فصلنامه هوش محاسباتی در مهندسی برق
سال دوازدهم شماره 4 (زمستان 1400)

In the game theory, the well-known solution to obtain the best profit in non-repeated games as much as possible is the Nash equilibrium. However, in some repeated non-cooperative games, agents can achieve more profit than the Nash equilibrium by tacit collusion. One of the methods to achieve profit more than Nash equilibriums in tacit collusion is reinforcement learning. However, reinforcement learning-based methods consider only one step in the learning process. To achieve and improve profit in these games, more than one step can be used. In this regard, a learning-based forwarding N-steps algorithm called Forwarding Steps (ForSts) is proposed in this paper. The main idea behind ForSts is to improve the performance of agents in non-cooperative games by observing the last N-step rewards. As ForSts is used in the game theory to learn tacit collusion, it is evaluated by the iterated prisoner’s dilemma and the Cournot market. Prisoner’s Dilemma is an example of a traditional game. The results show that in the iterated prisoner’s dilemma, the agents using ForSts achieve better profit than the agents playing in the Nash equilibrium. Also, in the Cournot electricity market, sum of the profit of agents using ForSts is 3.614% more than the sum of profit of agents` playing in the Nash equilibrium.

The stochastic nature of the active power generated by wind turbines (WTs) has posed new challenges to the reliability assessment, load flow analysis, and reconfiguration process of distribution systems penetrated with WTs. In this paper, a new optimization approach has been presented to find the best configuration of the distribution system along with optimal locations of WTs. This is aimed at maximizing the reliability of the system, while energy losses are minimized. To this end, based on the graph theory and the system topology, a simple technique is proposed for finding minimal cut sets that could be used for calculating reliability indices. Furthermore, the multiple flow directions created in the presence of WTs cause difficulties in calculating reliability indices and in performing load flow analysis. Accordingly, a new concept called the independent reliability zone is introduced to overcome these problems. In this paper, the multi-objective harmony search algorithm, due to its suitability for multi-objective problems, is employed as a solution for solving the proposed optimization problem. The effect of the proposed approach has been evaluated on both test and real systems. The results show that using the proposed approach, a more practical solution is achieved for low-cost and reliable 24-hour planning of the system in the presence of uncertainties in DGs.

Multiple Description Coding (MDC) is an error resilient coding technique in which multiple streams from a video source are generated, each is individually and mutually decodable. This error resiliency is obtained at the cost of redundancy, and the amount of redundancy depends on the channel loss rate as well as the frame position in the sequence. Due to the nature of video codecs, an erroneous frame will infect the successive frames and causes an unacceptable quality. MDC redundancy allocation more than required leads to compression inefficiency. Therefore, a channel adaptive optimization for frame-wise redundancy allocation is inevitable. In this paper, the MDC scheme known as Mixed Layer Multiple Description Coding (MLMDC) is under consideration for end-to-end distortion modeling and channel adaptive optimization. The model works based on the side and central decoder outputs mismatch. The performance of the model as well as the optimizer are verified by experimental results measured from JM19.0, H.264/AVC reference software. The experiments also show that the optimal MLMDC outperforms the conventional methods for high enough loss rates.

This paper presents the impact of simultaneous and coordinated control of the unified power flow controller (UPFC) and the doubly-fed induction generator (DFIG) on the multi-machine power system`s dynamic stability. All UPFC`s main basic PI controllers and its Power Oscillation Damping (POD) supplementary controller have been used. A more complete model of DFIG and both Rotor-Side Converter (RSC) and Grid-Side Converter (GSC) dynamics with their PI controllers are considered, too. UPFC and DFIG controllers are simultaneously coordinated and optimized with compromising between their control variables parameters. Particle Swarm Optimization (PSO) algorithm is used to optimize the objective function based on eigenvalues and damping ratio to reach the best parameters and variables of controllers of both UPFC and DFIG. During the studies of permanent and dynamic state, the thermal capacity of the lines and the nominal values of UPFC have also been considered. Simulation results in a 39-bus 10-machine New-England power system show the capability of the applied method. The results demonstrate that coordinated control of UPFC and DFIG tend to more damping system modes oscillation and more stability in the power system.

This paper proposes a multi-objective function for hourly optimization of microgrids performance through minimizing the operating costs, losses and, voltage deviation index. A fuzzy decision-maker is used in this study to select the best solution from the optimally set goals by the Pareto Front beam method. In this paper, the collections of microgrids are separated into several clusters and power transaction between clusters as well as between distribution system and MG clusters, with consideration for the uncertainty of renewable energy resources (RESs), is examined. An hourly robust energy management approach is presented for distribution systems under the penetration of renewable energy-based MGs. In addition to wind turbines and photovoltaics as RESs, the MGs are equipped with energy storage systems and micro-turbines. The uncertainty of renewable generation is demonstrated via the information gap decision theory (IGDT) technique. To validate the effectiveness of the proposed model, it is tested on a 94 bus distribution test system using the general algebraic modeling system (GAMS) software. The results show the prominence of MGs clustering in improving the techno-economic characteristics of the distribution system and indicate the important consequences of clustered microgrids in optimal power transaction and distribution system operation.

Training stability of a model in an identification process has been one of the primitive requirements in recent control researches. This paper aims at analyzing the training stability of the interval type 2 adaptive Neuro-Fuzzy inference system (IT2ANFIS) with a swarm-based hybrid algorithm. The antecedent and the consequent parts of the model are trained by particle swarm optimization (PSO) and Kalman filter (KF) algorithms, respectively (PSO+KF). The Lyapunov stability theorem with a newly found Lyapunov function is employed to assess the stability conditions. These conditions led to adaptive stabilizing boundaries in the adjustable parameters of the algorithms (APAs), such as the covariance matrix in KF, inertia factor, and maximum gain in PSO. The selection of APAs within these boundaries guaranteed the stability of the training process. The analytical approach of this study resulted in finding new and broader stabilizing boundaries for the APAs. Implementation of the theorem to the training and predicting the future values of the Mackey-Glass chaotic time series and a stochastic non‐linear system revealed the superiority of the theorem in terms of their root mean square errors (RMSEs), simulation times, and their entrapment in the local minimums.