero Voltage Switching (ZVS) is designed to overcome the limitations of ASL. The performance is analyzed in terms of its gain, input current ripple, switching loss and efficiency. An M-type resonant switch is used to achieve soft switching through ZVS technique.A proper value of resonant elements (inductor and Capacitor) is designed to achieve ZVS to improve the efficiency. Regulated voltage of proposed converter is required in DG systems and is possible with closed loop control. Fuzzy Logic controller (FLC) is designed to regulate the voltage and its dynamic performance is analyzed for different load values and also for sudden changes in load. Hardware model of the proposed ZVS based IASL converter with FLC is implemented and the results are verified with simulation results obtained from MATLAB Simulink.

Multi-objective optimization and sustainable design: a performance comparison of metaheuristic algorithms used for on-grid and off-grid hybrid energy systems

Alternative energy sources are needed for a sustainable world due to rapidly increasing energy consumption, fossil fuels, and greenhouse gases worldwide. A hybrid renewable energy system (HRES) must be optimally dimensioned to be responsive to sudden load changes and cost-effective. In this study, the aim is to reduce the carbon emissions of a university campus by generating electricity from a hybrid energy production system with solar panels, wind turbine, a diesel generator, and battery components. On the university campus where the hybrid energy system will be installed, the ambient temperature, solar radiation, wind speed, and load demands have been recorded in our database. Optimization algorithms were used to select the power values of the system components to be installed using these data in an efficient and inexpensive manner according to the ambient conditions. For optimal sizing of HRES components, gray wolf optimizer combined with cuckoo search (GWOCS) technique was investigated using MATLAB/Simulink. In this way, it has been tried to increase their efficiency by combining current optimization techniques. The cornerstone of our optimization efforts for both on-grid and off-grid models pivots on a constellation of critical decision variables: the power harvested from wind turbines, the productivity of solar panels, the capacity of battery storage, and the power contribution of diesel generators. In our pursuit of minimizing the annual cost metric, we employ a tailor-made function, meticulously upholding an array of constraints, such as the quotient of renewable energy and the potential risk of power disruption. A robust energy management system is integral to our design, orchestrating the delicate power flow balance among micro-grid components—vital for satisfying energy demand. Upon analyzing the outcomes of the study, it is apparent that the proposed Scenario 1 HRES effectively utilizes solar and battery components within the off-grid model, surpassing the efficiency of four other hybrid scenarios under consideration. Regarding optimization processes, the off-grid model exhibits superior results with the implementation of the GWOCS algorithm, delivering faster and more reliable solutions relative to other methodologies. Conversely, the optimization of the on-grid model reaches its optimal performance with the application of the cuckoo search algorithm. A comprehensive comparison from both technical and economic view points suggests the on-grid model as the most feasible and suitable choice. Upon completion of the optimization process, the load demand is catered to by a combination of a 2963.827-kW solar panel, a 201.8896-kW battery, and an additional purchase of 821.9 MWh from the grid. Additionally, an energy surplus sale of 1379.8 MWh to the grid culminates in an annual cost of system (ACS) of 475782.8240 USD, a total net present cost of 4815520.2794 USD, and a levelized cost of energy of 0.12754 USD/kWh. Solar panels cover the entire system, and the renewable energy fraction is 100%.

Simultaneous Damping and Frequency Control in AC Microgrid Using Coordinated Control Considering Time Delay and Noise

