fuzzy control of led tunnel lighting and energy conservation

TSINGHUA SCIENCE AND TECHNOLOGY ISSNll1007-0214ll03/12llpp576-582 Volume 16, Number 6, December 2011

Fuzzy Control of LED Tunnel Lighting and Energy Conservation*

Hong Zeng**, Jian Qiu, Xingfa Shen, Guojun Dai, Peng Liu, Shuping Le†

School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China; † School of Measuring and Optical Engineering, Nanchang Hangkong University, Nanchang 330063, China

Abstract: Current highway tunnel lighting control systems are often manually controlled, resulting in signifi-

cant energy waste. This article designs a fuzzy control algorithm for tunnel lighting energy control systems.

The system uses LED (Light Emitting Diode) lighting, so the fuzzy control algorithm is designed for LED

lights. The traffic and the natural illumination level are used as parameters in the intelligent lighting control

algorithm. This system has been deployed in the Lengshui tunnel on the 49th provincial highway of Zhejiang

province and operated for more than six months. The performance results show that the energy conserva-

tion system provides sufficient lighting levels for traffic safety with significant energy conservation.

Key words: tunnel lighting; LED (Light Emitting Diode); vehicle throughput; natural light illumination; fuzzy

algorithm; energy conservation

Introduction

Road tunnel lighting is one of the most important as-pects of highway driving safety. The rapid develop-ment of the Chinese highway traffic system has seen the completion of many new long, large road tunnels whose lighting systems consume large amounts of electric energy. Surveys show that lighting consumes 30% of the energy consumed by the mechanical and electrical systems in highway tunnels and that the tun-nel lighting costs are a loss for tunnel management departments[1]. To ensure good driver vision going into tunnels, the lighting systems maintain the same illu-mination as outside, but this results in conflict between driving safety and excessive lighting. Therefore,

energy conserving highway tunnel lighting systems are needed to improve tunnel energy use, reduce pollutant discharges and guarantee driving safety.

The high voltage sodium lamps are widely used for tunnel lighting, but their low power factor, high power consumption, narrow voltage range and long starting time make them ineffective for energy conservation systems. An LED (Light Emitting Diode) system is a new, green lighting source that is becoming more and more popular in recent years for urban road lighting, night building lighting and house lighting[2]. Compared to sodium lamps, LEDs require less maintenance, pro-vide better security, do not flicker, start up faster, pro-vide good optical homogeneity, and use less energy. With the improvement in LEDs, the luminous intensity and stability are greatly improved and LEDs are be-coming the first lighting choice in highway tunnels[3].

This paper describes the design deployment of a set of tunnel lighting and energy conservation control sys-tems based on fuzzy algorithms. The system includes a vehicle detection and throughput statistics module, a natural light tracking and illumination adjustment module and a main controller module. The system uses LEDs as the tunnel lighting source with measurements

Received: 2011-08-23; revised: 2011-10-13

** Supported by the National Basic Research and Development (973)Program of China (No. 2010CB334707), the National Natural Sci-ence Foundation of China (No. 60803126), the Program for Zheji-ang Provincial Key Innovative Research Team on Sensor Networks(No. 2009R50046), and the Zhejiang Provincial Natural ScienceFoundation (No. Y1101336)

** To whom correspondence should be addressed. E-mail: [email protected]; Tel: 86-571-86915043

Tsinghua Science and Technology, December 2011, 16(6): 576-582

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of the natural illumination outside the tunnel and vehi-cle throughput through the tunnel as input parameters, to control the light based on the natural light illumina-tion and the vehicle throughput using a real-time fuzzy control strategy with stepless dimming. In the daytime, when there are no vehicles in the tunnel, the LEDs maintain low power mode lighting, with the control system modifying the LED illumination in proportion to the outside illumination. During the night, the low power lighting is used when there are no vehicles in the tunnel and the full power lighting is used when vehicles are present. The system not only controls the LED illumination for safety, but also reduces electrical use by reducing the tunnel lighting operating costs. Actual operating experiment shows that the control method is effective and reduces costs.

1 Related Work

Most current tunnel lighting control systems use man-ual control of the lighting intensity with subjective control according to the time and environment. This type of control does not take into account factors such as weather, vehicle speed and vehicle throughput, which should affect tunnel illumination. These factors are very nonlinear and difficult to accurately model with automatic models from a macroscopic point of view[4].

