Loss Minimization Algorithm Research Articles

Comparative Study: Electric Distribution Optimization with Loss Minimization Algorithm and Particle Swarm Optimization

Comparative Study: Electric Distribution Optimization with Loss Minimization Algorithm and Particle Swarm Optimization

Loss Minimization Algorithm for Surface-Mounted PMSM Using Ripple-Based Extremum Seeking

Loss Minimization Algorithm for Surface-Mounted PMSM Using Ripple-Based Extremum Seeking

A novel decentralized inverter control algorithm for loss minimization and LVRT improvement

Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters’ fault-ride-through capability. This can become crucial for the reliable operation of the distribution grids, as they could lose valuable resources to support grid voltage at the time they need them the most. This paper explores how two novel loss-minimizing algorithms can both achieve high reduction of the system losses during normal operation and remain connected to support the voltage during faults. The algorithms we propose are decentralized and model-free: they require no communication and no knowledge of the grid topology or the grid location of the converters. Using local information, they control the reactive power injection to minimize the system losses. In this paper, we extend these algorithms to ensure the low voltage ride through (LVRT) capability of the converters, and we integrate them with state-of-the-art Wavelet-CNN–LSTM RES forecasting methods that enhance their performance. We perform extensive simulations on the real-time digital simulation (RTDS) platform, where we demonstrate that the algorithms we propose can achieve a substantial decrease in power losses while remaining compliant with the grid codes for LVRT makes them suitable for the implementation across the distribution system.

Development of Reactive Power Allocation Method for Radial Structure Wind Farm Considering Multiple Connections

In recent years, the number of wind farms consisting of type 3 and type 4 wind turbines located within the distribution system has been growing rapidly. Wind turbines can be utilized as a continuous reactive power source to support the system voltage by taking advantage of their reactive power control capability. This paper aims to further develop the reactive power assignment strategy in order to minimize losses in wind farms described in the published paper. We introduce the method of reconfiguration and numbering to apply the algorithm to the wind farm structure and develop the previously-defined allocation ratio into two types of allocation ratios. The goal is to apply the loss minimization algorithm to a wind farm configuration with up to two wind turbines connected to one ring main unit (RMU). The proposed strategy reduces power loss and increases the real power flow in the wind farm by allocating reactive power to connected wind turbines taking into account the resistance value. The proposed allocation technique is validated in a Real Time Digital Simulator (RTDS)-based Hardware-in-the-loop Simulation (HILS) environment considering the Dongbok wind farm configuration in Jeju, South Korea. In the simulation, a Raspberry Pi acts as a wind farm controller sending a reactive power dispatch signal to each wind turbine via Modbus TCP/IP protocol. The simulation results mean that, applying the proposed algorithm, we can expect loss reduction effects in the wind farm.

Efficiency Optimization Strategy of Permanent Magnet Synchronous Motor for Electric Vehicles Based on Energy Balance

This paper presents an efficiency optimization controller for a permanent magnet synchronous motor (PMSM) of an electric vehicle. A new loss model is obtained based on the permanent magnet synchronous motor’s energy balance equation utilizing the theory of the port-controlled Hamiltonian system. Since the energy balance equation is just the power loss of the PMSM, which provides great convenience for us to use the energy method for efficiency optimization. Then, a new loss minimization algorithm (LMA) is designed based on the new loss model by adjusting the ratio of the excitation current in the d–q axis. Moreover, the proposed algorithm is achieved by the principle of the energy shape method of the Hamiltonian system. Simulations are finally presented to verify effectiveness. The main results of these simulations indicate that the dynamic performance of the drive is maintained and the efficiency increase is up to about 7% compared with the id=0 control algorithm, and about 4.5% compared with the conventional LMA at a steady operation of a PMSM.

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios.