The incorporation of converter-based generating sources in utility-scale microgrids causes frequency instability and low-frequency oscillations (LFOs), which is also a reason for degeneration in system stability. Damping frequency control is an essential part of alternating current (AC) microgrid system operation and control. Sudden changes in load, variations in renewable power outputs due to changes in solar insolation or wind speed, and so on factors cause the system frequency to deviate from the nominal value. Therefore, the role of a frequency controller is to maintain the dynamic stability in an AC microgrid by retaining the system frequency at the nominal value. Again, AC microgrids with high renewable power penetration face even more difficulty in maintaining frequency stability because of their poor inertial response. The research presented here proposes a novel approach for grid-connected AC microgrid oscillation damping and frequency control that simultaneously takes into consideration time delay and noise. To improve the frequency response and dampen LFOs by providing the voltage and frequency within the specified range, a coordinated technique-based control approach is adopted. In the developed hybrid control, the frequency controller relies on active power modulation, while the power oscillation damping controller is dependent on reactive power modulation. To improve stability and reduce communication consequences such as noise and signal latency (time delay), the developed power oscillation damping controller and frequency controller are coordinated along with the robust linear quadratic Gaussian controller. The comparative investigation of the effectiveness of the coordinated control technique is employed in the software of MATLAB/Simulink for grid-connected AC microgrid. The outcome of the simulation illustrates the superior effectiveness of the suggested controller over the traditional droop controller for huge power flows under disturbance with various operating conditions, under/overfrequency events, time delay, and noise. This grid encouragement capability for AC microgrids is anticipated to lead to novel possibilities for generating revenue.

Forecasting-Aided State Estimation in Power Systems During Normal Load Variations Using Iterated Square-Root Cubature Kalman Filter

The main aim is to estimating the voltage profile at all the buses in the system before the arrival of next set of hybrid measurements from field. The effectiveness of the algorithm ISCKF during load variations is evaluated with respect to already implemented Kalman filter approaches for this application. This includes the sudden changes in loads which occurring in practical power systems. The utilization of an iterated square-root cubature Kalman filter (ISCKF) for power system forecasting-aided state estimation (FASE) is being studied during normal load variations. Its implementation involves "Newton-Gauss iterative method being embedded into the square-root cubature Kalman filter (SCKF)" at the measurement update step of Kalman filter. The square-root factor of error covariance matrices is calculated by utilizing QR decomposition to avoid losing of positive definiteness of the matrix. The estimation is carried out utilizing hybrid measurements from remote terminal units and phasor measurement units. The state vector is forecasted using the proposed method in the interval period between two measurement arrivals from the devices. Thereby, caters to state estimation of the voltages at buses in the system even when the measurements are unavailable. The efficacy of the proposed algorithm to FASE is evaluated for IEEE 30-bus system and Northern Region Power Grid (NRPG) 246-bus system. The simulation results show that the proposed ISCKF outperforms the CKF by significant improvement in accuracy of forecasting-aided state estimation. ISCKF will be able to give results before the next set of hybrid data arrives (expected from an estimation algorithm). Therefore, the proposed estimation algorithm is applicable for real-time practical application, with respect to large power systems as well.

Power Regulation of Islanded PV-Battery DC Microgrid With Seamless Transition

In this paper, a power regulation for islanded PV-battery DC microgrid with seamless transition is proposed under sudden load changes. In the power regulation, there are two modes, mode 1 for normal operation and mode 2 for the case where the load power is greater than the PV output, using droop control of PV to maintain the bus voltage and constant voltage control of energy storage to maintain the bus voltage, respectively. Mode 1 ensures that the battery is in a stable charging state and avoids frequent charging/discharging of battery. Mode 2 can maximize the use of PV energy. A seamless switching strategy without communication between the two modes is also established to suppress the fluctuation of bus voltage through the cooperation of PV and energy storage units, and a suitable switching timing is selected to avoid the sudden change of power during the switching process. Finally, the effectiveness and feasibility of the proposed control strategy is verified by the OPAL-5700 hardware-in-loop simulation platform.

Research on intelligent control and sensor application strategy of motor based on RFID technology

