Search

The rapid utilization of electricity has forced the stability of the power system for continuous operation. Due to difference in demand and generation, the frequency reference point changes that needs to be restored to its locus point for the stable operation of the power system. Therefore, a novel efficient control design is propounded to counter the phenomena of load frequency control. The designing of cascaded fractional model predictive controller coupled with fractional-order PID controller (CFMPC-FOPID) is designed for an efficient response of the power system under load disruption and system parameter variations. The controller is optimized by sooty tern optimization algorithm to identify the optimal parameters of the controller. The controller is tested under power mixing of renewable energy sources (i.e., PV and wind) and under varying load scenarios in multiarea hybrid power system. The proposed controller has effectively handled the frequency disruption under distinct load change by stabilizing it in 1.34 sec, 0.60 sec, and 0.41 sec for area-1 with an average time of 0.78 sec, and for area-2, the stabilizing time is 1.40 sec, 0.89 sec, and 0.56 sec with an average time of 0.95 sec, whereas the average time for MPC/PI, DSA-FOPID, GWO: PI-PD, and SCA: FOPI-FOPID is 7.67 sec, 4.68 sec, 1.77 sec, and 4.72 sec, respectively, for area-1 and 6.47 sec, 5.13 sec, 3.45 sec, and 5.02 sec, respectively, for area-2. The outcome result justifies the superiority of the studied technique.

The technology has proceeded so much that the power system should be substantial and explicit to give optimal results. Ever-increasing complexities of the power system and load disparity cause frequency fluctuations leading to efficiency degradation of the power system. In order to give a suitable real power output, the system entails an extremely perceptive control technique. Consequently, an advanced control method, that is, an adaptive model predictive controller (AMPC), is suggested for load frequency control (LFC) of the series power system which comprises photovoltaic (PV), wind, and thermal power. The suggested method is considered to enhance the power system execution as well as to decrease the oscillations due to a discrepancy in the system parameters and load disturbance under a multi-area power system network. The AMPC design verifies the constant frequency by maintaining a minimum steady state error under varying load conditions. The proposed control approach pledge that the steady-state error of frequencies and interchange of tie line powers is maintained in a given tolerance constraint. The effectiveness of the proposed controller is scrutinized by conventional controllers like genetic algorithm-tuned PI (GA-PI), firefly algorithm-tuned PI (FA-PI), and model predictive controller (MPC) to show the competence of the proposed method.

The challenge of controlling frequency becomes greater as the complexity of a power network increases. The stability of a power system is highly dependent upon the robustness of the controller. This paper presents automatic generation control (AGC) of a four-area interconnected power system along with integrated renewable energy sources of PV and wind energy. The designed model is a challenge given the increased penetration levels of PV and wind along with a thermal-hydropower system. The addition of a hydropower system as a fourth type results in the pole of the open loop system of the hydropower system being located at the right half side of the s-plan. This demands a robust control. A novel MPC-(1 + PIDN) is designed for high-order interconnected areas (HOIA) to stabilize the frequency in a robust way. The salp swarm algorithm is adopted to optimize the parameters of the PIDN controller. The performance of the proposed controller under HOIA is tested in a unbalanced load environment with uncertainty in the power system. The proposed controller can effectively handle the frequency disruption by stabilizing it in $$0.86 s$$ 0.86 s for Area-1, $$1.08 s$$ 1.08 s for Area-2, $$0.81 s$$ 0.81 s for Area-3, and $$0.84 s$$ 0.84 s for Area-4 with an average time of $$0.89 s$$ 0.89 s for all the areas, whereas the average time for GWO: PI-PD, MPC/PI and GA-PI is $$3.48 s$$ 3.48 s , $$10.36 s$$ 10.36 s and $$18.47 s$$ 18.47 s , respectively. The results demonstrate the effectiveness of the controller when compared to other controllers.

Importance of PV based energy systems cannot be denied with quickly increase in renewable energy demand. Due to inherent uncertainties and non-linear behaviour of grid tied PV system, conventional control strategies are unable to provide satisfactory performance. Therefore, key purpose of this paper is to design non linear controller for the control of grid tied PV system. A novel control strategy is devised by utilizing Lyapunov base finite time non-linear reactive power and dc link voltage control. This strategy employes Lyapunov approach to ensure stability and guarantees robustness. With this control strategy, better and superior performance is observed in comparison to proportional integral controller. Furthermore, simulation results are provided to illustrate the effectiveness of proposed algorithm. Various disturbances of system such as change in solar insolation level, change in reference parameters, faults on buses are considered. With this control strategy, settling time and oscillations are found significantly less than PI controller.

