ICTACT Journal on Soft Computing

Chia Seet Chin ¹, Bih Lii Chua ¹, Pei Yi Lim ¹, Hui Hwang Goh ¹, Kenneth Tze Kin Teo ¹

Affiliations
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY

Abstract

The photovoltaic (PV) array controlled by Maximum Power Point Tracking (MPPT) method for optimum PV power generation, particularly when the PV array is under partially shaded condition is presented in this paper. The system modelling is carried out in MATLAB-SIMULINK where the PV array is formed by five series connected identical PV modules. Under uniform solar irradiance conditions, the PV module and the PV array present nonlinear P-V characteristic but the maximum power point (MPP) can be easily identified. However, when the PV array is under shaded conditions, the P-V characteristic becomes more complex with the present of multiple MPP. While the PV array operated at local MPP, the generated power is limited. Thus, the investigation on MPPT approach is carried out to maximize the PV generated power even when the PV array is under partially shaded conditions (PSC). Fuzzy logic is adopted into the conventional MPPT to form fuzzy logic based MPPT (FMPPT) for better performance. The developed MPPT and FMPPT are compared, particularly the performances on the transient response and the steady state response when the array is under various shaded conditions. FMPPT shows better performance where the simulation results demonstrate FMPPT is able to facilitate the PV array to reach the MPP faster while it helps the PV array to produce a more stable output power.

Keywords

Photovoltaic, Partially Shaded Conditions, MPPT, Fuzzy Logic, Perturb and Observe.

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Yit Kwong Chin ¹, Heng Jin Tham ¹, N. S. V. Kameswara Rao ¹, Nurmin Bolong ¹, Kenneth Tze Kin Teo ¹

Affiliations
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY

Abstract

Congestions of the traffic flow within the urban traffic network have been a challenging task for all the urban developers. Many approaches have been introduced into the current system to solve the traffic congestion problems. Reconfiguration of the traffic signal timing plan has been carried out through implementation of different techniques. However, dynamic characteristics of the traffic flow increase the difficulties towards the ultimate solutions. Thus, traffic congestions still remain as unsolvable problems to the current traffic control system. In this study, artificial intelligence method has been introduced in the traffic light system to alter the traffic signal timing plan to optimize the traffic flows. Q-learning algorithm in this study has enhanced the traffic light system with learning ability. The learning mechanism of Q-learning enables traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each others to a common goal of ensuring the fluency of the traffic flows within the traffic network. The simulated results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimize the traffic flow accordingly.

Keywords

Reinforcement Learning, Q-Learning, Traffic Networks, Traffic Signal Timing Plan Management, Multi-Agents Systems.

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Nurul Izyan Kamaruddina ¹, Ahmad Razani Haron ¹, Bih Lii Chua ¹, Min Keng Tan ¹, Kit Guan Lim ¹, Kenneth Tze Kin Teo ¹

Affiliations
1 Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, MY

Abstract

Photovoltaic system (PV) is an important technological asset for renewable energy production. It works by converting solar cell energy from the sun into electrical direct current. In reality, the photovoltaic module usually receives non-uniform solar irradiance at different light intensity due to non-atmospheric hindrance. Under such conditions, the PV system exhibits multiple peaks on the energy characteristic curve, generally known as the partial shading condition (PSC). Therefore, in order to maximize the energy harvested by the photovoltaic system (PV), maximum power point tracking (MPPT) method is suggested to extract all possible maxima that have been produced by the PV system under various circumstances through the non-uniform irradiance of the sunlight. Based on previous researches, it is found that conventional method such as perturb and observed (P&O) method failed to track the maximum power and was trapped at the local maximum power (LMPP). This paper focuses on exploring a metaheuristic method which is the differential evolution (DE) algorithm in optimizing the energy harvested by the PV system. The platform chosen for modelling in this paper is a 3 × 3 PV array. The PV array is tested with different conditions of partial shading where random irradiance values are set. Comparing the performance of PV between P&O and DE based MPPT controller, the DE based MPPT controller is inferred to have a higher success rate to escape from being trapped in LMPP and thus produce more total energy.

Keywords

Photovoltaic system, Maximum Power Point Tracking, Partial Shading Condition, Perturb and Observed, Differential Evolution.

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Min Keng Tana ¹, Helen Sin Ee Chuo ¹, Kit Guan Lim ¹, Renee Ka Yin Chin ¹, Soo Siang Yang ¹, Kenneth Tze Kin Teo ¹

Affiliations
1 Modelling Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, MY

Abstract

Traffic congestion is a perennial issue for most cities. Various artificial intelligence (AI) algorithms, which can categorize as deterministic and metaheuristic algorithms have been suggested to mitigate congestion. Although traffic flow is dynamic and stochastic in nature, most of the previous works evaluated the algorithms with a deterministic or non-stochastic traffic flow pattern. As such, the adaptiveness of those AI algorithms in dealing with stochastic traffic flow patterns is yet to be investigated. Therefore, this paper aims to explore the feasibility of both algorithm types in controlling stochastic traffic flow. In this work, a benchmarked traffic flow model is modified and developed as the simulation platform with the parameters extracted from the guidelines of Public Works Department Malaysia (JKR). Normal distribution function is embedded in the developed model to simulate non-uniform headway for inflow and outflow vehicles. Two commonly used algorithms, namely Fuzzy Logic and Genetic Algorithm are proposed as the adaptive controller to optimize the traffic signalization based on the instant stochastic traffic demand. The simulation results show the metaheuristic algorithm performs better than the deterministic algorithm. The mutation mechanism of the metaheuristic algorithm improves the exploration ability of the algorithm in seeking the optimum solution within the solution space without bounded by a set of fixed-computational rules.

Keywords

Genetic Algorithm, Fuzzy Logic, Signal Optimization, Stochastic Flow, Saturated Condition.

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