Abstract People widely use permanent magnet brushless DC motors because of their simple structure, high operating efficiency, simple control, and easy maintenance. This paper constructs an EFID sensor network by merging EFID and sensor technologies and then designs the motor’s intelligent control system and strategy around it. In this paper, the SVR-PSO algorithm is used to localize the motor rotor, which nonlinear SVR trains to construct a nonlinear system of equations for calculating the position of the target tag, and then the PSO algorithm is used to seek the position coordinates of the target tag. The fuzzy PID is utilized to control the brushless DC motor, and the integral separation method is adopted to improve the control strategy. The SVR-PSO positioning algorithm and the improved fuzzy PID control strategy are applied and analyzed. For 12 tags to be measured, the positioning accuracy of SVR-PSO has improved by 56.8% compared to that of LANDMARC. Although SVR-PSO’s positioning algorithm is influenced by mutual coupling and multipath effects, the overall accuracy is still superior to that of LANDMARC. Based on the fuzzy PID controller optimized by the integral separation method, the motor rotational speed has a fast response, a small overshoot, and a small fluctuation of rotational speed, and can automatically regulate and restore stability under the situation of a sudden change of load. The motor speed response is fast, the overshoot is small, and the speed fluctuation is small, and the motor speed can be automatically adjusted and stabilized under the sudden load change, which has better dynamic and static performance than the traditional PID controller and fuzzy PID controller. The purpose of this paper is to provide a strategy reference for the use of RFID technology in the intelligent control of motors.

Harmonic current suppression method of virtual DC motor based on fuzzy sliding mode

Abstract In the operation of power grid, the low frequency subharmonics in power lines are easy to cause harmonic current problems. A harmonic current suppression method is constructed based on fuzzy sliding mode and virtual DC motor (VDC). First, the adaptive linear network is introduced to compare and analyze the output value and the actual value in the power line, and the possible harmonic current is obtained, which is detected and discretized. After that, this study adopts Fuzzy sliding mode control (FSMC) strategy for VDC, uses FSMC to participate in on-off control of VDC, and then realizes the suppression of harmonic current in VDC ports. In addition, the model also uses the circuit superposition theorem and the amount of harmonic data to be compensated by FSMC. The whole method uses VDC control technology to ensure the voltage stability of DC bus and load side when the load changes. In this physical experiment, harmonic current generated by load disturbance is eliminated by reducing DC harmonic voltage, so as to achieve the suppression of DC port harmonic current. Experimental results show that the third and fifth harmonics in power lines can be eliminated by this method to a large extent. Therefore, the proposed method can accurately detect and eliminate the harmonic current caused by sudden load change, and then realize the suppression of VDC harmonic current.

Optimization design of hybrid energy storage capacity configuration for electric ship

Electric propulsion has gained significant popularity in the marine vessel industry due to its numerous advantages. One of the key challenges faced in this domain is effectively managing the fluctuation of power grid caused by sudden load changes. While energy storage systems offer a viable solution, striking the right balance between cost and benefit remains a complex task. To address this issue, establish an optimization model and constraint conditions for capacity configuration of hybrid energy storage systems, and propose a decision-making method based on NSGA-II algorithm and cost-effectiveness method. To address the issue of NSGA-II algorithm being prone to local optima and premature convergence, chaotic thinking and adaptive crossover operators are introduced to improve the NSGA-II algorithm; In order to obtain the optimal configuration scheme from the obtained Pareto optimal solution set, a decision method based on cost-effectiveness was adopted to further analyze the bias between the Pareto optimal solution set and each objective. By comparing the decision results with the ideal solution (TOPSIS), the optimal capacity configuration scheme was obtained.

A Second-Order Sliding Mode Voltage Controller with Fast Convergence for a Permanent Magnet Synchronous Generator System

This paper studies an improved super-twisting sliding mode controller (IST-SMC) for the permanent magnet synchronous generator (PMSG) voltage loop to improve the anti-disturbance capability of the system. Compared to conventional voltage controllers, the control algorithm provides advantages in terms of system resistance to load disturbances. Conventional voltage controllers have significant voltage fluctuations and long recovery times during sudden load changes. To solve this problem, a voltage loop controller based on a super-twisting sliding mode (ST-SMC) is designed to enhance the immunity of the system. Also, the ST-SMC was improved to further increase the convergence rate of the system and enhance the dynamic performance. The convergence of the system away from the balance point is accelerated by introducing an exponential term, which in turn provides an improvement in the dynamic performance of the system. The effectiveness of the proposed control scheme was verified on a PMSG.