Load Frequency Control (LFC) has become of crucial importance in modern power systems on account of varying demand and generation trends. To maintain the frequency variations and minimize the tie line power fluctuations, various soft computing techniques have been used for fine tuning of PID controller in LFC problems. This paper presents a comparative analysis of various swarm optimization-based intelligent algorithms for LFC problems. From simulation results obtained via Simulink (MATLAB), the frequency variations in each area, tie-line power fluctuation profile as well as the convergence trend for each algorithm has been analyzed in this paper. Besides various system parameters such as percentage overshoot, settling time, rise timeand steady state error, different performance indices have also been computed against each swarm intelligence-based algorithm.

The controlling techniques of liquid level in a coupled conical tank system is a challenging task owing to its continuous changing cross-section and non-linearity in the system. In this paper, an adaptive model predictive controller (AMPC) is presented to control the valve speed of conical shaped tank to maintain the liquid level. In AMPC, the plant model states are changed in every cycle along with the MPC controller to update the plant parameters in a precise manner, which is a major concern due to its non-linear behavior. Moreover, the comparative analysis of coupled conical tank system with other controllers like Fractional order PID (FOPID) controller and PID controller is carried out. The simulation results represent the superiority of the AMPC controller as compared to the other controlling methods in terms of response time and overshoot.

This paper aimed at the prospect of an optimized algorithm for Selective Harmonic Elimination (SHE) through a hybrid control mechanism using fmincon and Newton–Raphson iterative method. In search of minimizing the Total Harmonic Distortion (THD), appropriate multi-level sequences and firing angles are used. This technique not only reduces the execution time but also improves the convergence rate. MATLAB simulations confirm that the algorithm is robust and the desired response is achieved within a few iterations for any range of modulation index. PSpice simulation verifies the successful removal of harmonics and confirms THD lies within limits specified by IEEE 519 standard. The implementation of this SHEPWM algorithm will improve the working of cascaded multi-level inverters by increasing their efficiency rate with respect to their speed.

In a grid, the choice of the point of common coupling (PCC) does not solely rely on the voltage level alone but also conjointly depends on the grid strength for many explicit purposes. Nowadays, the affinity of low SCR grid connections has become a crucial thought once it involves the integration of wind generation plants (WPPs). Since the quality of wind resources is a critical issue, these plants are usually placed in remote areas with a sophisticated potential of wind flow. These remote areas are typically less inhabited, where the grid does not perpetually always have to be sturdy. Moreover, the exceeded power demand loading and higher wind penetration affect the generation, transmission, and distribution utilities by permitting the flow of unbalanced voltages and currents in the power system. Therefore, the quality of transmitted power is becoming a crucial facet of distributed energy generation units. In this paper, a permanent-magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. It discusses a solution, which provides the low voltage ride through (LVRT) provision by the suppression of DC link overvoltage and active power limitation during an asymmetrical grid fault. With improved back-to-back converter control, the machine side converter (MSC) is employed to control the DC-link voltage. Furthermore, the grid side converter (GSC) is used to implement the active/reactive current injection according to the outlined limits. The need for external hardware is eventually avoided, which is typically required to dissipate the additional energy generated during a grid fault. Hence, it is proven to be an affordable solution.

Vehicle Cruise Control System (VCCS) is used to regulate the speed of the locomotive to a reference speed as desired by the driver. It is mainly used to reduce the driving exhaustion on the driver. This research paper deals with the control of VCCS based on Model Predictive Controller (MPC) in parallel with the conventional PID controller. The results of the proposed control system are compared with the conventional techniques including FLC, GA and ALO based PID controller. Simulation results show that the hybrid combination of MPC and classical PID controller has better outcome with regard to transient state response than numerous published techniques in recent times.

The technology is continuously advancing day by day and has transformed the personal computer (PC) from a very simple computing gadget to a highly advanced multi-processing and a multi-functional device. It is now capable of connecting and processing different devices at different places simultaneously. This improvement brings the system advancement for disabled persons so that they can be self-governed. This paper focuses on various interfacing techniques utilizing windows Operating System to assist the disabled persons and proposes a technique named as “Windows Interface for Mobility impaired and Blind people” (WIMB). Proposed model assists the disabled individuals (having blindness, mobility-impartment and deafness) to control real-life utilities through Microsoft Windows OS (Operating System). Furthermore, few available, applications are also reviewed and compared with the proposed technique.

