Ali Reza Seifi

Energy Flow Optimization in Multicarrier Systems

In this paper, a generalized heuristic approach is proposed to solve the optimal power flow problem in multicarrier energy systems. This technique omits the use of any extra variable, such as dispatch factors or dummy variables required for conventional techniques. The unified proposed approach can be utilized with all evolutionary algorithms. Modeling hub devices with constant efficiency may produce a considerable error in finding the actual optimal operating point of the whole network. However, using variable efficiency model adds complexity to the conventional methods while increasing the computation-demand of these techniques, but this target can be simply implemented by the proposed scheme. A multicarrier energy system consists of an electrical, a natural gas, and a district heating network is analyzed by the proposed algorithm using the modified teaching-learning-based optimization method. Results validate the utilized approach and show that it can successfully reach the global optimal solution of the problem.

An Integrated Steady-State Operation Assessment of Electrical, Natural Gas, and District Heating Networks

This paper studies a unified steady-state power flow analysis considering electrical, natural gas, and district heating networks all together. The important reason of such a view is the increasing utilization of cogeneration plants which makes a strong coupling between these networks. Another reason is the increasing usage of district heating networks because they have a higher efficiency besides a lower carbon dioxide emission than localized boilers. In this paper, interdependencies of the mentioned infrastructures are considered in detail including a nonlinear part-load efficiency performance for units. An integrated framework based on the Newton-Raphson technique is presented to solve the combined power flow problem and several case studies are utilized to demonstrate the applicability of the proposed methodology.

Comparative Studies of Different Control Strategies of a Dynamic Voltage Restorer Based on Matrix Converter

A dynamic voltage restorer (DVR) with no energy storage is studied. By using a matrix converter instead of the conventional AC/DC/AC converters, elimination of the DC-link capacitor is possible. The switching algorithm of matrix converter is the space vector modulation. There are different compensation algorithms to control the conventional DVR. These methods have been analyzed in this paper for the proposed matrix-converter-based DVR. A deep analysis through different diagrams would show the advantages or disadvantages of each compensation method. Equations for all methods are derived, and the characteristics of algorithms are compared with each other.

Adaptive self-tuning PID fuzzy sliding mode control for mitigating power system oscillations

In this paper, an adaptive self-tuning PID fuzzy sliding mode controller (ASPFSM) with a PID switching surface is proposed to damp power system oscillations. To overcome the problems in the design of a sliding-mode controller, which are the supposition of known uncertainty bounds and the chattering phenomenon in the control effort, an adaptive self-tuning PID based Lyapunov theory is studied. An adaption law is obtained from the Lyapunov stability theory, so the stability of the closed-loop system can be guaranteed. Then, the effectiveness of the ASPFSM is studied under different situations of a two-area four-machine power system. The simulation results confirm that performance of ASPFSM is much better than conventional power system stabilizer (CPSS).

Fuzzy-PSS and fuzzy neural network non-linear PI controller-based SSSC for damping inter-area oscillations

In this paper, a new approach is recommended for damping out power system oscillations. In this approach, a new fuzzy neural proportional–integral (PI) controller (FNPIC) based static synchronous series compensator (SSSC) and a fuzzy-power system stabilizer (fuzzy-PSS) with a new structure are simultaneously employed. Adaptive learning rates based on the Lyapunov stability theory to enhance the convergence speed of the proposed controller are obtained. In the structure of fuzzy-PSS, a pattern search algorithm is used to adjust four gains. A two-area four-machine power system and a three-area, three-machine power system are employed to investigate the efficiency of the proposed method. Simulations results confirm the capabilities of proposed controller in suppressing the power system oscillations.