Power synergistic control between of power‐to‐hydrogen and SCESS‐DFIG considering “source–load” turbulence

AbstractHydrogen production from wind power can reduce wind abandonment and improve the utilization rate of wind power. However, both “source” turbulence caused by unstable wind speed and “load” turbulence caused by sudden load changes will adversely affect the stable operation of power‐to‐hydrogen systems and the grid connection stability of wind farms. A synergistic control strategy between a supercapacitor and power‐to‐hydrogen system is proposed to solve this problem. The strategy is based on the ensemble empirical‐mode decomposition algorithm. By using the supercapacitor to stabilize the high‐frequency component in the “source–load” turbulence, the stability of the power‐to‐hydrogen system is improved. An optimal allocation method of supercapacitor capacity suitable for the control strategy is then proposed. Considering the different states of each fan and supercapacitor, the unit inertia time constant variance is reduced by reasonably distributing the output of each unit. This effectively improves the inertia time constant and grid connection stability of the wind farm. Finally, the effectiveness of the strategy is verified by Simulink simulations.

Cooperative control method considering efficiency and tracking performance for unmanned hybrid tractor based on rotary tillage prediction

In order to address the issues of poor trajectory tracking accuracy and low energy utilization efficiency in operation for unmanned hybrid tractors, a cooperative control method was proposed specifically for rotary tillage conditions. This method aims to improve the tractor efficiency and trajectory tracking performance. Firstly, a power take-off (PTO) power prediction method based on the Markov chain is designed to address the PTO of sudden load changes. Secondly, a trajectory tracking method based on model predictive control is designed to improve the accuracy of rotary tillage operation tracking. Next, in order to achieve efficient utilization of overall system energy, a real-time energy-saving control method based on instantaneous optimization is proposed. Finally, integrating the PTO power prediction method, trajectory tracking method, and real-time energy-saving control method with velocity and PTO power as the interactive variables, a cooperative control method based on Markov-trajectory tracking is established. The results show that the proposed control method simultaneously improves the energy utilization efficiency and trajectory tracking accuracy of the tractor under the rotary tillage condition. This study provides a reference for the cooperative control of the drive system and trajectory tracking of unmanned hybrid tractors.

Voltage and frequency control of solar – battery – diesel based islanded microgrid

Abstract Islanded microgrids with low-inertia distributed energy resources (DERs) are prone to frequency fluctuations. With the increasing integration of DERs in microgrids, the complexity of control and stability has also increased. Moreover, the integration of DERs into microgrids may result in a power imbalance between energy supply and demand during sudden changes in load or energy generation. This can cause frequency variations in the microgrid, which could have disastrous consequences such as equipment damage or even blackouts. This paper proposes a control strategy to ensure the efficient operation of an islanded hybrid microgrid consisting of a PV generator, battery energy storage system (BESS), and emergency diesel generator. According to Energy Exchange Model proposed in this paper, the hybrid system presented operates independently without being connected to the electrical grid, where the PV system and BESS act as the main energy sources, while the emergency diesel generator provides active power backup with voltage and frequency regulation. The novel in this paper is also that DER aids in frequency regulation during active power transients by delivering and absorbing active power in accordance with the inverter's suggested P droop control strategy. In this way inverter droop control decreases system frequency nadir emulating so called “synthetic inertia”. To design both the islanded hybrid system and the proposed control strategy, the MATLAB/Simulink environment is utilized. Based on the results, it can be concluded that the analyzed microgrid system is capable of maintaining stability and operating efficiently in a range of operating conditions.

Modified Chameleon Swarm Optimization Algorithm to Improve the Power System Stability

Power system stability has been difficult due to the occurrence of low frequency oscillation in the modern power system. Oscillation is mainly due to sudden changes of frequency, load, voltage, active power and reactive power. In this paper we provide a damping solution to oscillate the low frequency by using modified chameleon optimization algorithm. Damping performance and stability analysis of system is done by proposed modified chameleon Swarm Algorithm compared with conventional chameleon Swarm algorithm and Genetic algorithm with different operating condition in terms of Real power, Reactive power and Load disturbances. The comparative results confirmed that the proposed controller exhibit higher damping ratio, and better damping of deviations in speed and power angle to improve the stability of the system.