The shortest path searching algorithms are broadly utilized as an essential part of autonomous agents and navigation systems. As applications get extensively complex, the shortest path calculations implemented via programming produce a lot of time overhead. In this paper, different path planning techniques are analyzed on the bases of their time effectiveness under dynamic constraints; varying spaces and environments having a different level of complexities. Bi-RRT algorithm is proposed as the most time efficient algorithm in complex environments with high spaces. In addition, the algorithms are tested in different environments and comparison among all simulations results are presented in the form of time and path length.

This paper proposes a new Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems especially under Partial Shading (PS). This technique is based on Generalized Pattern Search (GPS) optimization algorithm. The proposed technique adopts the GPS algorithm owing to the reason that its major features like simple implementation, derivative free nature, fast convergence and high accuracy are ideally suited to deal with PS conditions. Furthermore, these features of GPS can well resolve the limitations and drawbacks of previous MPPT techniques, such as fluctuation, low efficiency, high complexity and slow convergence. For comparison, our proposed MPPT algorithm, the Particle Swarm Optimization (PSO) algorithm and the Perturb and Observe (P&O) algorithm have been implemented in MATLAB/Simulink. The comparison results demonstrate that the proposed MPPT technique can efficiently detect the global maximum (GM) in a fewer number of voltage samples, which is nearly 4 times less than the number of voltage samples required by PSO. Moreover, the steady state efficiency of the proposed MPPT is 99.8%, which is higher than the other MPPT techniques. Therefore, the proposed technique distinguishes itself from other MPPT techniques due to its superior quality in terms of convergence speed, dynamic and steady state efficiencies and low complexity.

Photovoltaic (PV) systems are gradually becoming the key renewable source of electrical energy. But the nonlinear characteristics of the PV module require special techniques to harvest maximum power. The techniques used to track the Maximum Power Point (MPP) from the nonlinear I-V characteristics through power electronic circuits are known as Maximum Power Point Tracking (MPPT) techniques. Although enormous MPPT techniques have been anticipated in literature, each endures its own advantages and disadvantages. This paper proposes a novel hybrid technique to enhance the performance of Incremental conductance (InC) technique. Our proposed technique removes the limitations of InC under rapid changing environmental condition. It improves the efficiency and reduces the oscillation power loss under steady state. A hybrid combination of variable-step sized Incremental Conductance with Fractional Short Circuit Current (FSCC) technique is proposed in this research work. Initially, the FSCC forces the PV system to work near MPP and then InC technique track the exact peak. Matlab/Simulink software is used to implement the proposed hybrid technique. Finally, the results verify its superior performance under rapidly changing environmental conditions.

Accurate and precise control of a robotic arm involves two main issues. The first issue is to calculate the desired angular displacements of the robotic arm joints based on the desired position (in the Euclidian space) of the tip (or gripper) of the arm. The second issue is the design of an efficient and effective control law to achieve the desired angular displacements. This paper attempts to resolve both of the issues for a 2DOF robotic arm by using Inverse Kinematics and linear control law designs. In particular, Inverse Kinematics equations have been implemented on a commercial off the shelf robotic arm in order to calculate the desired angular displacements of the arm-joints. In order to achieve the desired angular displacement, various linear control laws have been designed using the robotic arm joint model. The performance of the designed control laws has been analyzed and compared. A comparison of the control torque required to achieve the performance of each control law is also presented.

A 6-DoF robotic testbed has been proposed, designed and developed for university students to learn the concepts of control systems theory in robotics. Testbed is comprised of two parts namely; 6-DoF platform and ball-plate balancing payload. Robotic maneuvers in platform are generated through rotary Stewart-Gough mechanism using inverse kinematic algorithm. Ball-plate balancing payload is fixed on top of platform. This paper presents a comparison among generic testbeds, available at standard control system laboratories. Design of robotic platform along with payload has been detailed and tested with software simulations in MATLAB Simulink. This work explains complete electrical, mechanical and software design along with development, testing and results.

In this paper, a comprehensive review of essential components of the PV (Photovoltaic) system is elaborated, and their comparative unique features are discussed. The paper describes hardware design (power converters topologies specifically) employed in PV based energy generation systems to harvest maximum power from the available energy source. In this study, thirty different Maximum Power Point Tracking (MPPT) techniques have been critically analyzed and their response with respect to partial shading condition has been discussed. It is very difficult to say which technique is best as one must consider various factors and parameters while selecting a technique such as application, convergence speed, accuracy, efficiency, system reliability, and cost and performance of available hardware. Aiming at the complexity, hardware implementation, tracking speed, steady-state accuracy, or global maximum detection of the algorithm, an MPPT algorithm based on a rule table is proposed. In addition, the MPPT of a PV system based on bio inspired techniques is considered. The bio inspired algorithms and its application in PV system are compared for the authenticity of the review, and six different MPPT techniques are implemented on PV systems. A comparative analysis is made based on the results of four different cases of irradiance.