To solve the problems caused by manual control and to improve LED lighting and energy conservation, most researchers have focused on intelligent control of tunnel lighting systems, especially using fuzzy control algorithms to control stepless dimming strategies. Ha-gras et al.[5] introduced an intelligent energy control system for business building energy management. This system used various intelligent techniques, such as fuzzy systems, neural networks, and genetic algorithms, so as to study their effect on the thermal response of a building, including the effect of weather, indoor appli-cation requests and the impact of plants. This system used intelligent real time control to minimize building energy use. They described how the algorithm can theoretically efficiently reduce energy consumption. Fuzzy control algorithms have also been used to solve problems when classic control systems cannot be adapted to dynamic daylight illumination[6,7]. Cziker et al.[6] analyzed the feasibility of using fuzzy control for illumination control considering the impact of various

parameters on the illumination level. Wang et al.[7] de-signed a fuzzy controller using the MATLAB fuzzy logic control module with simulations using Simulink. Their results showed that the fuzzy algorithm im-proved the first-order linear time delay dynamic and static characteristics to give a good performance and a stable voltage.

Huang et al.[8] analyzed the most critical problem for road tunnel lighting systems, which is to provide safe driving conditions. Then, the authors analyzed how to reduce the energy capital, operating and maintenance costs. Their paper defined illumination requirements for different zones in the tunnel and a lighting system layout including illumination sensors, traffic sensors, and lamp control nodes. The authors then designed a fuzzy logic control algorithm using the number of ve-hicles per hour and traffic velocity as input parameters. However, their system was not implemented in a real tunnel to show any practical limits. Yang et al.[9] noted that highway tunnel lighting levels are usually for mesopic vision conditions. Traditional road illumina-tion is usually corrected using the photopic vision spectral luminous efficiency V(λ), but the test results then cannot reflect the real human eye response. This is particularly serious when measuring road lighting with different lighting sources. They studied the emission spectrum distributions of high-pressure sodium lamps (HPS) and white LEDs based on the mesopic vision luminous efficiency model. Their research gave sec-ondary correction methods, for lighting measurements by traditional V(λ) corrected instruments. Their con-clusions gave more accurate highway tunnel lighting values and an innovative method for analyzing lighting source performance.

Considering intelligent lighting systems, Wang et al.[10] designed an intelligent house lighting control system based on a ZigBee wireless sensor network and a fuzzy control algorithm. The sensors transmitted en-vironmental information and the illumination control signals. The environment data was used as input to the fuzzy lighting control system. In tests, the system re-duced energy use, especially for family use.

Most tunnel lighting system studies have mentioned the effect of different light sources on the mesopic vi-sion. As a solution for energy conservation, fuzzy con-trol algorithms are approved, deducted and tested by most researchers. There have been many theoretical

Hong Zeng et al.：Fuzzy Control of LED Tunnel Lighting and Energy Conservation 578

studies and simulations, but the tests have been in laboratories, not with large, realistic implementations. This paper describes the application of a fuzzy control algorithm on an LED lighting system using environ-mental and traffic data as input parameters for auto-matic lighting control. Results from a real traffic sys-tem show the effectiveness of the fuzzy algorithm for significant energy conservation.

2 Energy Conserving LED Lighting Control System

The system configuration shown in Fig. 1 has the con-troller as the core system component. The system also has traffic sensors, natural light sensors, LED illumina-tion sensors and the power supply module. The system collects the data and analyzes the traffic and natural light information to give stepless illumination control of the LED tunnel lights. The system also gives the real time traffic information in the tunnel.

The system power supply module uses three-phase AC input power to control the 24 lighting control cir-cuits. A laser receiver and a transmitter are installed at each end of the tunnel. The traffic flow detection mod-ule is connected to the laser vehicle inspection and testing component of the laser receiver to monitor the vehicles passing through the tunnel. The laser system has a long detection distance and good anti-interfer-ence performance. It adapts to weather and is not af-fected by the road conditions. The system effectively identifies the vehicle direction and throughput through the tunnel.

A natural light sensor and LED illumination adjust-ment module are installed outside the tunnel to collect the natural light data. The system can also identify whether there is a vehicle passing by according to the light illumination variation and feedback signals from the vehicle monitoring module. When a car is detected entering the tunnel, the LED illumination module adjusts the LED dimming signal. When there is no

Fig. 1 Diagram of LED lighting and energy conservation system


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vehicle inside the tunnel, the system reduces the LED illumination using fixed LED stepless dimming control signals.

