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In most applications the active power filters (APFs) are used to reduce harmonic distortion of a nonlinear load which is located near the APF installation point. This classic approach allows to reduce the distortion introduced to the power system but do not guarantee that the cost of the APFs installation is optimal. The purpose of this paper is to compare the classic approach to harmonic compensation with an optimization method of sizing and placement of the APFs in an existing distributed power network.

An exemplary real-life power system with distributed nonlinear loads was modeled using PCFLO power analysis software. Next, Matlab was used to implement the classic method and the optimization algorithm. Between Matlab and PCFLO a specially written Java middleware was used to provide a seamless workflow integration.

It was shown that the presented optimization method may lead to superior results in comparison with the classic approach. Simulation results clearly showed that the APFs installation cost can be significantly reduced when the optimization algorithm is used. Moreover, the proposed optimization method can overcome some problems connected with the nonlinearity and discontinuity of the APF's price/current function.

There are two main limitations of the presented method. First, the method needs much more computing power then the classic approach. Second, according to the authors’ knowledge, currently there are no commercially available APFs, which allow to directly apply the optimization method in industrial applications.

The presented results showed that the approach, which is the most popular in the industry, is far from being optimal from the cost perspective. As it has been shown in the investigated example, it might be possible to significantly reduce the total cost of APFs installed in the power system.

The optimization method presented in the paper as well as all simulation results are the original authors work. It was shown that the existing harmonic compensation strategies can be significantly upgraded and the proposed optimization method may be a basis and a reference point for future commercial solutions.

The change in the way of active power filters (APF) location can lead to overall cost reduction due to less number or less power of APFs required. The goal of this paper was to minimize the APF currents what is equivalent to solution with less apparent power of installed devices. The next step consists in development of new methods of APF optimal location.

Some scripts integrating optimization and harmonic analysis methods in Matlab and PCFLO software environments have been developed in order to achieve the goal.

Solution to the minimization problem determines the current spectrum of an APF connected to a selected system bus in accordance with some optimization strategies which among others enable minimization of THDV coefficients.

The APF control algorithm defined in the frequency domain and based on given current spectrum could lead to some problems with synchronization between APF instantaneous current and compensated current waveforms.

There are many papers on APFs but usually systems in which an APF is connected near a nonlinear load are analyzed. Some attempts to solve the more complex problems of synchronized multipoint compensation have been already made but there is still no generally accepted and commonly used solution.

This paper aims to present the prototype of a 140 kVA shunt active power filter (APF) for current harmonics and fundamental reactive power compensation of a 200 kW induction heating system.

Design issues of the power components, of the switching ripple filter and of the digital control are addressed and discussed. The APF control algorithm has been implemented on the 16‐bit, fixed‐point, TMS320LF2407 A DSP controller. The current control is based on proportional‐sinusoidal signal integrators with good performance in current harmonic elimination and power factor compensation.

The experimental tests, performed in real industrial environment for a 200 kW induction heating plant, show that the performance goals are fulfilled.

The sinusoidal signal integrators (for consistency with the other plural forms of acronyms) of the current controller are implemented in the rotating reference frame aligned with the voltage vector at the point of common connection. This allows the compensation of two harmonics with a single SSI, thus halving the computational effort of the DSP.

In industrial induction heating, the need for harmonic and reactive power compensation lasts a few seconds per minute, making passive solutions not suitable. The presented APF is a valid solution for this application, where only a few tailored implementations are available on the market.

Three different active power filter (APF) configurations are developed for harmonic elimination of a three‐phase electric arc furnace (EAF). Three single‐phase APF, a three‐wire APF and a four‐wire APF are developed for this purpose. A predictive control method of the APFs based on dynamic programming method is applied and the results of the simulation studies are compared. Finally the stability of the system is analyzed and its global asymptotic stability is shown.

This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM).

The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment.

Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close.

As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.

The purpose of this paper is to focus on the local minima issue encountered in motion planning by the artificial potential field (APF) method, investigate the currently existing approaches and analyze four types of previous methods. Based on the conclusions of analysis, this paper presents an improved wall‐following approach for real‐time application in mobile robots.

In the proposed method, new switching conditions among various behaviors are reasonably designed in order to guarantee the reliability and the generality of the method. In addition, path memory is incorporated in this method to enhance the robot's cognition capability to the environment. Therefore, the new method greatly weakens the blindness of decision making of robot and it is very helpful to select appropriate behaviors facing to the changeable situation. Comparing with the previous methods which are normally considering specific obstacles, the effectiveness of this proposed method for the environment with convex polygon‐shaped obstacles has been theoretically proved. The simulation and experimental results further demonstrate that the proposed method is adaptable for the environment with convex polygon‐shaped obstacles or non‐convex polygon‐shaped obstacles. It has more widely generality and adaptiveness than other existed methods in complicated unknown environment.

The proposed method can effectively realize real time motion planning with high reliability and generality. The cognition capability of mobile robot to the environment can be improved in order to adapt to the changeable situation. The proposed method can be suitable to more complex unknown environment. It is more applicable for actual environment comparing with other traditional APF methods.

This paper has widely investigated the currently existed approaches and analyzes deeply on four types of traditional APF methods adopted for real time motion planning in unknown environment with simulation works. Based on the conclusions of analysis, this paper presents an improved wall‐following approach. The proposed method can realize real time motion planning considering more complex environment with high reliability and generality. The simulation and experimental results further demonstrate that the proposed method is adaptable for the environment with convex polygon‐shaped obstacles or non‐convex polygon‐shaped obstacles. It has more widely generality and adaptiveness than other existed methods in complicated unknown environment.

Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance.

The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer.

The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials.

The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

This paper aims to study the encounter issues of the Tethered-Space Net Robot System (TSNRS) with non-target objects on orbit during the maneuver, including the collision issues with small space debris and the obstacle avoidance from large obstacles.

For the collision of TSNRS with small debris, the available collision model of the tethered net and its limitation is discussed, and the collision detection method is improved. Then the dynamic response of TSNRS is studied and a closed-loop controller is designed. For the obstacle avoidance, the variable enveloping circle of the TSNRS has coupled with the artificial potential field (APF) method. In addition, the APF is improved with a local trajectory correction method to avoid the overbending segment of the trajectory.

The collision model coupled with the improved collision detection method solves the detection failure and speeds up calculation efficiency by 12 times. Collisions of TSNRS with small debris make the local thread stretch and deforms finally making the net a mess. The boundary of the disturbance is obtained by a series of collision tests, and the designed controller not only achieved the tracking control of the TSNRS but also suppressed the disturbance of the net.

This paper fills the gap in the research on the collision of the tethered net with small debris and makes the collision model more general and efficient by improving the collision detection method. And the coupled obstacle avoidance method makes the process of obstacle avoidance safer and smoother.

The work in this paper provides a reference for the on-orbit application of TSNRS in the active space debris removal mission.

In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness.

This paper introduces a power quality enhancement model that is based on a new hybrid optimization algorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithm rider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections.

Through the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA); 0.09 per cent better than GMDA and WOA; and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models.

This paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors’ knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.