Electrical energy generation by using Photovoltaic system is becoming popular day by day. This paper presents a control topology for multilevel inverters used in standalone PV system. A hybrid technique is used to optimize the switching angles of thirteen levels single-phase cascaded multilevel inverter for selective harmonics elimination. The nonlinear transcendental equations problem is solved by dividing the whole system into two stages. In stage-I: it will find the convergence angles region by using Fmincon optimization technique and then in stage II it will find the accurate convergence point by using most common technique which is Newton Raphson (NR) method. This approach is fast, low cost, efficient and it produces very low total harmonics distortion. The ability of the proposed technique is simulated by using MATLAB and implemented on cascaded h-bridge circuits, opto-isolators and Arduino UNO microcontroller. The simulation and hardware results verify the capability of proposed technique.

Maximum Power Point Tracking (MPPT) under partial shading condition is the main issue in Photovoltaic (PV) system. This research work is based on a hybrid mechanism for MPPT problem by utilizing two commonly used techniques. The whole algorithm is divided into two stages. Initially it uses Fractional Open Circuit Voltage (FOCV) technique in order to detect Global Maxima (GM) region. Once the GM region is achieved the variable step sized incremental conductance technique can easily detect the Maximum Power Point (MPP). The proposed technique is fast, accurate and simple to implement. This algorithm is implemented and tested using MATLAB/Simulink and the robustness of this algorithm is verified by testing this algorithm under different partial shading conditions. Finally the superiority of this algorithm is judged by comparing this algorithm with other MPPT algorithms.

Passive dynamic walking (PDW) provides us better insight for understanding human walking, for developing prosthetic limbs and for designing superior bipedal robots. In this paper, we investigated the dynamics of a simple PDW, 2D compass-gait biped model that loosely look like human legs (without knees), using time-series analysis based on nonlinear dynamics. Previously, this passive biped model has been explored using only bifurcation diagrams and phase-space plots, but we studied it using nonlinear time-series analysis. The experimental results from walking experiments of prototype passive compass-gait biped validated the simulation results. These results can be useful for designing efficient bipedal robots.

Cooperative control consensus is one of the most actively studied topics within the realm of multi-agent systems. It generally aims to drive multi-agent systems to achieve a common group objective. The core aim of this paper is to promote research in cooperative control community by presenting the latest trends in this field. A summary of theoretical results regarding consensus for agreement analysis for complex dynamic systems and time-invariant information exchange topologies is briefly described in a unified way. The application under both non-formation and formation cooperative control consensus for multi-agent system also investigated. In addition, future recommendations and some open problems are also proposed.

Massive Open Online Courses (MOOCs) are the latest installment in the field of distance education. This paper discusses the pros and cons of MOOCs in the educational systems with a special emphasis on the engineering education. Since 2012, more and more universities have been joining MOOC revolution and the number of online courses has considerably increased over time. Engineering MOOCs are also increasing. Due to their access to large audience, massive online courses have been expanding their horizon of admission to engineering education at all levels and improving the in-campus learning.

This paper presents an improved path planning algorithm to avoid obstacles of critical shapes (obstacles having angles of around 90°). The proposed algorithm outperforms the reported bug algorithms by reaching the target safely in relatively less time and avoiding multiple obstacles without suffering into local minima. The main reason of such improvement is to schedule the speed and direction of the agent according to the edges of obstacles. More specifically, the agent's speed is set as a function of goal and the obstacle location. The direction to follow obstacle edges is not predefined, but generated online by taking the dot product between obstacle edges and the target behavior. In this way the optimal path can be achieved to reach the final target with safe, smooth and short trajectory. Simulation results and practical implementation validated the effectiveness of our proposed algorithm. Moreover, performance comparison of the proposed algorithm and other approaches confirms the efficiency of the presented approach.

The purpose of brain machine interface (BMI) is to provide a communication path between brain signals and external devices. Use of brain signals for restoring function of impaired body parts using scientific methods require brain machine interface. This article presents BMI to control five-fingered prosthetic hand using the electroencephalography (EEG) signals. The core objective of the system is to develop a control system that will be able to communicate with the brain thoughts. EEG signals captured from the human scalp are used as information carrier for brain control interface (BCI) system. This research article discusses the development of a system to assist disable persons using EEG based signals.