Significant Implication of Optimal Expansion Planning in Wind Turbines Toward Composite Generating System Reliability

Significant Implication of Optimal Expansion Planning in Wind Turbines toward Composite Generating System Reliability 1,2* 1 1,2 1,2 Muhammad Murtadha Othman , Nadia Hassin , Ismail Musirin & Nur Ashida Salim 1Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia; 2Committee of Research (CORE), Advanced Computing & Communication (ACC), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia This paper presents the stochastic approach deployed to perform expansion planning of the wind turbine generating (WTG) units in a power system. The expansion planning of WTG requires the information of its forced outage rate (FOR) determined by using the continuous-time Markov Chain. The FOR represents as an outage probability of a component emerged as it is taken out intentionally from a system customarily for the purpose of preventive maintenance or repair. In contrast with the conventional power plant, for the case study of WTG, the determination of FOR is utterly unique since it depends on the information of uncertain weather condition. Hence, the loss of load expectation (LOLE) and expected unserved energy (EUE) could be estimated so that it will not infringe its respective predetermined limits indispensable for the expansion planning of WTG using the stochastic approach. The modified IEEE RTS-79 system is used as a case study to verify effectiveness of the proposed method in the determination of WTG expansion planning. The results have shown improvement on the EUE and LOLE in relation with optimal expansion of the WTG units determined by using the proposed method. Keywords: Optimal Expansion Planning; Wind Turbine Generating Units; Continuous-Time Markov Chain; Loss of Load Expectation; Expected Unserved Energy. 1. INTRODUCTION In particular, the inconsistency of WTG The Global Wind Energy Council availability is usually incurred due to uncertain prognosticate that wind power could supply one generation of the electric supply attributed by third of the world’s electricity by 20501. Wind unpredictable and intermittent situations of wind energy has many advantages to offer pertaining speed at the same site throughout the years. with energy generation that evokes a faster Recently, there are enormous numbers of development of wind turbine generator (WTG) research study have been carried out to discuss in future years. There are more than 50 countries on several factors rendering to an adverse effect around the world that have large progress in the reliability of WTG farm. It is worthwhile pertinent with energy supply generated by the to mention that the uncertain weather condition WTG and is well accepted by the end users1. In has become upheavals to the maintenance spite of several advantages that could be activities conducted to the WTG system2. In provided by the WTG, there is also a conjunction with this matter, high wind seasons disadvantage attributed by the WTG pertaining and harsh or adverse weather conditions will with its inconsistency or less reliable in delay the maintenance process of WTG and generating electric power supplied to a system unplanned failure of WTG may also emerge. as compared with the conventional power plant. These uncertain environments will dwindle the performance and reliability of the WTG system. *Email Address: [email protected] The time-sequential simulation technique used to analyse the reliability of generation system connected with the WTG system has 1 3 1 been introduced in . The three-state Markov 휆 = (푓푎푖푙푢푟푒 / 푦푒푎푟) model of WTG has been deployed to determine 푀푇퐵퐹 its forced outage rate (FOR) required for the (1) reliability analysis of the whole generation The µ may occur for every hour is system. Moreover, a scheme of assessing the calculated using equation (2). 1 reliability of generation system for a Brazilian 휇 = (푟푒푝푎푖푟/ 푦푒푎푟) 푀푇푇푅 region by employing the Markov model taking (2) into account the FOR of its WTG system has 4 The information of λ and µ is then used in been discussed in . the continuous-time Markov Chain to determine This paper presents the expansion planning the FOR of a WTG unit for a given year as of WTG system using the stochastic approach. depicted in Figure 1. The continuous-time The expansion of WTG system requires the information of its FOR in a given year Markov Chain is emanating from a three-state determined based on the continuous-time model designed specifically for the WTG Markov Chain of WTG entailed with the repair operation influenced by uncertain weather rate, µ, and failure rate, λ, influenced by the conditions. The uncertain weather conditions is wind speed. The continuous-time Markov Chain one of the known factors