A statistical unsupervised method against false data injection attacks

An unsupervised method is proposed for detecting cyber-attacks after topology changes.Detection is performed by quantifying the probability distributions of the state vectors.Quantification is done through extracting several statistical measures.Localization of attacks is performed by Fuzzy c-means.Performance of classification methods is analyzed in managing system reconfigurations. To achieve intelligence in the future grid, a highly accurate state estimation is necessary as it is a prerequisite for many key functionalities in the successful operation of the power grid. Recent studies show that a new type of cyber-attack called False Data Injection (FDI) attack can bypass bad data detection mechanisms in the power system state estimation. Existing countermeasures might not be able to manage topology changes and integration of distributed generations because they are designed for a specific system configuration. To address this issue, an unsupervised method to distinguish between attack and normal patterns is proposed in this paper. This method can detect FDI attacks even after topology changes and integration of renewable energy sources. In this method, we assume that injecting false data into the power systems will lead to a deviation in the probability distribution of the state vector from the normal trend. The main phases of the proposed algorithm are: (1) Normalizing the dataset, (2) Adding several statistical measures as the new features to the dataset to quantify the probability distribution of the state vectors, (3) Employing principal component analysis to reduce the dimensionality of the dataset, (4) Visualizing the reduced data for humans and exploiting their creativity to detect attacks, and (5) Locating the attacks using Fuzzy C-means clustering algorithm.The proposed method is tested on both the IEEE 14-bus and IEEE 9-bus systems using real load data from the New York independent system operator with the following attack scenarios: (1) attacks without any topology change, (2) attacks after a contingency, and (3) attacks after integration of distributed generations. Experimental results show that our proposed method is superior to the state-of-the-art classification algorithms in dealing with changes. In addition, the reduced data which is helpful in distinguishing between attack and normal patterns can be fed into an expert system for further improvement of the security of the power grid.

Optimal voltage control and loss reduction in microgrid by active and reactive power generation

This study suggests a new algorithm based on a combination of fuzzy logic and genetic algorithm (GA) to improve voltage profile and reduce loss in a microgrid. Considered microgrid includes control variables such as onload tap changer (OLTC), active power output of distributed generators (DG) and reactive power output of feeder switched capacitors that are controlled in a microgrid controller (MGC) through communication links. The proposed method was used to obtain the optimum value of control variables to reduce the loss and improve the voltage profile. The problem formulations consider three distinct objectives related to cost of loss, cost of injected active power by infinity bus and cost of injected active power by DG. The novel formulation is a multi-objective and non-differentiable optimization problem. The control variables were changed base on fuzzy logic and the GA was employed for finding the optimum shape of membership functions. In order to verify the proposed method, a 34 bus microgrid was analyzed in varying load condition and was compared with previous works. Finally it has been tested on 33-buses system to be investigated the control variables status, cost function status and voltage fluctuation during one day.

Comparative Studies of Different Switching Patterns for Direct and Indirect Space Vector Modulated Matrix Converter

This paper presents a MATLAB/Simulink simulation of direct and indirect space vector modulation for matrix converter. Different switching patterns for both direct and indirect methods are simulated and compared. Three criteria are chosen to compare the performance of switching patterns: (1) total harmonic distortion (THD); (2) harmonic spectrum analysis of output voltages; and (3) number of switching in each switching period. Switching strategies are completely implemented using the power library in MATLAB/Simulink environment.

Dynamic Model and Small Signal Analysis of Z-Source Inverter

ABSTRACT Z-source converters, as one of the most noteworthy power electronic interfaces, have become so popular in the last decade. In this paper, the dynamic model of a three-phase six-switch Z-source inverter is presented. An accurate dynamic model is necessary for both determining the appropriate Z-source inverter with the efficient size of inductor and capacitor and designing the control system which guarantees the stability of the system. In this study, the steady state model and AC small signal analysis were used to obtain the dynamic model. To continue previous studies, in the proposed model, the state variables of the AC side were considered. Therefore, the model was more comprehensive and could be also used for three phase Z-source converter in the inverting mode. To validate the proposed model, the dynamic model was solved by MATLAB and compared with the real circuit implemented in the PSCAD/EMTDC. The simulation results demonstrated the accuracy of the proposed model for the three-phase Z-source inverter.

Designing a self-constructing fuzzy neural network controller for damping power system oscillations

Abstract This study presents a self-constructing fuzzy neural network (SCFNN) based static synchronous series control (SSSC) to mitigate the inter-area oscillations in interconnected power systems. The proposed intelligent system includes an on-line trained fuzzy neural network (FNN) controller with adaptive learning rates (ALRs) and self-constructing mechanism. The Lyapunov scheme is employed to obtain the adaptive learning rates. Therefore, convergence of the suggested controller can be ensured. The proposed approach is such that, at first, originally, no neurons exist in the structure of FNN. In fact, they are automatically created and if it is required, they will be created. Therefore, the total time for training algorithm is significantly reduced and the speed of controller is considerably increased. In addition, the Prony technique is utilized to guesstimate the damping ratio of oscillations. The results confirm the usefulness of the suggested controller.