Design of Constraints for Seeking Maximum Torque per Ampere Techniques in an Interior Permanent Magnet Synchronous Motor Control

The efficient control of permanent magnet synchronous motors (PMSM) requires the development of a technique for loss optimization. The best approach is the implementation of power loss minimization algorithms, which are hard to model and design. Therefore, the developers typically involve maximum torque per ampere (MTPA) control, which optimizes Joule loss only. The conventional MTPA control requires knowledge of motor parameters and can only properly operate when these parameters are constant. However, motor parameters vary depending on operating conditions; thus, conventional techniques cannot be used. Furthermore, many industrial drives are designed for self-commissioning, and they do not have prior information on motor parameters. In order to solve this problem, various MTPA-seeking techniques, which track the minimum of motor current, have been developed. The dynamic performance between these seeking algorithms and maximum deviation from the true MTPA trajectory are defined by the constraints in most cases, in which proper design improves the dynamic behavior of MTPA-seeking algorithms. This paper considers a PMSM, which was designed to operate in the saturation area and whose MTPA trajectory significantly deviates from the same curve constructed for the initial unsaturated parameters. This paper considers existing approaches, explains their pros and cons, and demonstrates that these methods do not utilize full potential of the motor. A new constraint design was proposed and explained step by step. The experiment verifies the proposed technique and demonstrates improvements in efficiency and dynamic behavior of the seeking algorithm.

A Combination of Deep Neural Networks and Physics to Solve the Inverse Problem of Burger's Equation.

One of the most basic nonlinear Partial Differential Equations (PDEs) to model the effects of propagation and diffusion is Burger's equation. This puts great emphasize on seeking efficient versatile methods for finding a solution to the forward and inverse problems of this equation. The focus of this paper is to introduce a method for solving the inverse problem of Burger's equation using neural networks. With recent advances in the area of deep learning, a Physics-Informed Neural Network (PINN) is a category of neural networks that proved efficient for handling PDEs. In our work, the 1D and 2D Burger's equations are simulated by applying a PINN to a set of domain points. The training process of PINNs is governed by the PDE formula, the initial conditions (ICs), the Boundary Conditions (BCs), and the loss minimization algorithm. After training the network to predict the coefficients of the nonlinear PDE, the inverse problem of the 1D and 2D Burger's equations are solved with an error as low as 0.047 and 0.2 for 1D and 2D case studies, respectively. The wave propagation model is accomplished with an approximate training loss value of 1×e-4. The utilization of PINNs for modeling Burger's equation is a mesh-free approach that competes with the commonly used numerical methods as it overcomes the curse of dimensionality. Training the PINN model to predict the propagation and diffusion effects can also be generalized to address further detailed applications of Burger's equation with complex domains. This contributes to clinical applications such as ultrasound therapeutics.

Internal Model Loss Minimization Control of Permanent Magnet Synchronous Machine

Electric drives are very useful in propelling the wheels of hybrid electric vehicles (HEVs). They also play a central function in the electric power steering (EPS). This paper describes studies carried out on the efficiency optimization of an interior permanent magnet synchronous machine (IPMSM) for application in the EPS. An analytic loss- minimization algorithm for an IPMSM was derived and the optimization problem took into consideration copper, iron and stray losses. The proposed loss minimization algorithm is simple and cost effective to implement. From the simulations carried out, significant efficiency gains are possible with this model. The internal model control (IMC) method was employed to achieve current and speed control with acceptable sensitivity to machine parameters.

Simple and Flexible Power Loss Minimizer With Low-Cost MCU Implementation for High-Efficiency Three-Phase Induction Motor Drives

This article presents a simple, practical, and low-cost implementation of a power losses minimization algorithm for three-phase induction motors taking also into account both iron core losses and magnetic saturation. The algorithm evaluates, in real time, the optimal direct axis magnetizing flux component for efficiency enhancement of the drive. A further advantage of the proposed technique is represented by a flexible and low-cost implementation by means of an ATMEL ATSAM3X8E microcontroller. The power loss minimization algorithm is tested preliminarily by means of several simulations, then, experimentally validated by applying the proposed control strategy on a 5.5-kW field-oriented control induction motor drive. Significant results in terms of efficiency enhancement are presented and discussed, demonstrating that the proposed online control can significantly decrease the power losses of the drive.