instigate to a common- is basically constructed based on the three-state cause failure of WTG operation. Therefore, the Markov model. The FOR can be referred to as a three-state model is composed with the two up level of security risk encountered by the WTG states and one down state. There is no output unit. The amalgamation of FOR obtained from power (Pt) produced during the particular hours both WTG and others generation systems are of exorbitant wind speed (sWt) that courses the used to determine reliability indices of expected down state (DOWN3) of WTG operation. The unserved energy (EUE) and loss-of-load WTG will restore back to its operational expectation (LOLE) for the whole generation condition when there is enough and acceptable system that will be an imperative information sWt at a given time. Hence, it is represented by for expansion planning. Robustness of the the transition state from DOWN3 to UP1 or stochastic approach used to undertake the from DOWN3 to UP2. On one hand, the WTG optimal expansion of WTG system is verified on also stops operating for preventive maintenance an IEEE RTS-79 system. and restore back after repair. Most of the wind farm customarily contains several numbers of 2. METHODOLOGY WTG unit. The sW for each WTG unit is This section explicates on the application t assumed to be the same. As a result, the of continuous-time Markov Chain for summation of all electric power generated by the determining the FOR of a WTG unit for a given WTG units will become P produced by the year. The subsequent subsection will explain in- t WTG farm. The cut-in wind speed (V ) that is depth on the stochastic approach used to ci less than 3 m/s to 5 m/s will not shove the WTG perform the optimal expansion of WTG system to operate and produce electricity. It is whilst ensuring there is no infringement on the preferable to have a lower value of V due to the specified limitation of EUE and LOLE. ci fact that the WTG will operate and produce 2.1 ESTIMATION OF FORCED OUTAGE electricity albeit the sWt is low. The cut-off wind RATE USING THE CONTINUOUS- speed (Vco) of 20 (m/s) is formerly specified by TIME MARKOV CHAIN the manufacturer to protect the WTG from any Initially, the mean time between failure damage during its operation at exorbitant wind (MTBF) and mean time to repair (MTTR) is speed and Pt will not be generated. estimated for determining the respective values of failure rate (λ) and repair rate (µ)1. The MTBF is defined as the total hours of observation over the total number of random failures. On the overleaf, the MTTR can be expressed as the sum of all corrective maintenance divided by the total number of failures during observation interval. Hence, the number of failures that may occur for every hour is, 2 푏 (푒+ µ) P1 = (푎+푏)(푒+휆+µ+푓) (8) 푎 (µ+푑) P2 = (푎+푏)(휆+µ+푐+푑) (9) (휆+푓) (휆+ 푐) P3 = (푒+휆+µ+푓)(휆+µ+푐+푑) (10) The second method utilizes the transition diagram that is rather ominous in contrast to the stochastic transitional probability matrix. Nevertheless, both of the methods exert to the same results. Fig. 1. Three-state model of WTG. Table I. State transition of three-state model By referring to Figure 1, the assigned of WTG. UP1 UP2 DOWN3 parameters resemble as given below. State (State 1) (State 2) (State 3) a = sWt < Vr ; b = sWt > Vr ; c = sWt < Vci ; d = sWt > Vci.; e = sWt < Vco ; f = sWt > Vco . WTG Operable Operable Failed where, Pr : rated power. Vr : rated wind speed. There are two methods used to compute Fig. 2. The transition diagram between UP1 and the FOR acquired from the three-state model of UP2. continuous-time Markov Chain. The first Figure 2 elucidates that the wind speed method uses the stochastic transitional (sWt) higher than the rated wind speed (Vr) is probability matrix to derive the transition delineated as the transition from UP1 to UP2 parameters of a, b, c, d, e, f, µ and λ imminent and vice versa. WTG are operable for both between the two up states, or between the up states to generate the electricity. states and down state, or vice versa5. The − 푎 푎 P = [ ] [P1 P2] (11) formation of stochastic transitional probability 푏 −푏 푏 matrix issuances from the three-state model of P1= (12) 푎+푏 푎 continuous-time Markov Chain can be obtained P2 = (13) from equation (3). 푎+푏 푃1̇ [푃2̇ ] = 푃3̇ −(푎 + 푓 + 휆) 푎 푓 + 휆 [ 푏 −(푏 + 푐 + 휆) 푐 + 휆 ] 푒 + µ 푑 + µ −(푒 + 푑 + 2µ) × [P1 P2 P3] (3) Detail derivation from the matrix may Fig. 3. The transition diagram between UP1 and divulge into, DOWN3. −(푎 + 푓 + 휆)푃1 + 푏푃2 + (푒 + µ)P3 = 0 In Figure 3, the WTG system is still (4) operable to generate electricity attributed by the aP1 + −(b + c + λ)P2 + (d + µ)P3 = 0 sW t that is lower than the cut-out wind speed (5) (Vco).

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