3 Fuzzy Control Algorithm 3.1 Fuzzy control method

Fuzzy control can be used for system level control by mimicing the behavior of an experienced operator with expert rules and regulations. The fuzzy language based regulations and rules are then converted to a numerical model of the automatic controller[11].

A key characteristic of tunnel illumination is that long-term operation may not necessarily require full power illumination. Therefore, the system fuzzy con-trol must consider the main parameters affecting the illumination, which are the outside illumination and the traffic flow to control the LED light output power. The detailed control procedure controls the illumina-tion when a vehicle passes by according to the traffic flow situation to some proportion of the natural illu-mination. When there are no vehicles or during the night, the LEDs automatically adjust to their minimum illumination, which is 10% of the maximum. Consider, for example, a traffic flow, in a retrograde two-lane two-way tunnel 1 km long with a daily traffic flow of 1500 vehicles at 40 km/h with 200 m safe distance between cars. The theoretical passing duration is 90 s for each car and the period between cars is at least 18 s. Therefore, there are only approximately 3.75 h each day when vehicles are passing through. The LEDs can then work in the lowest illumination mode for at least 20.25 h each day. This scheme effectively eliminates excessive lighting to reduce energy use.

3.2 Fuzzy control response table

The fuzzy control used a double-input, single- output algorithm. The two inputs are the natural light

illumination L (lx) outside the tunnel and the traffic flow Q (vehicles/day). The output is the LED illumina-tion change, U. The controller uses the Mamdani scheme.

Step 1 Define the fuzzy subset and control levels. Typically, the natural illumination during the sum-

mer with direct sunlight is 60 000 to 100 000 lx. The illumination is 20 000 to 25 000 lx on rainy days and 1000 to 10 000 lx indoors. During the winter, the illu-mination from direct sunlight is approximately 20 000 lx and 4000 to 5000 lx during rainy days. The illumi-nation also varies from early morning with 50 to 100 lx to 0.2 lx at night. Thus, the illumination, L, can be di-vided into 7 fuzzy subsets from 0 to 100 000 lx as NB, NM, NS, Z, PS, PM and PB listed in Table 1.

The tunnel traffic flow can be one-way or two-way traffic with a speed at 40 km/h and 1500 vehicles/day as an example. The traffic flow, Q, can then be sum-marized into 5 fuzzy subsets NB, NM, Z, PM, PB be-tween 0 and 1500 as listed in Table 2.

LED illumination adjustment is stepless adjusted from 10% to 100% illumination. 7 fuzzy LED illumi-nation subsets are defined as NB, NM, NS, Z, PS, PM and PB as listed in Table 3.

Step 2 Input and output functions. The membership functions L and Q are defined in

Step 1. The PS situation level for the illumination can be defined as with similar functions for the vehicle flow. Matlab simulation results are shown in Fig. 2.

1 4 , 20 000 45 000;25 000 5

PS1 16 , 45 000 80 000

35 000 7

L L

L L

⎧ − <⎪⎪= ⎨⎪− + <⎪⎩

(1)

Table 4 and Fig. 2 show the illumination levels for various conditions, which illustrates how the scheme reduces energy use, especially for low traffic flows.

Table 1 Fuzzy subsets of L

Subset name NB NM NS Z PS PM PB Meaning Negative Big Negative Middle Negative Small Zero Positive Small Positive Middle Positive Big

Table 2 Fuzzy subsets of Q

Subset name NB NM Z PM PB Meaning Negative Big Negative Middle Zero Positive Middle Positive Big


Table 3 Fuzzy subsets of U

Subset Name NB NM NS Z PS PM PB Meaning Negative Big Negative Middle Negative Small Zero Positive Small Positive Middle Positive Big

Table 4 LED lighting percentages for different flows and illuminations

Lighting percentage (%) L (lx)

Q = 0 75 150 225 300 525 600 675 750 975 1050 1125 1350 1425 1500

50 000 10 11 12 13 13 21 23 24 24 22 24 24 48 52 55 5000 10 13 23 24 24 37 40 43 43 50 50 50 53 57 60

Fig. 2 LED lighting output for different flows and illuminations

4 System Structure and Configuration

The system function structure shown in Fig. 3 includes the traffic detection and flow module, natural light tracking and LED illumination adjustment module, and the central control module.