Optimal Power Flow Control Variables using Slime Mould Algorithm for Generator Fuel Cost and Loss Minimization with Voltage Profile Enhancement Solution

Optimal Power Flow Control Variables using Slime Mould Algorithm for Generator Fuel Cost and Loss Minimization with Voltage Profile Enhancement Solution

Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation

This paper proposes the hybrid sequential quadratic programming (SQP) technique based on active set method for identifying the optimal placement and rating of distribution generation (DG) incorporated in radial distribution systems (RDS) for minimizing the real power loss satisfying power balance equations and voltage limits. SQP runs quadratic programming sequentially as a sub-program to obtain the best solution by using an active set method. In this paper, the best optimal solution is selected with less computation time by combining the benefits of both classical and meta-heuristic methods. SQP is a classical method that is more sensitive to initial value selection and the evolutionary methods give approximate solution. Hence, the initial values for the SQP technique were obtained from the meta–heuristic method of Parameter Improved Particle Swarm Optimization (PIPSO) algorithm. The proposed hybrid PIPSO–SQP method was implemented in IEEE 33-bus RDS, IEEE 69-bus RDS, and IEEE 118-bus RDS under different loading conditions. The results show that the proposed method has efficient reduction in real power loss minimization through the enhancement of the bus voltage profile.

Symbiotic Organism Search Algorithm for Power Loss Minimization in Radial Distribution Systems by Network Reconfiguration and Distributed Generation Placement

This paper proposes the Symbiotic Organism Search (SOS) algorithm to find the optimal network configuration and the placement of distributed generation (DG) units that minimize the real power loss in radial distribution networks. The proposed algorithm simulates symbiotic relationships such as mutualism, commensalism, and parasitism for solving the optimization problems. In the optimization process, the reconfiguration problem produces a large number of infeasible network configurations. To reduce these infeasible individuals and ensure the radial topology of the network, the graph theory was applied during the power flow. The implementation of the proposed SOS algorithm was carried out on 33-bus, 69-bus, 84-bus, and 119-bus distribution networks considering seven different scenarios. Simulation results and performance comparison with other optimization methods showed that the SOS-based approach was very effective in solving the network reconfiguration and DG placement problems, especially for complex and large-scale distribution networks.

Comparative study of Pareto optimal multi objective cuckoo search algorithm and multi objective particle swarm optimization for power loss minimization incorporating UPFC

The Flexible AC Transmission System (FACTS) devices are being commissioned in electrical power systems across the globe owing to the vast array of benefits they offer. The optimal performance of the FACTS devices can be harnessed only if they are installed at a strategic location. In this paper, the authors suggest the merit of multiobjective cuckoo search (MOCS) algorithm in mitigation of transmission losses by strategically installing unified power flower controller (UPFC) at an optimal location. Active power loss and reactive power loss reduction is the multiobjective optimization considered for the study. The Pareto-optimal technique is employed to extract the Pareto-optimal solution for the multiobjective problem considered. The Fuzzy logic method is utilized to yield the best-compromise solution from the pool of Pareto-optimal solution. The proposed approach is tested on a standard IEEE 30 bus test system. Furthermore, the efficacy of the MOCS algorithm is demonstrated by comparing the results with that of multiobjective particle swarm optimization (MOPSO).

Control Strategy for an Open-End Winding Induction Motor Drive System for Dual-Power Electric Vehicles

This work presents the control strategies for the configuration involving an open-end winding induction motor (OW-IM) fed by dual two-level inverters, which can realize the controllable power sharing between two isolated power sources without using the DC/DC converter. Based on the mathematical model of the OW-IM and the principles of the voltage vector distribution, two rotor flux calculation algorithms, namely the motor loss minimization (MLM) algorithm and the maximum power sharing capability (MPSC) algorithm are proposed and compared, with their selection strategy to select the suitable algorithm depending on the operating condition. The MLM algorithm has the optimal efficiency performance, while the MPSC algorithm can maximize the power sharing capability of the dual inverter while holding the widest operation range of the motor. Based on the dual space vector pulse-width modulation (SVPWM) control scheme, the golden section (GS) search method is introduced to allocate the stator voltage vector to the dual inverter for the power sharing demand, with a flexible iteration number to obtain a stable and efficient computational performance, which fully utilizes the power sharing capability via the voltage vector distribution. Simulation results prove the validity and the advantages of the proposed strategy. This system also lays a foundation for energy management function of the dual-power electric vehicles.