4.1 Traffic detection and flow module

The traffic detection and flow module consists of a power supply, laser transmitter, laser receiver, display and signal processing circuit, as shown in Fig. 4. This system uses a 12 V voltage electrical power supply with the laser transmitter and receiver using two pairs of beam detectors to collect the signals generated by passing vehicles. An ATMEGA8 processor is used for the signal processing of the signals received by the laser receiver. The order of the signals received is used to determine the direction and number of vehicles, which is then passed to the central processor.

4.2 Natural light tracking and LED illumination adjustment

The natural light tracking and LED illumination adjust-ment modules are described in Figs. 5 and 6. The natu-ral light tracking module measures the natural light level and outputs a digital signal to the LED adjustment

Fig. 3 System structure


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Fig. 4 Vehicle detection and flow statistics diagram

Fig. 5 Natural light tracking module

Fig. 6 LED illumination adjustment module

module which periodically reads the output data to obtain the average illumination level and displays it. The DAC output is connected to the LED light adjust-ment signal input as a DC 0 V to 5 V, where 0 V indi-cates “no vehicle” and 5 V indicates “vehicles passing”. The LED illumination is lowered when no vehicles are present. The natural light tracking mode is used for the LED illumination control when vehicles are present with 7 levels of LED illumination according to the 7 levels of natural light illumination.

4.3 Central control module

The central control module uses an MCU for data col-lection, analysis, and processing of the traffic detection, traffic flow, natural light tracking and LED illumination

signals. The MCU also sets the parameter, uses the fuzzy control algorithm and does system error detection. The output provides stepless LED illumination control.

5 System Implementation and Performance Evaluation

The main components of the tunnel LED energy con-trol system shown in Fig. 7 have been successfully implemented in more than 10 tunnels. The energy consumption and costs for the Lengshui tunnel on Zhejiang’s 49th provincial highway are listed in Table 6 from 2009 to 2011. This tunnel has a total length of 678 meters and a daily traffic flow of less than 1500 vehicles. The system was implemented in September, 2010 and reduced energy use by 87%, which is quite significant.

6 Conclusions

This paper presents a fuzzy algorithm highway tunnel lighting control system. The control system monitors the traffic flow, the natural illumination and the LED illumination. The system has been implemented in a highway tunnel for 10 months and has reduced energy use by 87%.

(a) Vehicle detection (b) Natural tracking

(c) Main control (d) Display

Fig. 7 Tunnel lighting and energy control system


Table 6 Power consumption and cost for the Lengshui tunnel

Date Monthly electricity

consumption (kw · h) Electricity charge

(RMB) 2009-06 9324 7962.69 2009-07 10 722 9156.58 2009-08 12 622 10 779.18 2009-09 14 929 12 749.36 2009-10 10 358 8845.73 2009-11 10 683 9123.28 2009-12 13 317 11 587.64 2010-01 22 480 19 804.88 2010-02 19 211 16 924.89 2010-03 10 155 8946.56 2010-04 15 133 13 332.17 2010-05 14 716 12 964.80 2010-06 11 286 9942.97 2010-07 12 812 11 287.37 2010-08 13 499 11 892.62 2010-09 1653 1456.29 2010-10 2995 2638.60 2010-11 2000 1762.00 2010-12 1561 1375.24 2011-01 1568 1381.41 2011-02 1610 1418.41 2011-03 1807 1591.97

References

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[2] Song Baihua, Li Hong, He Kexue. The research status and development of highway tunnel lighting. Hunan Commu-nication Science and Technology, 2005, 31(1): 96-98.

[3] He Yi, Lang Zheyan, Wu Aiguo, et al. Research on intelli-gent control of tunnel lighting system based on LED. In: 2010 International Conference on Optoleectronics and Im-age Processing. Haikou, China, 2010: 247-250.

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[9] Yang Yong, Bao Zuojun, Zhu Chuanzheng, et al. Study on the mesopic vision theory on the road tunnel lighting measurement. In: The Third International Conference on Measuring Technology and Mechatronics Automation. Shanghai, China, 2011: 565-567.

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fuzzy control of led tunnel lighting and energy conservation

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