Reactive Power Control using Sine Cosine Algorithm for Active Power Loss Minimization with Voltage Profile Enhancement

The major purpose of optimal reactive power dispatch (ORPD) is to detect control variables like the voltage ratings of the generator, switchable shunt condenser banks, and the tap ratio of the tap setting transformer to reduce the active power loss for fixed economic power planning when taking on a set of constraints. This paper introduces the application of the sine cosine algorithm (SCA) to find the novel solution for the reactive power dispatch of power systems with different test systems (Standard IEEE 14-bus, IEEE 57-bus and IEEE 118-bus) with specific cases like reducing active power losses and increasing bus voltages in test systems. Obtained results validate the high tone of the proposed algorithm, with better, fast and consistent results comparing with existing literatures.

Intrusion Detection Algorithm for Packet Loss Minimization in Wireless Sensor Networks

Wireless Sensor Network consists of a greater number of sensor nodes and recent advance is in wireless communications and it serves a backbone for controlling the real time applications. It consists of group of sensor nodes and that is sense the information from the event area and it is passes through the base station and which it reacts according to environment and to provide a large-scale monitoring and sensor measurement in a high temporal and the spatial resolution. The researcher uses a different algorithm in that they use a distributed energy fuzzy logic to reduce a packet loss. Wireless Sensor Networks are unprotected to many kinds of the security threats which can decrease the performance of network and cause the sensors to send wrong data to destination. The hostile node in the network is working as an attacker node and it takes all the information packets which is delivered through them. In this paper we propose an intrusion detection system algorithm against the packet dropping. Intrusion detection algorithm solves the problem by analyzing the network by detecting the abnormal node. Then the abnormal node is corrected into normal node with the help of intrusion detection algorithm.

Optimization of direct torque controlled induction motor drive by golden section method

A search control based energy loss minimization technique is proposed for Direct Torque Control (DTC) of three phase Induction Motor (IM) with Fuzzy Logic Controller (FLC) used in Electric Vehicles (EV). The performance parameters of the EV considered for analysis are Dynamic performance and Efficiency. DTC is used to achieve the excellent dynamic performance of the IM drive. FLC is employed to achieve accurate speed tracking and enhance the ruggedness against disturbance and uncertainties. Loss minimization using Golden section method is proposed to search optimum level of flux value for DTC of induction motor drive. This will lead to minimum core loss and enhanced efficiency of the system. The proposed work is simulated in MATLAB/SIMULINK. The simulation and experimental results are presented to validate the performance of the proposed loss minimization algorithm.

FACTS device installation in transmission system using whale optimization algorithm

As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the Whale Optimization Algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30-bus RTS was used as the test system to validate the effectiveness of the proposed algorithm.

Loss model based efficiency optimized control of brushless DC motor drive

Energy efficiency improvement of electrical motor drives has recently become a very interesting subject. Several methods have been proposed in the literature to improve the efficiency of permanent magnet synchronous machine (PMSM) with sinusoidal back-EMF. These methods are not precise and appropriate for brushless DC machine (BLDCM) with trapezoidal back-EMF. As a unique solution, this work introduces a flux controlled based loss minimization algorithm suitable for BLDCM that considers iron loss as well as influence of back-EMF harmonics; consequently promotes efficiency of machine. In this regard, the loss model of PMSM is extended based on multiple reference frame analysis to include back-EMF harmonics of BLDCM. As an advantage, proposed modified loss model does not require any additional data about the dimensions of machine that makes it suitable for industrial motor drive applications. The Proposed loss model is validated through experimental tests in different operating conditions. Afterward, by applying direct torque and indirect flux control of BLDCM as the control technique, d-axis current in the rotor reference frame is controlled to reduce the air gap flux and consequently iron loss of the machine. Finally, a procedure is presented to determine the optimum d-axis current which maximizes the efficiency. Effectiveness of proposed control system is evaluated using simulation results in MATLAB/Simulink and experimental results on a practical prototype. It is indicated that depending on the operating conditions, about 2% to 11% of efficiency improvement would be achieved in the proposed method.